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15,778 | 1 | 601,383 | 23,877 | 1 | linux-hosts-metrics-base | Linux Hosts Metrics | Base | Basic overview of linux host metrics, based on node_exporter | 2022-02-18 08:08:46 UTC | 2022-12-27 04:09:24 UTC | 0 | null | 0 | 0 | nodeExporter | null | 0 | 0 | 23,877 | 1 | 15,778 | 1 | 1 | Basic overview of linux host metrics, based on node_exporter | {
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} | 2022-02-18 08:08:46 UTC | null | 0 | 0 |
15,886 | 1 | 591,606 | 24,086 | 1 | seuranta | seuranta | koti | 2022-03-13 05:08:14 UTC | 2022-03-13 05:08:50 UTC | 0 | null | 0 | 0 | null | null | 0 | 0 | 24,086 | 1 | 15,886 | 1 | 1 | null | {
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16,750 | 1 | 696,805 | 25,684 | 1 | bmw-and-mini-dashboard | BMW (and MINI) dashboard | There are many companies that offer cars to their employees on a daily work. So maybe it's a good idea to have vehicle data on the Grafana Labs dashboard.
This way you can query and control the status vehicle. Exemple. the vehicle's is locked, secured, brake fluid, oil and checks if the vehicle's status is OK. In addition, there are other details about the remaining range-hybrid and electric and much more...
So far I have made a test #Grafana #dashboard for my vehicle #745Le. So my solution only works with #BMW (and #MINI) vehicles with a Connected Drive Account. | 2022-08-21 09:51:33 UTC | 2022-08-21 09:51:51 UTC | 0 | null | 0 | 0 | null | null | 0 | 0 | 25,684 | 1 | 16,750 | 1 | 1 | There are many companies that offer cars to their employees on a daily work. So maybe it's a good idea to have vehicle data on the Grafana Labs dashboard.
This way you can query and control the status vehicle. Exemple. the vehicle's is locked, secured, brake fluid, oil and checks if the vehicle's status is OK. In addition, there are other details about the remaining range-hybrid and electric and much more...
So far I have made a test #Grafana #dashboard for my vehicle #745Le. So my solution only works with #BMW (and #MINI) vehicles with a Connected Drive Account. | {
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} | 2022-08-21 09:51:33 UTC | 2022-08-21 09:51:51 UTC | 0 | 0 |
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} | 2022-09-01 07:56:10 UTC | 2022-09-01 08:01:02 UTC | 0 | 0 |
17,002 | 1 | 498,884 | 26,066 | 1 | pixie-service-overview | Pixie Service Overview | Dashboard shows latency, error, and throughput over time for all HTTP requests for the selected service. It also includes a sample of slow requests to the service. | 2022-09-16 15:34:27 UTC | 2022-09-16 15:34:57 UTC | 0 | To learn how to monitor service performance using Pixie, see the [tutorial](https://docs.px.dev/tutorials/pixie-101/service-performance/). | 76,986 | 76,987 | Other | null | 0 | 0 | 26,066 | 1 | 17,002 | 1 | 1 | Dashboard shows latency, error, and throughput over time for all HTTP requests for the selected service. It also includes a sample of slow requests to the service. To learn how to monitor service performance using Pixie, see https://docs.px.dev/tutorials/pixie-101/service-performance/ | {
"__inputs": [
{
"name": "DS_PIXIE_GRAFANA DATASOURCE PLUGIN",
"label": "Pixie Grafana Datasource Plugin",
"description": "",
"type": "datasource",
"pluginId": "pixie-pixie-datasource",
"pluginName": "Pixie Grafana Datasource Plugin"
}
],
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{
"type": "datasource",
"id": "pixie-pixie-datasource",
"name": "Pixie Grafana Datasource Plugin",
"version": "0.0.9"
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{
"type": "panel",
"id": "table",
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"version": ""
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{
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"id": "timeseries",
"name": "Time series",
"version": ""
}
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"annotations": {
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{
"builtIn": 1,
"datasource": "-- Grafana --",
"enable": true,
"hide": true,
"iconColor": "rgba(0, 211, 255, 1)",
"name": "Annotations & Alerts",
"target": {
"limit": 100,
"matchAny": false,
"tags": [],
"type": "dashboard"
},
"type": "dashboard"
}
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},
"editable": true,
"fiscalYearStartMonth": 0,
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"links": [],
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"panels": [
{
"datasource": "${DS_PIXIE_GRAFANA DATASOURCE PLUGIN}",
"fieldConfig": {
"defaults": {
"color": {
"mode": "palette-classic"
},
"custom": {
"axisLabel": "request throughput",
"axisPlacement": "auto",
"barAlignment": 0,
"drawStyle": "line",
"fillOpacity": 0,
"gradientMode": "none",
"hideFrom": {
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"lineInterpolation": "linear",
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"pointSize": 5,
"scaleDistribution": {
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"mode": "none"
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},
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"mode": "absolute",
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{
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"h": 12,
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"datasource": "${DS_PIXIE_GRAFANA DATASOURCE PLUGIN}",
"queryBody": {
"pxlScript": "# Copyright 2018- The Pixie Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n# SPDX-License-Identifier: Apache-2.0\n\n''' Service Overview\n\n This script gets an overview of an individual service, summarizing its request statistics.\n'''\nimport px\n\nns_per_ms = 1000 * 1000\nns_per_s = 1000 * ns_per_ms\n# Window size to use on time_ column for bucketing.\nwindow_ns = px.DurationNanos(10 * ns_per_s)\n# Flag to filter out requests that come from an unresolvable IP.\nfilter_unresolved_inbound = True\n# Flag to filter out health checks from the data.\nfilter_health_checks = True\n# Flag to filter out ready checks from the data.\nfilter_ready_checks = True\n\n# $pixieCluster - work around for grafana to update panel on variable change\n\nstart_time = __time_from\nservice = \"$pixieService\"\n\ndef pods_for_service(start_time: int, service: px.Service):\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[px.has_service_name(df.ctx['service'], service)]\n df.pod = df.ctx['pod_name']\n df = df.groupby('pod').agg()\n df.pod_create_time = px.pod_name_to_start_time(df.pod)\n df.pod_status = px.pod_name_to_status(df.pod)\n return df\n\n\ndef inbound_let_timeseries(start_time: int, service: px.Service):\n ''' Compute the let as a timeseries for requests received by `service`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @service: The name of the service to filter on.\n\n '''\n df = let_helper(start_time)\n df = df[px.has_service_name(df.service, service)]\n\n df = df.groupby(['timestamp']).agg(\n latency_quantiles=('latency', px.quantiles),\n error_rate_per_window=('failure', px.mean),\n throughput_total=('latency', px.count),\n bytes_total=('resp_body_size', px.sum)\n )\n\n # Format the result of LET aggregates into proper scalar formats and\n # time series.\n df.latency_p50 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p50')))\n df.latency_p90 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p90')))\n df.latency_p99 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p99')))\n df.request_throughput = df.throughput_total / window_ns\n df.errors_per_ns = df.error_rate_per_window * df.request_throughput / px.DurationNanos(1)\n df.error_rate = px.Percent(df.error_rate_per_window)\n df.bytes_per_ns = df.bytes_total / window_ns\n df.time_ = df.timestamp\n\n return df[['time_', 'latency_p50', 'latency_p90', 'latency_p99',\n 'request_throughput', 'errors_per_ns', 'error_rate', 'bytes_per_ns']]\n\n\ndef inbound_let_summary(start_time: int, service: px.Service):\n ''' Gets a summary of requests inbound to `service`.\n\n Args:\n @start_time Starting time of the data to examine.\n @service: The service to filter on.\n '''\n df = let_summary_helper(start_time)\n df = df[px.has_service_name(df.responder, service)]\n return df.drop('responder')\n\n\ndef outbound_let_summary(start_time: int, service: px.Service):\n ''' Gets a summary of requests outbound from `service`.\n\n Args:\n @start_time Starting time of the data to examine.\n @service: The service to filter on.\n '''\n df = let_summary_helper(start_time)\n df = df[px.has_service_name(df.requestor, service)]\n return df.drop(['requestor', 'remote_addr'])\n\n\ndef let_summary_helper(start_time: int):\n ''' Gets a summary of request statistics. This is a helper function, filtering by\n service is done elsewhere.\n\n Args:\n @start_time Starting time of the data to examine.\n '''\n df = let_helper(start_time)\n\n df = df.groupby(['service', 'remote_addr']).agg(\n latency=('latency', px.quantiles),\n total_request_count=('latency', px.count)\n error_rate=('failure', px.mean),\n )\n\n df.error_rate = px.Percent(df.error_rate)\n df.responder = df.service\n df.requesting_ip = df.remote_addr\n df.requesting_pod = px.pod_id_to_pod_name(px.ip_to_pod_id(df.remote_addr))\n df.requesting_svc = px.pod_id_to_service_name(px.ip_to_pod_id(df.remote_addr))\n\n return df[['responder', 'requesting_ip', 'requesting_pod', 'requesting_svc',\n 'latency', 'error_rate']]\n\n\ndef service_slow_requests(start_time: int, service: px.Service):\n df = let_helper(start_time)\n df = df[px.has_service_name(df.service, service)]\n quantiles = df.groupby('service').agg(\n latency_quantiles=('latency', px.quantiles)\n )\n quantiles.service_p99 = px.pluck_float64(quantiles.latency_quantiles, 'p99')\n quantiles = quantiles.drop('latency_quantiles')\n requests = df.merge(quantiles, left_on='service', right_on='service', how='inner',\n suffixes=['', '_x'])\n requests = requests[requests.latency >= px.floor(requests.service_p99)]\n\n return requests[['time_', 'pod', 'latency', 'req_method',\n 'req_path', 'req_body', 'resp_status',\n 'remote_addr', 'remote_port',\n 'resp_body']].head(100)\n\n\ndef let_helper(start_time: int):\n ''' Compute the initial part of the let for requests.\n Filtering to inbound/outbound traffic by service is done by the calling function.\n\n Args:\n @start_time: The timestamp of data to start at.\n\n '''\n df = px.DataFrame(table='http_events', start_time=start_time)\n # Filter only to inbound service traffic (server-side).\n # Don't include traffic initiated by this service to an external location.\n df = df[df.trace_role == 2]\n df.service = df.ctx['service']\n df.pod = df.ctx['pod']\n df.latency = df.latency\n\n df.timestamp = px.bin(df.time_, window_ns)\n\n df.failure = df.resp_status >= 400\n filter_out_conds = ((df.req_path != '/healthz' or not filter_health_checks) and (\n df.req_path != '/readyz' or not filter_ready_checks)) and (\n df['remote_addr'] != '-' or not filter_unresolved_inbound)\n\n df = df[filter_out_conds]\n return df\n \noutput = inbound_let_timeseries(start_time, service)\noutput.request_throughput = output.request_throughput * px.pow(10,9)\noutput = output[['time_', 'request_throughput']]\npx.display(output)\n"
},
"queryType": "run-script",
"refId": "A"
}
],
"title": "HTTP Requests",
"type": "timeseries"
},
{
"datasource": "${DS_PIXIE_GRAFANA DATASOURCE PLUGIN}",
"fieldConfig": {
"defaults": {
"color": {
"mode": "palette-classic"
},
"custom": {
"axisLabel": "error rate",
"axisPlacement": "auto",
"barAlignment": 0,
"drawStyle": "line",
"fillOpacity": 0,
"gradientMode": "none",
"hideFrom": {
"legend": false,
"tooltip": false,
"viz": false
},
"lineInterpolation": "linear",
"lineWidth": 1,
"pointSize": 5,
"scaleDistribution": {
"type": "linear"
},
"showPoints": "auto",
"spanNulls": false,
"stacking": {
"group": "A",
"mode": "none"
},
"thresholdsStyle": {
"mode": "off"
}
},
"mappings": [],
"thresholds": {
"mode": "absolute",
"steps": [
{
"color": "green",
"value": null
},
{
"color": "red",
"value": 80
}
]
},
"unit": "/s"
},
"overrides": []
},
"gridPos": {
"h": 12,
"w": 8,
"x": 8,
"y": 0
},
"id": 3,
"options": {
"legend": {
"calcs": [],
"displayMode": "list",
"placement": "bottom"
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"tooltip": {
"mode": "single",
"sort": "none"
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},
"targets": [
{
"datasource": "${DS_PIXIE_GRAFANA DATASOURCE PLUGIN}",
"queryBody": {
"pxlScript": "# Copyright 2018- The Pixie Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n# SPDX-License-Identifier: Apache-2.0\n\n''' Service Overview\n\n This script gets an overview of an individual service, summarizing its request statistics.\n'''\nimport px\n\nns_per_ms = 1000 * 1000\nns_per_s = 1000 * ns_per_ms\n# Window size to use on time_ column for bucketing.\nwindow_ns = px.DurationNanos(10 * ns_per_s)\n# Flag to filter out requests that come from an unresolvable IP.\nfilter_unresolved_inbound = True\n# Flag to filter out health checks from the data.\nfilter_health_checks = True\n# Flag to filter out ready checks from the data.\nfilter_ready_checks = True\n\n# $pixieCluster - work around for grafana to update panel on variable change\n\nstart_time = __time_from\nservice = \"$pixieService\"\n\ndef pods_for_service(start_time: int, service: px.Service):\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[px.has_service_name(df.ctx['service'], service)]\n df.pod = df.ctx['pod_name']\n df = df.groupby('pod').agg()\n df.pod_create_time = px.pod_name_to_start_time(df.pod)\n df.pod_status = px.pod_name_to_status(df.pod)\n return df\n\n\ndef inbound_let_timeseries(start_time: int, service: px.Service):\n ''' Compute the let as a timeseries for requests received by `service`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @service: The name of the service to filter on.\n\n '''\n df = let_helper(start_time)\n df = df[px.has_service_name(df.service, service)]\n\n df = df.groupby(['timestamp']).agg(\n latency_quantiles=('latency', px.quantiles),\n error_rate_per_window=('failure', px.mean),\n throughput_total=('latency', px.count),\n bytes_total=('resp_body_size', px.sum)\n )\n\n # Format the result of LET aggregates into proper scalar formats and\n # time series.\n df.latency_p50 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p50')))\n df.latency_p90 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p90')))\n df.latency_p99 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p99')))\n df.request_throughput = df.throughput_total / window_ns\n df.errors_per_ns = df.error_rate_per_window * df.request_throughput / px.DurationNanos(1)\n df.error_rate = px.Percent(df.error_rate_per_window)\n df.bytes_per_ns = df.bytes_total / window_ns\n df.time_ = df.timestamp\n\n return df[['time_', 'latency_p50', 'latency_p90', 'latency_p99',\n 'request_throughput', 'errors_per_ns', 'error_rate', 'bytes_per_ns']]\n\n\ndef inbound_let_summary(start_time: int, service: px.Service):\n ''' Gets a summary of requests inbound to `service`.\n\n Args:\n @start_time Starting time of the data to examine.\n @service: The service to filter on.\n '''\n df = let_summary_helper(start_time)\n df = df[px.has_service_name(df.responder, service)]\n return df.drop('responder')\n\n\ndef outbound_let_summary(start_time: int, service: px.Service):\n ''' Gets a summary of requests outbound from `service`.\n\n Args:\n @start_time Starting time of the data to examine.\n @service: The service to filter on.\n '''\n df = let_summary_helper(start_time)\n df = df[px.has_service_name(df.requestor, service)]\n return df.drop(['requestor', 'remote_addr'])\n\n\ndef let_summary_helper(start_time: int):\n ''' Gets a summary of request statistics. This is a helper function, filtering by\n service is done elsewhere.\n\n Args:\n @start_time Starting time of the data to examine.\n '''\n df = let_helper(start_time)\n\n df = df.groupby(['service', 'remote_addr']).agg(\n latency=('latency', px.quantiles),\n total_request_count=('latency', px.count)\n error_rate=('failure', px.mean),\n )\n\n df.error_rate = px.Percent(df.error_rate)\n df.responder = df.service\n df.requesting_ip = df.remote_addr\n df.requesting_pod = px.pod_id_to_pod_name(px.ip_to_pod_id(df.remote_addr))\n df.requesting_svc = px.pod_id_to_service_name(px.ip_to_pod_id(df.remote_addr))\n\n return df[['responder', 'requesting_ip', 'requesting_pod', 'requesting_svc',\n 'latency', 'error_rate']]\n\n\ndef service_slow_requests(start_time: int, service: px.Service):\n df = let_helper(start_time)\n df = df[px.has_service_name(df.service, service)]\n quantiles = df.groupby('service').agg(\n latency_quantiles=('latency', px.quantiles)\n )\n quantiles.service_p99 = px.pluck_float64(quantiles.latency_quantiles, 'p99')\n quantiles = quantiles.drop('latency_quantiles')\n requests = df.merge(quantiles, left_on='service', right_on='service', how='inner',\n suffixes=['', '_x'])\n requests = requests[requests.latency >= px.floor(requests.service_p99)]\n\n return requests[['time_', 'pod', 'latency', 'req_method',\n 'req_path', 'req_body', 'resp_status',\n 'remote_addr', 'remote_port',\n 'resp_body']].head(100)\n\n\ndef let_helper(start_time: int):\n ''' Compute the initial part of the let for requests.\n Filtering to inbound/outbound traffic by service is done by the calling function.\n\n Args:\n @start_time: The timestamp of data to start at.\n\n '''\n df = px.DataFrame(table='http_events', start_time=start_time)\n # Filter only to inbound service traffic (server-side).\n # Don't include traffic initiated by this service to an external location.\n df = df[df.trace_role == 2]\n df.service = df.ctx['service']\n df.pod = df.ctx['pod']\n df.latency = df.latency\n\n df.timestamp = px.bin(df.time_, window_ns)\n\n df.failure = df.resp_status >= 400\n filter_out_conds = ((df.req_path != '/healthz' or not filter_health_checks) and (\n df.req_path != '/readyz' or not filter_ready_checks)) and (\n df['remote_addr'] != '-' or not filter_unresolved_inbound)\n\n df = df[filter_out_conds]\n return df\n \noutput = inbound_let_timeseries(start_time, service)\noutput.errors_per_ns = output.errors_per_ns * px.pow(10,9)\noutput = output[['time_', 'errors_per_ns']]\n\npx.display(output)\n"
},
"queryType": "run-script",
"refId": "A"
}
],
"title": "HTTP Errors",
"type": "timeseries"
},
{
"datasource": "${DS_PIXIE_GRAFANA DATASOURCE PLUGIN}",
"fieldConfig": {
"defaults": {
"color": {
"mode": "palette-classic"
},
"custom": {
"axisLabel": "latency",
"axisPlacement": "auto",
"barAlignment": 0,
"drawStyle": "line",
"fillOpacity": 0,
"gradientMode": "none",
"hideFrom": {
"legend": false,
"tooltip": false,
"viz": false
},
"lineInterpolation": "linear",
"lineWidth": 1,
"pointSize": 5,
"scaleDistribution": {
"type": "linear"
},
"showPoints": "auto",
"spanNulls": false,
"stacking": {
"group": "A",
"mode": "none"
},
"thresholdsStyle": {
"mode": "off"
}
},
"mappings": [],
"thresholds": {
"mode": "absolute",
"steps": [
{
"color": "green",
"value": null
},
{
"color": "red",
"value": 80
}
]
},
"unit": "ms"
},
"overrides": []
},
"gridPos": {
"h": 12,
"w": 8,
"x": 16,
"y": 0
},
"id": 4,
"options": {
"legend": {
"calcs": [],
"displayMode": "list",
"placement": "bottom"
},
"tooltip": {
"mode": "single",
"sort": "none"
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},
"targets": [
{
"datasource": "${DS_PIXIE_GRAFANA DATASOURCE PLUGIN}",
"queryBody": {
"pxlScript": "# Copyright 2018- The Pixie Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n# SPDX-License-Identifier: Apache-2.0\n\n''' Service Overview\n\n This script gets an overview of an individual service, summarizing its request statistics.\n'''\nimport px\n\nns_per_ms = 1000 * 1000\nns_per_s = 1000 * ns_per_ms\n# Window size to use on time_ column for bucketing.\nwindow_ns = px.DurationNanos(10 * ns_per_s)\n# Flag to filter out requests that come from an unresolvable IP.\nfilter_unresolved_inbound = True\n# Flag to filter out health checks from the data.\nfilter_health_checks = True\n# Flag to filter out ready checks from the data.\nfilter_ready_checks = True\n\n# $pixieCluster - work around for grafana to update panel on variable change\n\nstart_time = __time_from\nservice = \"$pixieService\"\n\ndef pods_for_service(start_time: int, service: px.Service):\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[px.has_service_name(df.ctx['service'], service)]\n df.pod = df.ctx['pod_name']\n df = df.groupby('pod').agg()\n df.pod_create_time = px.pod_name_to_start_time(df.pod)\n df.pod_status = px.pod_name_to_status(df.pod)\n return df\n\n\ndef inbound_let_timeseries(start_time: int, service: px.Service):\n ''' Compute the let as a timeseries for requests received by `service`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @service: The name of the service to filter on.\n\n '''\n df = let_helper(start_time)\n df = df[px.has_service_name(df.service, service)]\n\n df = df.groupby(['timestamp']).agg(\n latency_quantiles=('latency', px.quantiles),\n error_rate_per_window=('failure', px.mean),\n throughput_total=('latency', px.count),\n bytes_total=('resp_body_size', px.sum)\n )\n\n # Format the result of LET aggregates into proper scalar formats and\n # time series.\n df.latency_p50 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p50')))\n df.latency_p90 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p90')))\n df.latency_p99 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p99')))\n df.request_throughput = df.throughput_total / window_ns\n df.errors_per_ns = df.error_rate_per_window * df.request_throughput / px.DurationNanos(1)\n df.error_rate = px.Percent(df.error_rate_per_window)\n df.bytes_per_ns = df.bytes_total / window_ns\n df.time_ = df.timestamp\n\n return df[['time_', 'latency_p50', 'latency_p90', 'latency_p99',\n 'request_throughput', 'errors_per_ns', 'error_rate', 'bytes_per_ns']]\n\n\ndef inbound_let_summary(start_time: int, service: px.Service):\n ''' Gets a summary of requests inbound to `service`.\n\n Args:\n @start_time Starting time of the data to examine.\n @service: The service to filter on.\n '''\n df = let_summary_helper(start_time)\n df = df[px.has_service_name(df.responder, service)]\n return df.drop('responder')\n\n\ndef outbound_let_summary(start_time: int, service: px.Service):\n ''' Gets a summary of requests outbound from `service`.\n\n Args:\n @start_time Starting time of the data to examine.\n @service: The service to filter on.\n '''\n df = let_summary_helper(start_time)\n df = df[px.has_service_name(df.requestor, service)]\n return df.drop(['requestor', 'remote_addr'])\n\n\ndef let_summary_helper(start_time: int):\n ''' Gets a summary of request statistics. This is a helper function, filtering by\n service is done elsewhere.\n\n Args:\n @start_time Starting time of the data to examine.\n '''\n df = let_helper(start_time)\n\n df = df.groupby(['service', 'remote_addr']).agg(\n latency=('latency', px.quantiles),\n total_request_count=('latency', px.count)\n error_rate=('failure', px.mean),\n )\n\n df.error_rate = px.Percent(df.error_rate)\n df.responder = df.service\n df.requesting_ip = df.remote_addr\n df.requesting_pod = px.pod_id_to_pod_name(px.ip_to_pod_id(df.remote_addr))\n df.requesting_svc = px.pod_id_to_service_name(px.ip_to_pod_id(df.remote_addr))\n\n return df[['responder', 'requesting_ip', 'requesting_pod', 'requesting_svc',\n 'latency', 'error_rate']]\n\n\ndef service_slow_requests(start_time: int, service: px.Service):\n df = let_helper(start_time)\n df = df[px.has_service_name(df.service, service)]\n quantiles = df.groupby('service').agg(\n latency_quantiles=('latency', px.quantiles)\n )\n quantiles.service_p99 = px.pluck_float64(quantiles.latency_quantiles, 'p99')\n quantiles = quantiles.drop('latency_quantiles')\n requests = df.merge(quantiles, left_on='service', right_on='service', how='inner',\n suffixes=['', '_x'])\n requests = requests[requests.latency >= px.floor(requests.service_p99)]\n\n return requests[['time_', 'pod', 'latency', 'req_method',\n 'req_path', 'req_body', 'resp_status',\n 'remote_addr', 'remote_port',\n 'resp_body']].head(100)\n\n\ndef let_helper(start_time: int):\n ''' Compute the initial part of the let for requests.\n Filtering to inbound/outbound traffic by service is done by the calling function.\n\n Args:\n @start_time: The timestamp of data to start at.\n\n '''\n df = px.DataFrame(table='http_events', start_time=start_time)\n # Filter only to inbound service traffic (server-side).\n # Don't include traffic initiated by this service to an external location.\n df = df[df.trace_role == 2]\n df.service = df.ctx['service']\n df.pod = df.ctx['pod']\n df.latency = df.latency\n\n df.timestamp = px.bin(df.time_, window_ns)\n\n df.failure = df.resp_status >= 400\n filter_out_conds = ((df.req_path != '/healthz' or not filter_health_checks) and (\n df.req_path != '/readyz' or not filter_ready_checks)) and (\n df['remote_addr'] != '-' or not filter_unresolved_inbound)\n\n df = df[filter_out_conds]\n return df\n \noutput = inbound_let_timeseries(start_time, service)\noutput.latency_p50 = output.latency_p50 / px.pow(10,6)\noutput.latency_p90 = output.latency_p90 / px.pow(10,6)\noutput.latency_p99 = output.latency_p99 / px.pow(10,6)\noutput = output[['time_', 'latency_p50', 'latency_p90', 'latency_p99']]\n\npx.display(output)\n"
},
"queryType": "run-script",
"refId": "A"
}
],
"title": "HTTP Latency",
"type": "timeseries"
},
{
"datasource": "${DS_PIXIE_GRAFANA DATASOURCE PLUGIN}",
"fieldConfig": {
"defaults": {
"color": {
"mode": "palette-classic"
},
"custom": {
"axisLabel": "response data throughput",
"axisPlacement": "auto",
"barAlignment": 0,
"drawStyle": "line",
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"mode": "absolute",
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"targets": [
{
"datasource": "${DS_PIXIE_GRAFANA DATASOURCE PLUGIN}",
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"pxlScript": "# Copyright 2018- The Pixie Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n# SPDX-License-Identifier: Apache-2.0\n\n''' Service Overview\n\n This script gets an overview of an individual service, summarizing its request statistics.\n'''\nimport px\n\nns_per_ms = 1000 * 1000\nns_per_s = 1000 * ns_per_ms\n# Window size to use on time_ column for bucketing.\nwindow_ns = px.DurationNanos(10 * ns_per_s)\n# Flag to filter out requests that come from an unresolvable IP.\nfilter_unresolved_inbound = True\n# Flag to filter out health checks from the data.\nfilter_health_checks = True\n# Flag to filter out ready checks from the data.\nfilter_ready_checks = True\n\n# $pixieCluster - work around for grafana to update panel on variable change\n\nstart_time = __time_from\nservice = \"$pixieService\"\n\ndef pods_for_service(start_time: int, service: px.Service):\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[px.has_service_name(df.ctx['service'], service)]\n df.pod = df.ctx['pod_name']\n df = df.groupby('pod').agg()\n df.pod_create_time = px.pod_name_to_start_time(df.pod)\n df.pod_status = px.pod_name_to_status(df.pod)\n return df\n\n\ndef inbound_let_timeseries(start_time: int, service: px.Service):\n ''' Compute the let as a timeseries for requests received by `service`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @service: The name of the service to filter on.\n\n '''\n df = let_helper(start_time)\n df = df[px.has_service_name(df.service, service)]\n\n df = df.groupby(['timestamp']).agg(\n latency_quantiles=('latency', px.quantiles),\n error_rate_per_window=('failure', px.mean),\n throughput_total=('latency', px.count),\n bytes_total=('resp_body_size', px.sum)\n )\n\n # Format the result of LET aggregates into proper scalar formats and\n # time series.\n df.latency_p50 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p50')))\n df.latency_p90 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p90')))\n df.latency_p99 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p99')))\n df.request_throughput = df.throughput_total / window_ns\n df.errors_per_ns = df.error_rate_per_window * df.request_throughput / px.DurationNanos(1)\n df.error_rate = px.Percent(df.error_rate_per_window)\n df.bytes_per_ns = df.bytes_total / window_ns\n df.time_ = df.timestamp\n\n return df[['time_', 'latency_p50', 'latency_p90', 'latency_p99',\n 'request_throughput', 'errors_per_ns', 'error_rate', 'bytes_per_ns']]\n\n\ndef inbound_let_summary(start_time: int, service: px.Service):\n ''' Gets a summary of requests inbound to `service`.\n\n Args:\n @start_time Starting time of the data to examine.\n @service: The service to filter on.\n '''\n df = let_summary_helper(start_time)\n df = df[px.has_service_name(df.responder, service)]\n return df.drop('responder')\n\n\ndef outbound_let_summary(start_time: int, service: px.Service):\n ''' Gets a summary of requests outbound from `service`.\n\n Args:\n @start_time Starting time of the data to examine.\n @service: The service to filter on.\n '''\n df = let_summary_helper(start_time)\n df = df[px.has_service_name(df.requestor, service)]\n return df.drop(['requestor', 'remote_addr'])\n\n\ndef let_summary_helper(start_time: int):\n ''' Gets a summary of request statistics. This is a helper function, filtering by\n service is done elsewhere.\n\n Args:\n @start_time Starting time of the data to examine.\n '''\n df = let_helper(start_time)\n\n df = df.groupby(['service', 'remote_addr']).agg(\n latency=('latency', px.quantiles),\n total_request_count=('latency', px.count)\n error_rate=('failure', px.mean),\n )\n\n df.error_rate = px.Percent(df.error_rate)\n df.responder = df.service\n df.requesting_ip = df.remote_addr\n df.requesting_pod = px.pod_id_to_pod_name(px.ip_to_pod_id(df.remote_addr))\n df.requesting_svc = px.pod_id_to_service_name(px.ip_to_pod_id(df.remote_addr))\n\n return df[['responder', 'requesting_ip', 'requesting_pod', 'requesting_svc',\n 'latency', 'error_rate']]\n\n\ndef service_slow_requests(start_time: int, service: px.Service):\n df = let_helper(start_time)\n df = df[px.has_service_name(df.service, service)]\n quantiles = df.groupby('service').agg(\n latency_quantiles=('latency', px.quantiles)\n )\n quantiles.service_p99 = px.pluck_float64(quantiles.latency_quantiles, 'p99')\n quantiles = quantiles.drop('latency_quantiles')\n requests = df.merge(quantiles, left_on='service', right_on='service', how='inner',\n suffixes=['', '_x'])\n requests = requests[requests.latency >= px.floor(requests.service_p99)]\n\n return requests[['time_', 'pod', 'latency', 'req_method',\n 'req_path', 'req_body', 'resp_status',\n 'remote_addr', 'remote_port',\n 'resp_body']].head(100)\n\n\ndef let_helper(start_time: int):\n ''' Compute the initial part of the let for requests.\n Filtering to inbound/outbound traffic by service is done by the calling function.\n\n Args:\n @start_time: The timestamp of data to start at.\n\n '''\n df = px.DataFrame(table='http_events', start_time=start_time)\n # Filter only to inbound service traffic (server-side).\n # Don't include traffic initiated by this service to an external location.\n df = df[df.trace_role == 2]\n df.service = df.ctx['service']\n df.pod = df.ctx['pod']\n df.latency = df.latency\n\n df.timestamp = px.bin(df.time_, window_ns)\n\n df.failure = df.resp_status >= 400\n filter_out_conds = ((df.req_path != '/healthz' or not filter_health_checks) and (\n df.req_path != '/readyz' or not filter_ready_checks)) and (\n df['remote_addr'] != '-' or not filter_unresolved_inbound)\n\n df = df[filter_out_conds]\n return df\n \noutput = inbound_let_timeseries(start_time, service)\noutput.bytes_per_ns = output.bytes_per_ns * px.pow(10,9) / px.pow(2,10)\noutput = output[['time_', 'bytes_per_ns']]\npx.display(output)\n"
},
"queryType": "run-script",
"refId": "A"
}
],
"title": "HTTP Response Data Throughput",
"type": "timeseries"
},
{
"datasource": "${DS_PIXIE_GRAFANA DATASOURCE PLUGIN}",
"fieldConfig": {
"defaults": {
"color": {
"mode": "thresholds"
},
"custom": {
"align": "auto",
"displayMode": "auto",
"inspect": false
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"mappings": [],
"thresholds": {
"mode": "absolute",
"steps": [
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"color": "green",
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"unit": "/s"
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"overrides": [
{
"matcher": {
"id": "byName",
"options": "state"
},
"properties": [
{
"id": "custom.width",
"value": 127
},
{
"id": "custom.displayMode",
"value": "color-text"
},
{
"id": "mappings",
"value": [
{
"options": {
"Failed": {
"color": "red",
"index": 5
},
"Pending": {
"color": "yellow",
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"Running": {
"color": "green",
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"Succeeded": {
"color": "green",
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"Terminated": {
"color": "red",
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"Unknown": {
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{
"matcher": {
"id": "byName",
"options": "pod"
},
"properties": [
{
"id": "links",
"value": [
{
"title": "",
"url": "/d/_t3foxCnz/px-pod?orgId=1&${pixieCluster:queryparam}&var-pixiePod=${__data.fields.pod}"
}
]
}
]
}
]
},
"gridPos": {
"h": 13,
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"options": {
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"fields": "",
"reducer": [
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"pluginVersion": "7.5.1",
"targets": [
{
"datasource": "${DS_PIXIE_GRAFANA DATASOURCE PLUGIN}",
"queryBody": {
"pxlScript": "# Copyright 2018- The Pixie Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n# SPDX-License-Identifier: Apache-2.0\n\n''' Service Overview\n\n This script gets an overview of an individual service, summarizing its request statistics.\n'''\nimport px\n\nns_per_ms = 1000 * 1000\nns_per_s = 1000 * ns_per_ms\n# Window size to use on time_ column for bucketing.\nwindow_ns = px.DurationNanos(10 * ns_per_s)\n# Flag to filter out requests that come from an unresolvable IP.\nfilter_unresolved_inbound = True\n# Flag to filter out health checks from the data.\nfilter_health_checks = True\n# Flag to filter out ready checks from the data.\nfilter_ready_checks = True\n\n# $pixieCluster - work around for grafana to update panel on variable change\n\nstart_time = __time_from\nservice = \"$pixieService\"\n\ndef pods_for_service(start_time: int, service: px.Service):\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[px.has_service_name(df.ctx['service'], service)]\n df.pod = df.ctx['pod_name']\n df = df.groupby('pod').agg()\n df.pod_create_time = px.pod_name_to_start_time(df.pod)\n df.pod_status = px.pod_name_to_status(df.pod)\n return df\n\n\ndef inbound_let_timeseries(start_time: int, service: px.Service):\n ''' Compute the let as a timeseries for requests received by `service`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @service: The name of the service to filter on.\n\n '''\n df = let_helper(start_time)\n df = df[px.has_service_name(df.service, service)]\n\n df = df.groupby(['timestamp']).agg(\n latency_quantiles=('latency', px.quantiles),\n error_rate_per_window=('failure', px.mean),\n throughput_total=('latency', px.count),\n bytes_total=('resp_body_size', px.sum)\n )\n\n # Format the result of LET aggregates into proper scalar formats and\n # time series.\n df.latency_p50 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p50')))\n df.latency_p90 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p90')))\n df.latency_p99 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p99')))\n df.request_throughput = df.throughput_total / window_ns\n df.errors_per_ns = df.error_rate_per_window * df.request_throughput / px.DurationNanos(1)\n df.error_rate = px.Percent(df.error_rate_per_window)\n df.bytes_per_ns = df.bytes_total / window_ns\n df.time_ = df.timestamp\n\n return df[['time_', 'latency_p50', 'latency_p90', 'latency_p99',\n 'request_throughput', 'errors_per_ns', 'error_rate', 'bytes_per_ns']]\n\n\ndef inbound_let_summary(start_time: int, service: px.Service):\n ''' Gets a summary of requests inbound to `service`.\n\n Args:\n @start_time Starting time of the data to examine.\n @service: The service to filter on.\n '''\n df = let_summary_helper(start_time)\n df = df[px.has_service_name(df.responder, service)]\n return df.drop('responder')\n\n\ndef outbound_let_summary(start_time: int, service: px.Service):\n ''' Gets a summary of requests outbound from `service`.\n\n Args:\n @start_time Starting time of the data to examine.\n @service: The service to filter on.\n '''\n df = let_summary_helper(start_time)\n df = df[px.has_service_name(df.requestor, service)]\n return df.drop(['requestor', 'remote_addr'])\n\n\ndef let_summary_helper(start_time: int):\n ''' Gets a summary of request statistics. This is a helper function, filtering by\n service is done elsewhere.\n\n Args:\n @start_time Starting time of the data to examine.\n '''\n df = let_helper(start_time)\n\n df = df.groupby(['service', 'remote_addr']).agg(\n latency=('latency', px.quantiles),\n total_request_count=('latency', px.count)\n error_rate=('failure', px.mean),\n )\n\n df.error_rate = px.Percent(df.error_rate)\n df.responder = df.service\n df.requesting_ip = df.remote_addr\n df.requesting_pod = px.pod_id_to_pod_name(px.ip_to_pod_id(df.remote_addr))\n df.requesting_svc = px.pod_id_to_service_name(px.ip_to_pod_id(df.remote_addr))\n\n return df[['responder', 'requesting_ip', 'requesting_pod', 'requesting_svc',\n 'latency', 'error_rate']]\n\n\ndef service_slow_requests(start_time: int, service: px.Service):\n df = let_helper(start_time)\n df = df[px.has_service_name(df.service, service)]\n quantiles = df.groupby('service').agg(\n latency_quantiles=('latency', px.quantiles)\n )\n quantiles.service_p99 = px.pluck_float64(quantiles.latency_quantiles, 'p99')\n quantiles = quantiles.drop('latency_quantiles')\n requests = df.merge(quantiles, left_on='service', right_on='service', how='inner',\n suffixes=['', '_x'])\n requests = requests[requests.latency >= px.floor(requests.service_p99)]\n\n return requests[['time_', 'pod', 'latency', 'req_method',\n 'req_path', 'req_body', 'resp_status',\n 'remote_addr', 'remote_port',\n 'resp_body']].head(100)\n\n\ndef let_helper(start_time: int):\n ''' Compute the initial part of the let for requests.\n Filtering to inbound/outbound traffic by service is done by the calling function.\n\n Args:\n @start_time: The timestamp of data to start at.\n\n '''\n df = px.DataFrame(table='http_events', start_time=start_time)\n # Filter only to inbound service traffic (server-side).\n # Don't include traffic initiated by this service to an external location.\n df = df[df.trace_role == 2]\n df.service = df.ctx['service']\n df.pod = df.ctx['pod']\n df.latency = df.latency\n\n df.timestamp = px.bin(df.time_, window_ns)\n\n df.failure = df.resp_status >= 400\n filter_out_conds = ((df.req_path != '/healthz' or not filter_health_checks) and (\n df.req_path != '/readyz' or not filter_ready_checks)) and (\n df['remote_addr'] != '-' or not filter_unresolved_inbound)\n\n df = df[filter_out_conds]\n return df\n \noutput = pods_for_service(start_time, service)\n\noutput.state = px.pluck(output.pod_status, \"phase\")\noutput.message = px.pluck(output.pod_status, \"message\") \n\noutput = output[['pod', 'state', 'message', 'pod_create_time']]\npx.display(output)\n"
},
"queryType": "run-script",
"refId": "A"
}
],
"title": "Pod List",
"type": "table"
},
{
"datasource": "${DS_PIXIE_GRAFANA DATASOURCE PLUGIN}",
"fieldConfig": {
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"color": {
"mode": "thresholds"
},
"custom": {
"align": "auto",
"displayMode": "auto",
"inspect": false
},
"mappings": [],
"thresholds": {
"mode": "absolute",
"steps": [
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"color": "green"
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{
"color": "red",
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"unit": "none"
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"overrides": [
{
"matcher": {
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"options": "error_rate"
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"properties": [
{
"id": "unit",
"value": "percentunit"
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{
"matcher": {
"id": "byName",
"options": "latency_p99"
},
"properties": [
{
"id": "unit",
"value": "ms"
},
{
"id": "custom.displayMode",
"value": "color-text"
},
{
"id": "thresholds",
"value": {
"mode": "absolute",
"steps": [
{
"color": "green"
},
{
"color": "light-orange",
"value": 150
},
{
"color": "red",
"value": 300
}
]
}
}
]
},
{
"matcher": {
"id": "byName",
"options": "requesting_pod"
},
"properties": [
{
"id": "links",
"value": [
{
"title": "",
"url": "/d/_t3foxCnz/px-pod?orgId=1&${pixieCluster:queryparam}&var-pixiePod=${__data.fields.requesting_pod}"
}
]
}
]
},
{
"matcher": {
"id": "byName",
"options": "requesting_svc"
},
"properties": [
{
"id": "links",
"value": [
{
"title": "",
"url": "/d/qhHtFyCnk/px-service?orgId=1&${pixieCluster:queryparam}&var-pixieService=${__data.fields.requesting_svc}"
}
]
}
]
}
]
},
"gridPos": {
"h": 14,
"w": 24,
"x": 0,
"y": 25
},
"id": 7,
"options": {
"footer": {
"fields": "",
"reducer": [
"sum"
],
"show": false
},
"showHeader": true,
"sortBy": [
{
"desc": true,
"displayName": "latency_p99"
}
]
},
"pluginVersion": "7.5.1",
"targets": [
{
"datasource": "${DS_PIXIE_GRAFANA DATASOURCE PLUGIN}",
"queryBody": {
"pxlScript": "# Copyright 2018- The Pixie Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n# SPDX-License-Identifier: Apache-2.0\n\n''' Service Overview\n\n This script gets an overview of an individual service, summarizing its request statistics.\n'''\nimport px\n\nns_per_ms = 1000 * 1000\nns_per_s = 1000 * ns_per_ms\n# Window size to use on time_ column for bucketing.\nwindow_ns = px.DurationNanos(10 * ns_per_s)\n# Flag to filter out requests that come from an unresolvable IP.\nfilter_unresolved_inbound = True\n# Flag to filter out health checks from the data.\nfilter_health_checks = True\n# Flag to filter out ready checks from the data.\nfilter_ready_checks = True\n\n# $pixieCluster - work around for grafana to update panel on variable change\n\nstart_time = __time_from\nservice = \"$pixieService\"\n\ndef pods_for_service(start_time: int, service: px.Service):\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[px.has_service_name(df.ctx['service'], service)]\n df.pod = df.ctx['pod_name']\n df = df.groupby('pod').agg()\n df.pod_create_time = px.pod_name_to_start_time(df.pod)\n df.pod_status = px.pod_name_to_status(df.pod)\n return df\n\n\ndef inbound_let_timeseries(start_time: int, service: px.Service):\n ''' Compute the let as a timeseries for requests received by `service`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @service: The name of the service to filter on.\n\n '''\n df = let_helper(start_time)\n df = df[px.has_service_name(df.service, service)]\n\n df = df.groupby(['timestamp']).agg(\n latency_quantiles=('latency', px.quantiles),\n error_rate_per_window=('failure', px.mean),\n throughput_total=('latency', px.count),\n bytes_total=('resp_body_size', px.sum)\n )\n\n # Format the result of LET aggregates into proper scalar formats and\n # time series.\n df.latency_p50 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p50')))\n df.latency_p90 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p90')))\n df.latency_p99 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p99')))\n df.request_throughput = df.throughput_total / window_ns\n df.errors_per_ns = df.error_rate_per_window * df.request_throughput / px.DurationNanos(1)\n df.error_rate = px.Percent(df.error_rate_per_window)\n df.bytes_per_ns = df.bytes_total / window_ns\n df.time_ = df.timestamp\n\n return df[['time_', 'latency_p50', 'latency_p90', 'latency_p99',\n 'request_throughput', 'errors_per_ns', 'error_rate', 'bytes_per_ns']]\n\n\ndef inbound_let_summary(start_time: int, service: px.Service):\n ''' Gets a summary of requests inbound to `service`.\n\n Args:\n @start_time Starting time of the data to examine.\n @service: The service to filter on.\n '''\n df = let_summary_helper(start_time)\n df = df[px.has_service_name(df.responder, service)]\n return df.drop('responder')\n\n\ndef outbound_let_summary(start_time: int, service: px.Service):\n ''' Gets a summary of requests outbound from `service`.\n\n Args:\n @start_time Starting time of the data to examine.\n @service: The service to filter on.\n '''\n df = let_summary_helper(start_time)\n df = df[px.has_service_name(df.requestor, service)]\n return df.drop(['requestor', 'remote_addr'])\n\n\ndef let_summary_helper(start_time: int):\n ''' Gets a summary of request statistics. This is a helper function, filtering by\n service is done elsewhere.\n\n Args:\n @start_time Starting time of the data to examine.\n '''\n df = let_helper(start_time)\n\n df = df.groupby(['service', 'remote_addr']).agg(\n latency=('latency', px.quantiles),\n total_request_count=('latency', px.count)\n error_rate=('failure', px.mean),\n )\n\n df.error_rate = px.Percent(df.error_rate)\n df.responder = df.service\n df.requesting_ip = df.remote_addr\n df.requesting_pod = px.pod_id_to_pod_name(px.ip_to_pod_id(df.remote_addr))\n df.requesting_svc = px.pod_id_to_service_name(px.ip_to_pod_id(df.remote_addr))\n\n return df[['responder', 'requesting_ip', 'requesting_pod', 'requesting_svc',\n 'latency', 'error_rate']]\n\n\ndef service_slow_requests(start_time: int, service: px.Service):\n df = let_helper(start_time)\n df = df[px.has_service_name(df.service, service)]\n quantiles = df.groupby('service').agg(\n latency_quantiles=('latency', px.quantiles)\n )\n quantiles.service_p99 = px.pluck_float64(quantiles.latency_quantiles, 'p99')\n quantiles = quantiles.drop('latency_quantiles')\n requests = df.merge(quantiles, left_on='service', right_on='service', how='inner',\n suffixes=['', '_x'])\n requests = requests[requests.latency >= px.floor(requests.service_p99)]\n\n return requests[['time_', 'pod', 'latency', 'req_method',\n 'req_path', 'req_body', 'resp_status',\n 'remote_addr', 'remote_port',\n 'resp_body']].head(100)\n\n\ndef let_helper(start_time: int):\n ''' Compute the initial part of the let for requests.\n Filtering to inbound/outbound traffic by service is done by the calling function.\n\n Args:\n @start_time: The timestamp of data to start at.\n\n '''\n df = px.DataFrame(table='http_events', start_time=start_time)\n # Filter only to inbound service traffic (server-side).\n # Don't include traffic initiated by this service to an external location.\n df = df[df.trace_role == 2]\n df.service = df.ctx['service']\n df.pod = df.ctx['pod']\n df.latency = df.latency\n\n df.timestamp = px.bin(df.time_, window_ns)\n\n df.failure = df.resp_status >= 400\n filter_out_conds = ((df.req_path != '/healthz' or not filter_health_checks) and (\n df.req_path != '/readyz' or not filter_ready_checks)) and (\n df['remote_addr'] != '-' or not filter_unresolved_inbound)\n\n df = df[filter_out_conds]\n return df\n \noutput = inbound_let_summary(start_time, service)\noutput.latency_p99 = px.pluck_float64(output.latency, \"p99\") / px.pow(10,6)\n\noutput = output[['requesting_ip', 'requesting_pod', 'requesting_svc', 'latency_p99', 'error_rate']]\npx.display(output)\n"
},
"queryType": "run-script",
"refId": "A"
}
],
"title": "Inbound HTTP Traffic To Service",
"type": "table"
},
{
"datasource": "${DS_PIXIE_GRAFANA DATASOURCE PLUGIN}",
"fieldConfig": {
"defaults": {
"color": {
"mode": "thresholds"
},
"custom": {
"align": "auto",
"displayMode": "auto",
"inspect": false
},
"mappings": [],
"thresholds": {
"mode": "absolute",
"steps": [
{
"color": "green"
},
{
"color": "red",
"value": 80
}
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"unit": "none"
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"overrides": [
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"matcher": {
"id": "byName",
"options": "latency"
},
"properties": [
{
"id": "unit",
"value": "ms"
},
{
"id": "custom.displayMode",
"value": "color-text"
},
{
"id": "thresholds",
"value": {
"mode": "absolute",
"steps": [
{
"color": "light-green"
},
{
"color": "light-orange",
"value": 150
},
{
"color": "red",
"value": 300
}
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{
"matcher": {
"id": "byName",
"options": "resp_status"
},
"properties": [
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"id": "custom.displayMode",
"value": "color-text"
},
{
"id": "thresholds",
"value": {
"mode": "absolute",
"steps": [
{
"color": "green"
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{
"color": "yellow",
"value": 300
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{
"color": "red",
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"matcher": {
"id": "byName",
"options": "pod"
},
"properties": [
{
"id": "links",
"value": [
{
"title": "",
"url": "/d/_t3foxCnz/px-pod?orgId=1&${pixieCluster:queryparam}&var-pixiePod=${__data.fields.pod}"
}
]
}
]
}
]
},
"gridPos": {
"h": 24,
"w": 24,
"x": 0,
"y": 39
},
"id": 8,
"options": {
"footer": {
"fields": "",
"reducer": [
"sum"
],
"show": false
},
"showHeader": true,
"sortBy": [
{
"desc": true,
"displayName": "latency"
}
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},
"pluginVersion": "7.5.1",
"targets": [
{
"datasource": "${DS_PIXIE_GRAFANA DATASOURCE PLUGIN}",
"queryBody": {
"pxlScript": "# Copyright 2018- The Pixie Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n# SPDX-License-Identifier: Apache-2.0\n\n''' Service Overview\n\n This script gets an overview of an individual service, summarizing its request statistics.\n'''\nimport px\n\nns_per_ms = 1000 * 1000\nns_per_s = 1000 * ns_per_ms\n# Window size to use on time_ column for bucketing.\nwindow_ns = px.DurationNanos(10 * ns_per_s)\n# Flag to filter out requests that come from an unresolvable IP.\nfilter_unresolved_inbound = True\n# Flag to filter out health checks from the data.\nfilter_health_checks = True\n# Flag to filter out ready checks from the data.\nfilter_ready_checks = True\n\n# $pixieCluster - work around for grafana to update panel on variable change\n\nstart_time = __time_from\nservice = \"$pixieService\"\n\ndef pods_for_service(start_time: int, service: px.Service):\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df = df[px.has_service_name(df.ctx['service'], service)]\n df.pod = df.ctx['pod_name']\n df = df.groupby('pod').agg()\n df.pod_create_time = px.pod_name_to_start_time(df.pod)\n df.pod_status = px.pod_name_to_status(df.pod)\n return df\n\n\ndef inbound_let_timeseries(start_time: int, service: px.Service):\n ''' Compute the let as a timeseries for requests received by `service`.\n\n Args:\n @start_time: The timestamp of data to start at.\n @service: The name of the service to filter on.\n\n '''\n df = let_helper(start_time)\n df = df[px.has_service_name(df.service, service)]\n\n df = df.groupby(['timestamp']).agg(\n latency_quantiles=('latency', px.quantiles),\n error_rate_per_window=('failure', px.mean),\n throughput_total=('latency', px.count),\n bytes_total=('resp_body_size', px.sum)\n )\n\n # Format the result of LET aggregates into proper scalar formats and\n # time series.\n df.latency_p50 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p50')))\n df.latency_p90 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p90')))\n df.latency_p99 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p99')))\n df.request_throughput = df.throughput_total / window_ns\n df.errors_per_ns = df.error_rate_per_window * df.request_throughput / px.DurationNanos(1)\n df.error_rate = px.Percent(df.error_rate_per_window)\n df.bytes_per_ns = df.bytes_total / window_ns\n df.time_ = df.timestamp\n\n return df[['time_', 'latency_p50', 'latency_p90', 'latency_p99',\n 'request_throughput', 'errors_per_ns', 'error_rate', 'bytes_per_ns']]\n\n\ndef inbound_let_summary(start_time: int, service: px.Service):\n ''' Gets a summary of requests inbound to `service`.\n\n Args:\n @start_time Starting time of the data to examine.\n @service: The service to filter on.\n '''\n df = let_summary_helper(start_time)\n df = df[px.has_service_name(df.responder, service)]\n return df.drop('responder')\n\n\ndef outbound_let_summary(start_time: int, service: px.Service):\n ''' Gets a summary of requests outbound from `service`.\n\n Args:\n @start_time Starting time of the data to examine.\n @service: The service to filter on.\n '''\n df = let_summary_helper(start_time)\n df = df[px.has_service_name(df.requestor, service)]\n return df.drop(['requestor', 'remote_addr'])\n\n\ndef let_summary_helper(start_time: int):\n ''' Gets a summary of request statistics. This is a helper function, filtering by\n service is done elsewhere.\n\n Args:\n @start_time Starting time of the data to examine.\n '''\n df = let_helper(start_time)\n\n df = df.groupby(['service', 'remote_addr']).agg(\n latency=('latency', px.quantiles),\n total_request_count=('latency', px.count)\n error_rate=('failure', px.mean),\n )\n\n df.error_rate = px.Percent(df.error_rate)\n df.responder = df.service\n df.requesting_ip = df.remote_addr\n df.requesting_pod = px.pod_id_to_pod_name(px.ip_to_pod_id(df.remote_addr))\n df.requesting_svc = px.pod_id_to_service_name(px.ip_to_pod_id(df.remote_addr))\n\n return df[['responder', 'requesting_ip', 'requesting_pod', 'requesting_svc',\n 'latency', 'error_rate']]\n\n\ndef service_slow_requests(start_time: int, service: px.Service):\n df = let_helper(start_time)\n df = df[px.has_service_name(df.service, service)]\n quantiles = df.groupby('service').agg(\n latency_quantiles=('latency', px.quantiles)\n )\n quantiles.service_p99 = px.pluck_float64(quantiles.latency_quantiles, 'p99')\n quantiles = quantiles.drop('latency_quantiles')\n requests = df.merge(quantiles, left_on='service', right_on='service', how='inner',\n suffixes=['', '_x'])\n requests = requests[requests.latency >= px.floor(requests.service_p99)]\n\n return requests[['time_', 'pod', 'latency', 'req_method',\n 'req_path', 'req_body', 'resp_status',\n 'remote_addr', 'remote_port',\n 'resp_body']].head(100)\n\n\ndef let_helper(start_time: int):\n ''' Compute the initial part of the let for requests.\n Filtering to inbound/outbound traffic by service is done by the calling function.\n\n Args:\n @start_time: The timestamp of data to start at.\n\n '''\n df = px.DataFrame(table='http_events', start_time=start_time)\n # Filter only to inbound service traffic (server-side).\n # Don't include traffic initiated by this service to an external location.\n df = df[df.trace_role == 2]\n df.service = df.ctx['service']\n df.pod = df.ctx['pod']\n df.latency = df.latency\n\n df.timestamp = px.bin(df.time_, window_ns)\n\n df.failure = df.resp_status >= 400\n filter_out_conds = ((df.req_path != '/healthz' or not filter_health_checks) and (\n df.req_path != '/readyz' or not filter_ready_checks)) and (\n df['remote_addr'] != '-' or not filter_unresolved_inbound)\n\n df = df[filter_out_conds]\n return df\n \noutput = service_slow_requests(start_time, service)\noutput.latency = output.latency / px.pow(10,6)\noutput.timestamp = output.time_\noutput = output.drop('time_')\n\noutput = output[['timestamp', 'pod', 'latency', 'req_method', 'req_path', 'req_body', 'resp_status', 'remote_addr', 'remote_port', 'resp_body']]\npx.display(output)\n"
},
"queryType": "run-script",
"refId": "A"
}
],
"title": "Sample Of Slow Inbound Requests",
"type": "table"
}
],
"schemaVersion": 36,
"style": "dark",
"tags": [],
"templating": {
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{
"current": {},
"datasource": "${DS_PIXIE_GRAFANA DATASOURCE PLUGIN}",
"definition": "Clusters",
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"datasource": "${DS_PIXIE_GRAFANA DATASOURCE PLUGIN}",
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} | 2022-09-16 15:34:27 UTC | 2022-09-16 15:34:57 UTC | 0 | 0 |
17,222 | 1 | 729,023 | 26,456 | 1 | pod-rightsizing | Pod rightsizing | Pod rightsizing | 2022-10-19 13:06:01 UTC | 2022-10-19 13:06:08 UTC | 0 | null | 0 | 0 | null | null | 0 | 0 | 26,456 | 1 | 17,222 | 1 | 1 | null | {
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"targets": [
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"editorMode": "code",
"expr": "sum by (pod,container)(rate(container_cpu_usage_seconds_total{container=~\"$container\"}[2m]))/sum by (pod,container)(kube_pod_container_resource_requests{resource=\"cpu\",container=~\"$container\"}) *100",
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16,438 | 1 | 662,546 | 25,065 | 1 | dataset-result-cmp | dataset_result_cmp | 查看「多个选定算法」在「选定数据集」下的整体检测指标的对比 | 2022-06-13 06:32:40 UTC | 2022-06-13 06:33:07 UTC | 0 | null | 0 | 0 | null | null | 0 | 0 | 25,065 | 1 | 16,438 | 1 | 1 | null | {
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17,016 | 1 | 711,753 | 26,089 | 1 | victron | Moot Victron | Multipass inverter | 2022-09-19 14:59:42 UTC | 2022-09-19 15:05:36 UTC | 0 | null | 0 | 0 | Other | null | 0 | 0 | 26,089 | 1 | 17,016 | 1 | 1 | null | {
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9,081 | 1 | 213,411 | 12,537 | 257 | nodes-by-instancegroup | Kubernetes Nodes by InstanceGroup | Graph Load and Memory usage of nodes grouped by instance-group. This is useful for planning the usage of your instance-groups. | 2018-11-17 22:34:42 UTC | 2018-11-17 22:39:20 UTC | 0 | null | 39,501 | 39,504 | nodeExporter | This dashboard has been tested with the prometheus-operator and kube-prometheus installations on a kops created Kubernetes cluster on AWS. | 0 | 0 | 12,537 | 1 | 9,081 | 1 | 257 | null | {
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"renderer": "flot",
"repeatDirection": "v",
"seriesOverrides": [],
"spaceLength": 10,
"stack": false,
"steppedLine": false,
"targets": [
{
"expr": "node_memory_MemTotal{instance=~\"$instance\"} - node_memory_MemFree{instance=~\"$instance\"} - node_memory_Buffers{instance=~\"$instance\"} - node_memory_Cached{instance=~\"$instance\"}",
"format": "time_series",
"hide": false,
"interval": "",
"intervalFactor": 1,
"legendFormat": "{{ instance }} used",
"refId": "B"
},
{
"expr": "min(node_memory_MemTotal{instance=~\"$instance\"})",
"format": "time_series",
"intervalFactor": 1,
"legendFormat": "{{ instance }} total",
"refId": "A"
}
],
"thresholds": [],
"timeFrom": null,
"timeShift": null,
"title": "Memory Usage",
"tooltip": {
"shared": true,
"sort": 0,
"value_type": "individual"
},
"transparent": false,
"type": "graph",
"xaxis": {
"buckets": null,
"mode": "time",
"name": null,
"show": true,
"values": []
},
"yaxes": [
{
"decimals": null,
"format": "bytes",
"label": "",
"logBase": 1,
"max": null,
"min": "0",
"show": true
},
{
"format": "short",
"label": null,
"logBase": 1,
"max": null,
"min": null,
"show": true
}
]
}
],
"schemaVersion": 16,
"style": "dark",
"tags": [],
"templating": {
"list": [
{
"allValue": null,
"current": null,
"datasource": "${DS_PROMETHEUS}",
"hide": 0,
"includeAll": false,
"label": "InstanceGroup",
"multi": false,
"name": "ig",
"options": [],
"query": "query_result(kube_node_labels)",
"refresh": 1,
"regex": "/label_igname=\"(\\w+)\"/",
"sort": 0,
"tagValuesQuery": "",
"tags": [],
"tagsQuery": "",
"type": "query",
"useTags": false
},
{
"allValue": null,
"current": null,
"datasource": "${DS_PROMETHEUS}",
"hide": 0,
"includeAll": true,
"label": "Instance",
"multi": false,
"name": "instance",
"options": [],
"query": "query_result((label_replace(kube_node_labels{label_igname=\"$ig\"}, \"nodename\", \"$1\", \"node\", \"(ip-\\\\d+-\\\\d+-\\\\d+-\\\\d+).+\") * on(nodename) group_left(instance) node_uname_info))",
"refresh": 1,
"regex": "/instance=\"(.+:9100)/",
"sort": 0,
"tagValuesQuery": "",
"tags": [],
"tagsQuery": "",
"type": "query",
"useTags": false
}
]
},
"time": {
"from": "now-1h",
"to": "now"
},
"timepicker": {
"refresh_intervals": [
"5s",
"10s",
"30s",
"1m",
"5m",
"15m",
"30m",
"1h",
"2h",
"1d"
],
"time_options": [
"5m",
"15m",
"1h",
"6h",
"12h",
"24h",
"2d",
"7d",
"30d"
]
},
"timezone": "",
"title": "Kubernetes Nodes by InstanceGroup",
"uid": "vRtDmfBmk",
"version": 16,
"description": "Graph Load and Memory usage of nodes grouped by instance-group. This is useful for planning the usage of your instance-groups."
} | 2018-11-17 22:34:42 UTC | 2018-11-17 22:39:20 UTC | 0 | 0 |
7,568 | 1 | 176,828 | 10,298 | 1,793 | my-glpi-dashboard | GLPI Dashboard (Tickets) | GLPI Dashboard
Show somes informations on tickets | 2018-08-16 18:40:17 UTC | 2018-08-16 18:52:19 UTC | 0 | # Informations
This dashboard is based on the original from the GLPI Plugin, he's provide you clears statistics about your tickets from GLPI.
## Setup
Your URL need to have the **/** at the end like :http://my-glpi-server.com/ and not http://my-glpi-server.com
# Changelogs
## v0.1
### Import
* Added the choice of the datasource ($DS_GLPI) during the import
* Added a field for the GLPI url ($URL_GLPI) when importing
### Dashboard
* Histogram of Incidents / Request
* SingleStat with: New Ticket, Ticket waiting for validation, Ticket unresolved, number of tickets created with time range 24h
* Modification of the "Last Ticket" table that displayed the HTML tags in the column Applicant
Resume variables for creating different panels.
# To improve
Retrieve the URL directly from the plugin configuration but removing the "apirest.php" | 33,851 | 33,854 | Other | null | 2 | 4.5 | 10,298 | 1 | 7,568 | 1 | 1,793 | null | {
"__inputs": [
{
"name": "DS_GLPI",
"label": "GLPI Datasource",
"description": "Select your GLPI Datasource",
"type": "datasource",
"pluginId": "ddurieux-glpi-datasource",
"pluginName": "GLPI API"
},
{
"name": "URL_GLPI",
"label": "GLPI URL",
"description": "Your URL must be like : http://my-glpi-server.com/ or http://10.0.0.1/",
"type": "constant"
}
],
"__requires": [
{
"type": "grafana",
"id": "grafana",
"name": "Grafana",
"version": "4.4.1"
},
{
"type": "datasource",
"id": "ddurieux-glpi-datasource",
"name": "GLPI API",
"version": "4.0.3"
},
{
"type": "panel",
"id": "singlestat",
"name": "Singlestat",
"version": ""
},
{
"type": "panel",
"id": "table",
"name": "Table",
"version": ""
}
],
"annotations": {
"list": [
{
"builtIn": 1,
"datasource": "-- Grafana --",
"enable": true,
"hide": true,
"iconColor": "rgba(0, 211, 255, 1)",
"name": "Annotations & Alerts",
"type": "dashboard"
}
]
},
"editable": true,
"gnetId": 7568,
"graphTooltip": 0,
"id": 32,
"iteration": 1534441585999,
"links": [],
"panels": [
{
"aliasColors": {},
"bars": true,
"dashLength": 10,
"dashes": false,
"datasource": "$DS_GLPI",
"decimals": null,
"fill": 1,
"gridPos": {
"h": 6,
"w": 5,
"x": 0,
"y": 0
},
"id": 15,
"legend": {
"alignAsTable": false,
"avg": false,
"current": false,
"max": false,
"min": false,
"rightSide": false,
"show": true,
"total": false,
"values": false
},
"lines": false,
"linewidth": 1,
"links": [],
"nullPointMode": "null",
"percentage": false,
"pointradius": 5,
"points": false,
"renderer": "flot",
"seriesOverrides": [],
"spaceLength": 10,
"stack": false,
"steppedLine": false,
"targets": [
{
"alias": "Incident",
"channel": {
"name": ""
},
"col_0": {
"group": "Default",
"label": "------",
"number": "0"
},
"col_1": {
"group": "Default",
"label": "------",
"number": "0"
},
"col_10": {
"group": "Default",
"label": "------",
"number": "0"
},
"col_11": {
"group": "Default",
"label": "------",
"number": "0"
},
"col_2": {
"group": "Default",
"label": "------",
"number": "0"
},
"col_3": {
"group": "Default",
"label": "------",
"number": "0"
},
"col_4": {
"group": "Default",
"label": "------",
"number": "0"
},
"col_5": {
"group": "Default",
"label": "------",
"number": "0"
},
"col_6": {
"group": "Default",
"label": "------",
"number": "0"
},
"col_7": {
"group": "Default",
"label": "------",
"number": "0"
},
"col_8": {
"group": "Default",
"label": "------",
"number": "0"
},
"col_9": {
"group": "Default",
"label": "------",
"number": "0"
},
"console": false,
"counter": true,
"datefield": {
"group": "Special / be careful",
"label": "Do not use date search (get all data)",
"number": "-1"
},
"dayhours": false,
"device": {
"name": ""
},
"dynamicsplit": {
"group": "Default",
"label": "------",
"number": "0"
},
"errors": {},
"functions": [],
"group": {
"name": ""
},
"histogram": false,
"options": {
"includeDeviceName": false,
"includeSensorName": false,
"mode": {
"filterProperty": {},
"name": "Metrics",
"textProperty": {},
"textValueFrom": {},
"value": 1
}
},
"query": "${URL_GLPI}front/ticket.php?is_deleted=0&as_map=0&criteria[0][field]=14&criteria[0][searchtype]=equals&criteria[0][value]=1&search=Rechercher&itemtype=Ticket&start=0",
"raw": {
"queryString": "",
"uri": ""
},
"refId": "A",
"sensor": {
"name": ""
},
"table": false
},
{
"alias": "Request",
"col_0": {
"group": "Default",
"label": "------",
"number": "0"
},
"col_1": {
"group": "Default",
"label": "------",
"number": "0"
},
"col_10": {
"group": "Default",
"label": "------",
"number": "0"
},
"col_11": {
"group": "Default",
"label": "------",
"number": "0"
},
"col_2": {
"group": "Default",
"label": "------",
"number": "0"
},
"col_3": {
"group": "Default",
"label": "------",
"number": "0"
},
"col_4": {
"group": "Default",
"label": "------",
"number": "0"
},
"col_5": {
"group": "Default",
"label": "------",
"number": "0"
},
"col_6": {
"group": "Default",
"label": "------",
"number": "0"
},
"col_7": {
"group": "Default",
"label": "------",
"number": "0"
},
"col_8": {
"group": "Default",
"label": "------",
"number": "0"
},
"col_9": {
"group": "Default",
"label": "------",
"number": "0"
},
"console": false,
"counter": true,
"datefield": {
"group": "Special / be careful",
"label": "Do not use date search (get all data)",
"number": "-1"
},
"dayhours": false,
"dynamicsplit": {
"group": "Default",
"label": "------",
"number": "0"
},
"histogram": false,
"query": "${URL_GLPI}front/ticket.php?is_deleted=0&as_map=0&criteria[0][field]=14&criteria[0][searchtype]=equals&criteria[0][value]=2&search=Rechercher&itemtype=Ticket&start=0",
"refId": "B",
"table": false
}
],
"thresholds": [],
"timeFrom": null,
"timeShift": null,
"title": "Incident / Request Stats",
"tooltip": {
"shared": false,
"sort": 0,
"value_type": "individual"
},
"type": "graph",
"xaxis": {
"buckets": null,
"mode": "series",
"name": null,
"show": true,
"values": [
"total"
]
},
"yaxes": [
{
"format": "none",
"label": null,
"logBase": 1,
"max": null,
"min": null,
"show": true
},
{
"format": "short",
"label": null,
"logBase": 1,
"max": null,
"min": null,
"show": false
}
],
"yaxis": {
"align": false,
"alignLevel": null
}
},
{
"cacheTimeout": null,
"colorBackground": true,
"colorValue": false,
"colors": [
"rgba(50, 172, 45, 0.97)",
"rgba(50, 172, 45, 0.97)",
"rgba(50, 172, 45, 0.97)"
],
"datasource": "GLPI",
"decimals": 0,
"format": "none",
"gauge": {
"maxValue": 100,
"minValue": 0,
"show": false,
"thresholdLabels": false,
"thresholdMarkers": true
},
"gridPos": {
"h": 6,
"w": 4,
"x": 5,
"y": 0
},
"id": 11,
"interval": null,
"links": [],
"mappingType": 1,
"mappingTypes": [
{
"name": "value to text",
"value": 1
},
{
"name": "range to text",
"value": 2
}
],
"maxDataPoints": 100,
"minSpan": 6,
"nullPointMode": "connected",
"nullText": null,
"postfix": "",
"postfixFontSize": "50%",
"prefix": "",
"prefixFontSize": "50%",
"rangeMaps": [
{
"from": "null",
"text": "N/A",
"to": "null"
}
],
"repeat": null,
"sparkline": {
"fillColor": "rgba(31, 118, 189, 0.18)",
"full": false,
"lineColor": "rgb(31, 120, 193)",
"show": false
},
"tableColumn": "",
"targets": [
{
"col_0": {
"group": "Default",
"label": "------",
"number": "0"
},
"col_1": {
"group": "Default",
"label": "------",
"number": "0"
},
"col_10": {
"group": "Default",
"label": "------",
"number": "0"
},
"col_11": {
"group": "Default",
"label": "------",
"number": "0"
},
"col_2": {
"group": "Default",
"label": "------",
"number": "0"
},
"col_3": {
"group": "Default",
"label": "------",
"number": "0"
},
"col_4": {
"group": "Default",
"label": "------",
"number": "0"
},
"col_5": {
"group": "Default",
"label": "------",
"number": "0"
},
"col_6": {
"group": "Default",
"label": "------",
"number": "0"
},
"col_7": {
"group": "Default",
"label": "------",
"number": "0"
},
"col_8": {
"group": "Default",
"label": "------",
"number": "0"
},
"col_9": {
"group": "Default",
"label": "------",
"number": "0"
},
"console": false,
"counter": true,
"datefield": {
"group": "Special / be careful",
"label": "Do not use date search (get all data)",
"number": "-1"
},
"dayhours": false,
"dynamicsplit": {
"group": "Default",
"label": "------",
"number": "0"
},
"histogram": false,
"query": "${URL_GLPI}front/ticket.php?is_deleted=0&as_map=0&criteria[0][field]=12&criteria[0][searchtype]=equals&criteria[0][value]=1&search=Search&itemtype=Ticket&start=0",
"refId": "A",
"table": false
}
],
"thresholds": "",
"title": "New ticket",
"transparent": false,
"type": "singlestat",
"valueFontSize": "120%",
"valueMaps": [
{
"op": "=",
"text": "0",
"value": "null"
}
],
"valueName": "total"
},
{
"cacheTimeout": null,
"colorBackground": true,
"colorValue": false,
"colors": [
"#3f6833",
"#cca300",
"rgba(245, 54, 54, 0.9)"
],
"datasource": "GLPI",
"format": "none",
"gauge": {
"maxValue": 100,
"minValue": 0,
"show": false,
"thresholdLabels": false,
"thresholdMarkers": true
},
"gridPos": {
"h": 6,
"w": 4,
"x": 9,
"y": 0
},
"id": 12,
"interval": null,
"links": [],
"mappingType": 1,
"mappingTypes": [
{
"name": "value to text",
"value": 1
},
{
"name": "range to text",
"value": 2
}
],
"maxDataPoints": 100,
"nullPointMode": "connected",
"nullText": null,
"postfix": "",
"postfixFontSize": "50%",
"prefix": "",
"prefixFontSize": "50%",
"rangeMaps": [
{
"from": "null",
"text": "N/A",
"to": "null"
}
],
"sparkline": {
"fillColor": "rgba(31, 118, 189, 0.18)",
"full": false,
"lineColor": "rgb(31, 120, 193)",
"show": false
},
"tableColumn": "",
"targets": [
{
"col_0": {
"group": "",
"label": "------",
"number": "0"
},
"col_1": {
"group": "",
"label": "------",
"number": "0"
},
"col_10": {
"group": "Default",
"label": "------",
"number": "0"
},
"col_11": {
"group": "Default",
"label": "------",
"number": "0"
},
"col_2": {
"group": "",
"label": "------",
"number": "0"
},
"col_3": {
"group": "",
"label": "------",
"number": "0"
},
"col_4": {
"group": "",
"label": "------",
"number": "0"
},
"col_5": {
"group": "",
"label": "------",
"number": "0"
},
"col_6": {
"group": "Default",
"label": "------",
"number": "0"
},
"col_7": {
"group": "Default",
"label": "------",
"number": "0"
},
"col_8": {
"group": "Default",
"label": "------",
"number": "0"
},
"col_9": {
"group": "Default",
"label": "------",
"number": "0"
},
"cols": {},
"console": false,
"counter": true,
"datefield": {
"group": "Special / be careful",
"label": "Do not use date search (get all data)",
"number": "-1"
},
"dayhours": false,
"dynamicsplit": {
"group": "Default",
"label": "------",
"number": "0"
},
"histogram": false,
"query": "${URL_GLPI}front/ticket.php?is_deleted=0&as_map=0&criteria[0][field]=52&criteria[0][searchtype]=equals&criteria[0][value]=2&criteria[1][link]=AND+NOT&criteria[1][field]=12&criteria[1][searchtype]=equals&criteria[1][value]=6&search=Search&itemtype=Ticket&start=0",
"refId": "A",
"table": false
}
],
"thresholds": "0,30",
"timerangefield": "date_creation",
"title": "Waiting for approval (not closed)",
"transparent": false,
"type": "singlestat",
"valueFontSize": "200%",
"valueMaps": [
{
"op": "=",
"text": "0",
"value": "null"
}
],
"valueName": "total"
},
{
"cacheTimeout": null,
"colorBackground": true,
"colorValue": false,
"colors": [
"#3f6833",
"#cca300",
"rgba(245, 54, 54, 0.9)"
],
"datasource": "GLPI",
"format": "none",
"gauge": {
"maxValue": 100,
"minValue": 0,
"show": false,
"thresholdLabels": false,
"thresholdMarkers": true
},
"gridPos": {
"h": 6,
"w": 4,
"x": 13,
"y": 0
},
"id": 5,
"interval": null,
"links": [],
"mappingType": 1,
"mappingTypes": [
{
"name": "value to text",
"value": 1
},
{
"name": "range to text",
"value": 2
}
],
"maxDataPoints": 100,
"nullPointMode": "connected",
"nullText": null,
"postfix": "",
"postfixFontSize": "50%",
"prefix": "",
"prefixFontSize": "50%",
"rangeMaps": [
{
"from": "null",
"text": "N/A",
"to": "null"
}
],
"sparkline": {
"fillColor": "rgba(31, 118, 189, 0.18)",
"full": false,
"lineColor": "rgb(31, 120, 193)",
"show": false
},
"tableColumn": "",
"targets": [
{
"col_0": {
"group": "",
"label": "------",
"number": "0"
},
"col_1": {
"group": "",
"label": "------",
"number": "0"
},
"col_10": {
"group": "Default",
"label": "------",
"number": "0"
},
"col_11": {
"group": "Default",
"label": "------",
"number": "0"
},
"col_2": {
"group": "",
"label": "------",
"number": "0"
},
"col_3": {
"group": "",
"label": "------",
"number": "0"
},
"col_4": {
"group": "",
"label": "------",
"number": "0"
},
"col_5": {
"group": "",
"label": "------",
"number": "0"
},
"col_6": {
"group": "Default",
"label": "------",
"number": "0"
},
"col_7": {
"group": "Default",
"label": "------",
"number": "0"
},
"col_8": {
"group": "Default",
"label": "------",
"number": "0"
},
"col_9": {
"group": "Default",
"label": "------",
"number": "0"
},
"cols": {},
"console": false,
"counter": true,
"datefield": {
"group": "Special / be careful",
"label": "Do not use date search (get all data)",
"number": "-1"
},
"dayhours": false,
"dynamicsplit": {
"group": "Default",
"label": "------",
"number": "0"
},
"histogram": false,
"query": "${URL_GLPI}front/ticket.php?is_deleted=0&as_map=0&criteria[0][field]=12&criteria[0][searchtype]=equals&criteria[0][value]=notold&search=Search&itemtype=Ticket&start=0",
"refId": "A",
"table": false
}
],
"thresholds": "0,30",
"timerangefield": "date_creation",
"title": "Ticket not solved",
"transparent": false,
"type": "singlestat",
"valueFontSize": "200%",
"valueMaps": [
{
"op": "=",
"text": "0",
"value": "null"
}
],
"valueName": "total"
},
{
"cacheTimeout": null,
"colorBackground": false,
"colorValue": false,
"colors": [
"rgba(50, 172, 45, 0.97)",
"rgba(237, 129, 40, 0.89)",
"rgba(245, 54, 54, 0.9)"
],
"datasource": "GLPI",
"format": "none",
"gauge": {
"maxValue": 1000,
"minValue": 0,
"show": true,
"thresholdLabels": false,
"thresholdMarkers": true
},
"gridPos": {
"h": 6,
"w": 6,
"x": 17,
"y": 0
},
"id": 1,
"interval": null,
"links": [],
"mappingType": 1,
"mappingTypes": [
{
"name": "value to text",
"value": 1
},
{
"name": "range to text",
"value": 2
}
],
"maxDataPoints": 100,
"minSpan": 4,
"nullPointMode": "connected",
"nullText": null,
"postfix": "",
"postfixFontSize": "50%",
"prefix": "",
"prefixFontSize": "50%",
"rangeMaps": [
{
"from": "null",
"text": "N/A",
"to": "null"
}
],
"sparkline": {
"fillColor": "rgba(31, 118, 189, 0.18)",
"full": false,
"lineColor": "rgb(31, 120, 193)",
"show": false
},
"tableColumn": "",
"targets": [
{
"col_0": {
"group": "Default",
"label": "------",
"number": "0"
},
"col_1": {
"group": "Default",
"label": "------",
"number": "0"
},
"col_10": {
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17,203 | 1 | 727,086 | 26,427 | 2 | pv-power-ahoy | PV Power Ahoy | Visualise power metrics coming from AHOY-DTU and OpenWeatherMap | 2022-10-16 17:40:22 UTC | 2022-10-16 17:40:57 UTC | 0 | null | 0 | 0 | null | null | 0 | 0 | 26,427 | 1 | 17,203 | 1 | 2 | Visualise power metrics coming from AHOY-DTU and OpenWeatherMap | {
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} | 2022-08-19 20:13:11 UTC | 2022-08-19 20:40:01 UTC | 0 | 0 |
16,915 | 1 | 499,492 | 25,947 | 2 | pfsense-router | pfSense Router | Dashboard for a pfsense router | 2022-09-10 20:54:58 UTC | 2022-09-10 21:03:07 UTC | 0 | null | 0 | 0 | nodeExporter | null | 0 | 0 | 25,947 | 1 | 16,915 | 1 | 2 | Dashboard for a pfsense router | {
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16,182 | 1 | 638,729 | 24,570 | 2 | windows-fabric-hyper-v-patchlevel | Windows - Fabric - Hyper-V - Patchlevel | This dashboard is for quickly see all patchlevels from the Hyper-V nodes. | 2022-04-28 13:11:53 UTC | 2022-04-28 13:13:11 UTC | 0 | [More information's](https://github.com/R-Studio/Zabbix_Hyper-V_Monitoring)
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16,645 | 1 | 684,547 | 25,457 | 2 | devopsmetrics | DevOpsMetrics | A Dashboard to display the Four Key Metrics. | 2022-07-26 13:33:42 UTC | 2022-07-26 14:35:00 UTC | 0 | Documentation to be found on corresponding GitHub:
[https://github.com/Justus-e/DevOpsMetrics](https://github.com/Justus-e/DevOpsMetrics) | 75,216 | 75,217 | null | null | 0 | 0 | 25,457 | 1 | 16,645 | 1 | 2 | null | {
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"uid": "${DS_INFLUXDB}"
},
"description": "",
"fieldConfig": {
"defaults": {
"color": {
"mode": "palette-classic"
},
"custom": {
"axisLabel": "",
"axisPlacement": "auto",
"barAlignment": 0,
"drawStyle": "line",
"fillOpacity": 0,
"gradientMode": "none",
"hideFrom": {
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"tooltip": false,
"viz": false
},
"lineInterpolation": "smooth",
"lineWidth": 5,
"pointSize": 5,
"scaleDistribution": {
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"showPoints": "auto",
"spanNulls": false,
"stacking": {
"group": "A",
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"thresholdsStyle": {
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},
"mappings": [],
"thresholds": {
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"steps": [
{
"color": "purple",
"value": null
},
{
"color": "green",
"value": 86400
},
{
"color": "#EAB839",
"value": 2592000
},
{
"color": "red",
"value": 15552000
}
]
},
"unit": "s"
},
"overrides": []
},
"gridPos": {
"h": 6,
"w": 15,
"x": 9,
"y": 1
},
"id": 25,
"options": {
"legend": {
"calcs": [],
"displayMode": "list",
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"tooltip": {
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"sort": "none"
}
},
"pluginVersion": "8.5.4",
"targets": [
{
"datasource": {
"type": "influxdb",
"uid": "${DS_INFLUXDB}"
},
"query": "import \"date\"\r\nfrom(bucket: \"${bucket}\")\r\n |> range(start: date.sub(from:${__from:date},d: ${AggregateInterval}), stop: ${__to:date})\r\n |> filter(fn: (r) => r[\"_measurement\"] == \"deployment\" and r[\"_field\"] == \"id\")\r\n |> unique(column: \"_value\")\r\n |> elapsed()\r\n |> map(fn: (r) => ({_value: r[\"elapsed\"], _time: r[\"_time\"]}))\r\n |> aggregateWindow(every: ${AggregateInterval}, fn: mean, createEmpty: false)",
"refId": "A"
}
],
"title": "Deployment Frequency",
"type": "timeseries"
},
{
"collapsed": false,
"gridPos": {
"h": 1,
"w": 24,
"x": 0,
"y": 7
},
"id": 18,
"panels": [],
"title": "Lead Time",
"type": "row"
},
{
"datasource": {
"type": "influxdb",
"uid": "${DS_INFLUXDB}"
},
"fieldConfig": {
"defaults": {
"color": {
"mode": "thresholds"
},
"mappings": [],
"thresholds": {
"mode": "absolute",
"steps": [
{
"color": "purple",
"value": null
},
{
"color": "green",
"value": 3600000000000
},
{
"color": "yellow",
"value": 604800000000000
},
{
"color": "red",
"value": 15552000000000000
}
]
},
"unit": "ns"
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"overrides": []
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"gridPos": {
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"w": 9,
"x": 0,
"y": 8
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"id": 6,
"options": {
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"graphMode": "area",
"justifyMode": "auto",
"orientation": "auto",
"reduceOptions": {
"calcs": [
"lastNotNull"
],
"fields": "",
"values": false
},
"textMode": "auto"
},
"pluginVersion": "9.0.2",
"targets": [
{
"datasource": {
"type": "influxdb",
"uid": "${DS_INFLUXDB}"
},
"query": "deploys = from(bucket: \"${bucket}\")\r\n |> range(start: ${__from:date}, stop: ${__to:date})\r\n |> filter(fn: (r) => r[\"_measurement\"] == \"deployment\" and r[\"_field\"] == \"change\")\r\n\r\nchanges = from(bucket: \"${bucket}\")\r\n |> range(start: ${__from:date}, stop: ${__to:date})\r\n |> filter(fn: (r) => r[\"_measurement\"] == \"change\" and r[\"_field\"] == \"id\")\r\n\r\njoin(\r\n tables: {change:changes, deploy:deploys},\r\n on: [\"_value\"],\r\n)\r\n |> map(fn: (r) => ({_value: uint(v: r[\"_time_deploy\"]) - uint(v: r[\"_time_change\"])}))\r\n |> mean()\r\n",
"refId": "A"
}
],
"title": "Lead Time",
"type": "stat"
},
{
"datasource": {
"type": "influxdb",
"uid": "${DS_INFLUXDB}"
},
"fieldConfig": {
"defaults": {
"color": {
"mode": "palette-classic"
},
"custom": {
"axisLabel": "",
"axisPlacement": "auto",
"barAlignment": 0,
"drawStyle": "line",
"fillOpacity": 0,
"gradientMode": "none",
"hideFrom": {
"legend": false,
"tooltip": false,
"viz": false
},
"lineInterpolation": "smooth",
"lineWidth": 5,
"pointSize": 5,
"scaleDistribution": {
"type": "linear"
},
"showPoints": "auto",
"spanNulls": false,
"stacking": {
"group": "A",
"mode": "none"
},
"thresholdsStyle": {
"mode": "area"
}
},
"mappings": [],
"thresholds": {
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"steps": [
{
"color": "purple",
"value": null
},
{
"color": "green",
"value": 3600000000000
},
{
"color": "#EAB839",
"value": 604800000000000
},
{
"color": "#6ED0E0",
"value": 15552000000000000
}
]
},
"unit": "ns"
},
"overrides": []
},
"gridPos": {
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"w": 15,
"x": 9,
"y": 8
},
"id": 8,
"options": {
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"displayMode": "list",
"placement": "bottom"
},
"tooltip": {
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"sort": "none"
}
},
"pluginVersion": "8.5.4",
"targets": [
{
"datasource": {
"type": "influxdb",
"uid": "${DS_INFLUXDB}"
},
"query": "import \"date\"\r\n\r\ndeploys = from(bucket: \"${bucket}\")\r\n |> range(start: date.sub(from:${__from:date}, d: ${AggregateInterval}), stop: ${__to:date})\r\n |> filter(fn: (r) => r[\"_measurement\"] == \"deployment\" and r[\"_field\"] == \"change\")\r\n\r\nchanges = from(bucket: \"${bucket}\")\r\n |> range(start: date.sub(from:${__from:date}, d: ${AggregateInterval}), stop: ${__to:date})\r\n |> filter(fn: (r) => r[\"_measurement\"] == \"change\" and r[\"_field\"] == \"id\")\r\n\r\njoin(\r\n tables: {change:changes, deploy:deploys},\r\n on: [\"_value\"],\r\n)\r\n |> map(fn: (r) => ({_value: uint(v: r[\"_time_deploy\"]) - uint(v: r[\"_time_change\"]), _time: r[\"_time_deploy\"]}))\r\n |> aggregateWindow(every: ${AggregateInterval}, fn: mean, createEmpty: false)\r\n",
"refId": "A"
}
],
"title": "Lead Time",
"type": "timeseries"
},
{
"collapsed": false,
"gridPos": {
"h": 1,
"w": 24,
"x": 0,
"y": 14
},
"id": 16,
"panels": [],
"title": "Change Fail Rate",
"type": "row"
},
{
"datasource": {
"type": "influxdb",
"uid": "${DS_INFLUXDB}"
},
"fieldConfig": {
"defaults": {
"color": {
"mode": "thresholds"
},
"mappings": [],
"thresholds": {
"mode": "absolute",
"steps": [
{
"color": "purple",
"value": null
},
{
"color": "green",
"value": 0.15
},
{
"color": "#EAB839",
"value": 0.2
},
{
"color": "dark-orange",
"value": 0.25
},
{
"color": "dark-red",
"value": 0.3
}
]
},
"unit": "percentunit"
},
"overrides": []
},
"gridPos": {
"h": 6,
"w": 9,
"x": 0,
"y": 15
},
"id": 12,
"options": {
"colorMode": "background",
"graphMode": "area",
"justifyMode": "auto",
"orientation": "auto",
"reduceOptions": {
"calcs": [
"lastNotNull"
],
"fields": "",
"values": false
},
"textMode": "auto"
},
"pluginVersion": "9.0.2",
"targets": [
{
"datasource": {
"type": "influxdb",
"uid": "${DS_INFLUXDB}"
},
"query": "import \"array\"\r\nincident = from(bucket: \"${bucket}\")\r\n |> range(start: ${__from:date}, stop: ${__to:date})\r\n |> filter(fn: (r) => r[\"_measurement\"] == \"incident\")\r\n |> count()\r\n |> findColumn(\r\n fn: (key) => key._field == \"id\",\r\n column: \"_value\",\r\n )\r\n\r\ndeployment = from(bucket: \"${bucket}\")\r\n |> range(start: ${__from:date}, stop: ${__to:date})\r\n |> filter(fn: (r) => r[\"_measurement\"] == \"deployment\" and r[\"_field\"] == \"id\")\r\n |> unique(column: \"_value\")\r\n |> count()\r\n |> findColumn(\r\n fn: (key) => key._field == \"id\",\r\n column: \"_value\",\r\n )\r\n\r\narray.from(rows: \r\n if deployment |> length() == 1 and incident |> length() == 1 then\r\n [{_value: float(v: incident[0]) / float(v: deployment[0]) }]\r\n else \r\n [{_value: float(v: 0)}] \r\n)",
"refId": "A"
}
],
"title": "Change Fail Rate",
"type": "stat"
},
{
"datasource": {
"type": "influxdb",
"uid": "${DS_INFLUXDB}"
},
"fieldConfig": {
"defaults": {
"color": {
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},
"custom": {
"axisLabel": "",
"axisPlacement": "auto",
"axisSoftMax": 0.3,
"barAlignment": 0,
"drawStyle": "line",
"fillOpacity": 0,
"gradientMode": "none",
"hideFrom": {
"legend": false,
"tooltip": false,
"viz": false
},
"lineInterpolation": "smooth",
"lineWidth": 5,
"pointSize": 5,
"scaleDistribution": {
"type": "linear"
},
"showPoints": "auto",
"spanNulls": false,
"stacking": {
"group": "A",
"mode": "none"
},
"thresholdsStyle": {
"mode": "area"
}
},
"mappings": [],
"thresholds": {
"mode": "absolute",
"steps": [
{
"color": "purple",
"value": null
},
{
"color": "green",
"value": 0.15
},
{
"color": "#EAB839",
"value": 0.2
},
{
"color": "dark-orange",
"value": 0.25
},
{
"color": "dark-red",
"value": 0.3
}
]
},
"unit": "percentunit"
},
"overrides": []
},
"gridPos": {
"h": 6,
"w": 15,
"x": 9,
"y": 15
},
"id": 21,
"options": {
"legend": {
"calcs": [],
"displayMode": "list",
"placement": "bottom"
},
"tooltip": {
"mode": "single",
"sort": "none"
}
},
"pluginVersion": "8.5.4",
"targets": [
{
"datasource": {
"type": "influxdb",
"uid": "${DS_INFLUXDB}"
},
"query": "import \"array\"\r\nimport \"date\"\r\n\r\nincident = from(bucket: \"${bucket}\")\r\n |> range(start: date.sub(from:${__from:date}, d: ${AggregateInterval}), stop: ${__to:date})\r\n |> filter(fn: (r) => r[\"_measurement\"] == \"incident\")\r\n |> aggregateWindow(every: ${AggregateInterval}, fn: count)\r\n \r\ndeployment = from(bucket: \"${bucket}\")\r\n |> range(start: date.sub(from:${__from:date}, d: ${AggregateInterval}), stop: ${__to:date})\r\n |> filter(fn: (r) => r[\"_measurement\"] == \"deployment\" and r[\"_field\"] == \"id\")\r\n |> unique(column: \"_value\")\r\n |> aggregateWindow(every: ${AggregateInterval}, fn: count)\r\n \r\njoin(\r\n tables: {incident:incident, deployment:deployment},\r\n on: [\"_time\"],\r\n)\r\n |> map(fn: (r) => ({ \r\n _value: \r\n if r[\"_value_deployment\"] == 0 then \r\n float(v: 0)\r\n else\r\n float(v: r[\"_value_incident\"]) / float(v: r[\"_value_deployment\"]),\r\n _time: r[\"_time\"]\r\n }))",
"refId": "A"
}
],
"title": "Change Fail Rate",
"type": "timeseries"
},
{
"collapsed": false,
"gridPos": {
"h": 1,
"w": 24,
"x": 0,
"y": 21
},
"id": 14,
"panels": [],
"title": "Mean Time to Restore",
"type": "row"
},
{
"datasource": {
"type": "influxdb",
"uid": "${DS_INFLUXDB}"
},
"fieldConfig": {
"defaults": {
"color": {
"mode": "thresholds"
},
"mappings": [],
"thresholds": {
"mode": "absolute",
"steps": [
{
"color": "purple",
"value": null
},
{
"color": "green",
"value": 3600000000000
},
{
"color": "#EAB839",
"value": 86400000000000
},
{
"color": "red",
"value": 604800000000000
}
]
},
"unit": "ns"
},
"overrides": []
},
"gridPos": {
"h": 6,
"w": 9,
"x": 0,
"y": 22
},
"id": 10,
"options": {
"colorMode": "background",
"graphMode": "area",
"justifyMode": "auto",
"orientation": "auto",
"reduceOptions": {
"calcs": [
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],
"fields": "",
"values": false
},
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},
"pluginVersion": "9.0.2",
"targets": [
{
"datasource": {
"type": "influxdb",
"uid": "${DS_INFLUXDB}"
},
"query": "incident = from(bucket: \"${bucket}\")\r\n |> range(start: ${__from:date}, stop: ${__to:date})\r\n |> filter(fn: (r) => r[\"_measurement\"] == \"incident\")\r\n\r\nrestore = from(bucket: \"${bucket}\")\r\n |> range(start: ${__from:date}, stop: ${__to:date})\r\n |> filter(fn: (r) => r[\"_measurement\"] == \"restore\")\r\n\r\njoin(\r\n tables: {incident:incident, restore:restore},\r\n on: [\"_value\"],\r\n )\r\n |> map(fn: (r) => ({_value: uint(v: r[\"_time_restore\"]) - uint(v: r[\"_time_incident\"])}))\r\n |> mean()",
"refId": "A"
}
],
"title": "Mean Time to Restore",
"type": "stat"
},
{
"datasource": {
"type": "influxdb",
"uid": "${DS_INFLUXDB}"
},
"fieldConfig": {
"defaults": {
"color": {
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},
"custom": {
"axisLabel": "",
"axisPlacement": "auto",
"barAlignment": 0,
"drawStyle": "line",
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"gradientMode": "none",
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"tooltip": false,
"viz": false
},
"lineInterpolation": "smooth",
"lineWidth": 5,
"pointSize": 5,
"scaleDistribution": {
"type": "linear"
},
"showPoints": "auto",
"spanNulls": false,
"stacking": {
"group": "A",
"mode": "none"
},
"thresholdsStyle": {
"mode": "area"
}
},
"mappings": [],
"thresholds": {
"mode": "absolute",
"steps": [
{
"color": "purple",
"value": null
},
{
"color": "green",
"value": 3600000000000
},
{
"color": "#EAB839",
"value": 86400000000000
},
{
"color": "#6ED0E0",
"value": 604800000000000
}
]
},
"unit": "ns"
},
"overrides": []
},
"gridPos": {
"h": 6,
"w": 15,
"x": 9,
"y": 22
},
"id": 22,
"options": {
"legend": {
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"displayMode": "list",
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}
},
"pluginVersion": "8.5.4",
"targets": [
{
"datasource": {
"type": "influxdb",
"uid": "${DS_INFLUXDB}"
},
"query": "import \"date\"\r\n\r\nincident = from(bucket: \"${bucket}\")\r\n |> range(start: date.sub(from:${__from:date}, d: ${AggregateInterval}), stop: ${__to:date})\r\n |> filter(fn: (r) => r[\"_measurement\"] == \"incident\")\r\n\r\nrestore = from(bucket: \"${bucket}\")\r\n |> range(start: date.sub(from:${__from:date}, d: ${AggregateInterval}), stop: ${__to:date})\r\n |> filter(fn: (r) => r[\"_measurement\"] == \"restore\")\r\n\r\njoin(\r\n tables: {incident:incident, restore:restore},\r\n on: [\"_value\"],\r\n )\r\n |> map(fn: (r) => ({_value: uint(v: r[\"_time_restore\"]) - uint(v: r[\"_time_incident\"]), _time: r[\"_time_incident\"]}))\r\n |> aggregateWindow(every: ${AggregateInterval}, fn: mean, createEmpty: false)",
"refId": "A"
}
],
"title": "Mean Time to Restore",
"type": "timeseries"
},
{
"collapsed": false,
"gridPos": {
"h": 1,
"w": 24,
"x": 0,
"y": 28
},
"id": 29,
"panels": [],
"title": "Overview",
"type": "row"
},
{
"datasource": {
"type": "influxdb",
"uid": "${DS_INFLUXDB}"
},
"fieldConfig": {
"defaults": {
"custom": {
"align": "auto",
"displayMode": "auto",
"inspect": false
},
"mappings": [],
"thresholds": {
"mode": "absolute",
"steps": [
{
"color": "green",
"value": null
},
{
"color": "red",
"value": 80
}
]
}
},
"overrides": []
},
"gridPos": {
"h": 13,
"w": 8,
"x": 0,
"y": 29
},
"id": 27,
"options": {
"footer": {
"fields": "",
"reducer": [
"sum"
],
"show": false
},
"showHeader": true
},
"pluginVersion": "9.0.2",
"targets": [
{
"datasource": {
"type": "influxdb",
"uid": "${DS_INFLUXDB}"
},
"query": "import \"date\"\r\n from(bucket: \"${bucket}\")\r\n |> range(start: ${__from:date}, stop: ${__to:date})\r\n |> filter(fn: (r) => r[\"_measurement\"] == \"deployment\" and r[\"_field\"] == \"id\")\r\n |> unique(column: \"_value\")\r\n |> map(fn: (r) => ({deployment: r[\"_value\"], time: r[\"_time\"]}))\r\n",
"refId": "A"
},
{
"datasource": {
"type": "influxdb",
"uid": "${DS_INFLUXDB}"
},
"hide": false,
"query": " from(bucket: \"${bucket}\")\r\n |> range(start: ${__from:date}, stop: ${__to:date})\r\n |> filter(fn: (r) => r[\"_measurement\"] == \"change\")\r\n |> map(fn: (r) => ({change: r[\"_value\"], time: r[\"_time\"]}))",
"refId": "B"
},
{
"datasource": {
"type": "influxdb",
"uid": "${DS_INFLUXDB}"
},
"hide": false,
"query": " from(bucket: \"${bucket}\")\r\n |> range(start: ${__from:date}, stop: ${__to:date})\r\n |> filter(fn: (r) => r[\"_measurement\"] == \"incident\")\r\n |> map(fn: (r) => ({incident: r[\"_value\"], time: r[\"_time\"]}))",
"refId": "C"
},
{
"datasource": {
"type": "influxdb",
"uid": "${DS_INFLUXDB}"
},
"hide": false,
"query": " from(bucket: \"${bucket}\")\r\n |> range(start: ${__from:date}, stop: ${__to:date})\r\n |> filter(fn: (r) => r[\"_measurement\"] == \"restore\")\r\n |> map(fn: (r) => ({restore: r[\"_value\"], time: r[\"_time\"]}))",
"refId": "D"
}
],
"title": "Events",
"type": "table"
},
{
"datasource": {
"type": "influxdb",
"uid": "${DS_INFLUXDB}"
},
"fieldConfig": {
"defaults": {
"color": {
"mode": "continuous-GrYlRd"
},
"displayName": "Deployments",
"mappings": [],
"thresholds": {
"mode": "absolute",
"steps": [
{
"color": "green",
"value": null
},
{
"color": "red",
"value": 80
}
]
}
},
"overrides": [
{
"matcher": {
"id": "byFrameRefID",
"options": "B"
},
"properties": [
{
"id": "displayName",
"value": "Changes"
},
{
"id": "color",
"value": {
"mode": "continuous-RdYlGr"
}
}
]
},
{
"matcher": {
"id": "byFrameRefID",
"options": "C"
},
"properties": [
{
"id": "displayName",
"value": "Incidents"
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{
"id": "color",
"value": {
"mode": "continuous-GrYlRd"
}
}
]
},
{
"matcher": {
"id": "byFrameRefID",
"options": "D"
},
"properties": [
{
"id": "displayName",
"value": "Restores"
},
{
"id": "color",
"value": {
"mode": "thresholds"
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}
]
},
{
"matcher": {
"id": "byFrameRefID",
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},
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"x": 8,
"y": 29
},
"id": 30,
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"minVizWidth": 0,
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"pluginVersion": "9.0.2",
"targets": [
{
"datasource": {
"type": "influxdb",
"uid": "${DS_INFLUXDB}"
},
"query": " from(bucket: \"${bucket}\")\r\n |> range(start: ${__from:date}, stop: ${__to:date})\r\n |> filter(fn: (r) => r[\"_measurement\"] == \"deployment\" and r[\"_field\"] == \"id\")\r\n |> unique(column: \"_value\")\r\n |> map(fn: (r) => ({_value: r[\"_value\"]}))\r\n |> count()\r\n",
"refId": "A"
},
{
"datasource": {
"type": "influxdb",
"uid": "${DS_INFLUXDB}"
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} | 2022-07-26 13:33:42 UTC | 2022-07-26 13:37:47 UTC | 0 | 0 |
17,103 | 1 | 718,677 | 26,241 | 2 | rabbitmq-overview | RabbitMQ-Overview | A new RabbitMQ Management Overview | 2022-09-30 18:40:24 UTC | 2022-09-30 18:41:12 UTC | 0 | null | 0 | 0 | null | null | 0 | 0 | 26,241 | 1 | 17,103 | 1 | 2 | A new RabbitMQ Management Overview | {
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"color": "#FFB357"
}
],
"spaceLength": 10,
"stack": false,
"steppedLine": false,
"targets": [
{
"expr": "(rabbitmq_resident_memory_limit_bytes * on(instance) group_left(rabbitmq_cluster, rabbitmq_node) rabbitmq_identity_info{rabbitmq_cluster=\"$rabbitmq_cluster\"}) -\n(rabbitmq_process_resident_memory_bytes * on(instance) group_left(rabbitmq_cluster, rabbitmq_node) rabbitmq_identity_info{rabbitmq_cluster=\"$rabbitmq_cluster\"})",
"format": "time_series",
"instant": false,
"intervalFactor": 1,
"legendFormat": "{{rabbitmq_node}}",
"refId": "A"
}
],
"thresholds": [
{
"colorMode": "warning",
"fill": true,
"line": true,
"op": "lt",
"value": 536870912,
"yaxis": "left"
},
{
"colorMode": "critical",
"fill": true,
"line": true,
"op": "lt",
"value": 0,
"yaxis": "left"
}
],
"timeFrom": null,
"timeRegions": [],
"timeShift": null,
"title": "Memory available before publishers blocked",
"tooltip": {
"shared": true,
"sort": 1,
"value_type": "individual"
},
"type": "graph",
"xaxis": {
"buckets": null,
"mode": "time",
"name": null,
"show": true,
"values": []
},
"yaxes": [
{
"decimals": null,
"format": "bytes",
"label": null,
"logBase": 1,
"max": null,
"min": null,
"show": true
},
{
"format": "short",
"label": null,
"logBase": 1,
"max": null,
"min": null,
"show": true
}
],
"yaxis": {
"align": false,
"alignLevel": null
}
},
{
"aliasColors": {},
"bars": false,
"cacheTimeout": null,
"dashLength": 10,
"dashes": false,
"datasource": "${DS_PROMETHEUS}",
"description": "This metric is reported for the partition where the RabbitMQ data directory is stored.\n\nIf the value is zero or less, the disk alarm will be triggered and all publishing connections across all cluster nodes will be blocked.\n\nThis value can temporarily go negative because the free disk space alarm is triggered with a slight delay.\n\n* [Alarms](https://www.rabbitmq.com/alarms.html)\n* [Disk Space Alarms](https://www.rabbitmq.com/disk-alarms.html)\n* [Disk Space](https://www.rabbitmq.com/production-checklist.html#resource-limits-disk-space)\n* [Persistence Configuration](https://www.rabbitmq.com/persistence-conf.html)\n* [Blocked Connection Notifications](https://www.rabbitmq.com/connection-blocked.html)",
"fill": 0,
"fillGradient": 0,
"gridPos": {
"h": 8,
"w": 6,
"x": 12,
"y": 4
},
"id": 8,
"legend": {
"alignAsTable": true,
"avg": false,
"current": true,
"max": true,
"min": true,
"show": false,
"total": false,
"values": true
},
"lines": true,
"linewidth": 1,
"links": [],
"nullPointMode": "null",
"options": {
"dataLinks": []
},
"percentage": false,
"pointradius": 2,
"points": false,
"renderer": "flot",
"seriesOverrides": [
{
"alias": "/^rabbit@\\w+0/",
"color": "#56A64B"
},
{
"alias": "/^rabbit@\\w+1/",
"color": "#F2CC0C"
},
{
"alias": "/^rabbit@\\w+2/",
"color": "#3274D9"
},
{
"alias": "/^rabbit@\\w+3/",
"color": "#A352CC"
},
{
"alias": "/^rabbit@\\w+4/",
"color": "#FF780A"
},
{
"alias": "/^rabbit@\\w+5/",
"color": "#96D98D"
},
{
"alias": "/^rabbit@\\w+6/",
"color": "#FFEE52"
},
{
"alias": "/^rabbit@\\w+7/",
"color": "#8AB8FF"
},
{
"alias": "/^rabbit@\\w+8/",
"color": "#CA95E5"
},
{
"alias": "/^rabbit@\\w+9/",
"color": "#FFB357"
}
],
"spaceLength": 10,
"stack": false,
"steppedLine": false,
"targets": [
{
"expr": "rabbitmq_disk_space_available_bytes * on(instance) group_left(rabbitmq_cluster, rabbitmq_node) rabbitmq_identity_info{rabbitmq_cluster=\"$rabbitmq_cluster\"}",
"format": "time_series",
"instant": false,
"intervalFactor": 1,
"legendFormat": "{{rabbitmq_node}}",
"refId": "A"
}
],
"thresholds": [
{
"colorMode": "critical",
"fill": true,
"line": true,
"op": "lt",
"value": 1073741824,
"yaxis": "left"
},
{
"colorMode": "warning",
"fill": true,
"line": true,
"op": "lt",
"value": 5368709120,
"yaxis": "left"
}
],
"timeFrom": null,
"timeRegions": [],
"timeShift": null,
"title": "Disk space available before publishers blocked",
"tooltip": {
"shared": true,
"sort": 1,
"value_type": "individual"
},
"type": "graph",
"xaxis": {
"buckets": null,
"mode": "time",
"name": null,
"show": true,
"values": []
},
"yaxes": [
{
"decimals": null,
"format": "bytes",
"label": null,
"logBase": 1,
"max": null,
"min": null,
"show": true
},
{
"format": "short",
"label": null,
"logBase": 1,
"max": null,
"min": null,
"show": true
}
],
"yaxis": {
"align": false,
"alignLevel": null
}
},
{
"aliasColors": {},
"bars": false,
"cacheTimeout": null,
"dashLength": 10,
"dashes": false,
"datasource": "${DS_PROMETHEUS}",
"description": "When this value reaches zero, new connections will not be accepted and disk write operations may fail.\n\nClient libraries, peer nodes and CLI tools will not be able to connect when the node runs out of available file descriptors.\n\n* [Open File Handles Limit](https://www.rabbitmq.com/production-checklist.html#resource-limits-file-handle-limit)",
"fill": 0,
"fillGradient": 0,
"gridPos": {
"h": 4,
"w": 6,
"x": 18,
"y": 4
},
"id": 2,
"legend": {
"alignAsTable": true,
"avg": false,
"current": true,
"max": true,
"min": true,
"rightSide": false,
"show": false,
"total": false,
"values": true
},
"lines": true,
"linewidth": 1,
"links": [],
"nullPointMode": "null",
"options": {
"dataLinks": []
},
"percentage": false,
"pointradius": 2,
"points": false,
"renderer": "flot",
"seriesOverrides": [
{
"alias": "/^rabbit@\\w+0/",
"color": "#56A64B"
},
{
"alias": "/^rabbit@\\w+1/",
"color": "#F2CC0C"
},
{
"alias": "/^rabbit@\\w+2/",
"color": "#3274D9"
},
{
"alias": "/^rabbit@\\w+3/",
"color": "#A352CC"
},
{
"alias": "/^rabbit@\\w+4/",
"color": "#FF780A"
},
{
"alias": "/^rabbit@\\w+5/",
"color": "#96D98D"
},
{
"alias": "/^rabbit@\\w+6/",
"color": "#FFEE52"
},
{
"alias": "/^rabbit@\\w+7/",
"color": "#8AB8FF"
},
{
"alias": "/^rabbit@\\w+8/",
"color": "#CA95E5"
},
{
"alias": "/^rabbit@\\w+9/",
"color": "#FFB357"
}
],
"spaceLength": 10,
"stack": false,
"steppedLine": false,
"targets": [
{
"expr": "(rabbitmq_process_max_fds * on(instance) group_left(rabbitmq_cluster, rabbitmq_node) rabbitmq_identity_info{rabbitmq_cluster=\"$rabbitmq_cluster\"}) -\n(rabbitmq_process_open_fds * on(instance) group_left(rabbitmq_cluster, rabbitmq_node) rabbitmq_identity_info{rabbitmq_cluster=\"$rabbitmq_cluster\"})",
"format": "time_series",
"instant": false,
"intervalFactor": 1,
"legendFormat": "{{rabbitmq_node}}",
"refId": "A"
}
],
"thresholds": [
{
"colorMode": "critical",
"fill": true,
"line": true,
"op": "lt",
"value": 500,
"yaxis": "left"
},
{
"colorMode": "warning",
"fill": true,
"line": true,
"op": "lt",
"value": 1000,
"yaxis": "left"
}
],
"timeFrom": null,
"timeRegions": [],
"timeShift": null,
"title": "File descriptors available",
"tooltip": {
"shared": true,
"sort": 1,
"value_type": "individual"
},
"type": "graph",
"xaxis": {
"buckets": null,
"mode": "time",
"name": null,
"show": true,
"values": []
},
"yaxes": [
{
"decimals": 0,
"format": "none",
"label": "",
"logBase": 1,
"max": null,
"min": null,
"show": true
},
{
"format": "short",
"label": null,
"logBase": 1,
"max": null,
"min": null,
"show": true
}
],
"yaxis": {
"align": false,
"alignLevel": null
}
},
{
"aliasColors": {},
"bars": false,
"cacheTimeout": null,
"dashLength": 10,
"dashes": false,
"datasource": "${DS_PROMETHEUS}",
"description": "When this value reaches zero, new connections will not be accepted.\n\nClient libraries, peer nodes and CLI tools will not be able to connect when the node runs out of available file descriptors.\n\n* [Networking and RabbitMQ](https://www.rabbitmq.com/networking.html)",
"fill": 0,
"fillGradient": 0,
"gridPos": {
"h": 4,
"w": 6,
"x": 18,
"y": 8
},
"id": 5,
"legend": {
"alignAsTable": true,
"avg": false,
"current": true,
"max": true,
"min": true,
"show": false,
"total": false,
"values": true
},
"lines": true,
"linewidth": 1,
"links": [],
"nullPointMode": "null",
"options": {
"dataLinks": []
},
"percentage": false,
"pointradius": 2,
"points": false,
"renderer": "flot",
"seriesOverrides": [
{
"alias": "/^rabbit@\\w+0/",
"color": "#56A64B"
},
{
"alias": "/^rabbit@\\w+1/",
"color": "#F2CC0C"
},
{
"alias": "/^rabbit@\\w+2/",
"color": "#3274D9"
},
{
"alias": "/^rabbit@\\w+3/",
"color": "#A352CC"
},
{
"alias": "/^rabbit@\\w+4/",
"color": "#FF780A"
},
{
"alias": "/^rabbit@\\w+5/",
"color": "#96D98D"
},
{
"alias": "/^rabbit@\\w+6/",
"color": "#FFEE52"
},
{
"alias": "/^rabbit@\\w+7/",
"color": "#8AB8FF"
},
{
"alias": "/^rabbit@\\w+8/",
"color": "#CA95E5"
},
{
"alias": "/^rabbit@\\w+9/",
"color": "#FFB357"
}
],
"spaceLength": 10,
"stack": false,
"steppedLine": false,
"targets": [
{
"expr": "(rabbitmq_process_max_tcp_sockets * on(instance) group_left(rabbitmq_cluster, rabbitmq_node) rabbitmq_identity_info{rabbitmq_cluster=\"$rabbitmq_cluster\"}) -\n(rabbitmq_process_open_tcp_sockets * on(instance) group_left(rabbitmq_cluster, rabbitmq_node) rabbitmq_identity_info{rabbitmq_cluster=\"$rabbitmq_cluster\"})",
"format": "time_series",
"instant": false,
"intervalFactor": 1,
"legendFormat": "{{rabbitmq_node}}",
"refId": "A"
}
],
"thresholds": [
{
"colorMode": "critical",
"fill": true,
"line": true,
"op": "lt",
"value": 500,
"yaxis": "left"
},
{
"colorMode": "warning",
"fill": true,
"line": true,
"op": "lt",
"value": 1000,
"yaxis": "left"
}
],
"timeFrom": null,
"timeRegions": [],
"timeShift": null,
"title": "TCP sockets available",
"tooltip": {
"shared": true,
"sort": 1,
"value_type": "individual"
},
"type": "graph",
"xaxis": {
"buckets": null,
"mode": "time",
"name": null,
"show": true,
"values": []
},
"yaxes": [
{
"decimals": 0,
"format": "none",
"label": null,
"logBase": 1,
"max": null,
"min": null,
"show": true
},
{
"format": "short",
"label": null,
"logBase": 1,
"max": null,
"min": null,
"show": true
}
],
"yaxis": {
"align": false,
"alignLevel": null
}
},
{
"collapsed": false,
"datasource": "${DS_PROMETHEUS}",
"gridPos": {
"h": 1,
"w": 24,
"x": 0,
"y": 12
},
"id": 27,
"panels": [],
"title": "QUEUED MESSAGES",
"type": "row"
},
{
"aliasColors": {},
"bars": false,
"cacheTimeout": null,
"dashLength": 10,
"dashes": false,
"datasource": "${DS_PROMETHEUS}",
"description": "Total number of ready messages ready to be delivered to consumers.\n\nAim to keep this value as low as possible. RabbitMQ behaves best when messages are flowing through it. It's OK for publishers to occasionally outpace consumers, but the expectation is that consumers will eventually process all ready messages.\n\nIf this metric keeps increasing, your system will eventually run out of memory and/or disk space. Consider using TTL or Queue Length Limit to prevent unbounded message growth.\n\n* [Queues](https://www.rabbitmq.com/queues.html)\n* [Consumers](https://www.rabbitmq.com/consumers.html)\n* [Queue Length Limit](https://www.rabbitmq.com/maxlength.html)\n* [Time-To-Live and Expiration](https://www.rabbitmq.com/ttl.html)",
"fill": 10,
"fillGradient": 0,
"gridPos": {
"h": 5,
"w": 12,
"x": 0,
"y": 13
},
"id": 9,
"legend": {
"alignAsTable": true,
"avg": false,
"current": true,
"max": true,
"min": true,
"rightSide": false,
"show": false,
"total": false,
"values": true
},
"lines": true,
"linewidth": 1,
"links": [],
"nullPointMode": "null as zero",
"options": {
"dataLinks": []
},
"percentage": false,
"pointradius": 2,
"points": false,
"renderer": "flot",
"seriesOverrides": [
{
"alias": "/^rabbit@\\w+0/",
"color": "#56A64B"
},
{
"alias": "/^rabbit@\\w+1/",
"color": "#F2CC0C"
},
{
"alias": "/^rabbit@\\w+2/",
"color": "#3274D9"
},
{
"alias": "/^rabbit@\\w+3/",
"color": "#A352CC"
},
{
"alias": "/^rabbit@\\w+4/",
"color": "#FF780A"
},
{
"alias": "/^rabbit@\\w+5/",
"color": "#96D98D"
},
{
"alias": "/^rabbit@\\w+6/",
"color": "#FFEE52"
},
{
"alias": "/^rabbit@\\w+7/",
"color": "#8AB8FF"
},
{
"alias": "/^rabbit@\\w+8/",
"color": "#CA95E5"
},
{
"alias": "/^rabbit@\\w+9/",
"color": "#FFB357"
}
],
"spaceLength": 10,
"stack": true,
"steppedLine": false,
"targets": [
{
"expr": "sum(rabbitmq_queue_messages_ready * on(instance) group_left(rabbitmq_cluster, rabbitmq_node) rabbitmq_identity_info{rabbitmq_cluster=\"$rabbitmq_cluster\"}) by(rabbitmq_node)",
"format": "time_series",
"instant": false,
"intervalFactor": 1,
"legendFormat": "{{rabbitmq_node}}",
"refId": "A"
}
],
"thresholds": [],
"timeFrom": null,
"timeRegions": [],
"timeShift": null,
"title": "Messages ready to be delivered to consumers",
"tooltip": {
"shared": true,
"sort": 2,
"value_type": "individual"
},
"type": "graph",
"xaxis": {
"buckets": null,
"mode": "time",
"name": null,
"show": true,
"values": []
},
"yaxes": [
{
"decimals": null,
"format": "short",
"label": "",
"logBase": 1,
"max": null,
"min": "0",
"show": true
},
{
"format": "short",
"label": null,
"logBase": 1,
"max": null,
"min": null,
"show": true
}
],
"yaxis": {
"align": false,
"alignLevel": null
}
},
{
"aliasColors": {},
"bars": false,
"cacheTimeout": null,
"dashLength": 10,
"dashes": false,
"datasource": "${DS_PROMETHEUS}",
"description": "The total number of messages that are either in-flight to consumers, currently being processed by consumers or simply waiting for the consumer acknowledgements to be processed by the queue. Until the queue processes the message acknowledgement, the message will remain unacknowledged.\n\n* [Queues](https://www.rabbitmq.com/queues.html)\n* [Confirms and Acknowledgements](https://www.rabbitmq.com/confirms.html)\n* [Consumer Prefetch](https://www.rabbitmq.com/consumer-prefetch.html)",
"fill": 10,
"fillGradient": 0,
"gridPos": {
"h": 5,
"w": 12,
"x": 12,
"y": 13
},
"id": 19,
"legend": {
"alignAsTable": true,
"avg": false,
"current": true,
"max": true,
"min": true,
"rightSide": false,
"show": false,
"total": false,
"values": true
},
"lines": true,
"linewidth": 1,
"links": [],
"nullPointMode": "null as zero",
"options": {
"dataLinks": []
},
"percentage": false,
"pointradius": 2,
"points": false,
"renderer": "flot",
"seriesOverrides": [
{
"alias": "/^rabbit@\\w+0/",
"color": "#56A64B"
},
{
"alias": "/^rabbit@\\w+1/",
"color": "#F2CC0C"
},
{
"alias": "/^rabbit@\\w+2/",
"color": "#3274D9"
},
{
"alias": "/^rabbit@\\w+3/",
"color": "#A352CC"
},
{
"alias": "/^rabbit@\\w+4/",
"color": "#FF780A"
},
{
"alias": "/^rabbit@\\w+5/",
"color": "#96D98D"
},
{
"alias": "/^rabbit@\\w+6/",
"color": "#FFEE52"
},
{
"alias": "/^rabbit@\\w+7/",
"color": "#8AB8FF"
},
{
"alias": "/^rabbit@\\w+8/",
"color": "#CA95E5"
},
{
"alias": "/^rabbit@\\w+9/",
"color": "#FFB357"
}
],
"spaceLength": 10,
"stack": true,
"steppedLine": false,
"targets": [
{
"expr": "sum(rabbitmq_queue_messages_unacked * on(instance) group_left(rabbitmq_cluster, rabbitmq_node) rabbitmq_identity_info{rabbitmq_cluster=\"$rabbitmq_cluster\"}) by(rabbitmq_node)",
"format": "time_series",
"instant": false,
"intervalFactor": 1,
"legendFormat": "{{rabbitmq_node}}",
"refId": "A"
}
],
"thresholds": [],
"timeFrom": null,
"timeRegions": [],
"timeShift": null,
"title": "Messages pending consumer acknowledgement",
"tooltip": {
"shared": true,
"sort": 2,
"value_type": "individual"
},
"type": "graph",
"xaxis": {
"buckets": null,
"mode": "time",
"name": null,
"show": true,
"values": []
},
"yaxes": [
{
"decimals": null,
"format": "short",
"label": "",
"logBase": 1,
"max": null,
"min": "0",
"show": true
},
{
"format": "short",
"label": null,
"logBase": 1,
"max": null,
"min": null,
"show": true
}
],
"yaxis": {
"align": false,
"alignLevel": null
}
},
{
"collapsed": false,
"datasource": "${DS_PROMETHEUS}",
"gridPos": {
"h": 1,
"w": 24,
"x": 0,
"y": 18
},
"id": 11,
"panels": [],
"title": "INCOMING MESSAGES",
"type": "row"
},
{
"aliasColors": {},
"bars": false,
"cacheTimeout": null,
"dashLength": 10,
"dashes": false,
"datasource": "${DS_PROMETHEUS}",
"description": "The incoming message rate before any routing rules are applied.\n\nIf this value is lower than the number of messages published to queues, it may indicate that some messages are delivered to more than one queue.\n\nIf this value is higher than the number of messages published to queues, messages cannot be routed and will either be dropped or returned to publishers.\n\n* [Publishers](https://www.rabbitmq.com/publishers.html)",
"fill": 10,
"fillGradient": 0,
"gridPos": {
"h": 5,
"w": 8,
"x": 0,
"y": 19
},
"id": 13,
"legend": {
"alignAsTable": true,
"avg": false,
"current": true,
"max": true,
"min": true,
"rightSide": false,
"show": false,
"total": false,
"values": true
},
"lines": true,
"linewidth": 1,
"links": [],
"nullPointMode": "null as zero",
"options": {
"dataLinks": []
},
"percentage": false,
"pointradius": 2,
"points": false,
"renderer": "flot",
"seriesOverrides": [
{
"alias": "/^rabbit@\\w+0/",
"color": "#56A64B"
},
{
"alias": "/^rabbit@\\w+1/",
"color": "#F2CC0C"
},
{
"alias": "/^rabbit@\\w+2/",
"color": "#3274D9"
},
{
"alias": "/^rabbit@\\w+3/",
"color": "#A352CC"
},
{
"alias": "/^rabbit@\\w+4/",
"color": "#FF780A"
},
{
"alias": "/^rabbit@\\w+5/",
"color": "#96D98D"
},
{
"alias": "/^rabbit@\\w+6/",
"color": "#FFEE52"
},
{
"alias": "/^rabbit@\\w+7/",
"color": "#8AB8FF"
},
{
"alias": "/^rabbit@\\w+8/",
"color": "#CA95E5"
},
{
"alias": "/^rabbit@\\w+9/",
"color": "#FFB357"
}
],
"spaceLength": 10,
"stack": true,
"steppedLine": false,
"targets": [
{
"expr": "sum(rate(rabbitmq_channel_messages_published_total[60s]) * on(instance) group_left(rabbitmq_cluster, rabbitmq_node) rabbitmq_identity_info{rabbitmq_cluster=\"$rabbitmq_cluster\"}) by(rabbitmq_node)",
"format": "time_series",
"instant": false,
"intervalFactor": 1,
"legendFormat": "{{rabbitmq_node}}",
"refId": "A"
}
],
"thresholds": [],
"timeFrom": null,
"timeRegions": [],
"timeShift": null,
"title": "Messages published / s",
"tooltip": {
"shared": true,
"sort": 2,
"value_type": "individual"
},
"type": "graph",
"xaxis": {
"buckets": null,
"mode": "time",
"name": null,
"show": true,
"values": []
},
"yaxes": [
{
"decimals": null,
"format": "short",
"label": "",
"logBase": 1,
"max": null,
"min": "0",
"show": true
},
{
"format": "short",
"label": null,
"logBase": 1,
"max": null,
"min": null,
"show": true
}
],
"yaxis": {
"align": false,
"alignLevel": null
}
},
{
"aliasColors": {},
"bars": false,
"cacheTimeout": null,
"dashLength": 10,
"dashes": false,
"datasource": "${DS_PROMETHEUS}",
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"x": 8,
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"id": 18,
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"current": true,
"max": true,
"min": true,
"rightSide": false,
"show": false,
"total": false,
"values": true
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"lines": true,
"linewidth": 1,
"links": [],
"nullPointMode": "null as zero",
"options": {
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"percentage": false,
"pointradius": 2,
"points": false,
"renderer": "flot",
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{
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{
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{
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{
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{
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{
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"stack": true,
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"targets": [
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"max": true,
"min": true,
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"show": false,
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"lines": true,
"linewidth": 1,
"links": [],
"nullPointMode": "null as zero",
"options": {
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"pointradius": 2,
"points": false,
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"spaceLength": 10,
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"timeRegions": [],
"timeShift": null,
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"linewidth": 1,
"links": [],
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"pointradius": 2,
"points": false,
"renderer": "flot",
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{
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},
{
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},
{
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},
{
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"color": "#96D98D"
},
{
"alias": "/^rabbit@\\w+6/",
"color": "#FFEE52"
},
{
"alias": "/^rabbit@\\w+7/",
"color": "#8AB8FF"
},
{
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},
{
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"spaceLength": 10,
"stack": true,
"steppedLine": false,
"targets": [
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"timeRegions": [],
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"x": 8,
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"lines": true,
"linewidth": 1,
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"percentage": false,
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{
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{
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},
{
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{
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"color": "#FF780A"
},
{
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"color": "#96D98D"
},
{
"alias": "/^rabbit@\\w+6/",
"color": "#FFEE52"
},
{
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{
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{
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"spaceLength": 10,
"stack": true,
"steppedLine": false,
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"thresholds": [],
"timeFrom": null,
"timeRegions": [],
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"yaxis": {
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"alignLevel": null
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},
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"bars": false,
"cacheTimeout": null,
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"dashes": false,
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"fillGradient": 0,
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"x": 16,
"y": 24
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"id": 34,
"legend": {
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"current": true,
"max": true,
"min": true,
"rightSide": false,
"show": false,
"total": false,
"values": true
},
"lines": true,
"linewidth": 1,
"links": [],
"nullPointMode": "null as zero",
"options": {
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},
"percentage": false,
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"points": false,
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],
"spaceLength": 10,
"stack": true,
"steppedLine": false,
"targets": [
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"thresholds": [
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],
"timeFrom": null,
"timeRegions": [],
"timeShift": null,
"title": "Unroutable messages dropped / s",
"tooltip": {
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"sort": 2,
"value_type": "individual"
},
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"max": null,
"min": null,
"show": true
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],
"yaxis": {
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"alignLevel": null
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},
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"datasource": "${DS_PROMETHEUS}",
"gridPos": {
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"x": 0,
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"id": 29,
"panels": [],
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},
{
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"cacheTimeout": null,
"dashLength": 10,
"dashes": false,
"datasource": "${DS_PROMETHEUS}",
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"fillGradient": 0,
"gridPos": {
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"w": 8,
"x": 0,
"y": 30
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"id": 14,
"legend": {
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"current": true,
"max": true,
"min": true,
"rightSide": false,
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"lines": true,
"linewidth": 1,
"links": [],
"nullPointMode": "null as zero",
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},
"percentage": false,
"pointradius": 2,
"points": false,
"renderer": "flot",
"seriesOverrides": [
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"color": "#56A64B"
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{
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},
{
"alias": "/^rabbit@\\w+2/",
"color": "#3274D9"
},
{
"alias": "/^rabbit@\\w+3/",
"color": "#A352CC"
},
{
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},
{
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"color": "#96D98D"
},
{
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},
{
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"color": "#8AB8FF"
},
{
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{
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"spaceLength": 10,
"stack": true,
"steppedLine": false,
"targets": [
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"timeRegions": [],
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},
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"show": false,
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"values": true
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"lines": true,
"linewidth": 1,
"links": [],
"nullPointMode": "null as zero",
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{
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{
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{
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{
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{
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{
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"stack": true,
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"targets": [
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"thresholds": [],
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},
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"datasource": "${DS_PROMETHEUS}",
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"y": 30
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"min": true,
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"values": true
},
"lines": true,
"linewidth": 1,
"links": [],
"nullPointMode": "null as zero",
"options": {
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},
"percentage": false,
"pointradius": 2,
"points": false,
"renderer": "flot",
"seriesOverrides": [
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],
"spaceLength": 10,
"stack": true,
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"targets": [
{
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"thresholds": [
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"dashLength": 10,
"dashes": false,
"datasource": "${DS_PROMETHEUS}",
"description": "The rate of new queues created (as opposed to redeclarations).\n\nLow sustained values above zero are to be expected. High rates may be indicative of queue churn or high rates of connection recovery. Confirm connection recovery rates by using the _Connections opened_ metric.\n\n* [Queues](https://www.rabbitmq.com/queues.html)",
"fill": 10,
"fillGradient": 0,
"gridPos": {
"h": 5,
"w": 4,
"x": 12,
"y": 46
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"id": 60,
"legend": {
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"avg": false,
"current": true,
"max": true,
"min": true,
"rightSide": false,
"show": false,
"total": false,
"values": true
},
"lines": true,
"linewidth": 1,
"links": [],
"nullPointMode": "null as zero",
"options": {
"dataLinks": []
},
"percentage": false,
"pointradius": 2,
"points": false,
"renderer": "flot",
"seriesOverrides": [
{
"alias": "/^rabbit@\\w+0/",
"color": "#56A64B"
},
{
"alias": "/^rabbit@\\w+1/",
"color": "#F2CC0C"
},
{
"alias": "/^rabbit@\\w+2/",
"color": "#3274D9"
},
{
"alias": "/^rabbit@\\w+3/",
"color": "#A352CC"
},
{
"alias": "/^rabbit@\\w+4/",
"color": "#FF780A"
},
{
"alias": "/^rabbit@\\w+5/",
"color": "#96D98D"
},
{
"alias": "/^rabbit@\\w+6/",
"color": "#FFEE52"
},
{
"alias": "/^rabbit@\\w+7/",
"color": "#8AB8FF"
},
{
"alias": "/^rabbit@\\w+8/",
"color": "#CA95E5"
},
{
"alias": "/^rabbit@\\w+9/",
"color": "#FFB357"
}
],
"spaceLength": 10,
"stack": true,
"steppedLine": false,
"targets": [
{
"expr": "sum(rate(rabbitmq_queues_created_total[60s]) * on(instance) group_left(rabbitmq_cluster, rabbitmq_node) rabbitmq_identity_info{rabbitmq_cluster=\"$rabbitmq_cluster\"}) by(rabbitmq_node)",
"format": "time_series",
"instant": false,
"intervalFactor": 1,
"legendFormat": "{{rabbitmq_node}}",
"refId": "A"
}
],
"thresholds": [
{
"colorMode": "warning",
"fill": true,
"line": true,
"op": "gt",
"value": 2,
"yaxis": "left"
},
{
"colorMode": "critical",
"fill": true,
"line": true,
"op": "gt",
"value": 10,
"yaxis": "left"
}
],
"timeFrom": null,
"timeRegions": [],
"timeShift": null,
"title": "Queues created / s",
"tooltip": {
"shared": true,
"sort": 2,
"value_type": "individual"
},
"type": "graph",
"xaxis": {
"buckets": null,
"mode": "time",
"name": null,
"show": true,
"values": []
},
"yaxes": [
{
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"format": "short",
"label": "",
"logBase": 1,
"max": null,
"min": "0",
"show": true
},
{
"format": "short",
"label": null,
"logBase": 1,
"max": null,
"min": null,
"show": true
}
],
"yaxis": {
"align": false,
"alignLevel": null
}
},
{
"aliasColors": {},
"bars": false,
"cacheTimeout": null,
"dashLength": 10,
"dashes": false,
"datasource": "${DS_PROMETHEUS}",
"description": "The rate of queues deleted.\n\nLow sustained values above zero are to be expected. High rates may be indicative of queue churn or high rates of connection recovery. Confirm connection recovery rates by using the _Connections opened_ metric.\n\n* [Queues](https://www.rabbitmq.com/queues.html)",
"fill": 10,
"fillGradient": 0,
"gridPos": {
"h": 5,
"w": 8,
"x": 16,
"y": 46
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"id": 59,
"legend": {
"alignAsTable": true,
"avg": false,
"current": true,
"max": true,
"min": true,
"rightSide": false,
"show": false,
"total": false,
"values": true
},
"lines": true,
"linewidth": 1,
"links": [],
"nullPointMode": "null as zero",
"options": {
"dataLinks": []
},
"percentage": false,
"pointradius": 2,
"points": false,
"renderer": "flot",
"seriesOverrides": [
{
"alias": "/^rabbit@\\w+0/",
"color": "#56A64B"
},
{
"alias": "/^rabbit@\\w+1/",
"color": "#F2CC0C"
},
{
"alias": "/^rabbit@\\w+2/",
"color": "#3274D9"
},
{
"alias": "/^rabbit@\\w+3/",
"color": "#A352CC"
},
{
"alias": "/^rabbit@\\w+4/",
"color": "#FF780A"
},
{
"alias": "/^rabbit@\\w+5/",
"color": "#96D98D"
},
{
"alias": "/^rabbit@\\w+6/",
"color": "#FFEE52"
},
{
"alias": "/^rabbit@\\w+7/",
"color": "#8AB8FF"
},
{
"alias": "/^rabbit@\\w+8/",
"color": "#CA95E5"
},
{
"alias": "/^rabbit@\\w+9/",
"color": "#FFB357"
}
],
"spaceLength": 10,
"stack": true,
"steppedLine": false,
"targets": [
{
"expr": "sum(rate(rabbitmq_queues_deleted_total[60s]) * on(instance) group_left(rabbitmq_cluster, rabbitmq_node) rabbitmq_identity_info{rabbitmq_cluster=\"$rabbitmq_cluster\"}) by(rabbitmq_node)",
"format": "time_series",
"instant": false,
"intervalFactor": 1,
"legendFormat": "{{rabbitmq_node}}",
"refId": "A"
}
],
"thresholds": [
{
"colorMode": "warning",
"fill": true,
"line": true,
"op": "gt",
"value": 2,
"yaxis": "left"
},
{
"colorMode": "critical",
"fill": true,
"line": true,
"op": "gt",
"value": 10,
"yaxis": "left"
}
],
"timeFrom": null,
"timeRegions": [],
"timeShift": null,
"title": "Queues deleted / s",
"tooltip": {
"shared": true,
"sort": 2,
"value_type": "individual"
},
"type": "graph",
"xaxis": {
"buckets": null,
"mode": "time",
"name": null,
"show": true,
"values": []
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"yaxes": [
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"format": "short",
"label": "",
"logBase": 1,
"max": null,
"min": "0",
"show": true
},
{
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"label": null,
"logBase": 1,
"max": null,
"min": null,
"show": true
}
],
"yaxis": {
"align": false,
"alignLevel": null
}
},
{
"collapsed": false,
"datasource": "${DS_PROMETHEUS}",
"gridPos": {
"h": 1,
"w": 24,
"x": 0,
"y": 51
},
"id": 51,
"panels": [],
"title": "CHANNELS",
"type": "row"
},
{
"aliasColors": {},
"bars": false,
"cacheTimeout": null,
"dashLength": 10,
"dashes": false,
"datasource": "${DS_PROMETHEUS}",
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"fill": 10,
"fillGradient": 0,
"gridPos": {
"h": 5,
"w": 8,
"x": 0,
"y": 52
},
"id": 54,
"legend": {
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"avg": false,
"current": true,
"max": true,
"min": true,
"rightSide": false,
"show": false,
"total": false,
"values": true
},
"lines": true,
"linewidth": 1,
"links": [],
"nullPointMode": "null as zero",
"options": {
"dataLinks": []
},
"percentage": false,
"pointradius": 2,
"points": false,
"renderer": "flot",
"seriesOverrides": [
{
"alias": "/^rabbit@\\w+0/",
"color": "#56A64B"
},
{
"alias": "/^rabbit@\\w+1/",
"color": "#F2CC0C"
},
{
"alias": "/^rabbit@\\w+2/",
"color": "#3274D9"
},
{
"alias": "/^rabbit@\\w+3/",
"color": "#A352CC"
},
{
"alias": "/^rabbit@\\w+4/",
"color": "#FF780A"
},
{
"alias": "/^rabbit@\\w+5/",
"color": "#96D98D"
},
{
"alias": "/^rabbit@\\w+6/",
"color": "#FFEE52"
},
{
"alias": "/^rabbit@\\w+7/",
"color": "#8AB8FF"
},
{
"alias": "/^rabbit@\\w+8/",
"color": "#CA95E5"
},
{
"alias": "/^rabbit@\\w+9/",
"color": "#FFB357"
}
],
"spaceLength": 10,
"stack": true,
"steppedLine": false,
"targets": [
{
"expr": "rabbitmq_channels * on(instance) group_left(rabbitmq_cluster, rabbitmq_node) rabbitmq_identity_info{rabbitmq_cluster=\"$rabbitmq_cluster\"}",
"format": "time_series",
"instant": false,
"intervalFactor": 1,
"legendFormat": "{{rabbitmq_node}}",
"refId": "A"
}
],
"thresholds": [],
"timeFrom": null,
"timeRegions": [],
"timeShift": null,
"title": "Total channels",
"tooltip": {
"shared": true,
"sort": 2,
"value_type": "individual"
},
"type": "graph",
"xaxis": {
"buckets": null,
"mode": "time",
"name": null,
"show": true,
"values": []
},
"yaxes": [
{
"decimals": null,
"format": "short",
"label": "",
"logBase": 1,
"max": null,
"min": "0",
"show": true
},
{
"format": "short",
"label": null,
"logBase": 1,
"max": null,
"min": null,
"show": true
}
],
"yaxis": {
"align": false,
"alignLevel": null
}
},
{
"aliasColors": {},
"bars": false,
"cacheTimeout": null,
"dashLength": 10,
"dashes": false,
"datasource": "${DS_PROMETHEUS}",
"description": "The rate of new channels opened by applications across all connections. Channels are expected to be long-lived.\n\nLow sustained values above zero are to be expected. High rates may be indicative of channel churn or mass connection recovery. Confirm connection recovery rates by using the _Connections opened_ metric.\n\n* [High Channel Churn](https://www.rabbitmq.com/channels.html#high-channel-churn)\n* [Channels](https://www.rabbitmq.com/channels.html)",
"fill": 10,
"fillGradient": 0,
"gridPos": {
"h": 5,
"w": 8,
"x": 8,
"y": 52
},
"id": 55,
"legend": {
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"avg": false,
"current": true,
"max": true,
"min": true,
"rightSide": false,
"show": false,
"total": false,
"values": true
},
"lines": true,
"linewidth": 1,
"links": [],
"nullPointMode": "null as zero",
"options": {
"dataLinks": []
},
"percentage": false,
"pointradius": 2,
"points": false,
"renderer": "flot",
"seriesOverrides": [
{
"alias": "/^rabbit@\\w+0/",
"color": "#56A64B"
},
{
"alias": "/^rabbit@\\w+1/",
"color": "#F2CC0C"
},
{
"alias": "/^rabbit@\\w+2/",
"color": "#3274D9"
},
{
"alias": "/^rabbit@\\w+3/",
"color": "#A352CC"
},
{
"alias": "/^rabbit@\\w+4/",
"color": "#FF780A"
},
{
"alias": "/^rabbit@\\w+5/",
"color": "#96D98D"
},
{
"alias": "/^rabbit@\\w+6/",
"color": "#FFEE52"
},
{
"alias": "/^rabbit@\\w+7/",
"color": "#8AB8FF"
},
{
"alias": "/^rabbit@\\w+8/",
"color": "#CA95E5"
},
{
"alias": "/^rabbit@\\w+9/",
"color": "#FFB357"
}
],
"spaceLength": 10,
"stack": true,
"steppedLine": false,
"targets": [
{
"expr": "sum(rate(rabbitmq_channels_opened_total[60s]) * on(instance) group_left(rabbitmq_cluster, rabbitmq_node) rabbitmq_identity_info{rabbitmq_cluster=\"$rabbitmq_cluster\"}) by(rabbitmq_node)",
"format": "time_series",
"instant": false,
"intervalFactor": 1,
"legendFormat": "{{rabbitmq_node}}",
"refId": "A"
}
],
"thresholds": [
{
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"fill": true,
"line": true,
"op": "gt",
"value": 2,
"yaxis": "left"
},
{
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"fill": true,
"line": true,
"op": "gt",
"value": 10,
"yaxis": "left"
}
],
"timeFrom": null,
"timeRegions": [],
"timeShift": null,
"title": "Channels opened / s",
"tooltip": {
"shared": true,
"sort": 2,
"value_type": "individual"
},
"type": "graph",
"xaxis": {
"buckets": null,
"mode": "time",
"name": null,
"show": true,
"values": []
},
"yaxes": [
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"format": "short",
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"logBase": 1,
"max": null,
"min": "0",
"show": true
},
{
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"label": null,
"logBase": 1,
"max": null,
"min": null,
"show": true
}
],
"yaxis": {
"align": false,
"alignLevel": null
}
},
{
"aliasColors": {},
"bars": false,
"cacheTimeout": null,
"dashLength": 10,
"dashes": false,
"datasource": "${DS_PROMETHEUS}",
"description": "The rate of channels closed by applications across all connections. Channels are expected to be long-lived.\n\nLow sustained values above zero are to be expected. High rates may be indicative of channel churn or mass connection recovery. Confirm connection recovery rates by using the _Connections opened_ metric.\n\n* [High Channel Churn](https://www.rabbitmq.com/channels.html#high-channel-churn)\n* [Channels](https://www.rabbitmq.com/channels.html)",
"fill": 10,
"fillGradient": 0,
"gridPos": {
"h": 5,
"w": 8,
"x": 16,
"y": 52
},
"id": 56,
"legend": {
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"avg": false,
"current": true,
"max": true,
"min": true,
"rightSide": false,
"show": false,
"total": false,
"values": true
},
"lines": true,
"linewidth": 1,
"links": [],
"nullPointMode": "null as zero",
"options": {
"dataLinks": []
},
"percentage": false,
"pointradius": 2,
"points": false,
"renderer": "flot",
"seriesOverrides": [
{
"alias": "/^rabbit@\\w+0/",
"color": "#56A64B"
},
{
"alias": "/^rabbit@\\w+1/",
"color": "#F2CC0C"
},
{
"alias": "/^rabbit@\\w+2/",
"color": "#3274D9"
},
{
"alias": "/^rabbit@\\w+3/",
"color": "#A352CC"
},
{
"alias": "/^rabbit@\\w+4/",
"color": "#FF780A"
},
{
"alias": "/^rabbit@\\w+5/",
"color": "#96D98D"
},
{
"alias": "/^rabbit@\\w+6/",
"color": "#FFEE52"
},
{
"alias": "/^rabbit@\\w+7/",
"color": "#8AB8FF"
},
{
"alias": "/^rabbit@\\w+8/",
"color": "#CA95E5"
},
{
"alias": "/^rabbit@\\w+9/",
"color": "#FFB357"
}
],
"spaceLength": 10,
"stack": true,
"steppedLine": false,
"targets": [
{
"expr": "sum(rate(rabbitmq_channels_closed_total[60s]) * on(instance) group_left(rabbitmq_cluster, rabbitmq_node) rabbitmq_identity_info{rabbitmq_cluster=\"$rabbitmq_cluster\"}) by(rabbitmq_node)",
"format": "time_series",
"instant": false,
"intervalFactor": 1,
"legendFormat": "{{rabbitmq_node}}",
"refId": "A"
}
],
"thresholds": [
{
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"line": true,
"op": "gt",
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"yaxis": "left"
},
{
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"line": true,
"op": "gt",
"value": 10,
"yaxis": "left"
}
],
"timeFrom": null,
"timeRegions": [],
"timeShift": null,
"title": "Channels closed / s",
"tooltip": {
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"sort": 2,
"value_type": "individual"
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"mode": "time",
"name": null,
"show": true,
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"min": null,
"show": true
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],
"yaxis": {
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"alignLevel": null
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},
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"datasource": "${DS_PROMETHEUS}",
"gridPos": {
"h": 1,
"w": 24,
"x": 0,
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"id": 46,
"panels": [],
"title": "CONNECTIONS",
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},
{
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"cacheTimeout": null,
"dashLength": 10,
"dashes": false,
"datasource": "${DS_PROMETHEUS}",
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"x": 0,
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"max": true,
"min": true,
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"values": true
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"lines": true,
"linewidth": 1,
"links": [],
"nullPointMode": "null as zero",
"options": {
"dataLinks": []
},
"percentage": false,
"pointradius": 2,
"points": false,
"renderer": "flot",
"seriesOverrides": [
{
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"color": "#56A64B"
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{
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{
"alias": "/^rabbit@\\w+2/",
"color": "#3274D9"
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{
"alias": "/^rabbit@\\w+3/",
"color": "#A352CC"
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{
"alias": "/^rabbit@\\w+4/",
"color": "#FF780A"
},
{
"alias": "/^rabbit@\\w+5/",
"color": "#96D98D"
},
{
"alias": "/^rabbit@\\w+6/",
"color": "#FFEE52"
},
{
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"color": "#8AB8FF"
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{
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{
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"spaceLength": 10,
"stack": true,
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"refId": "A"
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"thresholds": [],
"timeFrom": null,
"timeRegions": [],
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},
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"dashLength": 10,
"dashes": false,
"datasource": "${DS_PROMETHEUS}",
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"x": 8,
"y": 58
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"avg": false,
"current": true,
"max": true,
"min": true,
"rightSide": false,
"show": false,
"total": false,
"values": true
},
"lines": true,
"linewidth": 1,
"links": [],
"nullPointMode": "null as zero",
"options": {
"dataLinks": []
},
"percentage": false,
"pointradius": 2,
"points": false,
"renderer": "flot",
"seriesOverrides": [
{
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"color": "#56A64B"
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{
"alias": "/^rabbit@\\w+1/",
"color": "#F2CC0C"
},
{
"alias": "/^rabbit@\\w+2/",
"color": "#3274D9"
},
{
"alias": "/^rabbit@\\w+3/",
"color": "#A352CC"
},
{
"alias": "/^rabbit@\\w+4/",
"color": "#FF780A"
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{
"alias": "/^rabbit@\\w+5/",
"color": "#96D98D"
},
{
"alias": "/^rabbit@\\w+6/",
"color": "#FFEE52"
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{
"alias": "/^rabbit@\\w+7/",
"color": "#8AB8FF"
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{
"alias": "/^rabbit@\\w+8/",
"color": "#CA95E5"
},
{
"alias": "/^rabbit@\\w+9/",
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}
],
"spaceLength": 10,
"stack": true,
"steppedLine": false,
"targets": [
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"interval": "",
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16,189 | 1 | 639,751 | 24,587 | 2 | netdata-graphite | netdata Graphite | Massive collection of netdata graphs via Graphite. | 2022-04-30 18:32:08 UTC | 2022-05-02 18:44:56 UTC | 0 | null | 0 | 0 | nodeExporter | null | 0 | 0 | 24,587 | 1 | 16,189 | 1 | 2 | Massive collection of netdata graphs via Graphite. | {
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"transparent": true,
"type": "text"
},
{
"aliasColors": {
"iowait": "#962d82",
"load1": "#eab839",
"load15": "#0a50a1",
"load5": "#bf1b00",
"nice": "#508642",
"softirq": "#890f02",
"system": "#0a50a1",
"user": "#eab839"
},
"bars": false,
"dashLength": 10,
"dashes": false,
"datasource": "$datasource",
"decimals": 2,
"fill": 0,
"height": "150",
"id": 2,
"legend": {
"alignAsTable": true,
"avg": false,
"current": true,
"max": false,
"min": false,
"rightSide": true,
"show": true,
"sideWidth": 200,
"total": false,
"values": true
},
"lines": true,
"linewidth": 2,
"links": [],
"nullPointMode": "connected",
"percentage": false,
"pointradius": 5,
"points": false,
"renderer": "flot",
"seriesOverrides": [],
"spaceLength": 10,
"span": 12,
"stack": false,
"steppedLine": false,
"targets": [
{
"hide": false,
"refId": "A",
"target": "aliasByMetric($rootdir.$server.system.load.load1)",
"textEditor": false
},
{
"hide": false,
"refId": "B",
"target": "aliasByMetric($rootdir.$server.system.load.load5)",
"textEditor": false
},
{
"hide": false,
"refId": "C",
"target": "aliasByMetric($rootdir.$server.system.load.load15)",
"textEditor": false
}
],
"thresholds": [],
"timeFrom": null,
"timeShift": null,
"title": "System Load Average",
"tooltip": {
"shared": true,
"sort": 0,
"value_type": "individual"
},
"transparent": true,
"type": "graph",
"xaxis": {
"buckets": null,
"mode": "time",
"name": null,
"show": true,
"values": []
},
"yaxes": [
{
"format": "short",
"label": "load",
"logBase": 1,
"max": null,
"min": null,
"show": true
},
{
"format": "short",
"label": null,
"logBase": 1,
"max": null,
"min": null,
"show": true
}
]
},
{
"content": "## disk",
"height": "10",
"id": 1190,
"links": [],
"mode": "markdown",
"span": 12,
"title": "",
"transparent": true,
"type": "text"
},
{
"content": "Total Disk I/O, for all physical disks. You can get detailed information about each disk at the **Disks** section and per application Disk usage at the **Applications Monitoring** section. Physical are all the disks that are listed in `/sys/block`, but do not exist in `/sys/devices/virtual/block`.",
"height": "10",
"id": 2036,
"links": [],
"mode": "markdown",
"span": 12,
"title": "",
"transparent": true,
"type": "text"
},
{
"aliasColors": {
"in": "#cca300",
"iowait": "#962d82",
"load1": "#eab839",
"load15": "#0a50a1",
"load5": "#bf1b00",
"nice": "#508642",
"out": "#bf1b00",
"reads": "#508642",
"softirq": "#890f02",
"system": "#0a50a1",
"user": "#eab839",
"writes": "#bf1b00"
},
"bars": false,
"dashLength": 10,
"dashes": false,
"datasource": "$datasource",
"decimals": null,
"fill": 4,
"id": 3,
"legend": {
"alignAsTable": true,
"avg": false,
"current": true,
"max": false,
"min": false,
"rightSide": true,
"show": true,
"sideWidth": 200,
"total": false,
"values": true
},
"lines": true,
"linewidth": 1,
"links": [],
"nullPointMode": "connected",
"percentage": false,
"pointradius": 5,
"points": false,
"renderer": "flot",
"seriesOverrides": [],
"spaceLength": 10,
"span": 12,
"stack": false,
"steppedLine": false,
"targets": [
{
"hide": false,
"refId": "A",
"target": "aliasByMetric($rootdir.$server.system.io.out)",
"textEditor": false
},
{
"hide": false,
"refId": "B",
"target": "aliasByMetric($rootdir.$server.system.io.in)",
"textEditor": false
}
],
"thresholds": [],
"timeFrom": null,
"timeShift": null,
"title": "Disk I/O",
"tooltip": {
"shared": true,
"sort": 0,
"value_type": "individual"
},
"transparent": true,
"type": "graph",
"xaxis": {
"buckets": null,
"mode": "time",
"name": null,
"show": true,
"values": []
},
"yaxes": [
{
"format": "KBs",
"label": "bytes/s",
"logBase": 1,
"max": null,
"min": null,
"show": true
},
{
"format": "short",
"label": null,
"logBase": 1,
"max": null,
"min": null,
"show": true
}
]
},
{
"content": "Memory paged from/to disk. This is usually the total disk I/O of the system.",
"height": "10",
"id": 1192,
"links": [],
"mode": "markdown",
"span": 12,
"title": "",
"transparent": true,
"type": "text"
},
{
"aliasColors": {
"in": "#cca300",
"iowait": "#962d82",
"load1": "#eab839",
"load15": "#0a50a1",
"load5": "#bf1b00",
"nice": "#508642",
"out": "#bf1b00",
"reads": "#508642",
"softirq": "#890f02",
"system": "#0a50a1",
"user": "#eab839",
"writes": "#bf1b00"
},
"bars": false,
"dashLength": 10,
"dashes": false,
"datasource": "$datasource",
"decimals": null,
"fill": 4,
"id": 1191,
"legend": {
"alignAsTable": true,
"avg": false,
"current": true,
"max": false,
"min": false,
"rightSide": true,
"show": true,
"sideWidth": 200,
"total": false,
"values": true
},
"lines": true,
"linewidth": 1,
"links": [],
"nullPointMode": "connected",
"percentage": false,
"pointradius": 5,
"points": false,
"renderer": "flot",
"seriesOverrides": [],
"spaceLength": 10,
"span": 12,
"stack": false,
"steppedLine": false,
"targets": [
{
"hide": false,
"refId": "A",
"target": "aliasByMetric($rootdir.$server.system.pgpgio.out)",
"textEditor": false
},
{
"hide": false,
"refId": "B",
"target": "aliasByMetric($rootdir.$server.system.pgpgio.in)",
"textEditor": false
}
],
"thresholds": [],
"timeFrom": null,
"timeShift": null,
"title": "Memory Paged from/to disk",
"tooltip": {
"shared": true,
"sort": 0,
"value_type": "individual"
},
"transparent": true,
"type": "graph",
"xaxis": {
"buckets": null,
"mode": "time",
"name": null,
"show": true,
"values": []
},
"yaxes": [
{
"format": "KBs",
"label": "bytes/s",
"logBase": 1,
"max": null,
"min": null,
"show": true
},
{
"format": "short",
"label": null,
"logBase": 1,
"max": null,
"min": null,
"show": true
}
]
},
{
"content": "## ram",
"height": "10",
"id": 2037,
"links": [],
"mode": "markdown",
"span": 12,
"title": "",
"transparent": true,
"type": "text"
},
{
"content": "System Random Access Memory (i.e. physical memory) usage.",
"height": "10",
"id": 1193,
"links": [],
"mode": "markdown",
"span": 12,
"title": "",
"transparent": true,
"type": "text"
},
{
"aliasColors": {
"buffers": "#967302",
"cached": "#1f78c1",
"free": "rgb(112, 155, 55)",
"iowait": "#962d82",
"load1": "#eab839",
"load15": "#0a50a1",
"load5": "#bf1b00",
"nice": "#508642",
"softirq": "#890f02",
"system": "#0a50a1",
"used": "#e24d42",
"user": "#eab839"
},
"bars": false,
"dashLength": 10,
"dashes": false,
"datasource": "$datasource",
"decimals": 2,
"fill": 10,
"height": "210",
"id": 4,
"legend": {
"alignAsTable": true,
"avg": false,
"current": true,
"max": false,
"min": false,
"rightSide": true,
"show": true,
"sideWidth": 200,
"total": false,
"values": true
},
"lines": true,
"linewidth": 0,
"links": [],
"nullPointMode": "connected",
"percentage": false,
"pointradius": 5,
"points": false,
"renderer": "flot",
"seriesOverrides": [],
"spaceLength": 10,
"span": 12,
"stack": true,
"steppedLine": false,
"targets": [
{
"hide": false,
"refId": "A",
"target": "aliasByMetric($rootdir.$server.system.ram.buffers)",
"textEditor": false
},
{
"hide": false,
"refId": "B",
"target": "aliasByMetric($rootdir.$server.system.ram.cached)",
"textEditor": false
},
{
"hide": false,
"refId": "C",
"target": "aliasByMetric($rootdir.$server.system.ram.used)",
"textEditor": false
},
{
"hide": false,
"refId": "D",
"target": "aliasByMetric($rootdir.$server.system.ram.free)",
"textEditor": false
}
],
"thresholds": [],
"timeFrom": null,
"timeShift": null,
"title": "System RAM",
"tooltip": {
"shared": true,
"sort": 0,
"value_type": "individual"
},
"transparent": true,
"type": "graph",
"xaxis": {
"buckets": null,
"mode": "time",
"name": null,
"show": true,
"values": []
},
"yaxes": [
{
"decimals": null,
"format": "decmbytes",
"label": "bytes",
"logBase": 1,
"max": null,
"min": null,
"show": true
},
{
"format": "short",
"label": null,
"logBase": 1,
"max": null,
"min": null,
"show": true
}
]
},
{
"content": "## swap",
"height": "10",
"id": 2038,
"links": [],
"mode": "markdown",
"span": 12,
"title": "",
"transparent": true,
"type": "text"
},
{
"content": "System swap memory usage. Swap space is used when the amount of physical memory (RAM) is full. When the system needs more memory resources and the RAM is full, inactive pages in memory are moved to the swap space (usually a disk, a disk partition or a file).",
"height": "10",
"id": 1194,
"links": [],
"mode": "markdown",
"span": 12,
"title": "",
"transparent": true,
"type": "text"
},
{
"aliasColors": {
"buffers": "#967302",
"cached": "#0a50a1",
"free": "rgb(112, 155, 55)",
"iowait": "#962d82",
"load1": "#eab839",
"load15": "#0a50a1",
"load5": "#bf1b00",
"nice": "#508642",
"softirq": "#890f02",
"system": "#0a50a1",
"used": "#bf1b00",
"user": "#eab839"
},
"bars": false,
"dashLength": 10,
"dashes": false,
"datasource": "$datasource",
"decimals": 1,
"fill": 10,
"height": "210",
"id": 1195,
"legend": {
"alignAsTable": true,
"avg": false,
"current": true,
"max": false,
"min": false,
"rightSide": true,
"show": true,
"sideWidth": 200,
"total": false,
"values": true
},
"lines": true,
"linewidth": 0,
"links": [],
"nullPointMode": "connected",
"percentage": false,
"pointradius": 5,
"points": false,
"renderer": "flot",
"seriesOverrides": [],
"spaceLength": 10,
"span": 12,
"stack": true,
"steppedLine": false,
"targets": [
{
"hide": false,
"refId": "A",
"target": "aliasByMetric($rootdir.$server.system.swap.used)",
"textEditor": false
},
{
"hide": false,
"refId": "B",
"target": "aliasByMetric($rootdir.$server.system.swap.free)",
"textEditor": false
}
],
"thresholds": [],
"timeFrom": null,
"timeShift": null,
"title": "System Swap",
"tooltip": {
"shared": true,
"sort": 0,
"value_type": "individual"
},
"transparent": true,
"type": "graph",
"xaxis": {
"buckets": null,
"mode": "time",
"name": null,
"show": true,
"values": []
},
"yaxes": [
{
"format": "decmbytes",
"label": "bytes",
"logBase": 1,
"max": null,
"min": "0",
"show": true
},
{
"format": "short",
"label": null,
"logBase": 1,
"max": null,
"min": null,
"show": true
}
]
},
{
"content": "Total Swap I/O. (netdata measures both `in` and `out`. If either of them is not shown in the chart, it is because it is zero - you can change the page settings to always render all the available dimensions on all charts).",
"height": "10",
"id": 1197,
"links": [],
"mode": "markdown",
"span": 12,
"title": "",
"transparent": true,
"type": "text"
},
{
"aliasColors": {
"buffers": "#967302",
"cached": "#0a50a1",
"free": "rgb(112, 155, 55)",
"in": "#cca300",
"iowait": "#962d82",
"load1": "#eab839",
"load15": "#0a50a1",
"load5": "#bf1b00",
"nice": "#508642",
"out": "#bf1b00",
"softirq": "#890f02",
"system": "#0a50a1",
"used": "#e24d42",
"user": "#eab839"
},
"bars": false,
"dashLength": 10,
"dashes": false,
"datasource": "$datasource",
"decimals": 1,
"fill": 4,
"id": 1196,
"legend": {
"alignAsTable": true,
"avg": false,
"current": true,
"max": false,
"min": false,
"rightSide": true,
"show": true,
"sideWidth": 200,
"total": false,
"values": true
},
"lines": true,
"linewidth": 1,
"links": [],
"nullPointMode": "connected",
"percentage": false,
"pointradius": 5,
"points": false,
"renderer": "flot",
"seriesOverrides": [],
"spaceLength": 10,
"span": 12,
"stack": false,
"steppedLine": false,
"targets": [
{
"hide": false,
"refId": "A",
"target": "aliasByMetric($rootdir.$server.system.swapio.out)",
"textEditor": false
},
{
"hide": false,
"refId": "B",
"target": "aliasByMetric($rootdir.$server.system.swapio.in)",
"textEditor": false
}
],
"thresholds": [],
"timeFrom": null,
"timeShift": null,
"title": "Swap I/O",
"tooltip": {
"shared": true,
"sort": 0,
"value_type": "individual"
},
"transparent": true,
"type": "graph",
"xaxis": {
"buckets": null,
"mode": "time",
"name": null,
"show": true,
"values": []
},
"yaxes": [
{
"decimals": null,
"format": "MBs",
"label": "bytes/s",
"logBase": 1,
"max": null,
"min": null,
"show": true
},
{
"format": "short",
"label": null,
"logBase": 1,
"max": null,
"min": null,
"show": true
}
]
},
{
"content": "## network",
"height": "10",
"id": 2039,
"links": [],
"mode": "markdown",
"span": 12,
"title": "",
"transparent": true,
"type": "text"
},
{
"content": "Total bandwidth of all physical network interfaces. This does not include `lo`, VPNs, network bridges, IFB devices, bond interfaces, etc. Only the bandwidth of physical network interfaces is aggregated. Physical are all the network interfaces that are listed in `/proc/net/dev`, but do not exist in `/sys/devices/virtual/net`.",
"height": "10",
"id": 1198,
"links": [],
"mode": "markdown",
"span": 12,
"title": "",
"transparent": true,
"type": "text"
},
{
"aliasColors": {
"iowait": "#962d82",
"load1": "#eab839",
"load15": "#0a50a1",
"load5": "#bf1b00",
"nice": "#508642",
"received": "#508642",
"sent": "#bf1b00",
"softirq": "#890f02",
"system": "#0a50a1",
"user": "#eab839"
},
"bars": false,
"dashLength": 10,
"dashes": false,
"datasource": "$datasource",
"decimals": 1,
"fill": 4,
"id": 5,
"legend": {
"alignAsTable": true,
"avg": false,
"current": true,
"max": false,
"min": false,
"rightSide": true,
"show": true,
"sideWidth": 200,
"total": false,
"values": true
},
"lines": true,
"linewidth": 1,
"links": [],
"nullPointMode": "connected",
"percentage": false,
"pointradius": 5,
"points": false,
"renderer": "flot",
"seriesOverrides": [],
"spaceLength": 10,
"span": 12,
"stack": false,
"steppedLine": false,
"targets": [
{
"hide": false,
"refId": "A",
"target": "aliasByMetric($rootdir.$server.system.net.sent)",
"textEditor": false
},
{
"hide": false,
"refId": "B",
"target": "aliasByMetric($rootdir.$server.system.net.received)",
"textEditor": false
}
],
"thresholds": [],
"timeFrom": null,
"timeShift": null,
"title": "Total IPv4 Traffic",
"tooltip": {
"shared": true,
"sort": 0,
"value_type": "individual"
},
"transparent": true,
"type": "graph",
"xaxis": {
"buckets": null,
"mode": "time",
"name": null,
"show": true,
"values": []
},
"yaxes": [
{
"format": "Kbits",
"label": "bits/s",
"logBase": 1,
"max": null,
"min": null,
"show": true
},
{
"format": "short",
"label": null,
"logBase": 1,
"max": null,
"min": null,
"show": true
}
]
},
{
"content": "Total IPv4 Traffic.",
"height": "10",
"id": 1199,
"links": [],
"mode": "markdown",
"span": 12,
"title": "",
"transparent": true,
"type": "text"
},
{
"aliasColors": {
"iowait": "#962d82",
"load1": "#eab839",
"load15": "#0a50a1",
"load5": "#bf1b00",
"nice": "#508642",
"received": "#508642",
"sent": "#bf1b00",
"softirq": "#890f02",
"system": "#0a50a1",
"user": "#eab839"
},
"bars": false,
"dashLength": 10,
"dashes": false,
"datasource": "$datasource",
"decimals": 1,
"fill": 4,
"id": 1204,
"legend": {
"alignAsTable": true,
"avg": false,
"current": true,
"max": false,
"min": false,
"rightSide": true,
"show": true,
"sideWidth": 200,
"total": false,
"values": true
},
"lines": true,
"linewidth": 1,
"links": [],
"nullPointMode": "connected",
"percentage": false,
"pointradius": 5,
"points": false,
"renderer": "flot",
"seriesOverrides": [],
"spaceLength": 10,
"span": 12,
"stack": false,
"steppedLine": false,
"targets": [
{
"hide": false,
"refId": "A",
"target": "aliasByMetric($rootdir.$server.system.ipv4.sent)",
"textEditor": false
},
{
"hide": false,
"refId": "B",
"target": "aliasByMetric($rootdir.$server.system.ipv4.received)",
"textEditor": false
}
],
"thresholds": [],
"timeFrom": null,
"timeShift": null,
"title": "IPv4 Bandwidth",
"tooltip": {
"shared": true,
"sort": 0,
"value_type": "individual"
},
"transparent": true,
"type": "graph",
"xaxis": {
"buckets": null,
"mode": "time",
"name": null,
"show": true,
"values": []
},
"yaxes": [
{
"format": "Kbits",
"label": "bits/s",
"logBase": 1,
"max": null,
"min": null,
"show": true
},
{
"format": "short",
"label": null,
"logBase": 1,
"max": null,
"min": null,
"show": true
}
]
},
{
"content": "## processes",
"height": "10",
"id": 2040,
"links": [],
"mode": "markdown",
"span": 12,
"title": "",
"transparent": true,
"type": "text"
},
{
"content": "System processes. **Running** are the processes in the CPU. **Blocked** are processes that are willing to enter the CPU, but they cannot, e.g. because they wait for disk activity.",
"height": "10",
"id": 1200,
"links": [],
"mode": "markdown",
"span": 12,
"title": "",
"transparent": true,
"type": "text"
},
{
"aliasColors": {
"blocked": "#e24d42",
"iowait": "#962d82",
"load1": "#eab839",
"load15": "#0a50a1",
"load5": "#bf1b00",
"nice": "#508642",
"running": "#cca300",
"softirq": "#890f02",
"system": "#0a50a1",
"user": "#eab839"
},
"bars": false,
"dashLength": 10,
"dashes": false,
"datasource": "$datasource",
"decimals": 1,
"fill": 0,
"id": 6,
"legend": {
"alignAsTable": true,
"avg": false,
"current": true,
"max": false,
"min": false,
"rightSide": true,
"show": true,
"sideWidth": 200,
"total": false,
"values": true
},
"lines": true,
"linewidth": 2,
"links": [],
"nullPointMode": "connected",
"percentage": false,
"pointradius": 5,
"points": false,
"renderer": "flot",
"seriesOverrides": [],
"spaceLength": 10,
"span": 12,
"stack": false,
"steppedLine": false,
"targets": [
{
"hide": false,
"refId": "A",
"target": "aliasByMetric($rootdir.$server.system.processes.running)",
"textEditor": false
},
{
"hide": false,
"refId": "B",
"target": "aliasByMetric($rootdir.$server.system.processes.blocked)",
"textEditor": false
}
],
"thresholds": [],
"timeFrom": null,
"timeShift": null,
"title": "System Processes",
"tooltip": {
"shared": true,
"sort": 0,
"value_type": "individual"
},
"transparent": true,
"type": "graph",
"xaxis": {
"buckets": null,
"mode": "time",
"name": null,
"show": true,
"values": []
},
"yaxes": [
{
"format": "short",
"label": "processes",
"logBase": 1,
"max": null,
"min": null,
"show": true
},
{
"format": "short",
"label": null,
"logBase": 1,
"max": null,
"min": null,
"show": true
}
]
},
{
"content": "Number of new processes created.",
"height": "10",
"id": 1201,
"links": [],
"mode": "markdown",
"span": 12,
"title": "",
"transparent": true,
"type": "text"
},
{
"aliasColors": {
"iowait": "#962d82",
"load1": "#eab839",
"load15": "#0a50a1",
"load5": "#bf1b00",
"nice": "#508642",
"softirq": "#890f02",
"started": "#705da0",
"system": "#0a50a1",
"user": "#eab839"
},
"bars": false,
"dashLength": 10,
"dashes": false,
"datasource": "$datasource",
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"fill": 0,
"id": 7,
"legend": {
"alignAsTable": true,
"avg": false,
"current": true,
"max": false,
"min": false,
"rightSide": true,
"show": true,
"sideWidth": 200,
"total": false,
"values": true
},
"lines": true,
"linewidth": 2,
"links": [],
"nullPointMode": "connected",
"percentage": false,
"pointradius": 5,
"points": false,
"renderer": "flot",
"seriesOverrides": [],
"spaceLength": 10,
"span": 12,
"stack": false,
"steppedLine": false,
"targets": [
{
"hide": false,
"refId": "A",
"target": "aliasByMetric($rootdir.$server.system.forks.started)",
"textEditor": false
}
],
"thresholds": [],
"timeFrom": null,
"timeShift": null,
"title": "Started Processes",
"tooltip": {
"shared": true,
"sort": 0,
"value_type": "individual"
},
"transparent": true,
"type": "graph",
"xaxis": {
"buckets": null,
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"min": null,
"show": true
},
{
"format": "short",
"label": null,
"logBase": 1,
"max": null,
"min": null,
"show": true
}
]
},
{
"content": "All system processes.",
"height": "10",
"id": 1202,
"links": [],
"mode": "markdown",
"span": 12,
"title": "",
"transparent": true,
"type": "text"
},
{
"aliasColors": {
"active": "#cca300",
"iowait": "#962d82",
"load1": "#eab839",
"load15": "#0a50a1",
"load5": "#bf1b00",
"nice": "#508642",
"softirq": "#890f02",
"system": "#0a50a1",
"user": "#eab839"
},
"bars": false,
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"span": 12,
"stack": false,
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{
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"target": "aliasByMetric($rootdir.$server.system.active_processes.active)",
"textEditor": false
}
],
"thresholds": [],
"timeFrom": null,
"timeShift": null,
"title": "System Active Processes",
"tooltip": {
"shared": true,
"sort": 0,
"value_type": "individual"
},
"transparent": true,
"type": "graph",
"xaxis": {
"buckets": null,
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"show": true
},
{
"format": "short",
"label": null,
"logBase": 1,
"max": null,
"min": null,
"show": true
}
]
},
{
"content": "[Context Switches](https://en.wikipedia.org/wiki/Context_switch), is the switching of the CPU from one process, task or thread to another. If there are many processes or threads willing to execute and very few CPU cores available to handle them, the system is making more context switching to balance the CPU resources among them. The whole process is computationally intensive. The more the context switches, the slower the system gets.",
"height": "10",
"id": 1203,
"links": [],
"mode": "markdown",
"span": 12,
"title": "",
"transparent": true,
"type": "text"
},
{
"aliasColors": {
"iowait": "#962d82",
"load1": "#eab839",
"load15": "#0a50a1",
"load5": "#bf1b00",
"nice": "#508642",
"softirq": "#890f02",
"switches": "#cca300",
"system": "#0a50a1",
"user": "#eab839"
},
"bars": false,
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"dashes": false,
"datasource": "$datasource",
"decimals": 1,
"fill": 0,
"id": 9,
"legend": {
"alignAsTable": true,
"avg": false,
"current": true,
"max": false,
"min": false,
"rightSide": true,
"show": true,
"sideWidth": 200,
"total": false,
"values": true
},
"lines": true,
"linewidth": 2,
"links": [],
"nullPointMode": "connected",
"percentage": false,
"pointradius": 5,
"points": false,
"renderer": "flot",
"seriesOverrides": [],
"spaceLength": 10,
"span": 12,
"stack": false,
"steppedLine": false,
"targets": [
{
"hide": false,
"refId": "A",
"target": "aliasByMetric($rootdir.$server.system.ctxt.switches)",
"textEditor": false
}
],
"thresholds": [],
"timeFrom": null,
"timeShift": null,
"title": "CPU Context Switches",
"tooltip": {
"shared": true,
"sort": 0,
"value_type": "individual"
},
"transparent": true,
"type": "graph",
"xaxis": {
"buckets": null,
"mode": "time",
"name": null,
"show": true,
"values": []
},
"yaxes": [
{
"format": "short",
"label": "context switches",
"logBase": 1,
"max": null,
"min": null,
"show": true
},
{
"format": "short",
"label": null,
"logBase": 1,
"max": null,
"min": null,
"show": true
}
]
},
{
"content": "Idle jitter is calculated by netdata. A thread is spawned that requests to sleep for a few microseconds. When the system wakes it up, it measures how many microseconds have passed. The difference between the requested and the actual duration of the sleep, is the idle jitter. This number is useful in real-time environments, where CPU jitter can affect the quality of the service (like VoIP media gateways).",
"height": "10",
"id": 1207,
"links": [],
"mode": "markdown",
"span": 12,
"title": "",
"transparent": true,
"type": "text"
},
{
"aliasColors": {
"average": "#0a50a1",
"iowait": "#962d82",
"load1": "#eab839",
"load15": "#0a50a1",
"load5": "#bf1b00",
"max": "#bf1b00",
"min": "#cca300",
"nice": "#508642",
"softirq": "#890f02",
"system": "#0a50a1",
"user": "#eab839"
},
"bars": false,
"dashLength": 10,
"dashes": false,
"datasource": "$datasource",
"decimals": 1,
"fill": 4,
"id": 10,
"legend": {
"alignAsTable": true,
"avg": false,
"current": true,
"max": false,
"min": false,
"rightSide": true,
"show": true,
"sideWidth": 200,
"total": false,
"values": true
},
"lines": true,
"linewidth": 1,
"links": [],
"nullPointMode": "connected",
"percentage": false,
"pointradius": 5,
"points": false,
"renderer": "flot",
"seriesOverrides": [],
"spaceLength": 10,
"span": 12,
"stack": false,
"steppedLine": false,
"targets": [
{
"hide": false,
"refId": "A",
"target": "aliasByMetric($rootdir.$server.system.idlejitter.average)",
"textEditor": false
},
{
"hide": false,
"refId": "B",
"target": "aliasByMetric($rootdir.$server.system.idlejitter.max)",
"textEditor": false
},
{
"hide": false,
"refId": "C",
"target": "aliasByMetric($rootdir.$server.system.idlejitter.min)",
"textEditor": false
}
],
"thresholds": [],
"timeFrom": null,
"timeShift": null,
"title": "CPU Idle Jitter",
"tooltip": {
"shared": true,
"sort": 0,
"value_type": "individual"
},
"transparent": true,
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"buckets": null,
"mode": "time",
"name": null,
"show": true,
"values": []
},
"yaxes": [
{
"format": "short",
"label": "miroseconds lost/s",
"logBase": 1,
"max": null,
"min": null,
"show": true
},
{
"format": "short",
"label": null,
"logBase": 1,
"max": null,
"min": null,
"show": true
}
]
},
{
"content": "## interrupts",
"height": "10",
"id": 1205,
"links": [],
"mode": "markdown",
"span": 12,
"title": "",
"transparent": true,
"type": "text"
},
{
"content": "Total number of CPU interrupts. Check `system.interrupts` that gives more detail about each interrupt and also the **CPUs** section where interrupts are analyzed per CPU core.",
"height": "10",
"id": 2041,
"links": [],
"mode": "markdown",
"span": 12,
"title": "",
"transparent": true,
"type": "text"
},
{
"aliasColors": {
"interrupts": "#e24d42",
"iowait": "#962d82",
"load1": "#eab839",
"load15": "#0a50a1",
"load5": "#bf1b00",
"nice": "#508642",
"softirq": "#890f02",
"system": "#0a50a1",
"user": "#eab839"
},
"bars": false,
"dashLength": 10,
"dashes": false,
"datasource": "$datasource",
"decimals": 1,
"fill": 0,
"id": 11,
"legend": {
"alignAsTable": true,
"avg": false,
"current": true,
"max": false,
"min": false,
"rightSide": true,
"show": true,
"sideWidth": 200,
"total": false,
"values": true
},
"lines": true,
"linewidth": 2,
"links": [],
"nullPointMode": "connected",
"percentage": false,
"pointradius": 5,
"points": false,
"renderer": "flot",
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"spaceLength": 10,
"span": 12,
"stack": false,
"steppedLine": false,
"targets": [
{
"hide": false,
"refId": "A",
"target": "aliasByMetric($rootdir.$server.system.intr.interrupts)",
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}
],
"thresholds": [],
"timeFrom": null,
"timeShift": null,
"title": "CPU Interrupts",
"tooltip": {
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"show": true,
"values": []
},
"yaxes": [
{
"format": "short",
"label": "interrupts/s",
"logBase": 1,
"max": null,
"min": null,
"show": true
},
{
"format": "short",
"label": null,
"logBase": 1,
"max": null,
"min": null,
"show": true
}
]
},
{
"content": "CPU interrupts in detail. At the **CPUs** section, interrupts are analyzed per CPU core.",
"height": "10",
"id": 1206,
"links": [],
"mode": "markdown",
"span": 12,
"title": "",
"transparent": true,
"type": "text"
},
{
"aliasColors": {
"LOC": "#967302",
"MCP": "#99440a",
"ahci": "#bf1b00",
"ata": "#cca300",
"eth": "#3f6833",
"iowait": "#962d82",
"load1": "#eab839",
"load15": "#0a50a1",
"load5": "#bf1b00",
"nice": "#508642",
"softirq": "#890f02",
"system": "#0a50a1",
"user": "#eab839"
},
"bars": false,
"dashLength": 10,
"dashes": false,
"datasource": "$datasource",
"decimals": 1,
"fill": 8,
"id": 12,
"legend": {
"alignAsTable": true,
"avg": false,
"current": true,
"max": false,
"min": false,
"rightSide": true,
"show": true,
"sideWidth": 200,
"total": false,
"values": true
},
"lines": true,
"linewidth": 0,
"links": [],
"nullPointMode": "connected",
"percentage": false,
"pointradius": 5,
"points": false,
"renderer": "flot",
"seriesOverrides": [],
"spaceLength": 10,
"span": 12,
"stack": true,
"steppedLine": false,
"targets": [
{
"hide": false,
"refId": "A",
"target": "aliasByMetric(maximumAbove($rootdir.$server.system.interrupts.*, 0))",
"textEditor": true
},
{
"hide": false,
"refId": "C",
"target": "aliasByNode(maximumAbove($rootdir.$server.system.interrupts.*.*, 0), 4, 5)",
"textEditor": false
}
],
"thresholds": [],
"timeFrom": null,
"timeShift": null,
"title": "System interrupts",
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{
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"label": null,
"logBase": 1,
"max": null,
"min": null,
"show": true
}
]
},
{
"content": "## softirqs",
"height": "10",
"id": 2042,
"links": [],
"mode": "markdown",
"span": 12,
"title": "",
"transparent": true,
"type": "text"
},
{
"content": "CPU softirqs in detail. At the **CPUs** section, softirqs are analyzed per CPU core.",
"height": "10",
"id": 1208,
"links": [],
"mode": "markdown",
"span": 12,
"title": "",
"transparent": true,
"type": "text"
},
{
"aliasColors": {
"BLOCK": "#aea2e0",
"NET_RX": "#e0752d",
"NET_TX": "#447ebc",
"RCU": "#e24d42",
"TASKLET": "#0a50a1",
"TIMER": "#e24d42",
"iowait": "#962d82",
"load1": "#eab839",
"load15": "#0a50a1",
"load5": "#bf1b00",
"nice": "#508642",
"softirq": "#890f02",
"system": "#0a50a1",
"user": "#eab839"
},
"bars": false,
"dashLength": 10,
"dashes": false,
"datasource": "$datasource",
"decimals": 1,
"fill": 8,
"id": 13,
"legend": {
"alignAsTable": true,
"avg": false,
"current": true,
"max": false,
"min": false,
"rightSide": true,
"show": true,
"sideWidth": 200,
"total": false,
"values": true
},
"lines": true,
"linewidth": 0,
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"points": false,
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"spaceLength": 10,
"span": 12,
"stack": true,
"steppedLine": false,
"targets": [
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"refId": "A",
"target": "aliasByMetric($rootdir.$server.system.softirqs.RCU)",
"textEditor": false
},
{
"hide": false,
"refId": "B",
"target": "aliasByMetric($rootdir.$server.system.softirqs.TASKLET)",
"textEditor": false
},
{
"hide": false,
"refId": "C",
"target": "aliasByMetric($rootdir.$server.system.softirqs.BLOCK)",
"textEditor": false
},
{
"hide": false,
"refId": "D",
"target": "aliasByMetric($rootdir.$server.system.softirqs.NET_RX)",
"textEditor": false
},
{
"hide": false,
"refId": "E",
"target": "aliasByMetric($rootdir.$server.system.softirqs.NET_TX)",
"textEditor": false
},
{
"hide": false,
"refId": "F",
"target": "aliasByMetric($rootdir.$server.system.softirqs.TIMER)",
"textEditor": false
}
],
"thresholds": [],
"timeFrom": null,
"timeShift": null,
"title": "System softirqs",
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"sort": 0,
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"xaxis": {
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"name": null,
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"label": "softirqs/s",
"logBase": 1,
"max": null,
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"show": true
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{
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"label": null,
"logBase": 1,
"max": null,
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"show": true
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]
},
{
"content": "## softnet",
"height": "10",
"id": 2043,
"links": [],
"mode": "markdown",
"span": 12,
"title": "",
"transparent": true,
"type": "text"
},
{
"content": "Statistics for CPUs SoftIRQs related to network receive work. Break down per CPU core can be found at **CPU / softnet statistics**. processed states the number of packets processed, _dropped_ is the number packets dropped because the network device backlog was full (to fix them on Linux use `sysctl` to increase `net.core.netdev_max_backlog`), _squeezed_ is the number of packets dropped because the network device budget ran out (to fix them on Linux use `sysctl` to increase `net.core.netdev_budget`). More information about identifying and troubleshooting network driver related issues can be found at [Red Hat Enterprise Linux Network Performance Tuning Guide](https://access.redhat.com/sites/default/files/attachments/20150325_network_performance_tuning.pdf).",
"height": "10",
"id": 1209,
"links": [],
"mode": "markdown",
"span": 12,
"title": "",
"transparent": true,
"type": "text"
},
{
"aliasColors": {
"dropped": "#bf1b00",
"iowait": "#962d82",
"load1": "#eab839",
"load15": "#0a50a1",
"load5": "#bf1b00",
"nice": "#508642",
"processed": "#cca300",
"softirq": "#890f02",
"squeezed": "#ba43a9",
"system": "#0a50a1",
"user": "#eab839"
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"min": false,
"rightSide": true,
"show": true,
"sideWidth": 200,
"total": false,
"values": true
},
"lines": true,
"linewidth": 2,
"links": [],
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"spaceLength": 10,
"span": 12,
"stack": false,
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"targets": [
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"target": "aliasByMetric($rootdir.$server.system.softnet_stat.squeezed)",
"textEditor": false
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"hide": false,
"refId": "A",
"target": "aliasByMetric($rootdir.$server.system.softnet_stat.processed)",
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"timeFrom": null,
"timeShift": null,
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"tooltip": {
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"max": null,
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"show": true
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{
"format": "short",
"label": null,
"logBase": 1,
"max": null,
"min": null,
"show": true
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]
},
{
"content": "## entropy",
"height": "10",
"id": 1210,
"links": [],
"mode": "markdown",
"span": 12,
"title": "",
"transparent": true,
"type": "text"
},
{
"content": "[Entropy](https://en.wikipedia.org/wiki/Entropy_(computing)), is a pool of random numbers ([/dev/random](https://en.wikipedia.org/wiki//dev/random)) that is mainly used in cryptography. If the pool of entropy gets empty, processes requiring random numbers may run a lot slower (it depends on the interface each program uses), waiting for the pool to be replenished. Ideally a system with high entropy demands should have a hardware device for that purpose (TPM is one such device). There are also several software-only options you may install, like `haveged`, although these are generally useful only in servers.)",
"height": "10",
"id": 2044,
"links": [],
"mode": "markdown",
"span": 12,
"title": "",
"transparent": true,
"type": "text"
},
{
"aliasColors": {
"entropy": "#962d82",
"iowait": "#962d82",
"load1": "#eab839",
"load15": "#0a50a1",
"load5": "#bf1b00",
"nice": "#508642",
"softirq": "#890f02",
"system": "#0a50a1",
"user": "#eab839"
},
"bars": false,
"dashLength": 10,
"dashes": false,
"datasource": "$datasource",
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"fill": 0,
"id": 15,
"legend": {
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"avg": false,
"current": true,
"max": false,
"min": false,
"rightSide": true,
"show": true,
"sideWidth": 200,
"total": false,
"values": true
},
"lines": true,
"linewidth": 2,
"links": [],
"nullPointMode": "connected",
"percentage": false,
"pointradius": 5,
"points": false,
"renderer": "flot",
"seriesOverrides": [],
"spaceLength": 10,
"span": 12,
"stack": false,
"steppedLine": false,
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"refId": "A",
"target": "aliasByMetric($rootdir.$server.system.entropy.entropy)",
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"timeFrom": null,
"timeShift": null,
"title": "Available Entropy",
"tooltip": {
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]
},
{
"content": "## ipc semaphores",
"height": "10",
"id": 1211,
"links": [],
"mode": "markdown",
"span": 12,
"title": "",
"transparent": true,
"type": "text"
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"content": "A [page fault](https://en.wikipedia.org/wiki/Page_fault) is a type of interrupt, called trap, raised by computer hardware when a running program accesses a memory page that is mapped into the virtual address space, but not actually loaded into main memory. If the page is loaded in memory at the time the fault is generated, but is not marked in the memory management unit as being loaded in memory, then it is called a _minor_ or soft page fault. A _major_ page fault is generated when the system needs to load the memory page from disk or swap memory.",
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"content": "Committed Memory, is the sum of all memory which has been allocated by processes.",
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"content": "## kernel",
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},
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"content": "_Dirty_ is the amount of memory waiting to be written to disk. _Writeback_ is how much memory is actively being written to disk.",
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"content": "The total amount of memory being used by the kernel. _Slab_ is the amount of memory used by the kernel to cache data structures for its own use. _KernelStack_ is the amount of memory allocated for each task done by the kernel. _PageTables_ is the amount of memory decicated to the lowest level of page tables (A page table is used to turn a virtual address into a physical memory address). _VmallocUsed_ is the amount of memory being used as virtual address space.",
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"id": 1220,
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},
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"hide": false,
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"content": "## slab",
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"transparent": true,
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"content": "_Reclaimable_ is the amount of memory which the kernel can reuse. _Unreclaimable_ can not be reused even when the kernel is lacking memory.",
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"content": "## interrupts",
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},
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"content": "## softirqs",
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"content": "## softnet",
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"content": "Statistics for CPUs SoftIRQs related to network receive work. Break down per CPU core can be found at **CPU / softnet statistics**. processed states the number of packets processed, _dropped_ is the number packets dropped because the network device backlog was full (to fix them on Linux use `sysctl` to increase `net.core.netdev_max_backlog`), _squeezed_ is the number of packets dropped because the network device budget ran out (to fix them on Linux use `sysctl` to increase `net.core.netdev_budget`). More information about identifying and troubleshooting network driver related issues can be found at **Red Hat Enterprise Linux Network Performance Tuning Guide**.",
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"content": "Charts with performance information for all the system disks. Special care has been given to present disk performance metrics in a way compatible with `iostat -x`. netdata by default prevents rendering performance charts for individual partitions and unmounted virtual disks. Disabled charts can still be enabled by configuring the relative settings in the netdata configuration file.",
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"xaxis": {
"buckets": null,
"mode": "time",
"name": null,
"show": true,
"values": []
},
"yaxes": [
{
"format": "ms",
"label": "time per operation",
"logBase": 1,
"max": null,
"min": "0",
"show": true
},
{
"format": "short",
"label": null,
"logBase": 1,
"max": null,
"min": null,
"show": true
}
]
},
{
"content": "The sum of the duration of all completed I/O operations. This number can exceed the interval if the disk is able to execute I/O operations in parallel.",
"height": "10",
"id": 1316,
"links": [],
"minSpan": 12,
"mode": "markdown",
"span": 12,
"title": "",
"transparent": true,
"type": "text"
},
{
"aliasColors": {
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"load1": "#eab839",
"load15": "#0a50a1",
"load5": "#bf1b00",
"major": "#e24d42",
"minor": "#cca300",
"nice": "#508642",
"operations": "#cca300",
"processed": "#cca300",
"reads": "#cca300",
"reclaimable": "#cca300",
"softirq": "#99440a",
"squeezed": "#705da0",
"system": "#0a50a1",
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"uptime": "#eab839",
"user": "#eab839",
"utilization": "#ea6460",
"writes": "#e24d42"
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"bars": false,
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"datasource": "$datasource",
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"fill": 0,
"height": "150",
"id": 691,
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"sideWidth": 200,
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"lines": true,
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"spaceLength": 10,
"span": 12,
"stack": false,
"steppedLine": false,
"targets": [
{
"hide": false,
"refId": "B",
"target": "aliasByMetric($rootdir.$server.disk_iotime.$disk.writes)",
"textEditor": false
},
{
"hide": false,
"refId": "A",
"target": "aliasByMetric($rootdir.$server.disk_iotime.$disk.reads)",
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}
],
"thresholds": [],
"timeFrom": null,
"timeShift": null,
"title": "Disk Total I/O Time",
"tooltip": {
"shared": true,
"sort": 0,
"value_type": "individual"
},
"transparent": true,
"type": "graph",
"xaxis": {
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]
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],
"repeat": "disk",
"repeatIteration": null,
"repeatRowId": null,
"showTitle": true,
"title": "Disk $disk",
"titleSize": "h6"
},
{
"collapse": true,
"height": "200",
"panels": [
{
"content": "Disk space utilization. reserved for root is automatically reserved by the system to prevent the root user from getting out of space.",
"height": "10",
"id": 1317,
"links": [],
"minSpan": 12,
"mode": "markdown",
"span": 12,
"title": "",
"transparent": true,
"type": "text"
},
{
"aliasColors": {
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"iowait": "#962d82",
"load1": "#eab839",
"load15": "#0a50a1",
"load5": "#bf1b00",
"major": "#e24d42",
"minor": "#cca300",
"nice": "#508642",
"operations": "#cca300",
"processed": "#cca300",
"reads": "#cca300",
"reclaimable": "#cca300",
"reserved_for_root": "#0a50a1",
"softirq": "#99440a",
"squeezed": "#705da0",
"system": "#0a50a1",
"unreclaimable": "#bf1b00",
"uptime": "#eab839",
"used": "#bf1b00",
"user": "#eab839",
"utilization": "#ea6460",
"writes": "#bf1b00"
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"bars": false,
"dashLength": 10,
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"datasource": "$datasource",
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"fill": 10,
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"lines": true,
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"spaceLength": 10,
"span": 12,
"stack": true,
"steppedLine": false,
"targets": [
{
"hide": false,
"refId": "A",
"target": "aliasByMetric($rootdir.$server.disk_space.$disk_space.reserved_for_root)",
"textEditor": false
},
{
"hide": false,
"refId": "B",
"target": "aliasByMetric($rootdir.$server.disk_space.$disk_space.used)",
"textEditor": false
},
{
"hide": false,
"refId": "C",
"target": "aliasByMetric($rootdir.$server.disk_space.$disk_space.avail)",
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],
"thresholds": [],
"timeFrom": null,
"timeShift": null,
"title": "Disk Space Usage for $disk_space",
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"shared": true,
"sort": 0,
"value_type": "individual"
},
"transparent": true,
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]
},
{
"content": "inodes (or index nodes) are filesystem objects (e.g. files and directories). On many types of file system implementations, the maximum number of inodes is fixed at filesystem creation, limiting the maximum number of files the filesystem can hold. It is possible for a device to run out of inodes. When this happens, new files cannot be created on the device, even though there may be free space available.",
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"id": 1318,
"links": [],
"minSpan": 12,
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"span": 12,
"title": "",
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"load15": "#0a50a1",
"load5": "#bf1b00",
"major": "#e24d42",
"minor": "#cca300",
"nice": "#508642",
"operations": "#cca300",
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"reclaimable": "#cca300",
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"softirq": "#99440a",
"squeezed": "#705da0",
"system": "#0a50a1",
"unreclaimable": "#bf1b00",
"uptime": "#eab839",
"used": "#bf1b00",
"user": "#eab839",
"utilization": "#ea6460",
"writes": "#bf1b00"
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"datasource": "$datasource",
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"fill": 10,
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},
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"hide": false,
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"sort": 0,
"value_type": "individual"
},
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"showTitle": true,
"title": "Disk Space $disk_space",
"titleSize": "h6"
},
{
"collapse": true,
"height": "200",
"panels": [
{
"content": "Metrics for the IPv4 stack of the system. [Internet Protocol version 4 (IPv4)](https://en.wikipedia.org/wiki/IPv4) is the fourth version of the Internet Protocol (IP). It is one of the core protocols of standards-based internetworking methods in the Internet. IPv4 is a connectionless protocol for use on packet-switched networks. It operates on a best effort delivery model, in that it does not guarantee delivery, nor does it assure proper sequencing or avoidance of duplicate delivery. These aspects, including data integrity, are addressed by an upper layer transport protocol, such as the Transmission Control Protocol (TCP).\n",
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"id": 1319,
"links": [],
"minSpan": 12,
"mode": "markdown",
"span": 12,
"title": "",
"transparent": true,
"type": "text"
},
{
"content": "## sockets",
"height": "10",
"id": 2266,
"links": [],
"minSpan": 12,
"mode": "markdown",
"span": 12,
"title": "",
"transparent": true,
"type": "text"
},
{
"aliasColors": {
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"forwarded": "#0a50a1",
"iowait": "#962d82",
"load1": "#eab839",
"load15": "#0a50a1",
"load5": "#bf1b00",
"major": "#e24d42",
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"nice": "#508642",
"received": "#967302",
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"sent": "#bf1b00",
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"system": "#0a50a1",
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"uptime": "#eab839",
"used": "#cca300",
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"lines": true,
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"spaceLength": 10,
"span": 12,
"stack": false,
"steppedLine": false,
"targets": [
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"hide": false,
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"target": "aliasByMetric($rootdir.$server.ipv4.sockstat_sockets.used)",
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],
"thresholds": [],
"timeFrom": null,
"timeShift": null,
"title": "IPv4 Sockets Used",
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{
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]
},
{
"content": "## packets",
"height": "10",
"id": 1325,
"links": [],
"minSpan": 12,
"mode": "markdown",
"span": 12,
"title": "",
"transparent": true,
"type": "text"
},
{
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"forwarded": "#0a50a1",
"iowait": "#962d82",
"load1": "#eab839",
"load15": "#0a50a1",
"load5": "#bf1b00",
"major": "#e24d42",
"minor": "#cca300",
"nice": "#508642",
"received": "#967302",
"reclaimable": "#cca300",
"sent": "#e24d42",
"softirq": "#890f02",
"system": "#0a50a1",
"unreclaimable": "#bf1b00",
"uptime": "#eab839",
"user": "#eab839"
},
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"target": "aliasByMetric($rootdir.$server.ipv4.packets.delivered)",
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},
{
"hide": false,
"refId": "B",
"target": "aliasByMetric(maximumAbove($rootdir.$server.ipv4.packets.forwarded, 0))",
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},
{
"hide": false,
"refId": "C",
"target": "aliasByMetric($rootdir.$server.ipv4.packets.sent)",
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},
{
"hide": false,
"refId": "D",
"target": "aliasByMetric($rootdir.$server.ipv4.packets.received)",
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],
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"timeFrom": null,
"timeShift": null,
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},
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]
},
{
"content": "## errors",
"height": "10",
"id": 1326,
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"minSpan": 12,
"mode": "markdown",
"span": 12,
"title": "",
"transparent": true,
"type": "text"
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"InHdrErrors": "#0a50a1",
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"OutNoRoutes": "#99440a",
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"load15": "#0a50a1",
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"minor": "#cca300",
"nice": "#508642",
"reclaimable": "#cca300",
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"uptime": "#eab839",
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"hide": false,
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],
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"hide": false,
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],
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]
},
{
"content": "## tcp",
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],
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},
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"mode": "markdown",
"span": 12,
"title": "",
"transparent": true,
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"lines": true,
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},
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"hide": false,
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"aliasColors": {
"connections": "#cca300",
"iowait": "#962d82",
"load1": "#eab839",
"load15": "#0a50a1",
"load5": "#bf1b00",
"major": "#e24d42",
"minor": "#cca300",
"nice": "#508642",
"received": "#cca300",
"reclaimable": "#cca300",
"sent": "#bf1b00",
"softirq": "#890f02",
"system": "#0a50a1",
"unreclaimable": "#bf1b00",
"uptime": "#eab839",
"user": "#eab839"
},
"bars": false,
"dashLength": 10,
"dashes": false,
"datasource": "$datasource",
"decimals": 1,
"fill": 0,
"id": 2235,
"legend": {
"alignAsTable": true,
"avg": false,
"current": true,
"max": false,
"min": false,
"rightSide": true,
"show": true,
"sideWidth": 200,
"total": false,
"values": true
},
"lines": true,
"linewidth": 2,
"links": [],
"nullPointMode": "connected",
"percentage": false,
"pointradius": 5,
"points": false,
"renderer": "flot",
"seriesOverrides": [],
"spaceLength": 10,
"span": 12,
"stack": false,
"steppedLine": false,
"targets": [
{
"hide": false,
"refId": "A",
"target": "aliasByMetric(maximumAbove($rootdir.$server.ipv4.mcastpkts.*, 0))",
"textEditor": false
}
],
"thresholds": [],
"timeFrom": null,
"timeShift": null,
"title": "IPv4 Multicast Packets",
"tooltip": {
"shared": true,
"sort": 0,
"value_type": "individual"
},
"transparent": true,
"type": "graph",
"xaxis": {
"buckets": null,
"mode": "time",
"name": null,
"show": true,
"values": []
},
"yaxes": [
{
"format": "short",
"label": "packets/s",
"logBase": 1,
"max": null,
"min": null,
"show": true
},
{
"format": "short",
"label": null,
"logBase": 1,
"max": null,
"min": null,
"show": true
}
]
},
{
"aliasColors": {
"connections": "#cca300",
"iowait": "#962d82",
"load1": "#eab839",
"load15": "#0a50a1",
"load5": "#bf1b00",
"major": "#e24d42",
"minor": "#cca300",
"nice": "#508642",
"received": "#508642",
"reclaimable": "#cca300",
"sent": "#bf1b00",
"softirq": "#890f02",
"system": "#0a50a1",
"unreclaimable": "#bf1b00",
"uptime": "#eab839",
"user": "#eab839"
},
"bars": false,
"dashLength": 10,
"dashes": false,
"datasource": "$datasource",
"decimals": 1,
"fill": 4,
"id": 2234,
"legend": {
"alignAsTable": true,
"avg": false,
"current": true,
"max": false,
"min": false,
"rightSide": true,
"show": true,
"sideWidth": 200,
"total": false,
"values": true
},
"lines": true,
"linewidth": 2,
"links": [],
"nullPointMode": "connected",
"percentage": false,
"pointradius": 5,
"points": false,
"renderer": "flot",
"seriesOverrides": [],
"spaceLength": 10,
"span": 12,
"stack": false,
"steppedLine": false,
"targets": [
{
"hide": false,
"refId": "A",
"target": "aliasByMetric(maximumAbove($rootdir.$server.ipv4.mcast.*, 0))",
"textEditor": false
}
],
"thresholds": [],
"timeFrom": null,
"timeShift": null,
"title": "IPv4 Multicast Bandwidth",
"tooltip": {
"shared": true,
"sort": 0,
"value_type": "individual"
},
"transparent": true,
"type": "graph",
"xaxis": {
"buckets": null,
"mode": "time",
"name": null,
"show": true,
"values": []
},
"yaxes": [
{
"format": "Kbits",
"label": "bits/s",
"logBase": 1,
"max": null,
"min": null,
"show": true
},
{
"format": "short",
"label": null,
"logBase": 1,
"max": null,
"min": null,
"show": true
}
]
},
{
"content": "## ecn",
"height": "10",
"id": 2236,
"links": [],
"minSpan": 12,
"mode": "markdown",
"span": 12,
"title": "",
"transparent": true,
"type": "text"
},
{
"content": "[Explicit Congestion Notification (ECN)](https://en.wikipedia.org/wiki/Explicit_Congestion_Notification) is a TCP extension that allows end-to-end notification of network congestion without dropping packets. ECN is an optional feature that may be used between two ECN-enabled endpoints when the underlying network infrastructure also supports it.",
"height": "10",
"id": 1329,
"links": [],
"minSpan": 12,
"mode": "markdown",
"span": 12,
"title": "",
"transparent": true,
"type": "text"
},
{
"aliasColors": {
"CEP": "#cca300",
"ECTP0": "#0a50a1",
"NoECTP": "#e24d42",
"connections": "#cca300",
"iowait": "#962d82",
"load1": "#eab839",
"load15": "#0a50a1",
"load5": "#bf1b00",
"major": "#e24d42",
"minor": "#cca300",
"nice": "#508642",
"reclaimable": "#cca300",
"softirq": "#890f02",
"system": "#0a50a1",
"unreclaimable": "#bf1b00",
"uptime": "#eab839",
"user": "#eab839"
},
"bars": false,
"dashLength": 10,
"dashes": false,
"datasource": "$datasource",
"decimals": 1,
"fill": 0,
"id": 49,
"legend": {
"alignAsTable": true,
"avg": false,
"current": true,
"max": false,
"min": false,
"rightSide": true,
"show": true,
"sideWidth": 200,
"total": false,
"values": true
},
"lines": true,
"linewidth": 2,
"links": [],
"nullPointMode": "connected",
"percentage": false,
"pointradius": 5,
"points": false,
"renderer": "flot",
"seriesOverrides": [],
"spaceLength": 10,
"span": 12,
"stack": false,
"steppedLine": false,
"targets": [
{
"hide": false,
"refId": "A",
"target": "aliasByMetric(maximumAbove($rootdir.$server.ipv4.ecnpkts.ECTP0, 0))",
"textEditor": false
},
{
"hide": false,
"refId": "B",
"target": "aliasByMetric($rootdir.$server.ipv4.ecnpkts.NoECTP)",
"textEditor": false
},
{
"hide": false,
"refId": "C",
"target": "aliasByMetric(maximumAbove($rootdir.$server.ipv4.ecnpkts.ECTP1, 0))",
"textEditor": false
},
{
"hide": false,
"refId": "D",
"target": "aliasByMetric(maximumAbove($rootdir.$server.ipv4.ecnpkts.CEP, 0))",
"textEditor": false
}
],
"thresholds": [],
"timeFrom": null,
"timeShift": null,
"title": "IPv4 ECN Statistics",
"tooltip": {
"shared": true,
"sort": 0,
"value_type": "individual"
},
"transparent": true,
"type": "graph",
"xaxis": {
"buckets": null,
"mode": "time",
"name": null,
"show": true,
"values": []
},
"yaxes": [
{
"format": "short",
"label": "packets/s",
"logBase": 1,
"max": null,
"min": null,
"show": true
},
{
"format": "short",
"label": null,
"logBase": 1,
"max": null,
"min": null,
"show": true
}
]
}
],
"repeat": null,
"repeatIteration": null,
"repeatRowId": null,
"showTitle": true,
"title": "IPv4 Networking",
"titleSize": "h6"
},
{
"collapse": true,
"height": "200",
"panels": [
{
"content": "Metrics for the IPv6 stack of the system. [Internet Protocol version 6 (IPv6)](https://en.wikipedia.org/wiki/IPv6) is the most recent version of the Internet Protocol (IP), the communications protocol that provides an identification and location system for computers on networks and routes traffic across the Internet. IPv6 was developed by the Internet Engineering Task Force (IETF) to deal with the long-anticipated problem of IPv4 address exhaustion. IPv6 is intended to replace IPv4.",
"height": "10",
"id": 2292,
"links": [],
"minSpan": 12,
"mode": "markdown",
"span": 12,
"title": "",
"transparent": true,
"type": "text"
},
{
"content": "## packets",
"height": "10",
"id": 2293,
"links": [],
"minSpan": 12,
"mode": "markdown",
"span": 12,
"title": "",
"transparent": true,
"type": "text"
},
{
"aliasColors": {
"connections": "#cca300",
"delivered": "#99440a",
"forwarded": "#0a50a1",
"iowait": "#962d82",
"load1": "#eab839",
"load15": "#0a50a1",
"load5": "#bf1b00",
"major": "#e24d42",
"minor": "#cca300",
"nice": "#508642",
"received": "#967302",
"reclaimable": "#cca300",
"sent": "#e24d42",
"softirq": "#890f02",
"system": "#0a50a1",
"unreclaimable": "#bf1b00",
"uptime": "#eab839",
"used": "#cca300",
"user": "#eab839"
},
"bars": false,
"dashLength": 10,
"dashes": false,
"datasource": "$datasource",
"decimals": 1,
"fill": 0,
"id": 2294,
"legend": {
"alignAsTable": true,
"avg": false,
"current": true,
"max": false,
"min": false,
"rightSide": true,
"show": true,
"sideWidth": 200,
"total": false,
"values": true
},
"lines": true,
"linewidth": 2,
"links": [],
"nullPointMode": "connected",
"percentage": false,
"pointradius": 5,
"points": false,
"renderer": "flot",
"seriesOverrides": [],
"spaceLength": 10,
"span": 12,
"stack": false,
"steppedLine": false,
"targets": [
{
"hide": false,
"refId": "A",
"target": "aliasByMetric($rootdir.$server.ipv6.packets.*)",
"textEditor": false
}
],
"thresholds": [],
"timeFrom": null,
"timeShift": null,
"title": "IPv6 Packets",
"tooltip": {
"shared": true,
"sort": 0,
"value_type": "individual"
},
"transparent": true,
"type": "graph",
"xaxis": {
"buckets": null,
"mode": "time",
"name": null,
"show": true,
"values": []
},
"yaxes": [
{
"format": "pps",
"label": "packets/s",
"logBase": 1,
"max": null,
"min": null,
"show": true
},
{
"format": "short",
"label": null,
"logBase": 1,
"max": null,
"min": null,
"show": true
}
]
},
{
"aliasColors": {
"InNoECTPkts": "#cca300",
"connections": "#cca300",
"delivered": "#99440a",
"forwarded": "#0a50a1",
"iowait": "#962d82",
"load1": "#eab839",
"load15": "#0a50a1",
"load5": "#bf1b00",
"major": "#e24d42",
"minor": "#cca300",
"nice": "#508642",
"received": "#967302",
"reclaimable": "#cca300",
"sent": "#bf1b00",
"softirq": "#890f02",
"system": "#0a50a1",
"unreclaimable": "#bf1b00",
"uptime": "#eab839",
"used": "#cca300",
"user": "#eab839"
},
"bars": false,
"dashLength": 10,
"dashes": false,
"datasource": "$datasource",
"decimals": 1,
"fill": 0,
"id": 2303,
"legend": {
"alignAsTable": true,
"avg": false,
"current": true,
"max": false,
"min": false,
"rightSide": true,
"show": true,
"sideWidth": 200,
"total": false,
"values": true
},
"lines": true,
"linewidth": 2,
"links": [],
"nullPointMode": "connected",
"percentage": false,
"pointradius": 5,
"points": false,
"renderer": "flot",
"seriesOverrides": [],
"spaceLength": 10,
"span": 12,
"stack": false,
"steppedLine": false,
"targets": [
{
"hide": false,
"refId": "A",
"target": "aliasByMetric(maximumAbove($rootdir.$server.ipv6.ect.*, 0))",
"textEditor": false
}
],
"thresholds": [],
"timeFrom": null,
"timeShift": null,
"title": "IPv6 ECT Packets",
"tooltip": {
"shared": true,
"sort": 0,
"value_type": "individual"
},
"transparent": true,
"type": "graph",
"xaxis": {
"buckets": null,
"mode": "time",
"name": null,
"show": true,
"values": []
},
"yaxes": [
{
"format": "pps",
"label": "packets/s",
"logBase": 1,
"max": null,
"min": null,
"show": true
},
{
"format": "short",
"label": null,
"logBase": 1,
"max": null,
"min": null,
"show": true
}
]
},
{
"content": "## errors",
"height": "10",
"id": 2304,
"links": [],
"minSpan": 12,
"mode": "markdown",
"span": 12,
"title": "",
"transparent": true,
"type": "text"
},
{
"aliasColors": {
"connections": "#cca300",
"delivered": "#99440a",
"forwarded": "#0a50a1",
"iowait": "#962d82",
"load1": "#eab839",
"load15": "#0a50a1",
"load5": "#bf1b00",
"major": "#e24d42",
"minor": "#cca300",
"nice": "#508642",
"received": "#967302",
"reclaimable": "#cca300",
"sent": "#bf1b00",
"softirq": "#890f02",
"system": "#0a50a1",
"unreclaimable": "#bf1b00",
"uptime": "#eab839",
"used": "#cca300",
"user": "#eab839"
},
"bars": false,
"dashLength": 10,
"dashes": false,
"datasource": "$datasource",
"decimals": 1,
"fill": 0,
"id": 2305,
"legend": {
"alignAsTable": true,
"avg": false,
"current": true,
"max": false,
"min": false,
"rightSide": true,
"show": true,
"sideWidth": 200,
"total": false,
"values": true
},
"lines": true,
"linewidth": 2,
"links": [],
"nullPointMode": "connected",
"percentage": false,
"pointradius": 5,
"points": false,
"renderer": "flot",
"seriesOverrides": [],
"spaceLength": 10,
"span": 12,
"stack": false,
"steppedLine": false,
"targets": [
{
"hide": false,
"refId": "A",
"target": "aliasByMetric($rootdir.$server.ipv6.errors.*)",
"textEditor": false
}
],
"thresholds": [],
"timeFrom": null,
"timeShift": null,
"title": "IPv6 Errors",
"tooltip": {
"shared": true,
"sort": 0,
"value_type": "individual"
},
"transparent": true,
"type": "graph",
"xaxis": {
"buckets": null,
"mode": "time",
"name": null,
"show": true,
"values": []
},
"yaxes": [
{
"format": "pps",
"label": "packets/s",
"logBase": 1,
"max": null,
"min": null,
"show": true
},
{
"format": "short",
"label": null,
"logBase": 1,
"max": null,
"min": null,
"show": true
}
]
},
{
"content": "## tcp6",
"height": "10",
"id": 2307,
"links": [],
"minSpan": 12,
"mode": "markdown",
"span": 12,
"title": "",
"transparent": true,
"type": "text"
},
{
"aliasColors": {
"connections": "#cca300",
"delivered": "#99440a",
"forwarded": "#0a50a1",
"inuse": "#cca300",
"iowait": "#962d82",
"load1": "#eab839",
"load15": "#0a50a1",
"load5": "#bf1b00",
"major": "#e24d42",
"minor": "#cca300",
"nice": "#508642",
"received": "#967302",
"reclaimable": "#cca300",
"sent": "#bf1b00",
"softirq": "#890f02",
"system": "#0a50a1",
"unreclaimable": "#bf1b00",
"uptime": "#eab839",
"used": "#cca300",
"user": "#eab839"
},
"bars": false,
"dashLength": 10,
"dashes": false,
"datasource": "$datasource",
"decimals": null,
"fill": 0,
"id": 2306,
"legend": {
"alignAsTable": true,
"avg": false,
"current": true,
"max": false,
"min": false,
"rightSide": true,
"show": true,
"sideWidth": 200,
"total": false,
"values": true
},
"lines": true,
"linewidth": 2,
"links": [],
"nullPointMode": "connected",
"percentage": false,
"pointradius": 5,
"points": false,
"renderer": "flot",
"seriesOverrides": [],
"spaceLength": 10,
"span": 12,
"stack": false,
"steppedLine": false,
"targets": [
{
"hide": false,
"refId": "A",
"target": "aliasByMetric($rootdir.$server.ipv6.sockstat6_tcp_sockets.inuse)",
"textEditor": false
}
],
"thresholds": [],
"timeFrom": null,
"timeShift": null,
"title": "IPv6 TCP Sockets",
"tooltip": {
"shared": true,
"sort": 0,
"value_type": "individual"
},
"transparent": true,
"type": "graph",
"xaxis": {
"buckets": null,
"mode": "time",
"name": null,
"show": true,
"values": []
},
"yaxes": [
{
"format": "short",
"label": "sockets",
"logBase": 1,
"max": null,
"min": null,
"show": true
},
{
"format": "short",
"label": null,
"logBase": 1,
"max": null,
"min": null,
"show": true
}
]
},
{
"content": "## udp6",
"height": "10",
"id": 2308,
"links": [],
"minSpan": 12,
"mode": "markdown",
"span": 12,
"title": "",
"transparent": true,
"type": "text"
},
{
"aliasColors": {
"connections": "#cca300",
"delivered": "#99440a",
"forwarded": "#0a50a1",
"inuse": "#cca300",
"iowait": "#962d82",
"load1": "#eab839",
"load15": "#0a50a1",
"load5": "#bf1b00",
"major": "#e24d42",
"minor": "#cca300",
"nice": "#508642",
"received": "#967302",
"reclaimable": "#cca300",
"sent": "#bf1b00",
"softirq": "#890f02",
"system": "#0a50a1",
"unreclaimable": "#bf1b00",
"uptime": "#eab839",
"used": "#cca300",
"user": "#eab839"
},
"bars": false,
"dashLength": 10,
"dashes": false,
"datasource": "$datasource",
"decimals": null,
"fill": 0,
"id": 2309,
"legend": {
"alignAsTable": true,
"avg": false,
"current": true,
"max": false,
"min": false,
"rightSide": true,
"show": true,
"sideWidth": 200,
"total": false,
"values": true
},
"lines": true,
"linewidth": 2,
"links": [],
"nullPointMode": "connected",
"percentage": false,
"pointradius": 5,
"points": false,
"renderer": "flot",
"seriesOverrides": [],
"spaceLength": 10,
"span": 12,
"stack": false,
"steppedLine": false,
"targets": [
{
"hide": false,
"refId": "A",
"target": "aliasByMetric($rootdir.$server.ipv6.sockstat6_udp_sockets.inuse)",
"textEditor": false
}
],
"thresholds": [],
"timeFrom": null,
"timeShift": null,
"title": "IPv6 UDP Sockets",
"tooltip": {
"shared": true,
"sort": 0,
"value_type": "individual"
},
"transparent": true,
"type": "graph",
"xaxis": {
"buckets": null,
"mode": "time",
"name": null,
"show": true,
"values": []
},
"yaxes": [
{
"format": "short",
"label": "sockets",
"logBase": 1,
"max": null,
"min": null,
"show": true
},
{
"format": "short",
"label": null,
"logBase": 1,
"max": null,
"min": null,
"show": true
}
]
},
{
"content": "## multicast6",
"height": "10",
"id": 2310,
"links": [],
"minSpan": 12,
"mode": "markdown",
"span": 12,
"title": "",
"transparent": true,
"type": "text"
},
{
"aliasColors": {
"connections": "#cca300",
"delivered": "#99440a",
"forwarded": "#0a50a1",
"inuse": "#cca300",
"iowait": "#962d82",
"load1": "#eab839",
"load15": "#0a50a1",
"load5": "#bf1b00",
"major": "#e24d42",
"minor": "#cca300",
"nice": "#508642",
"received": "#967302",
"reclaimable": "#cca300",
"sent": "#bf1b00",
"softirq": "#890f02",
"system": "#0a50a1",
"unreclaimable": "#bf1b00",
"uptime": "#eab839",
"used": "#cca300",
"user": "#eab839"
},
"bars": false,
"dashLength": 10,
"dashes": false,
"datasource": "$datasource",
"decimals": null,
"fill": 4,
"id": 2311,
"legend": {
"alignAsTable": true,
"avg": false,
"current": true,
"max": false,
"min": false,
"rightSide": true,
"show": true,
"sideWidth": 200,
"total": false,
"values": true
},
"lines": true,
"linewidth": 2,
"links": [],
"nullPointMode": "connected",
"percentage": false,
"pointradius": 5,
"points": false,
"renderer": "flot",
"seriesOverrides": [],
"spaceLength": 10,
"span": 12,
"stack": false,
"steppedLine": false,
"targets": [
{
"hide": false,
"refId": "A",
"target": "aliasByMetric(maximumAbove($rootdir.$server.ipv6.mcast.received, 0))",
"textEditor": false
},
{
"hide": false,
"refId": "B",
"target": "aliasByMetric(maximumBelow($rootdir.$server.ipv6.mcast.sent, 0))",
"textEditor": false
}
],
"thresholds": [],
"timeFrom": null,
"timeShift": null,
"title": "IPv6 Multicast Bandwidth",
"tooltip": {
"shared": true,
"sort": 0,
"value_type": "individual"
},
"transparent": true,
"type": "graph",
"xaxis": {
"buckets": null,
"mode": "time",
"name": null,
"show": true,
"values": []
},
"yaxes": [
{
"format": "bps",
"label": "bits/s",
"logBase": 1,
"max": null,
"min": null,
"show": true
},
{
"format": "short",
"label": null,
"logBase": 1,
"max": null,
"min": null,
"show": true
}
]
},
{
"aliasColors": {
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"unreclaimable": "#bf1b00",
"uptime": "#eab839",
"user": "#eab839"
},
"bars": false,
"dashLength": 10,
"dashes": false,
"datasource": "$datasource",
"decimals": 1,
"fill": 4,
"id": 2241,
"legend": {
"alignAsTable": true,
"avg": false,
"current": true,
"max": false,
"min": false,
"rightSide": true,
"show": true,
"sideWidth": 200,
"total": false,
"values": true
},
"lines": true,
"linewidth": 1,
"links": [],
"nullPointMode": "connected",
"percentage": false,
"pointradius": 5,
"points": false,
"renderer": "flot",
"seriesOverrides": [],
"spaceLength": 10,
"span": 12,
"stack": false,
"steppedLine": false,
"targets": [
{
"hide": false,
"refId": "B",
"target": "aliasByMetric(maximumAbove($rootdir.$server.tc.[[tc]]_packets.*, 0))",
"textEditor": false
}
],
"thresholds": [],
"timeFrom": null,
"timeShift": null,
"title": "Class Packets $tc",
"tooltip": {
"shared": true,
"sort": 0,
"value_type": "individual"
},
"transparent": true,
"type": "graph",
"xaxis": {
"buckets": null,
"mode": "time",
"name": null,
"show": true,
"values": []
},
"yaxes": [
{
"format": "pps",
"label": " packets/s",
"logBase": 1,
"max": null,
"min": null,
"show": true
},
{
"format": "short",
"label": null,
"logBase": 1,
"max": null,
"min": null,
"show": true
}
]
},
{
"aliasColors": {
"8002_": "#508642",
"ECTP0": "#0a50a1",
"NoECTP": "#bf1b00",
"connections": "#cca300",
"ignore": "#bf1b00",
"invalid": "#0a50a1",
"iowait": "#962d82",
"load1": "#eab839",
"load15": "#0a50a1",
"load5": "#bf1b00",
"major": "#e24d42",
"minor": "#cca300",
"nice": "#508642",
"received": "#cca300",
"reclaimable": "#cca300",
"restarted": "#bf1b00",
"sent": "#e24d42",
"softirq": "#890f02",
"system": "#0a50a1",
"unreclaimable": "#bf1b00",
"uptime": "#eab839",
"user": "#eab839"
},
"bars": false,
"dashLength": 10,
"dashes": false,
"datasource": "$datasource",
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"fill": 4,
"id": 2245,
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"min": false,
"rightSide": true,
"show": true,
"sideWidth": 200,
"total": false,
"values": true
},
"lines": true,
"linewidth": 1,
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"nullPointMode": "connected",
"percentage": false,
"pointradius": 5,
"points": false,
"renderer": "flot",
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"spaceLength": 10,
"span": 12,
"stack": false,
"steppedLine": false,
"targets": [
{
"hide": false,
"refId": "B",
"target": "aliasByMetric($rootdir.$server.tc.[[tc]]_dropped.*)",
"textEditor": false
}
],
"thresholds": [],
"timeFrom": null,
"timeShift": null,
"title": "Class Dropped Packets $tc",
"tooltip": {
"shared": true,
"sort": 0,
"value_type": "individual"
},
"transparent": true,
"type": "graph",
"xaxis": {
"buckets": null,
"mode": "time",
"name": null,
"show": true,
"values": []
},
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"format": "pps",
"label": " packets/s",
"logBase": 1,
"max": null,
"min": null,
"show": true
},
{
"format": "short",
"label": null,
"logBase": 1,
"max": null,
"min": null,
"show": true
}
]
}
],
"repeat": "tc",
"repeatIteration": null,
"repeatRowId": null,
"showTitle": true,
"title": "Quality of Service $tc",
"titleSize": "h6"
},
{
"collapse": true,
"height": "200",
"panels": [
{
"content": "Resources utilization of systemd services. netdata monitors all systemd services via cgroups (the resources accounting used by containers). ",
"height": "10",
"id": 1858,
"links": [],
"minSpan": 12,
"mode": "markdown",
"span": 12,
"title": "",
"transparent": true,
"type": "text"
},
{
"content": "## cpu",
"height": "10",
"id": 1859,
"links": [],
"minSpan": 12,
"mode": "markdown",
"span": 12,
"title": "",
"transparent": true,
"type": "text"
},
{
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"ECTP0": "#0a50a1",
"NoECTP": "#bf1b00",
"connections": "#cca300",
"ignore": "#bf1b00",
"invalid": "#0a50a1",
"iowait": "#962d82",
"load1": "#eab839",
"load15": "#0a50a1",
"load5": "#bf1b00",
"major": "#e24d42",
"minor": "#cca300",
"nice": "#508642",
"reclaimable": "#cca300",
"restarted": "#bf1b00",
"softirq": "#890f02",
"system": "#0a50a1",
"unreclaimable": "#bf1b00",
"uptime": "#eab839",
"user": "#eab839"
},
"bars": false,
"dashLength": 10,
"dashes": false,
"datasource": "$datasource",
"decimals": 1,
"fill": 8,
"id": 70,
"legend": {
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"avg": false,
"current": true,
"max": false,
"min": false,
"rightSide": true,
"show": true,
"sideWidth": 200,
"sort": "current",
"sortDesc": true,
"total": false,
"values": true
},
"lines": true,
"linewidth": 1,
"links": [],
"nullPointMode": "connected",
"percentage": false,
"pointradius": 5,
"points": false,
"renderer": "flot",
"seriesOverrides": [],
"spaceLength": 10,
"span": 12,
"stack": true,
"steppedLine": false,
"targets": [
{
"hide": false,
"refId": "A",
"target": "aliasByMetric(maximumAbove($rootdir.$server.services.cpu.*, 0))",
"textEditor": true
}
],
"thresholds": [],
"timeFrom": null,
"timeShift": null,
"title": "Systemd Services CPU utilization",
"tooltip": {
"shared": true,
"sort": 2,
"value_type": "individual"
},
"transparent": true,
"type": "graph",
"xaxis": {
"buckets": null,
"mode": "time",
"name": null,
"show": true,
"values": []
},
"yaxes": [
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"format": "percent",
"label": " %",
"logBase": 1,
"max": null,
"min": null,
"show": true
},
{
"format": "short",
"label": null,
"logBase": 1,
"max": null,
"min": null,
"show": true
}
]
},
{
"content": "## mem",
"height": "10",
"id": 1860,
"links": [],
"minSpan": 12,
"mode": "markdown",
"span": 12,
"title": "",
"transparent": true,
"type": "text"
},
{
"aliasColors": {
"ECTP0": "#0a50a1",
"NoECTP": "#bf1b00",
"connections": "#cca300",
"ignore": "#bf1b00",
"invalid": "#0a50a1",
"iowait": "#962d82",
"load1": "#eab839",
"load15": "#0a50a1",
"load5": "#bf1b00",
"major": "#e24d42",
"minor": "#cca300",
"nice": "#508642",
"reclaimable": "#cca300",
"restarted": "#bf1b00",
"softirq": "#890f02",
"system": "#0a50a1",
"unreclaimable": "#bf1b00",
"uptime": "#eab839",
"user": "#eab839"
},
"bars": false,
"dashLength": 10,
"dashes": false,
"datasource": "$datasource",
"decimals": 1,
"fill": 8,
"id": 72,
"legend": {
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"avg": false,
"current": true,
"max": false,
"min": false,
"rightSide": true,
"show": true,
"sideWidth": 200,
"sort": "current",
"sortDesc": true,
"total": false,
"values": true
},
"lines": true,
"linewidth": 1,
"links": [],
"nullPointMode": "connected",
"percentage": false,
"pointradius": 5,
"points": false,
"renderer": "flot",
"seriesOverrides": [],
"spaceLength": 10,
"span": 12,
"stack": true,
"steppedLine": false,
"targets": [
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"hide": false,
"refId": "A",
"target": "aliasByMetric(maximumAbove($rootdir.$server.services.mem_usage.*, 0))",
"textEditor": true
}
],
"thresholds": [],
"timeFrom": null,
"timeShift": null,
"title": "Systemd Services Used Memory without Cache",
"tooltip": {
"shared": true,
"sort": 2,
"value_type": "individual"
},
"transparent": true,
"type": "graph",
"xaxis": {
"buckets": null,
"mode": "time",
"name": null,
"show": true,
"values": []
},
"yaxes": [
{
"format": "decmbytes",
"label": " bytes",
"logBase": 1,
"max": null,
"min": null,
"show": true
},
{
"format": "short",
"label": null,
"logBase": 1,
"max": null,
"min": null,
"show": true
}
]
},
{
"content": "## disk",
"height": "10",
"id": 1861,
"links": [],
"minSpan": 12,
"mode": "markdown",
"span": 12,
"title": "",
"transparent": true,
"type": "text"
},
{
"aliasColors": {
"ECTP0": "#0a50a1",
"NoECTP": "#bf1b00",
"connections": "#cca300",
"ignore": "#bf1b00",
"invalid": "#0a50a1",
"iowait": "#962d82",
"load1": "#eab839",
"load15": "#0a50a1",
"load5": "#bf1b00",
"major": "#e24d42",
"minor": "#cca300",
"nice": "#508642",
"reclaimable": "#cca300",
"restarted": "#bf1b00",
"softirq": "#890f02",
"system": "#0a50a1",
"unreclaimable": "#bf1b00",
"uptime": "#eab839",
"user": "#eab839"
},
"bars": false,
"dashLength": 10,
"dashes": false,
"datasource": "$datasource",
"decimals": 1,
"fill": 8,
"id": 71,
"legend": {
"alignAsTable": true,
"avg": false,
"current": true,
"max": false,
"min": false,
"rightSide": true,
"show": true,
"sideWidth": 200,
"sort": "current",
"sortDesc": true,
"total": false,
"values": true
},
"lines": true,
"linewidth": 1,
"links": [],
"nullPointMode": "connected",
"percentage": false,
"pointradius": 5,
"points": false,
"renderer": "flot",
"seriesOverrides": [],
"spaceLength": 10,
"span": 12,
"stack": true,
"steppedLine": false,
"targets": [
{
"hide": false,
"refId": "A",
"target": "aliasByMetric(maximumAbove($rootdir.$server.services.throttle_io_read.*, 0))",
"textEditor": true
}
],
"thresholds": [],
"timeFrom": null,
"timeShift": null,
"title": "Systemd Services Throttle Disk Read Bandwidth",
"tooltip": {
"shared": true,
"sort": 2,
"value_type": "individual"
},
"transparent": true,
"type": "graph",
"xaxis": {
"buckets": null,
"mode": "time",
"name": null,
"show": true,
"values": []
},
"yaxes": [
{
"format": "KBs",
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"logBase": 1,
"max": null,
"min": null,
"show": true
},
{
"format": "short",
"label": null,
"logBase": 1,
"max": null,
"min": null,
"show": true
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"NoECTP": "#bf1b00",
"connections": "#cca300",
"ignore": "#bf1b00",
"invalid": "#0a50a1",
"iowait": "#962d82",
"load1": "#eab839",
"load15": "#0a50a1",
"load5": "#bf1b00",
"major": "#e24d42",
"minor": "#cca300",
"nice": "#508642",
"reclaimable": "#cca300",
"restarted": "#bf1b00",
"softirq": "#890f02",
"system": "#0a50a1",
"unreclaimable": "#bf1b00",
"uptime": "#eab839",
"user": "#eab839"
},
"bars": false,
"dashLength": 10,
"dashes": false,
"datasource": "$datasource",
"decimals": 1,
"fill": 8,
"id": 73,
"legend": {
"alignAsTable": true,
"avg": false,
"current": true,
"max": false,
"min": false,
"rightSide": true,
"show": true,
"sideWidth": 200,
"sort": "current",
"sortDesc": true,
"total": false,
"values": true
},
"lines": true,
"linewidth": 1,
"links": [],
"nullPointMode": "connected",
"percentage": false,
"pointradius": 5,
"points": false,
"renderer": "flot",
"seriesOverrides": [],
"spaceLength": 10,
"span": 12,
"stack": true,
"steppedLine": false,
"targets": [
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"hide": false,
"refId": "A",
"target": "aliasByMetric(maximumAbove($rootdir.$server.services.throttle_io_write.*, 0))",
"textEditor": true
}
],
"thresholds": [],
"timeFrom": null,
"timeShift": null,
"title": "Systemd Services Throttle Disk Write Bandwidth",
"tooltip": {
"shared": true,
"sort": 2,
"value_type": "individual"
},
"transparent": true,
"type": "graph",
"xaxis": {
"buckets": null,
"mode": "time",
"name": null,
"show": true,
"values": []
},
"yaxes": [
{
"format": "KBs",
"label": " bytes/s",
"logBase": 1,
"max": null,
"min": null,
"show": true
},
{
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"label": null,
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"max": null,
"min": null,
"show": true
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},
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"NoECTP": "#bf1b00",
"connections": "#cca300",
"ignore": "#bf1b00",
"invalid": "#0a50a1",
"iowait": "#962d82",
"load1": "#eab839",
"load15": "#0a50a1",
"load5": "#bf1b00",
"major": "#e24d42",
"minor": "#cca300",
"nice": "#508642",
"reclaimable": "#cca300",
"restarted": "#bf1b00",
"softirq": "#890f02",
"system": "#0a50a1",
"unreclaimable": "#bf1b00",
"uptime": "#eab839",
"user": "#eab839"
},
"bars": false,
"dashLength": 10,
"dashes": false,
"datasource": "$datasource",
"decimals": 1,
"fill": 8,
"id": 2433,
"legend": {
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"current": true,
"max": false,
"min": false,
"rightSide": true,
"show": true,
"sideWidth": 200,
"sort": "current",
"sortDesc": true,
"total": false,
"values": true
},
"lines": true,
"linewidth": 1,
"links": [],
"nullPointMode": "connected",
"percentage": false,
"pointradius": 5,
"points": false,
"renderer": "flot",
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"span": 12,
"stack": true,
"steppedLine": false,
"targets": [
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"hide": false,
"refId": "A",
"target": "aliasByMetric(maximumAbove($rootdir.$server.services.throttle_io_ops_read.*, 0))",
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],
"thresholds": [],
"timeFrom": null,
"timeShift": null,
"title": "Systemd Services Throttle Disk Read Operations",
"tooltip": {
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"sort": 2,
"value_type": "individual"
},
"transparent": true,
"type": "graph",
"xaxis": {
"buckets": null,
"mode": "time",
"name": null,
"show": true,
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},
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"max": null,
"min": null,
"show": true
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{
"format": "short",
"label": null,
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"max": null,
"min": null,
"show": true
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"NoECTP": "#bf1b00",
"connections": "#cca300",
"ignore": "#bf1b00",
"invalid": "#0a50a1",
"iowait": "#962d82",
"load1": "#eab839",
"load15": "#0a50a1",
"load5": "#bf1b00",
"major": "#e24d42",
"minor": "#cca300",
"nice": "#508642",
"reclaimable": "#cca300",
"restarted": "#bf1b00",
"softirq": "#890f02",
"system": "#0a50a1",
"unreclaimable": "#bf1b00",
"uptime": "#eab839",
"user": "#eab839"
},
"bars": false,
"dashLength": 10,
"dashes": false,
"datasource": "$datasource",
"decimals": 1,
"fill": 8,
"id": 2434,
"legend": {
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"avg": false,
"current": true,
"max": false,
"min": false,
"rightSide": true,
"show": true,
"sideWidth": 200,
"sort": "current",
"sortDesc": true,
"total": false,
"values": true
},
"lines": true,
"linewidth": 1,
"links": [],
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"percentage": false,
"pointradius": 5,
"points": false,
"renderer": "flot",
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"spaceLength": 10,
"span": 12,
"stack": true,
"steppedLine": false,
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"refId": "A",
"target": "aliasByMetric(maximumAbove($rootdir.$server.services.throttle_io_ops_write.*, 0))",
"textEditor": true
}
],
"thresholds": [],
"timeFrom": null,
"timeShift": null,
"title": "Systemd Services Throttle Disk Write Operations",
"tooltip": {
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"value_type": "individual"
},
"transparent": true,
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"name": null,
"show": true,
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"max": null,
"min": null,
"show": true
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{
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"max": null,
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"show": true
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],
"repeat": null,
"repeatIteration": null,
"repeatRowId": null,
"showTitle": true,
"title": "systemd Services",
"titleSize": "h6"
},
{
"collapse": true,
"height": "200",
"panels": [
{
"content": "Per application statistics are collected using netdata's `apps.plugin`. This plugin walks through all processes and aggregates statistics for applications of interest, defined in `/etc/netdata/apps_groups.conf` (the default is [here](https://github.com/firehol/netdata/blob/master/conf.d/apps_groups.conf)). The plugin internally builds a process tree (much like `ps fax` does), and groups processes together (evaluating both child and parent processes) so that the result is always a chart with a predefined set of dimensions (of course, only application groups found running are reported). The reported values are compatible with `top`, although the netdata plugin counts also the resources of exited children (unlike `top` which shows only the resources of the currently running processes). So for processes like shell scripts, the reported values include the resources used by the commands these scripts run within each timeframe.",
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"id": 1862,
"links": [],
"minSpan": 12,
"mode": "markdown",
"span": 12,
"title": "",
"transparent": true,
"type": "text"
},
{
"content": "## cpu",
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"id": 1863,
"links": [],
"minSpan": 12,
"mode": "markdown",
"span": 12,
"title": "",
"transparent": true,
"type": "text"
},
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"load1": "#eab839",
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"system": "#0a50a1",
"unreclaimable": "#bf1b00",
"uptime": "#eab839",
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"thresholds": [],
"timeFrom": null,
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"title": "Apps CPU Time (100% = 1 core)",
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"label": "page faults/s",
"logBase": 1,
"max": null,
"min": null,
"show": true
},
{
"format": "short",
"label": null,
"logBase": 1,
"max": null,
"min": null,
"show": true
}
]
},
{
"content": "## processes",
"height": "10",
"id": 1866,
"links": [],
"minSpan": 12,
"mode": "markdown",
"span": 12,
"title": "",
"transparent": true,
"type": "text"
},
{
"aliasColors": {
"ECTP0": "#0a50a1",
"NoECTP": "#bf1b00",
"connections": "#cca300",
"ignore": "#bf1b00",
"invalid": "#0a50a1",
"iowait": "#962d82",
"load1": "#eab839",
"load15": "#0a50a1",
"load5": "#bf1b00",
"major": "#e24d42",
"minor": "#cca300",
"nice": "#508642",
"reclaimable": "#cca300",
"restarted": "#bf1b00",
"softirq": "#890f02",
"system": "#0a50a1",
"unreclaimable": "#bf1b00",
"uptime": "#eab839",
"user": "#eab839"
},
"bars": false,
"dashLength": 10,
"dashes": false,
"datasource": "$datasource",
"decimals": null,
"fill": 10,
"id": 89,
"legend": {
"alignAsTable": true,
"avg": false,
"current": true,
"max": false,
"min": false,
"rightSide": true,
"show": true,
"sideWidth": 200,
"sort": "current",
"sortDesc": true,
"total": false,
"values": true
},
"lines": true,
"linewidth": 0,
"links": [],
"nullPointMode": "connected",
"percentage": false,
"pointradius": 5,
"points": false,
"renderer": "flot",
"seriesOverrides": [],
"spaceLength": 10,
"span": 12,
"stack": true,
"steppedLine": false,
"targets": [
{
"hide": false,
"refId": "A",
"target": "aliasByMetric(maximumAbove($rootdir.$server.apps.processes.*, 0))",
"textEditor": true
}
],
"thresholds": [],
"timeFrom": null,
"timeShift": null,
"title": "Apps Processes",
"tooltip": {
"shared": true,
"sort": 2,
"value_type": "individual"
},
"transparent": true,
"type": "graph",
"xaxis": {
"buckets": null,
"mode": "time",
"name": null,
"show": true,
"values": []
},
"yaxes": [
{
"format": "short",
"label": "processes",
"logBase": 1,
"max": null,
"min": null,
"show": true
},
{
"format": "short",
"label": null,
"logBase": 1,
"max": null,
"min": null,
"show": true
}
]
},
{
"aliasColors": {
"ECTP0": "#0a50a1",
"NoECTP": "#bf1b00",
"connections": "#cca300",
"ignore": "#bf1b00",
"invalid": "#0a50a1",
"iowait": "#962d82",
"load1": "#eab839",
"load15": "#0a50a1",
"load5": "#bf1b00",
"major": "#e24d42",
"minor": "#cca300",
"nice": "#508642",
"reclaimable": "#cca300",
"restarted": "#bf1b00",
"softirq": "#890f02",
"system": "#0a50a1",
"unreclaimable": "#bf1b00",
"uptime": "#eab839",
"user": "#eab839"
},
"bars": false,
"dashLength": 10,
"dashes": false,
"datasource": "$datasource",
"decimals": null,
"fill": 10,
"id": 90,
"legend": {
"alignAsTable": true,
"avg": false,
"current": true,
"max": false,
"min": false,
"rightSide": true,
"show": true,
"sideWidth": 200,
"sort": "current",
"sortDesc": true,
"total": false,
"values": true
},
"lines": true,
"linewidth": 0,
"links": [],
"nullPointMode": "connected",
"percentage": false,
"pointradius": 5,
"points": false,
"renderer": "flot",
"seriesOverrides": [],
"spaceLength": 10,
"span": 12,
"stack": true,
"steppedLine": false,
"targets": [
{
"hide": false,
"refId": "A",
"target": "aliasByMetric(maximumAbove($rootdir.$server.apps.threads.*, 0))",
"textEditor": true
}
],
"thresholds": [],
"timeFrom": null,
"timeShift": null,
"title": "Apps Threads",
"tooltip": {
"shared": true,
"sort": 2,
"value_type": "individual"
},
"transparent": true,
"type": "graph",
"xaxis": {
"buckets": null,
"mode": "time",
"name": null,
"show": true,
"values": []
},
"yaxes": [
{
"format": "short",
"label": "threads",
"logBase": 1,
"max": null,
"min": null,
"show": true
},
{
"format": "short",
"label": null,
"logBase": 1,
"max": null,
"min": null,
"show": true
}
]
},
{
"aliasColors": {
"ECTP0": "#0a50a1",
"NoECTP": "#bf1b00",
"connections": "#cca300",
"ignore": "#bf1b00",
"invalid": "#0a50a1",
"iowait": "#962d82",
"load1": "#eab839",
"load15": "#0a50a1",
"load5": "#bf1b00",
"major": "#e24d42",
"minor": "#cca300",
"nice": "#508642",
"reclaimable": "#cca300",
"restarted": "#bf1b00",
"softirq": "#890f02",
"system": "#0a50a1",
"unreclaimable": "#bf1b00",
"uptime": "#eab839",
"user": "#eab839"
},
"bars": false,
"dashLength": 10,
"dashes": false,
"datasource": "$datasource",
"decimals": null,
"fill": 10,
"id": 91,
"legend": {
"alignAsTable": true,
"avg": false,
"current": true,
"max": false,
"min": false,
"rightSide": true,
"show": true,
"sideWidth": 200,
"sort": "current",
"sortDesc": true,
"total": false,
"values": true
},
"lines": true,
"linewidth": 0,
"links": [],
"nullPointMode": "connected",
"percentage": false,
"pointradius": 5,
"points": false,
"renderer": "flot",
"seriesOverrides": [],
"spaceLength": 10,
"span": 12,
"stack": true,
"steppedLine": false,
"targets": [
{
"hide": false,
"refId": "A",
"target": "aliasByMetric(maximumAbove($rootdir.$server.apps.pipes.*, 0))",
"textEditor": true
}
],
"thresholds": [],
"timeFrom": null,
"timeShift": null,
"title": "Apps Pipes",
"tooltip": {
"shared": true,
"sort": 2,
"value_type": "individual"
},
"transparent": true,
"type": "graph",
"xaxis": {
"buckets": null,
"mode": "time",
"name": null,
"show": true,
"values": []
},
"yaxes": [
{
"format": "short",
"label": "open pipes",
"logBase": 1,
"max": null,
"min": null,
"show": true
},
{
"format": "short",
"label": null,
"logBase": 1,
"max": null,
"min": null,
"show": true
}
]
},
{
"content": "## swap",
"height": "10",
"id": 1867,
"links": [],
"minSpan": 12,
"mode": "markdown",
"span": 12,
"title": "",
"transparent": true,
"type": "text"
},
{
"aliasColors": {
"ECTP0": "#0a50a1",
"NoECTP": "#bf1b00",
"connections": "#cca300",
"ignore": "#bf1b00",
"invalid": "#0a50a1",
"iowait": "#962d82",
"load1": "#eab839",
"load15": "#0a50a1",
"load5": "#bf1b00",
"major": "#e24d42",
"minor": "#cca300",
"nice": "#508642",
"reclaimable": "#cca300",
"restarted": "#bf1b00",
"softirq": "#890f02",
"system": "#bf1b00",
"unreclaimable": "#bf1b00",
"uptime": "#eab839",
"user": "#eab839"
},
"bars": false,
"dashLength": 10,
"dashes": false,
"datasource": "$datasource",
"decimals": 1,
"fill": 10,
"id": 92,
"legend": {
"alignAsTable": true,
"avg": false,
"current": true,
"max": false,
"min": false,
"rightSide": true,
"show": true,
"sideWidth": 200,
"sort": "current",
"sortDesc": true,
"total": false,
"values": true
},
"lines": true,
"linewidth": 0,
"links": [],
"nullPointMode": "connected",
"percentage": false,
"pointradius": 5,
"points": false,
"renderer": "flot",
"seriesOverrides": [],
"spaceLength": 10,
"span": 12,
"stack": true,
"steppedLine": false,
"targets": [
{
"hide": false,
"refId": "A",
"target": "aliasByMetric(maximumAbove($rootdir.$server.apps.major_faults.*, 0))",
"textEditor": true
}
],
"thresholds": [],
"timeFrom": null,
"timeShift": null,
"title": "Apps Major Page Faults (swap read)",
"tooltip": {
"shared": true,
"sort": 2,
"value_type": "individual"
},
"transparent": true,
"type": "graph",
"xaxis": {
"buckets": null,
"mode": "time",
"name": null,
"show": true,
"values": []
},
"yaxes": [
{
"format": "short",
"label": "page faults/s",
"logBase": 1,
"max": null,
"min": null,
"show": true
},
{
"format": "short",
"label": null,
"logBase": 1,
"max": null,
"min": null,
"show": true
}
]
},
{
"content": "## net",
"height": "10",
"id": 1871,
"links": [],
"minSpan": 12,
"mode": "markdown",
"span": 12,
"title": "",
"transparent": true,
"type": "text"
},
{
"aliasColors": {
"ECTP0": "#0a50a1",
"NoECTP": "#bf1b00",
"connections": "#cca300",
"ignore": "#bf1b00",
"invalid": "#0a50a1",
"iowait": "#962d82",
"load1": "#eab839",
"load15": "#0a50a1",
"load5": "#bf1b00",
"major": "#e24d42",
"minor": "#cca300",
"nice": "#508642",
"reclaimable": "#cca300",
"restarted": "#bf1b00",
"softirq": "#890f02",
"system": "#bf1b00",
"unreclaimable": "#bf1b00",
"uptime": "#eab839",
"user": "#eab839"
},
"bars": false,
"dashLength": 10,
"dashes": false,
"datasource": "$datasource",
"decimals": null,
"fill": 10,
"id": 93,
"legend": {
"alignAsTable": true,
"avg": false,
"current": true,
"max": false,
"min": false,
"rightSide": true,
"show": true,
"sideWidth": 200,
"sort": "current",
"sortDesc": true,
"total": false,
"values": true
},
"lines": true,
"linewidth": 0,
"links": [],
"nullPointMode": "connected",
"percentage": false,
"pointradius": 5,
"points": false,
"renderer": "flot",
"seriesOverrides": [],
"spaceLength": 10,
"span": 12,
"stack": true,
"steppedLine": false,
"targets": [
{
"hide": false,
"refId": "A",
"target": "aliasByMetric(maximumAbove($rootdir.$server.apps.sockets.*, 0))",
"textEditor": true
}
],
"thresholds": [],
"timeFrom": null,
"timeShift": null,
"title": "Apps Open Sockets",
"tooltip": {
"shared": true,
"sort": 2,
"value_type": "individual"
},
"transparent": true,
"type": "graph",
"xaxis": {
"buckets": null,
"mode": "time",
"name": null,
"show": true,
"values": []
},
"yaxes": [
{
"format": "short",
"label": "open sockets",
"logBase": 1,
"max": null,
"min": null,
"show": true
},
{
"format": "short",
"label": null,
"logBase": 1,
"max": null,
"min": null,
"show": true
}
]
}
],
"repeat": null,
"repeatIteration": null,
"repeatRowId": null,
"showTitle": true,
"title": "Applications",
"titleSize": "h6"
},
{
"collapse": true,
"height": "200",
"panels": [
{
"content": "Per user group statistics are collected using netdata's `apps.plugin`. This plugin walks through all processes and aggregates statistics per user group. The reported values are compatible with `top`, although the netdata plugin counts also the resources of exited children (unlike `top` which shows only the resources of the currently running processes). So for processes like shell scripts, the reported values include the resources used by the commands these scripts run within each timeframe.",
"height": "10",
"id": 1874,
"links": [],
"minSpan": 12,
"mode": "markdown",
"span": 12,
"title": "",
"transparent": true,
"type": "text"
},
{
"aliasColors": {
"ECTP0": "#0a50a1",
"NoECTP": "#bf1b00",
"connections": "#cca300",
"ignore": "#bf1b00",
"invalid": "#0a50a1",
"iowait": "#962d82",
"load1": "#eab839",
"load15": "#0a50a1",
"load5": "#bf1b00",
"major": "#e24d42",
"minor": "#cca300",
"nice": "#508642",
"reclaimable": "#cca300",
"restarted": "#bf1b00",
"softirq": "#890f02",
"system": "#0a50a1",
"unreclaimable": "#bf1b00",
"uptime": "#eab839",
"user": "#eab839"
},
"bars": false,
"dashLength": 10,
"dashes": false,
"datasource": "$datasource",
"decimals": 1,
"fill": 8,
"height": "450",
"id": 1876,
"legend": {
"alignAsTable": true,
"avg": false,
"current": true,
"max": false,
"min": false,
"rightSide": true,
"show": true,
"sideWidth": 200,
"sort": "current",
"sortDesc": true,
"total": false,
"values": true
},
"lines": true,
"linewidth": 1,
"links": [],
"minSpan": 12,
"nullPointMode": "connected",
"percentage": false,
"pointradius": 5,
"points": false,
"renderer": "flot",
"seriesOverrides": [],
"spaceLength": 10,
"span": 12,
"stack": true,
"steppedLine": false,
"targets": [
{
"hide": false,
"refId": "A",
"target": "aliasByMetric(maximumAbove($rootdir.$server.groups.cpu.*, 0))",
"textEditor": true
}
],
"thresholds": [],
"timeFrom": null,
"timeShift": null,
"title": "User Groups CPU Time (100% = 1 core)",
"tooltip": {
"shared": true,
"sort": 2,
"value_type": "individual"
},
"transparent": true,
"type": "graph",
"xaxis": {
"buckets": null,
"mode": "time",
"name": null,
"show": true,
"values": []
},
"yaxes": [
{
"format": "percent",
"label": " cpu time %",
"logBase": 1,
"max": null,
"min": null,
"show": true
},
{
"format": "short",
"label": null,
"logBase": 1,
"max": null,
"min": null,
"show": true
}
]
},
{
"aliasColors": {
"ECTP0": "#0a50a1",
"NoECTP": "#bf1b00",
"connections": "#cca300",
"ignore": "#bf1b00",
"invalid": "#0a50a1",
"iowait": "#962d82",
"load1": "#eab839",
"load15": "#0a50a1",
"load5": "#bf1b00",
"major": "#e24d42",
"minor": "#cca300",
"nice": "#508642",
"reclaimable": "#cca300",
"restarted": "#bf1b00",
"softirq": "#890f02",
"system": "#0a50a1",
"unreclaimable": "#bf1b00",
"uptime": "#eab839",
"user": "#eab839"
},
"bars": false,
"dashLength": 10,
"dashes": false,
"datasource": "$datasource",
"decimals": 1,
"fill": 8,
"id": 1877,
"legend": {
"alignAsTable": true,
"avg": false,
"current": true,
"max": false,
"min": false,
"rightSide": true,
"show": true,
"sideWidth": 200,
"sort": "current",
"sortDesc": true,
"total": false,
"values": true
},
"lines": true,
"linewidth": 1,
"links": [],
"nullPointMode": "connected",
"percentage": false,
"pointradius": 5,
"points": false,
"renderer": "flot",
"seriesOverrides": [],
"spaceLength": 10,
"span": 12,
"stack": true,
"steppedLine": false,
"targets": [
{
"hide": false,
"refId": "A",
"target": "aliasByMetric(maximumAbove($rootdir.$server.groups.cpu_user.*, 0))",
"textEditor": true
}
],
"thresholds": [],
"timeFrom": null,
"timeShift": null,
"title": "User Groups CPU User Time (100% = 1 core)",
"tooltip": {
"shared": true,
"sort": 2,
"value_type": "individual"
},
"transparent": true,
"type": "graph",
"xaxis": {
"buckets": null,
"mode": "time",
"name": null,
"show": true,
"values": []
},
"yaxes": [
{
"format": "percent",
"label": " cpu time %",
"logBase": 1,
"max": null,
"min": null,
"show": true
},
{
"format": "short",
"label": null,
"logBase": 1,
"max": null,
"min": null,
"show": true
}
]
},
{
"aliasColors": {
"ECTP0": "#0a50a1",
"NoECTP": "#bf1b00",
"connections": "#cca300",
"ignore": "#bf1b00",
"invalid": "#0a50a1",
"iowait": "#962d82",
"load1": "#eab839",
"load15": "#0a50a1",
"load5": "#bf1b00",
"major": "#e24d42",
"minor": "#cca300",
"nice": "#508642",
"reclaimable": "#cca300",
"restarted": "#bf1b00",
"softirq": "#890f02",
"system": "#0a50a1",
"unreclaimable": "#bf1b00",
"uptime": "#eab839",
"user": "#eab839"
},
"bars": false,
"dashLength": 10,
"dashes": false,
"datasource": "$datasource",
"decimals": null,
"fill": 8,
"id": 1879,
"legend": {
"alignAsTable": true,
"avg": false,
"current": true,
"max": false,
"min": false,
"rightSide": true,
"show": true,
"sideWidth": 200,
"sort": "current",
"sortDesc": true,
"total": false,
"values": true
},
"lines": true,
"linewidth": 1,
"links": [],
"nullPointMode": "connected",
"percentage": false,
"pointradius": 5,
"points": false,
"renderer": "flot",
"seriesOverrides": [],
"spaceLength": 10,
"span": 12,
"stack": true,
"steppedLine": false,
"targets": [
{
"hide": false,
"refId": "A",
"target": "aliasByMetric(maximumAbove($rootdir.$server.groups.cpu_system.*, 0))",
"textEditor": true
}
],
"thresholds": [],
"timeFrom": null,
"timeShift": null,
"title": "User Groups CPU System Time (100% = 1 core)",
"tooltip": {
"shared": true,
"sort": 2,
"value_type": "individual"
},
"transparent": true,
"type": "graph",
"xaxis": {
"buckets": null,
"mode": "time",
"name": null,
"show": true,
"values": []
},
"yaxes": [
{
"format": "percent",
"label": " cpu time %",
"logBase": 1,
"max": null,
"min": null,
"show": true
},
{
"format": "short",
"label": null,
"logBase": 1,
"max": null,
"min": null,
"show": true
}
]
},
{
"content": "## disk",
"height": "10",
"id": 1882,
"links": [],
"minSpan": 12,
"mode": "markdown",
"span": 12,
"title": "",
"transparent": true,
"type": "text"
},
{
"aliasColors": {
"ECTP0": "#0a50a1",
"NoECTP": "#bf1b00",
"connections": "#cca300",
"ignore": "#bf1b00",
"invalid": "#0a50a1",
"iowait": "#962d82",
"load1": "#eab839",
"load15": "#0a50a1",
"load5": "#bf1b00",
"major": "#e24d42",
"minor": "#cca300",
"nice": "#508642",
"reclaimable": "#cca300",
"restarted": "#bf1b00",
"softirq": "#890f02",
"system": "#0a50a1",
"unreclaimable": "#bf1b00",
"uptime": "#eab839",
"user": "#eab839"
},
"bars": false,
"dashLength": 10,
"dashes": false,
"datasource": "$datasource",
"decimals": 1,
"fill": 8,
"height": "450",
"id": 1884,
"legend": {
"alignAsTable": true,
"avg": false,
"current": true,
"max": false,
"min": false,
"rightSide": true,
"show": true,
"sideWidth": 200,
"sort": "current",
"sortDesc": true,
"total": false,
"values": true
},
"lines": true,
"linewidth": 0,
"links": [],
"minSpan": 12,
"nullPointMode": "connected",
"percentage": false,
"pointradius": 5,
"points": false,
"renderer": "flot",
"seriesOverrides": [],
"spaceLength": 10,
"span": 12,
"stack": true,
"steppedLine": false,
"targets": [
{
"hide": false,
"refId": "A",
"target": "aliasByMetric(maximumAbove($rootdir.$server.groups.preads.*, 0))",
"textEditor": true
}
],
"thresholds": [],
"timeFrom": null,
"timeShift": null,
"title": "User Groups Disk Reads",
"tooltip": {
"shared": true,
"sort": 2,
"value_type": "individual"
},
"transparent": true,
"type": "graph",
"xaxis": {
"buckets": null,
"mode": "time",
"name": null,
"show": true,
"values": []
},
"yaxes": [
{
"format": "KBs",
"label": "bytes/s",
"logBase": 1,
"max": null,
"min": null,
"show": true
},
{
"format": "short",
"label": null,
"logBase": 1,
"max": null,
"min": null,
"show": true
}
]
},
{
"aliasColors": {
"ECTP0": "#0a50a1",
"NoECTP": "#bf1b00",
"connections": "#cca300",
"ignore": "#bf1b00",
"invalid": "#0a50a1",
"iowait": "#962d82",
"load1": "#eab839",
"load15": "#0a50a1",
"load5": "#bf1b00",
"major": "#e24d42",
"minor": "#cca300",
"nice": "#508642",
"reclaimable": "#cca300",
"restarted": "#bf1b00",
"softirq": "#890f02",
"system": "#0a50a1",
"unreclaimable": "#bf1b00",
"uptime": "#eab839",
"user": "#eab839"
},
"bars": false,
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"decimals": 1,
"fill": 8,
"height": "450",
"id": 1886,
"legend": {
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"max": false,
"min": false,
"rightSide": true,
"show": true,
"sideWidth": 200,
"sort": "current",
"sortDesc": true,
"total": false,
"values": true
},
"lines": true,
"linewidth": 0,
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"pointradius": 5,
"points": false,
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"spaceLength": 10,
"span": 12,
"stack": true,
"steppedLine": false,
"targets": [
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"hide": false,
"refId": "A",
"target": "aliasByMetric(maximumAbove($rootdir.$server.groups.pwrites.*, 0))",
"textEditor": true
}
],
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"timeShift": null,
"title": "User Groups Disk Writes",
"tooltip": {
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},
"transparent": true,
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"xaxis": {
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},
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},
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},
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"minor": "#cca300",
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"system": "#0a50a1",
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"uptime": "#eab839",
"user": "#eab839"
},
"bars": false,
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"dashes": false,
"datasource": "$datasource",
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"fill": 8,
"id": 1899,
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"show": true,
"sideWidth": 200,
"sort": "current",
"sortDesc": true,
"total": false,
"values": true
},
"lines": true,
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"stack": true,
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"targets": [
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],
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"title": "User Groups Disk Logical Writes",
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"value_type": "individual"
},
"transparent": true,
"type": "graph",
"xaxis": {
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"show": true,
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},
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},
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},
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"invalid": "#0a50a1",
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"load1": "#eab839",
"load15": "#0a50a1",
"load5": "#bf1b00",
"major": "#e24d42",
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"nice": "#508642",
"reclaimable": "#cca300",
"restarted": "#bf1b00",
"softirq": "#890f02",
"system": "#0a50a1",
"unreclaimable": "#bf1b00",
"uptime": "#eab839",
"user": "#eab839"
},
"bars": false,
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"dashes": false,
"datasource": "$datasource",
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"fill": 10,
"id": 1900,
"legend": {
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"avg": false,
"current": true,
"max": false,
"min": false,
"rightSide": true,
"show": true,
"sideWidth": 200,
"sort": "current",
"sortDesc": true,
"total": false,
"values": true
},
"lines": true,
"linewidth": 0,
"links": [],
"nullPointMode": "connected",
"percentage": false,
"pointradius": 5,
"points": false,
"renderer": "flot",
"seriesOverrides": [],
"spaceLength": 10,
"span": 12,
"stack": true,
"steppedLine": false,
"targets": [
{
"hide": false,
"refId": "A",
"target": "aliasByMetric(maximumAbove($rootdir.$server.groups.files.*, 0))",
"textEditor": true
}
],
"thresholds": [],
"timeFrom": null,
"timeShift": null,
"title": "User Groups Open Files",
"tooltip": {
"shared": true,
"sort": 2,
"value_type": "individual"
},
"transparent": true,
"type": "graph",
"xaxis": {
"buckets": null,
"mode": "time",
"name": null,
"show": true,
"values": []
},
"yaxes": [
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"show": true
},
{
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"label": null,
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"max": null,
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}
]
},
{
"content": "## mem",
"height": "10",
"id": 1902,
"links": [],
"minSpan": 12,
"mode": "markdown",
"span": 12,
"title": "",
"transparent": true,
"type": "text"
},
{
"content": "Real memory (RAM) used per user group. This does not include shared memory.",
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"id": 1922,
"links": [],
"minSpan": 12,
"mode": "markdown",
"span": 12,
"title": "",
"transparent": true,
"type": "text"
},
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"iowait": "#962d82",
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"restarted": "#bf1b00",
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"unreclaimable": "#bf1b00",
"uptime": "#eab839",
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},
"bars": false,
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"fill": 10,
"id": 1904,
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"show": true,
"sideWidth": 200,
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},
"lines": true,
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"target": "aliasByMetric(maximumAbove($rootdir.$server.groups.mem.*, 0))",
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],
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"title": "User Groups Real Memory (w/o shared)",
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},
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},
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},
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"show": true
}
]
},
{
"content": "Virtual memory allocated per user group. Please check [this article](https://github.com/firehol/netdata/wiki/netdata-virtual-memory-size) for more information.",
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"id": 1923,
"links": [],
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"mode": "markdown",
"span": 12,
"title": "",
"transparent": true,
"type": "text"
},
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"uptime": "#eab839",
"user": "#eab839"
},
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"fill": 10,
"id": 1907,
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"show": true,
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"values": true
},
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"stack": true,
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"targets": [
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"target": "aliasByMetric(maximumAbove($rootdir.$server.groups.vmem.*, 0))",
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}
],
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"title": "User Groups Virtual Memory Size",
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},
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},
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},
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"show": true
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]
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"invalid": "#0a50a1",
"iowait": "#962d82",
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"major": "#e24d42",
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"restarted": "#bf1b00",
"softirq": "#890f02",
"system": "#0a50a1",
"unreclaimable": "#bf1b00",
"uptime": "#eab839",
"user": "#eab839"
},
"bars": false,
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"dashes": false,
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"fill": 10,
"id": 1908,
"legend": {
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"current": true,
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"min": false,
"rightSide": true,
"show": true,
"sideWidth": 200,
"sort": "current",
"sortDesc": true,
"total": false,
"values": true
},
"lines": true,
"linewidth": 0,
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"pointradius": 5,
"points": false,
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"seriesOverrides": [],
"spaceLength": 10,
"span": 12,
"stack": true,
"steppedLine": false,
"targets": [
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"hide": false,
"refId": "A",
"target": "aliasByMetric(maximumAbove($rootdir.$server.groups.minor_faults.*, 0))",
"textEditor": true
}
],
"thresholds": [],
"timeFrom": null,
"timeShift": null,
"title": "User Groups Minor Page Faults",
"tooltip": {
"shared": true,
"sort": 2,
"value_type": "individual"
},
"transparent": true,
"type": "graph",
"xaxis": {
"buckets": null,
"mode": "time",
"name": null,
"show": true,
"values": []
},
"yaxes": [
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"max": null,
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"show": true
},
{
"format": "short",
"label": null,
"logBase": 1,
"max": null,
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"show": true
}
]
},
{
"content": "## processes",
"height": "10",
"id": 1911,
"links": [],
"minSpan": 12,
"mode": "markdown",
"span": 12,
"title": "",
"transparent": true,
"type": "text"
},
{
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"connections": "#cca300",
"ignore": "#bf1b00",
"invalid": "#0a50a1",
"iowait": "#962d82",
"load1": "#eab839",
"load15": "#0a50a1",
"load5": "#bf1b00",
"major": "#e24d42",
"minor": "#cca300",
"nice": "#508642",
"reclaimable": "#cca300",
"restarted": "#bf1b00",
"softirq": "#890f02",
"system": "#0a50a1",
"unreclaimable": "#bf1b00",
"uptime": "#eab839",
"user": "#eab839"
},
"bars": false,
"dashLength": 10,
"dashes": false,
"datasource": "$datasource",
"decimals": null,
"fill": 10,
"id": 1912,
"legend": {
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"avg": false,
"current": true,
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"min": false,
"rightSide": true,
"show": true,
"sideWidth": 200,
"sort": "current",
"sortDesc": true,
"total": false,
"values": true
},
"lines": true,
"linewidth": 0,
"links": [],
"nullPointMode": "connected",
"percentage": false,
"pointradius": 5,
"points": false,
"renderer": "flot",
"seriesOverrides": [],
"spaceLength": 10,
"span": 12,
"stack": true,
"steppedLine": false,
"targets": [
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"refId": "A",
"target": "aliasByMetric(maximumAbove($rootdir.$server.groups.processes.*, 0))",
"textEditor": true
}
],
"thresholds": [],
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"timeShift": null,
"title": "User Groups Processes",
"tooltip": {
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"sort": 2,
"value_type": "individual"
},
"transparent": true,
"type": "graph",
"xaxis": {
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"name": null,
"show": true,
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},
"yaxes": [
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"label": "processes",
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"max": null,
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"show": true
},
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"show": true
}
]
},
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"iowait": "#962d82",
"load1": "#eab839",
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"load5": "#bf1b00",
"major": "#e24d42",
"minor": "#cca300",
"nice": "#508642",
"reclaimable": "#cca300",
"restarted": "#bf1b00",
"softirq": "#890f02",
"system": "#0a50a1",
"unreclaimable": "#bf1b00",
"uptime": "#eab839",
"user": "#eab839"
},
"bars": false,
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"dashes": false,
"datasource": "$datasource",
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"fill": 10,
"id": 1915,
"legend": {
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"avg": false,
"current": true,
"max": false,
"min": false,
"rightSide": true,
"show": true,
"sideWidth": 200,
"sort": "current",
"sortDesc": true,
"total": false,
"values": true
},
"lines": true,
"linewidth": 0,
"links": [],
"nullPointMode": "connected",
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"pointradius": 5,
"points": false,
"renderer": "flot",
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"spaceLength": 10,
"span": 12,
"stack": true,
"steppedLine": false,
"targets": [
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"hide": false,
"refId": "A",
"target": "aliasByMetric(maximumAbove($rootdir.$server.groups.threads.*, 0))",
"textEditor": true
}
],
"thresholds": [],
"timeFrom": null,
"timeShift": null,
"title": "User Groups Threads",
"tooltip": {
"shared": true,
"sort": 2,
"value_type": "individual"
},
"transparent": true,
"type": "graph",
"xaxis": {
"buckets": null,
"mode": "time",
"name": null,
"show": true,
"values": []
},
"yaxes": [
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"label": "threads",
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"max": null,
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"show": true
},
{
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"label": null,
"logBase": 1,
"max": null,
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"show": true
}
]
},
{
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"ignore": "#bf1b00",
"invalid": "#0a50a1",
"iowait": "#962d82",
"load1": "#eab839",
"load15": "#0a50a1",
"load5": "#bf1b00",
"major": "#e24d42",
"minor": "#cca300",
"nice": "#508642",
"reclaimable": "#cca300",
"restarted": "#bf1b00",
"softirq": "#890f02",
"system": "#0a50a1",
"unreclaimable": "#bf1b00",
"uptime": "#eab839",
"user": "#eab839"
},
"bars": false,
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"dashes": false,
"datasource": "$datasource",
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"fill": 10,
"id": 1916,
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"avg": false,
"current": true,
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"min": false,
"rightSide": true,
"show": true,
"sideWidth": 200,
"sort": "current",
"sortDesc": true,
"total": false,
"values": true
},
"lines": true,
"linewidth": 0,
"links": [],
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"pointradius": 5,
"points": false,
"renderer": "flot",
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"spaceLength": 10,
"span": 12,
"stack": true,
"steppedLine": false,
"targets": [
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"hide": false,
"refId": "A",
"target": "aliasByMetric(maximumAbove($rootdir.$server.groups.pipes.*, 0))",
"textEditor": true
}
],
"thresholds": [],
"timeFrom": null,
"timeShift": null,
"title": "User Groups Pipes",
"tooltip": {
"shared": true,
"sort": 2,
"value_type": "individual"
},
"transparent": true,
"type": "graph",
"xaxis": {
"buckets": null,
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"name": null,
"show": true,
"values": []
},
"yaxes": [
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"label": "open pipes",
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"max": null,
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"show": true
},
{
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"label": null,
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]
},
{
"content": "## swap",
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"id": 1919,
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"title": "",
"transparent": true,
"type": "text"
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"target": "aliasByMetric(maximumAbove($rootdir.$server.groups.major_faults.*, 0))",
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},
{
"content": "## net",
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"uptime": "#eab839",
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"fill": 10,
"id": 1869,
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"title": "User Groups Open Sockets",
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],
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"repeatRowId": null,
"showTitle": true,
"title": "User Groups",
"titleSize": "h6"
},
{
"collapse": true,
"height": "200",
"panels": [
{
"content": "Per user statistics are collected using netdata's `apps.plugin`. This plugin walks through all processes and aggregates statistics per user. The reported values are compatible with `top`, although the netdata plugin counts also the resources of exited children (unlike `top` which shows only the resources of the currently running processes). So for processes like shell scripts, the reported values include the resources used by the commands these scripts run within each timeframe.",
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"id": 1873,
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"uptime": "#eab839",
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"height": "420",
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],
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},
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},
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},
{
"content": "## disk",
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"id": 1881,
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"mode": "markdown",
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"transparent": true,
"type": "text"
},
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"links": [],
"nullPointMode": "connected",
"percentage": false,
"pointradius": 5,
"points": false,
"renderer": "flot",
"seriesOverrides": [],
"spaceLength": 10,
"span": 12,
"stack": true,
"steppedLine": false,
"targets": [
{
"hide": false,
"refId": "A",
"target": "aliasByMetric(maximumAbove($rootdir.$server.users.files.*, 0))",
"textEditor": true
}
],
"thresholds": [],
"timeFrom": null,
"timeShift": null,
"title": "Users Open Files",
"tooltip": {
"shared": true,
"sort": 2,
"value_type": "individual"
},
"transparent": true,
"type": "graph",
"xaxis": {
"buckets": null,
"mode": "time",
"name": null,
"show": true,
"values": []
},
"yaxes": [
{
"format": "short",
"label": "open files",
"logBase": 1,
"max": null,
"min": null,
"show": true
},
{
"format": "short",
"label": null,
"logBase": 1,
"max": null,
"min": null,
"show": true
}
]
},
{
"content": "## mem",
"height": "10",
"id": 1903,
"links": [],
"minSpan": 12,
"mode": "markdown",
"span": 12,
"title": "",
"transparent": true,
"type": "text"
},
{
"content": "Real memory (RAM) used per user. This does not include shared memory.",
"height": "10",
"id": 1924,
"links": [],
"minSpan": 12,
"mode": "markdown",
"span": 12,
"title": "",
"transparent": true,
"type": "text"
},
{
"aliasColors": {
"ECTP0": "#0a50a1",
"NoECTP": "#bf1b00",
"connections": "#cca300",
"ignore": "#bf1b00",
"invalid": "#0a50a1",
"iowait": "#962d82",
"load1": "#eab839",
"load15": "#0a50a1",
"load5": "#bf1b00",
"major": "#e24d42",
"minor": "#cca300",
"nice": "#508642",
"reclaimable": "#cca300",
"restarted": "#bf1b00",
"softirq": "#890f02",
"system": "#0a50a1",
"unreclaimable": "#bf1b00",
"uptime": "#eab839",
"user": "#eab839"
},
"bars": false,
"dashLength": 10,
"dashes": false,
"datasource": "$datasource",
"decimals": 1,
"fill": 10,
"id": 1905,
"legend": {
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"avg": false,
"current": true,
"max": false,
"min": false,
"rightSide": true,
"show": true,
"sideWidth": 200,
"sort": "current",
"sortDesc": true,
"total": false,
"values": true
},
"lines": true,
"linewidth": 0,
"links": [],
"nullPointMode": "connected",
"percentage": false,
"pointradius": 5,
"points": false,
"renderer": "flot",
"seriesOverrides": [],
"spaceLength": 10,
"span": 12,
"stack": true,
"steppedLine": false,
"targets": [
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"hide": false,
"refId": "A",
"target": "aliasByMetric(maximumAbove($rootdir.$server.users.mem.*, 0))",
"textEditor": true
}
],
"thresholds": [],
"timeFrom": null,
"timeShift": null,
"title": "Users Real Memory (w/o shared)",
"tooltip": {
"shared": true,
"sort": 2,
"value_type": "individual"
},
"transparent": true,
"type": "graph",
"xaxis": {
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"name": null,
"show": true,
"values": []
},
"yaxes": [
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"format": "decmbytes",
"label": "bytes",
"logBase": 1,
"max": null,
"min": null,
"show": true
},
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"format": "short",
"label": null,
"logBase": 1,
"max": null,
"min": null,
"show": true
}
]
},
{
"content": "Virtual memory allocated per user. Please check [this article](https://github.com/firehol/netdata/wiki/netdata-virtual-memory-size) for more information.",
"height": "10",
"id": 1925,
"links": [],
"minSpan": 12,
"mode": "markdown",
"span": 12,
"title": "",
"transparent": true,
"type": "text"
},
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"aliasColors": {
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"NoECTP": "#bf1b00",
"connections": "#cca300",
"ignore": "#bf1b00",
"invalid": "#0a50a1",
"iowait": "#962d82",
"load1": "#eab839",
"load15": "#0a50a1",
"load5": "#bf1b00",
"major": "#e24d42",
"minor": "#cca300",
"nice": "#508642",
"reclaimable": "#cca300",
"restarted": "#bf1b00",
"softirq": "#890f02",
"system": "#0a50a1",
"unreclaimable": "#bf1b00",
"uptime": "#eab839",
"user": "#eab839"
},
"bars": false,
"dashLength": 10,
"dashes": false,
"datasource": "$datasource",
"decimals": 1,
"fill": 10,
"id": 1906,
"legend": {
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"avg": false,
"current": true,
"max": false,
"min": false,
"rightSide": true,
"show": true,
"sideWidth": 200,
"sort": "current",
"sortDesc": true,
"total": false,
"values": true
},
"lines": true,
"linewidth": 0,
"links": [],
"nullPointMode": "connected",
"percentage": false,
"pointradius": 5,
"points": false,
"renderer": "flot",
"seriesOverrides": [],
"spaceLength": 10,
"span": 12,
"stack": true,
"steppedLine": false,
"targets": [
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"hide": false,
"refId": "A",
"target": "aliasByMetric(maximumAbove($rootdir.$server.users.vmem.*, 0))",
"textEditor": true
}
],
"thresholds": [],
"timeFrom": null,
"timeShift": null,
"title": "Users Virtual Memory Size",
"tooltip": {
"shared": true,
"sort": 2,
"value_type": "individual"
},
"transparent": true,
"type": "graph",
"xaxis": {
"buckets": null,
"mode": "time",
"name": null,
"show": true,
"values": []
},
"yaxes": [
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"format": "decmbytes",
"label": "bytes",
"logBase": 1,
"max": null,
"min": null,
"show": true
},
{
"format": "short",
"label": null,
"logBase": 1,
"max": null,
"min": null,
"show": true
}
]
},
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"aliasColors": {
"ECTP0": "#0a50a1",
"NoECTP": "#bf1b00",
"connections": "#cca300",
"ignore": "#bf1b00",
"invalid": "#0a50a1",
"iowait": "#962d82",
"load1": "#eab839",
"load15": "#0a50a1",
"load5": "#bf1b00",
"major": "#e24d42",
"minor": "#cca300",
"nice": "#508642",
"reclaimable": "#cca300",
"restarted": "#bf1b00",
"softirq": "#890f02",
"system": "#0a50a1",
"unreclaimable": "#bf1b00",
"uptime": "#eab839",
"user": "#eab839"
},
"bars": false,
"dashLength": 10,
"dashes": false,
"datasource": "$datasource",
"decimals": 1,
"fill": 10,
"id": 1909,
"legend": {
"alignAsTable": true,
"avg": false,
"current": true,
"max": false,
"min": false,
"rightSide": true,
"show": true,
"sideWidth": 200,
"sort": "current",
"sortDesc": true,
"total": false,
"values": true
},
"lines": true,
"linewidth": 0,
"links": [],
"nullPointMode": "connected",
"percentage": false,
"pointradius": 5,
"points": false,
"renderer": "flot",
"seriesOverrides": [],
"spaceLength": 10,
"span": 12,
"stack": true,
"steppedLine": false,
"targets": [
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"hide": false,
"refId": "A",
"target": "aliasByMetric(maximumAbove($rootdir.$server.users.minor_faults.*, 0))",
"textEditor": true
}
],
"thresholds": [],
"timeFrom": null,
"timeShift": null,
"title": "Users Minor Page Faults",
"tooltip": {
"shared": true,
"sort": 2,
"value_type": "individual"
},
"transparent": true,
"type": "graph",
"xaxis": {
"buckets": null,
"mode": "time",
"name": null,
"show": true,
"values": []
},
"yaxes": [
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"format": "short",
"label": "page faults/s",
"logBase": 1,
"max": null,
"min": null,
"show": true
},
{
"format": "short",
"label": null,
"logBase": 1,
"max": null,
"min": null,
"show": true
}
]
},
{
"content": "## processes",
"height": "10",
"id": 1910,
"links": [],
"minSpan": 12,
"mode": "markdown",
"span": 12,
"title": "",
"transparent": true,
"type": "text"
},
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"NoECTP": "#bf1b00",
"connections": "#cca300",
"ignore": "#bf1b00",
"invalid": "#0a50a1",
"iowait": "#962d82",
"load1": "#eab839",
"load15": "#0a50a1",
"load5": "#bf1b00",
"major": "#e24d42",
"minor": "#cca300",
"nice": "#508642",
"reclaimable": "#cca300",
"restarted": "#bf1b00",
"softirq": "#890f02",
"system": "#0a50a1",
"unreclaimable": "#bf1b00",
"uptime": "#eab839",
"user": "#eab839"
},
"bars": false,
"dashLength": 10,
"dashes": false,
"datasource": "$datasource",
"decimals": null,
"fill": 10,
"id": 1913,
"legend": {
"alignAsTable": true,
"avg": false,
"current": true,
"max": false,
"min": false,
"rightSide": true,
"show": true,
"sideWidth": 200,
"sort": "current",
"sortDesc": true,
"total": false,
"values": true
},
"lines": true,
"linewidth": 0,
"links": [],
"nullPointMode": "connected",
"percentage": false,
"pointradius": 5,
"points": false,
"renderer": "flot",
"seriesOverrides": [],
"spaceLength": 10,
"span": 12,
"stack": true,
"steppedLine": false,
"targets": [
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"hide": false,
"refId": "A",
"target": "aliasByMetric(maximumAbove($rootdir.$server.users.processes.*, 0))",
"textEditor": true
}
],
"thresholds": [],
"timeFrom": null,
"timeShift": null,
"title": "Users Processes",
"tooltip": {
"shared": true,
"sort": 2,
"value_type": "individual"
},
"transparent": true,
"type": "graph",
"xaxis": {
"buckets": null,
"mode": "time",
"name": null,
"show": true,
"values": []
},
"yaxes": [
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"format": "short",
"label": "processes",
"logBase": 1,
"max": null,
"min": null,
"show": true
},
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"format": "short",
"label": null,
"logBase": 1,
"max": null,
"min": null,
"show": true
}
]
},
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"aliasColors": {
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"NoECTP": "#bf1b00",
"connections": "#cca300",
"ignore": "#bf1b00",
"invalid": "#0a50a1",
"iowait": "#962d82",
"load1": "#eab839",
"load15": "#0a50a1",
"load5": "#bf1b00",
"major": "#e24d42",
"minor": "#cca300",
"nice": "#508642",
"reclaimable": "#cca300",
"restarted": "#bf1b00",
"softirq": "#890f02",
"system": "#0a50a1",
"unreclaimable": "#bf1b00",
"uptime": "#eab839",
"user": "#eab839"
},
"bars": false,
"dashLength": 10,
"dashes": false,
"datasource": "$datasource",
"decimals": null,
"fill": 10,
"id": 1914,
"legend": {
"alignAsTable": true,
"avg": false,
"current": true,
"max": false,
"min": false,
"rightSide": true,
"show": true,
"sideWidth": 200,
"sort": "current",
"sortDesc": true,
"total": false,
"values": true
},
"lines": true,
"linewidth": 0,
"links": [],
"nullPointMode": "connected",
"percentage": false,
"pointradius": 5,
"points": false,
"renderer": "flot",
"seriesOverrides": [],
"spaceLength": 10,
"span": 12,
"stack": true,
"steppedLine": false,
"targets": [
{
"hide": false,
"refId": "A",
"target": "aliasByMetric(maximumAbove($rootdir.$server.users.threads.*, 0))",
"textEditor": true
}
],
"thresholds": [],
"timeFrom": null,
"timeShift": null,
"title": "Users Threads",
"tooltip": {
"shared": true,
"sort": 2,
"value_type": "individual"
},
"transparent": true,
"type": "graph",
"xaxis": {
"buckets": null,
"mode": "time",
"name": null,
"show": true,
"values": []
},
"yaxes": [
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"label": "threads",
"logBase": 1,
"max": null,
"min": null,
"show": true
},
{
"format": "short",
"label": null,
"logBase": 1,
"max": null,
"min": null,
"show": true
}
]
},
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"NoECTP": "#bf1b00",
"connections": "#cca300",
"ignore": "#bf1b00",
"invalid": "#0a50a1",
"iowait": "#962d82",
"load1": "#eab839",
"load15": "#0a50a1",
"load5": "#bf1b00",
"major": "#e24d42",
"minor": "#cca300",
"nice": "#508642",
"reclaimable": "#cca300",
"restarted": "#bf1b00",
"softirq": "#890f02",
"system": "#0a50a1",
"unreclaimable": "#bf1b00",
"uptime": "#eab839",
"user": "#eab839"
},
"bars": false,
"dashLength": 10,
"dashes": false,
"datasource": "$datasource",
"decimals": null,
"fill": 10,
"id": 1917,
"legend": {
"alignAsTable": true,
"avg": false,
"current": true,
"max": false,
"min": false,
"rightSide": true,
"show": true,
"sideWidth": 200,
"sort": "current",
"sortDesc": true,
"total": false,
"values": true
},
"lines": true,
"linewidth": 0,
"links": [],
"nullPointMode": "connected",
"percentage": false,
"pointradius": 5,
"points": false,
"renderer": "flot",
"seriesOverrides": [],
"spaceLength": 10,
"span": 12,
"stack": true,
"steppedLine": false,
"targets": [
{
"hide": false,
"refId": "A",
"target": "aliasByMetric(maximumAbove($rootdir.$server.users.pipes.*, 0))",
"textEditor": true
}
],
"thresholds": [],
"timeFrom": null,
"timeShift": null,
"title": "Users Pipes",
"tooltip": {
"shared": true,
"sort": 2,
"value_type": "individual"
},
"transparent": true,
"type": "graph",
"xaxis": {
"buckets": null,
"mode": "time",
"name": null,
"show": true,
"values": []
},
"yaxes": [
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"format": "short",
"label": "open pipes",
"logBase": 1,
"max": null,
"min": null,
"show": true
},
{
"format": "short",
"label": null,
"logBase": 1,
"max": null,
"min": null,
"show": true
}
]
},
{
"content": "## swap",
"height": "10",
"id": 1918,
"links": [],
"minSpan": 12,
"mode": "markdown",
"span": 12,
"title": "",
"transparent": true,
"type": "text"
},
{
"aliasColors": {
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"NoECTP": "#bf1b00",
"connections": "#cca300",
"ignore": "#bf1b00",
"invalid": "#0a50a1",
"iowait": "#962d82",
"load1": "#eab839",
"load15": "#0a50a1",
"load5": "#bf1b00",
"major": "#e24d42",
"minor": "#cca300",
"nice": "#508642",
"reclaimable": "#cca300",
"restarted": "#bf1b00",
"softirq": "#890f02",
"system": "#bf1b00",
"unreclaimable": "#bf1b00",
"uptime": "#eab839",
"user": "#eab839"
},
"bars": false,
"dashLength": 10,
"dashes": false,
"datasource": "$datasource",
"decimals": 1,
"fill": 10,
"id": 1921,
"legend": {
"alignAsTable": true,
"avg": false,
"current": true,
"max": false,
"min": false,
"rightSide": true,
"show": true,
"sideWidth": 200,
"sort": "current",
"sortDesc": true,
"total": false,
"values": true
},
"lines": true,
"linewidth": 0,
"links": [],
"nullPointMode": "connected",
"percentage": false,
"pointradius": 5,
"points": false,
"renderer": "flot",
"seriesOverrides": [],
"spaceLength": 10,
"span": 12,
"stack": true,
"steppedLine": false,
"targets": [
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"hide": false,
"refId": "A",
"target": "aliasByMetric(maximumAbove($rootdir.$server.users.major_faults.*, 0))",
"textEditor": true
}
],
"thresholds": [],
"timeFrom": null,
"timeShift": null,
"title": "Users Major Page Faults (swap read)",
"tooltip": {
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"value_type": "individual"
},
"transparent": true,
"type": "graph",
"xaxis": {
"buckets": null,
"mode": "time",
"name": null,
"show": true,
"values": []
},
"yaxes": [
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"label": "page faults/s",
"logBase": 1,
"max": null,
"min": null,
"show": true
},
{
"format": "short",
"label": null,
"logBase": 1,
"max": null,
"min": null,
"show": true
}
]
},
{
"content": "## net",
"height": "10",
"id": 1872,
"links": [],
"minSpan": 12,
"mode": "markdown",
"span": 12,
"title": "",
"transparent": true,
"type": "text"
},
{
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"connections": "#cca300",
"ignore": "#bf1b00",
"invalid": "#0a50a1",
"iowait": "#962d82",
"load1": "#eab839",
"load15": "#0a50a1",
"load5": "#bf1b00",
"major": "#e24d42",
"minor": "#cca300",
"nice": "#508642",
"reclaimable": "#cca300",
"restarted": "#bf1b00",
"softirq": "#890f02",
"system": "#bf1b00",
"unreclaimable": "#bf1b00",
"uptime": "#eab839",
"user": "#eab839"
},
"bars": false,
"dashLength": 10,
"dashes": false,
"datasource": "$datasource",
"decimals": null,
"fill": 10,
"id": 1870,
"legend": {
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"avg": false,
"current": true,
"max": false,
"min": false,
"rightSide": true,
"show": true,
"sideWidth": 200,
"sort": "current",
"sortDesc": true,
"total": false,
"values": true
},
"lines": true,
"linewidth": 0,
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"pointradius": 5,
"points": false,
"renderer": "flot",
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"span": 12,
"stack": true,
"steppedLine": false,
"targets": [
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"refId": "A",
"target": "aliasByMetric(maximumAbove($rootdir.$server.users.sockets.*, 0))",
"textEditor": true
}
],
"thresholds": [],
"timeFrom": null,
"timeShift": null,
"title": "Users Open Sockets",
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"sort": 2,
"value_type": "individual"
},
"transparent": true,
"type": "graph",
"xaxis": {
"buckets": null,
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"name": null,
"show": true,
"values": []
},
"yaxes": [
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"label": "open sockets",
"logBase": 1,
"max": null,
"min": null,
"show": true
},
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"label": null,
"logBase": 1,
"max": null,
"min": null,
"show": true
}
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],
"repeat": null,
"repeatIteration": null,
"repeatRowId": null,
"showTitle": true,
"title": "Users",
"titleSize": "h6"
},
{
"collapse": true,
"height": "200",
"panels": [
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"height": "150",
"id": 2435,
"links": [],
"minSpan": 12,
"mode": "markdown",
"span": 2,
"title": "",
"transparent": true,
"type": "text"
},
{
"cacheTimeout": null,
"colorBackground": false,
"colorValue": false,
"colors": [
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"datasource": "$datasource",
"decimals": null,
"format": "percent",
"gauge": {
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"minValue": 0,
"show": false,
"thresholdLabels": false,
"thresholdMarkers": true
},
"height": "150",
"id": 2436,
"interval": null,
"links": [],
"mappingType": 1,
"mappingTypes": [
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"name": "value to text",
"value": 1
},
{
"name": "range to text",
"value": 2
}
],
"maxDataPoints": 100,
"nullPointMode": "connected",
"nullText": null,
"postfix": "",
"postfixFontSize": "50%",
"prefix": "",
"prefixFontSize": "50%",
"rangeMaps": [
{
"from": "null",
"text": "N/A",
"to": "null"
}
],
"span": 2,
"sparkline": {
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"full": false,
"lineColor": "rgb(31, 120, 193)",
"show": true
},
"tableColumn": "",
"targets": [
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],
"thresholds": "",
"title": "CPU",
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"type": "singlestat",
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"valueMaps": [
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"format": "deckbytes",
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"minValue": 0,
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"thresholdLabels": false,
"thresholdMarkers": true
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"lineColor": "rgb(31, 120, 193)",
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"thresholdMarkers": true
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"lineColor": "rgb(31, 120, 193)",
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"target": "aliasByMetric($rootdir.$server.cgroup_[[cgroupplugin]].throttle_io.read)",
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"format": "KBs",
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"minValue": 0,
"show": false,
"thresholdLabels": false,
"thresholdMarkers": true
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"lineColor": "rgb(31, 120, 193)",
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"mode": "markdown",
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"minSpan": 12,
"mode": "markdown",
"span": 12,
"title": "",
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"show": true,
"sideWidth": 200,
"sortDesc": true,
"total": false,
"values": true
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"lines": true,
"linewidth": 1,
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"pointradius": 5,
"points": false,
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"load5": "#bf1b00",
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"restarted": "#bf1b00",
"softirq": "#890f02",
"system": "#bf1b00",
"unreclaimable": "#bf1b00",
"uptime": "#eab839",
"user": "#cca300"
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"min": false,
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"show": true,
"sideWidth": 200,
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"values": true
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"lines": true,
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"lines": true,
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"id": 1177,
"legend": {
"alignAsTable": true,
"avg": false,
"current": true,
"max": false,
"min": false,
"rightSide": true,
"show": true,
"sideWidth": 200,
"sortDesc": true,
"total": false,
"values": true
},
"lines": true,
"linewidth": 1,
"links": [],
"minSpan": 12,
"nullPointMode": "connected",
"percentage": false,
"pointradius": 5,
"points": false,
"renderer": "flot",
"seriesOverrides": [],
"spaceLength": 10,
"span": 12,
"stack": true,
"steppedLine": false,
"targets": [
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"hide": false,
"refId": "A",
"target": "aliasByMetric($rootdir.$server.netdata.plugin_diskspace_dt.duration)",
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"thresholds": [],
"timeFrom": null,
"timeShift": null,
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"tooltip": {
"shared": true,
"sort": 2,
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"transparent": true,
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"xaxis": {
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"format": "short",
"label": null,
"logBase": 1,
"max": null,
"min": null,
"show": true
}
]
},
{
"content": "## tc.helper",
"height": "10",
"id": 2252,
"links": [],
"minSpan": 12,
"mode": "markdown",
"span": 12,
"title": "",
"transparent": true,
"type": "text"
},
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"aliasColors": {
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"average": "#967302",
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"clients": "#cca300",
"connections": "#cca300",
"dirty": "#3f6833",
"ignore": "#bf1b00",
"in": "#cca300",
"invalid": "#0a50a1",
"iowait": "#962d82",
"ipc": "#bf1b00",
"load1": "#eab839",
"load15": "#0a50a1",
"load5": "#bf1b00",
"major": "#e24d42",
"max": "#bf1b00",
"minor": "#cca300",
"nice": "#508642",
"out": "#bf1b00",
"pgfault": "#cca300",
"ram": "#3f6833",
"read": "#cca300",
"reclaimable": "#cca300",
"requests": "#cca300",
"restarted": "#bf1b00",
"savings": "#cca300",
"sent": "#614d93",
"softirq": "#890f02",
"swap": "#bf1b00",
"system": "#e24d42",
"unreclaimable": "#bf1b00",
"uptime": "#eab839",
"user": "#cca300",
"write": "#cca300"
},
"bars": false,
"dashLength": 10,
"dashes": false,
"datasource": "$datasource",
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"fill": 8,
"id": 1178,
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"current": true,
"max": false,
"min": false,
"rightSide": true,
"show": true,
"sideWidth": 200,
"sortDesc": true,
"total": false,
"values": true
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"lines": true,
"linewidth": 0,
"links": [],
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"pointradius": 5,
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"spaceLength": 10,
"span": 12,
"stack": true,
"steppedLine": false,
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"hide": false,
"refId": "A",
"target": "aliasByMetric($rootdir.$server.netdata.plugin_tc_cpu.system)",
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"hide": false,
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"target": "aliasByMetric($rootdir.$server.netdata.plugin_tc_cpu.user)",
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"duration": "#967302",
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"in": "#cca300",
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"iowait": "#962d82",
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"load15": "#0a50a1",
"load5": "#bf1b00",
"major": "#e24d42",
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"nice": "#508642",
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"pgfault": "#cca300",
"ram": "#3f6833",
"read": "#cca300",
"reclaimable": "#cca300",
"requests": "#cca300",
"restarted": "#bf1b00",
"run_time": "#cca300",
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"sent": "#614d93",
"softirq": "#890f02",
"swap": "#bf1b00",
"system": "#bf1b00",
"unreclaimable": "#bf1b00",
"uptime": "#eab839",
"user": "#cca300",
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"bars": false,
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"dashes": false,
"datasource": "$datasource",
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"fill": 4,
"id": 1179,
"legend": {
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"max": false,
"min": false,
"rightSide": true,
"show": true,
"sideWidth": 200,
"sortDesc": true,
"total": false,
"values": true
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"lines": true,
"linewidth": 1,
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"minSpan": 12,
"nullPointMode": "connected",
"percentage": false,
"pointradius": 5,
"points": false,
"renderer": "flot",
"seriesOverrides": [],
"spaceLength": 10,
"span": 12,
"stack": true,
"steppedLine": false,
"targets": [
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"hide": false,
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"timeFrom": null,
"timeShift": null,
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"label": null,
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"max": null,
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"show": true
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]
},
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"content": "## apps.plugin",
"height": "10",
"id": 2253,
"links": [],
"minSpan": 12,
"mode": "markdown",
"span": 12,
"title": "",
"transparent": true,
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"out": "#bf1b00",
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"ram": "#3f6833",
"read": "#cca300",
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"savings": "#cca300",
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"swap": "#bf1b00",
"system": "#e24d42",
"unreclaimable": "#bf1b00",
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"dashes": false,
"datasource": "$datasource",
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"fill": 8,
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"rightSide": true,
"show": true,
"sideWidth": 200,
"sortDesc": true,
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"values": true
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"lines": true,
"linewidth": 0,
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"pointradius": 5,
"points": false,
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"spaceLength": 10,
"span": 12,
"stack": true,
"steppedLine": false,
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"hide": false,
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"target": "aliasByMetric($rootdir.$server.netdata.apps_cpu.system)",
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},
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"hide": false,
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"transparent": true,
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"show": true
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"show": true
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"read": "#cca300",
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"stack": false,
"steppedLine": false,
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"target": "aliasByMetric($rootdir.$server.netdata.apps_sizes.new_pids)",
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"refId": "B",
"target": "aliasByMetric($rootdir.$server.netdata.apps_sizes.targets)",
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},
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"hide": false,
"refId": "C",
"target": "aliasByMetric($rootdir.$server.netdata.apps_sizes.fds)",
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{
"hide": false,
"refId": "D",
"target": "aliasByMetric($rootdir.$server.netdata.apps_sizes.pids)",
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"hide": false,
"refId": "E",
"target": "aliasByMetric($rootdir.$server.netdata.apps_sizes.files)",
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"hide": false,
"refId": "F",
"target": "aliasByMetric($rootdir.$server.netdata.apps_sizes.calls)",
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"thresholds": [],
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"title": "Apps Plugin Files",
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"transparent": true,
"type": "graph",
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"lines": true,
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"lines": true,
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9,968 | 1 | 180,333 | 13,919 | 514 | loadrunner | LoadRunner | Share Micro Focus LoadRunner test data with your colleagues and customers. | 2019-03-25 14:35:40 UTC | 2019-03-25 15:34:31 UTC | 0 | This dashboard is compatible with LoadRunner 12.61 and higher.
It allows visualizing data exported to InfluxDB from the raw results of a test run.
Read more about it in LoadRunner help. | 0 | 0 | Other | null | 0 | 0 | 13,919 | 1 | 9,968 | 1 | 514 | null | {
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} | 2019-03-25 14:35:40 UTC | 2019-03-25 15:34:31 UTC | 0 | 0 |
16,270 | 1 | 646,863 | 24,726 | 3 | ercot | Ercot | Texas power grid state and information | 2022-05-14 18:41:50 UTC | 2022-05-14 18:49:09 UTC | 0 | For information on how to set up the database, read the README at the github project:
https://github.com/josbraden/ercot
The basic steps are:
1. Set up a MySQL database and user
2. Run the python script to pull the data from the Ercot reporting site.
I'm not affiliated with Ercot in any way. | 73,272 | 73,273 | Other | null | 0 | 0 | 24,726 | 1 | 16,270 | 1 | 3 | null | {
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} | 2022-05-14 18:41:50 UTC | 2022-05-14 18:49:05 UTC | 0 | 0 |
15,775 | 1 | 604,221 | 23,871 | 3 | common-voice-public-dashboard | Common Voice Public Dashboard | This is a dashboard for a time-bounded analysis of top 10 active languages on the site | 2022-02-17 09:40:10 UTC | 2022-02-17 09:40:46 UTC | 0 | null | 0 | 0 | null | null | 0 | 0 | 23,871 | 1 | 15,775 | 1 | 3 | This is a dashboard for a time-bounded analysis of top 10 active languages on the site | {
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16,760 | 1 | 692,266 | 25,700 | 3 | kubernetes-workload | Kubernetes Workload | An extensive Kubernetes Workload Dashboard based on existing recording rules | 2022-08-23 06:22:04 UTC | 2022-08-23 06:22:55 UTC | 0 | null | 0 | 0 | null | null | 0 | 0 | 25,700 | 1 | 16,760 | 1 | 3 | null | {
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15,887 | 1 | 612,770 | 24,089 | 3 | insert-datas-2 | Insert_datas_2 | Dashboard de pe linux cu date din influxdb | 2022-03-13 09:52:09 UTC | 2022-03-13 09:54:08 UTC | 0 | null | 0 | 0 | Other | null | 0 | 0 | 24,089 | 1 | 15,887 | 1 | 3 | null | {
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}
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"alignLevel": null
}
},
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"dashLength": 10,
"dashes": false,
"datasource": "${DS_PROMETHEUS}",
"fill": 1,
"gridPos": {
"h": 9,
"w": 12,
"x": 12,
"y": 9
},
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"format": "time_series",
"intervalFactor": 1,
"legendFormat": "{{namespace}}",
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"show": true
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{
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"max": null,
"min": null,
"show": true
}
],
"yaxis": {
"align": false,
"alignLevel": null
}
}
],
"refresh": "30s",
"schemaVersion": 16,
"style": "dark",
"tags": [],
"templating": {
"list": []
},
"time": {
"from": "now-1h",
"to": "now"
},
"timepicker": {
"refresh_intervals": [
"5s",
"10s",
"30s",
"1m",
"5m",
"15m",
"30m",
"1h",
"2h",
"1d"
],
"time_options": [
"5m",
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},
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"title": "JupyterHub",
"uid": "AN4KQwWiz",
"version": 5,
"description": "IDR JupyterHub"
} | 2022-10-03 03:52:48 UTC | null | 0 | 0 |
17,251 | 1 | 730,959 | 26,508 | 3 | wireguard | Wireguard | Metrics for Wireguard VPN | 2022-10-22 22:29:21 UTC | 2022-10-22 22:36:08 UTC | 0 | ### Wireguard Dashboard
After taking a careful look at [Mind Flavor's Wireguard Prometheus Exporter](https://github.com/MindFlavor/prometheus_wireguard_exporter/blob/master/src/main.rs) I decided to make a decent dashboard for it since the original one was sadly broken, I hope you enjoy it <3 | 77,907 | 77,908 | null | null | 0 | 0 | 26,508 | 1 | 17,251 | 1 | 3 | Metrics for Wireguard VPN | {
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"pluginName": "Prometheus"
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"__elements": {},
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"annotations": {
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"builtIn": 1,
"datasource": {
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"enable": true,
"hide": true,
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{
"color": "orange",
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"id": 30,
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"title": "Connected Clients (SI)",
"type": "gauge"
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"uid": "${DS_PROMETHEUS}"
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"range": true,
"refId": "A"
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"type": "state-timeline"
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"to": "now"
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"timezone": "",
"title": "Wireguard",
"uid": "CJRCJWNVk",
"version": 6,
"weekStart": "",
"gnetId": 17251
} | 2022-10-22 22:29:21 UTC | null | 0 | 0 |
16,926 | 1 | 707,680 | 25,959 | 3 | server | Server | Monitor overall health, and resource usage of a Nomad cluster, Raft usage, RPC usage etc. | 2022-09-11 10:38:36 UTC | 2022-09-11 10:39:01 UTC | 0 | null | 0 | 0 | null | null | 0 | 0 | 25,959 | 1 | 16,926 | 1 | 3 | null | {
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} | 2022-09-11 10:38:36 UTC | 2022-09-11 10:39:01 UTC | 0 | 0 |
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- noload_exporter: Userspace and PCIe counters
- nlfs_exporter: Filesystem counters for NLFS
- node_exporter: System level counters | 76,179 | 76,180 | null | null | 0 | 0 | 25,736 | 1 | 16,785 | 1 | 3 | A quick dashboard for showing the NLFS and NoLoad exporter values together. | {
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} | 2022-05-21 18:33:06 UTC | 2022-05-21 18:33:34 UTC | 0 | 0 |
16,262 | 1 | 645,259 | 24,715 | 3 | boot4go-gateway | Boot4GO-Gateway | Process status published by Go Prometheus client library, e.g. memory used, fds open, GC details | 2022-05-12 08:20:43 UTC | 2022-05-12 08:23:47 UTC | 0 | A Prometheus scraping Golang Prometheus metrics endpoints fot boot4go-gateway.
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} | 2022-05-12 08:20:43 UTC | null | 0 | 0 |
16,276 | 1 | 269,813 | 24,739 | 3 | http-response-monitoring | HTTP Response Monitoring | A very simple and visual Dashboard to monitor HTTP Responses using the native http_response from Telegraf. It just works automatically once the sources are added to telegraf. | 2022-05-15 22:44:40 UTC | 2022-05-15 22:44:50 UTC | 0 | null | 0 | 0 | null | null | 0 | 0 | 24,739 | 1 | 16,276 | 1 | 3 | A very simple and visual Dashboard to monitor HTTP Responses using the native http_response from Telegraf. It just works automatically once the sources are added to telegraf. | {
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"interval": "",
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},
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},
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"interval": "",
"intervalFactor": 1,
"legendFormat": "Current",
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"step": 30
},
{
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"intervalFactor": 1,
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},
{
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{
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"legendFormat": "precent of unavailable",
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],
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}
],
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}
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"title": "traefik-ingress ALERTS",
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},
{
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},
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]
},
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},
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},
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},
"type": "query"
}
],
"executionErrorState": "alerting",
"for": "5m",
"frequency": "1m",
"handler": 1,
"message": "ingress-nginx's memory usage percentage is too high",
"name": "Aggregated Memory Usage consul alert",
"noDataState": "no_data",
"notifications": [
{
"uid": "000000071"
}
]
},
"aliasColors": {},
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},
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},
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},
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},
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},
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"renderer": "flot",
"seriesOverrides": [],
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"steppedLine": false,
"targets": [
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"legendFormat": "percentageUsageMain",
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},
{
"expr": "sum(workload:container_memory_working_set_bytes:aggr{aggr=\"avg\", cluster=~\"eks.*mgmt.*\", workload=\"ingress-nginx\"}) /\nsum(workload:container_resource_requests:aggr{workload=\"ingress-nginx\",cluster=~\"eks.*mgmt.*\"}) * 100",
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}
],
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"op": "gt",
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"visible": true
}
],
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"title": "Aggregated Memory Usage ingress-nginx",
"tooltip": {
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"value_type": "individual"
},
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{
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],
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},
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},
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]
},
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},
"type": "query"
},
{
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100
],
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},
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],
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"name": "Aggregated CPU Usage coredns alert",
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"notifications": [
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}
]
},
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"dashes": false,
"datasource": {
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"uid": "${DS_PROMETHEUS}"
},
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},
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},
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},
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},
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},
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"pluginVersion": "8.4.4",
"pointradius": 2,
"points": false,
"renderer": "flot",
"seriesOverrides": [],
"spaceLength": 10,
"stack": false,
"steppedLine": false,
"targets": [
{
"expr": "sum(workload:container_cpu_usage_seconds:rate5m:aggr{aggr=\"avg\", cluster=~\"eks.*main.*\", workload=\"ingress-nginx\"}) / sum(workload:container_resource_requests:aggr{workload=\"ingress-nginx\", cluster=~\"eks.*main.*\", resource=\"cpu\"}) * 100",
"hide": false,
"interval": "",
"legendFormat": "percentageUsageMain",
"refId": "percentageUsageMain"
},
{
"expr": "sum(workload:container_cpu_usage_seconds:rate5m:aggr{aggr=\"avg\", cluster=~\"eks.*mgmt.*\", workload=\"ingress-nginx\"}) / sum(workload:container_resource_requests:aggr{workload=\"ingress-nginx\", cluster=~\"eks.*mgmt.*\", resource=\"cpu\"}) * 100",
"hide": false,
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}
],
"thresholds": [
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"colorMode": "critical",
"fill": true,
"line": true,
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"visible": true
}
],
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"title": "Aggregated CPU Usage ingress-nginx",
"tooltip": {
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},
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},
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"show": true
},
{
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}
],
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}
},
{
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0.3
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},
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},
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"5m",
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},
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},
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}
],
"executionErrorState": "alerting",
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},
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"datasource": {
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"uid": "${DS_PROMETHEUS}"
},
"description": "`Current` - The number of replicas that are currently running in the system\n\n`Available` - The number of replicas that are passing readiness probes\n\n`Unavailable` - The opposite of available\n\n`Desired` - The number of replicas you asked for in the deployment file\n\n`Updated` - The number of replicas that are up to date with the desired state",
"editable": true,
"error": false,
"fieldConfig": {
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},
"overrides": []
},
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"grid": {},
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"x": 5,
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},
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},
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},
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{
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{
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},
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},
{
"expr": "(avg without(instance, pod) (kube_deployment_status_replicas_unavailable{cluster=\"eks-prod-main-1\", deployment=\"ingress-nginx\"})+0.1)/avg without(instance, pod) (kube_deployment_spec_replicas{cluster=\"eks-prod-main-1\", deployment=\"ingress-nginx\"})",
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}
],
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}
}
],
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}
],
"refresh": false,
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"style": "dark",
"tags": [
"SRE",
"Devops",
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},
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{
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"definition": "label_values({label_cluster=\"$cluster\"}, label_nodegroup)",
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},
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},
"definition": "up{job=\"coredns\"}",
"hide": 0,
"includeAll": true,
"label": "instance",
"multi": false,
"name": "instance",
"options": [],
"query": {
"query": "up{job=\"coredns\"}",
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},
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"sort": 1,
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"tagsQuery": "",
"type": "query",
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]
},
"time": {
"from": "now-30m",
"to": "now"
},
"timepicker": {
"refresh_intervals": [
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"1m",
"5m",
"15m",
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"time_options": [
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"gnetId": 16759
} | 2022-08-23 06:20:52 UTC | 2022-08-23 06:21:44 UTC | 0 | 0 |
15,728 | 1 | 355,487 | 23,766 | 3 | apc-sua1500i | APC SUA1500I for XigmaNAS | Smart-UPS APC SUA1500I created for XigmaNAS | 2022-02-09 07:06:24 UTC | 2022-02-09 07:22:34 UTC | 0 | It is required to create extra script to retrieve the UPS data /mnt/pool/ext/extensions/telegraf/custom_scripts/upsc_status
#!/bin/sh
#
# Set environment.
PATH=/sbin:/bin:/usr/sbin:/usr/bin:/usr/local/sbin:/usr/local/bin
# UPS device name
UPS_DEVNAME="apc1500"
UPS_DEVNAME_LIST=$(upsc -l)
if [ "${UPS_DEVNAME_LIST}" = "${UPS_DEVNAME}" ]; then
UPS_BATTERY_CHARGE=$(upsc ${UPS_DEVNAME} battery.charge)
UPS_BATTERY_RUNTIME=$(upsc ${UPS_DEVNAME} battery.runtime)
UPS_BATTERY_TEMPERATURE=$(upsc ${UPS_DEVNAME} battery.temperature)
UPS_BATTERY_VOLTAGE=$(upsc ${UPS_DEVNAME} battery.voltage)
UPS_INPUT_VOLTAGE=$(upsc ${UPS_DEVNAME} input.voltage)
UPS_OUTPUT_VOLTAGE=$(upsc ${UPS_DEVNAME} output.voltage)
UPS_MODEL=$(upsc ${UPS_DEVNAME} ups.model)
UPS_LOAD=$(upsc ${UPS_DEVNAME} ups.load)
UPS_STATUS=$(upsc ${UPS_DEVNAME} ups.status)
# if [ "${UPS_STATUS}" = "OL" ]; then
# # UPS is on ac power.
# UPS_STATUS="AC power"
# elif [ "${UPS_STATUS}" = "OL CHRG" ]; then
# # UPS is on ac/charging.
# UPS_STATUS="AC power/charging"
# elif [ "${UPS_STATUS}" = "OB DISCHRG" ]; then
# # UPS is on battery power.
# UPS_STATUS="On battery"
# fi
printf "ups_details battery.charge=${UPS_BATTERY_CHARGE},battery.runtime=${UPS_BATTERY_RUNTIME},battery.temperature=${UPS_BATTERY_TEMPERATURE},battery.voltage=${UPS_BATTERY_VOLTAGE},input.voltage=${UPS_INPUT_VOLTAGE},output.voltage=${UPS_OUTPUT_VOLTAGE},ups.model=\"${UPS_MODEL}\",ups.status=\"${UPS_STATUS}\",ups.load=${UPS_LOAD}"
fi
| 70,807 | 70,808 | Telegraf | [[inputs.exec]]
commands = ["/mnt/pool/ext/extensions/telegraf/custom_scripts/upsc_status"]
timeout = "5s"
data_format = "influx"
| 0 | 0 | 23,766 | 1 | 15,728 | 1 | 3 | A re-creation of FreeNAS's builtin reporting page | {
"__inputs": [
{
"name": "DS_XIGMANAS",
"label": "xigmanas",
"description": "",
"type": "datasource",
"pluginId": "influxdb",
"pluginName": "InfluxDB"
}
],
"__requires": [
{
"type": "panel",
"id": "gauge",
"name": "Gauge",
"version": ""
},
{
"type": "grafana",
"id": "grafana",
"name": "Grafana",
"version": "7.4.5"
},
{
"type": "panel",
"id": "graph",
"name": "Graph",
"version": ""
},
{
"type": "datasource",
"id": "influxdb",
"name": "InfluxDB",
"version": "1.0.0"
},
{
"type": "panel",
"id": "singlestat",
"name": "Singlestat",
"version": ""
}
],
"annotations": {
"list": [
{
"$$hashKey": "object:1405",
"builtIn": 1,
"datasource": "-- Grafana --",
"enable": true,
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"yaxis": {
"align": false,
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}
},
{
"aliasColors": {},
"bars": false,
"dashLength": 10,
"dashes": false,
"datasource": "${DS_PROMETHEUS}",
"decimals": 0,
"fieldConfig": {
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},
"overrides": []
},
"fill": 0,
"fillGradient": 1,
"gridPos": {
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"rightSide": true,
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},
"lines": true,
"linewidth": 4,
"nullPointMode": "connected",
"options": {
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},
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"targets": [
{
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],
"thresholds": [],
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"timeRegions": [],
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{
"$$hashKey": "object:46",
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{
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"yaxis": {
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},
{
"datasource": "${DS_PROMETHEUS}",
"fieldConfig": {
"defaults": {
"color": {
"mode": "continuous-YlBl"
},
"custom": {},
"mappings": [],
"max": 100,
"min": 0,
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{
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{
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{
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{
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},
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},
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},
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"colorMode": "value",
"graphMode": "none",
"justifyMode": "center",
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"reduceOptions": {
"calcs": [
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"fields": "",
"values": false
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"textMode": "auto"
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"pluginVersion": "7.3.6",
"targets": [
{
"expr": "shelly_ht_humidity",
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"intervalFactor": 1,
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],
"timeFrom": null,
"timeShift": null,
"title": "Humidity",
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{
"datasource": "${DS_PROMETHEUS}",
"fieldConfig": {
"defaults": {
"color": {
"mode": "thresholds"
},
"custom": {
"align": "left",
"displayMode": "color-text",
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},
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"thresholds": {
"mode": "absolute",
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{
"color": "super-light-blue",
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}
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},
"unit": "none"
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"overrides": [
{
"matcher": {
"id": "byName",
"options": "Battery"
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"properties": [
{
"id": "custom.width",
"value": 297
},
{
"id": "unit",
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{
"id": "custom.displayMode",
"value": "lcd-gauge"
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{
"id": "thresholds",
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"mode": "absolute",
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{
"color": "dark-red",
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{
"color": "#EAB839",
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},
{
"color": "green",
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]
}
}
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},
{
"matcher": {
"id": "byName",
"options": "Status"
},
"properties": [
{
"id": "custom.displayMode",
"value": "basic"
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{
"id": "color",
"value": {
"mode": "thresholds"
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},
{
"id": "thresholds",
"value": {
"mode": "absolute",
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{
"color": "dark-red",
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},
{
"color": "dark-green",
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}
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}
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},
{
"matcher": {
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},
{
"matcher": {
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},
{
"matcher": {
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},
{
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}
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},
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"options": {
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],
"timeFrom": null,
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"title": "Sensor Overview",
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{
"id": "organize",
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"battery": 2,
"firmware": 4,
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"mac": 5,
"name": 0,
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"updated": 3
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"renameByName": {
"__name__": "",
"battery": "Battery",
"config_battery": "Battery Level",
"config_on": "",
"firmware": "Firmware",
"isok": "Status",
"mac": "MAC",
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"shelly_name": "ID",
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"state_lastupdated": "Last Updated",
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"type": "Sensor Type",
"updated": "Updated"
}
}
}
],
"type": "table"
}
],
"refresh": "1m",
"schemaVersion": 26,
"style": "dark",
"tags": [],
"templating": {
"list": []
},
"time": {
"from": "now-24h",
"to": "now"
},
"timepicker": {},
"timezone": "",
"title": "Shelly Temperature",
"uid": "soUBz7Wgk",
"version": 20,
"description": "https://github.com/aexel90/shelly_exporter"
} | 2022-10-02 09:01:22 UTC | null | 0 | 0 |
16,982 | 1 | 498,884 | 26,042 | 4 | pixie-k8s-cluster-overview | Pixie K8s Cluster Overview | Dashboard includes a service map of the HTTP traffic between the services in your cluster, along with the latency, error rate, and throughput per service. It also lists the nodes, namespaces and pods available in your cluster. | 2022-09-15 23:35:39 UTC | 2022-09-16 01:20:54 UTC | 0 | To learn how to monitor service performance and infrastructure health using Pixie, see the [tutorial](https://docs.px.dev/tutorials/pixie-101/service-performance/). | 76,943 | 76,944 | Other | null | 0 | 0 | 26,042 | 1 | 16,982 | 1 | 4 | Dashboard includes a service map of the HTTP traffic between the services in your cluster, along with the latency, error rate, and throughput per service. It also lists the nodes, namespaces and pods available in your cluster. To learn how to monitor service performance and infrastructure health using Pixie, see https://docs.px.dev/tutorials/pixie-101/service-performance/ | {
"__inputs": [
{
"name": "DS_PIXIE_GRAFANA DATASOURCE PLUGIN",
"label": "Pixie Grafana Datasource Plugin",
"description": "",
"type": "datasource",
"pluginId": "pixie-pixie-datasource",
"pluginName": "Pixie Grafana Datasource Plugin"
}
],
"__requires": [
{
"type": "panel",
"id": "nodeGraph",
"name": "Node Graph",
"version": ""
},
{
"type": "datasource",
"id": "pixie-pixie-datasource",
"name": "Pixie Grafana Datasource Plugin",
"version": "0.0.9"
},
{
"type": "panel",
"id": "table",
"name": "Table",
"version": ""
}
],
"annotations": {
"list": [
{
"builtIn": 1,
"datasource": "-- Grafana --",
"enable": true,
"hide": true,
"iconColor": "rgba(0, 211, 255, 1)",
"name": "Annotations & Alerts",
"target": {
"limit": 100,
"matchAny": false,
"tags": [],
"type": "dashboard"
},
"type": "dashboard"
}
]
},
"editable": true,
"graphTooltip": 0,
"id": null,
"iteration": 1657152736466,
"links": [],
"panels": [
{
"datasource": "${DS_PIXIE_GRAFANA DATASOURCE PLUGIN}",
"gridPos": {
"h": 14,
"w": 24,
"x": 0,
"y": 0
},
"id": 2,
"targets": [
{
"datasource": "${DS_PIXIE_GRAFANA DATASOURCE PLUGIN}",
"queryBody": {
"pxlScript": "# Copyright 2018- The Pixie Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n# SPDX-License-Identifier: Apache-2.0\n\n'''\nThis query outputs a graph of the HTTP traffic between the services in\nyour cluster. Use with Grafana's node graph panel.\n\nThis query is for use with Grafana's Pixie Datasource Plugin only,\nas it uses Grafana macros for adding Grafana dashboard context.\nThe functions in this query are pulled from the px/cluster script:\nhttps://github.com/pixie-io/pixie/tree/main/src/pxl_scripts/px/cluster\n'''\n\n# Import Pixie's module for querying data.\nimport px\n\n# Window size to use on time_ column for bucketing.\nns_per_s = 1000 * 1000 * 1000\nns_per_ms = 1000 * 1000\nwindow_ns = px.DurationNanos(10 * ns_per_s)\n\n# Flag to filter out health checks from the data.\nfilter_health_checks = True\n\n# Whether or not to include traffic from IPs that don't resolve to a known pod/service.\ninclude_ips = True\n\n# $pixieCluster - work around for grafana to update panel on variable change\n\n# Hack to get the time window for the script.\n# TODO(philkuz): Replace this with a built-in.\ndef get_time_window(start_time: int):\n ''' Converts the start_time string into a table with a single column and single row.\n The approach is hacky, and will round to roughly 1 second.\n '''\n df = px.DataFrame('process_stats', start_time=start_time)\n\n df = df.agg(\n time_min=('time_', px.min),\n time_max=('time_', px.max),\n )\n\n df.window = px.DurationNanos(df.time_max - df.time_min)\n df = df[['window']]\n\n return df\n\n\ndef add_time_window_column(df, start_time):\n tw = get_time_window(start_time)\n df = df.merge(tw, how='inner', left_on=[], right_on=[])\n return df\n\n\ndef process_stats_by_entity(start_time: int, entity: str):\n ''' Gets the windowed process stats (CPU, memory, etc) per node or pod.\n Args:\n @start_time Starting time of the data to examine.\n @entity: Either pod or node_name.\n '''\n # Window size to use on time_ column for bucketing.\n ns_per_s = 1000 * 1000 * 1000\n window_ns = px.DurationNanos(10 * ns_per_s)\n\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df[entity] = df.ctx[entity]\n df.timestamp = px.bin(df.time_, window_ns)\n # First calculate CPU usage by process (UPID) in each k8s_object\n # over all windows.\n df = df.groupby([entity, 'upid', 'timestamp']).agg(\n rss=('rss_bytes', px.mean),\n vsize=('vsize_bytes', px.mean),\n # The fields below are counters, so we take the min and the max to subtract them.\n cpu_utime_ns_max=('cpu_utime_ns', px.max),\n cpu_utime_ns_min=('cpu_utime_ns', px.min),\n cpu_ktime_ns_max=('cpu_ktime_ns', px.max),\n cpu_ktime_ns_min=('cpu_ktime_ns', px.min),\n read_bytes_max=('read_bytes', px.max),\n read_bytes_min=('read_bytes', px.min),\n write_bytes_max=('write_bytes', px.max),\n write_bytes_min=('write_bytes', px.min),\n rchar_bytes_max=('rchar_bytes', px.max),\n rchar_bytes_min=('rchar_bytes', px.min),\n wchar_bytes_max=('wchar_bytes', px.max),\n wchar_bytes_min=('wchar_bytes', px.min),\n )\n # Next calculate cpu usage and memory stats per window.\n df.cpu_utime_ns = df.cpu_utime_ns_max - df.cpu_utime_ns_min\n df.cpu_ktime_ns = df.cpu_ktime_ns_max - df.cpu_ktime_ns_min\n df.read_bytes = df.read_bytes_max - df.read_bytes_min\n df.write_bytes = df.write_bytes_max - df.write_bytes_min\n df.rchar_bytes = df.rchar_bytes_max - df.rchar_bytes_min\n df.wchar_bytes = df.wchar_bytes_max - df.wchar_bytes_min\n # Sum by UPID.\n df = df.groupby([entity, 'timestamp']).agg(\n cpu_ktime_ns=('cpu_ktime_ns', px.sum),\n cpu_utime_ns=('cpu_utime_ns', px.sum),\n read_bytes=('read_bytes', px.sum),\n write_bytes=('write_bytes', px.sum),\n rchar_bytes=('rchar_bytes', px.sum),\n wchar_bytes=('wchar_bytes', px.sum),\n rss=('rss', px.sum),\n vsize=('vsize', px.sum),\n )\n df.actual_disk_read_throughput = df.read_bytes / window_ns\n df.actual_disk_write_throughput = df.write_bytes / window_ns\n df.total_disk_read_throughput = df.rchar_bytes / window_ns\n df.total_disk_write_throughput = df.wchar_bytes / window_ns\n # Now take the mean value over the various timestamps.\n df = df.groupby(entity).agg(\n cpu_ktime_ns=('cpu_ktime_ns', px.mean),\n cpu_utime_ns=('cpu_utime_ns', px.mean),\n actual_disk_read_throughput=('actual_disk_read_throughput', px.mean),\n actual_disk_write_throughput=('actual_disk_write_throughput', px.mean),\n total_disk_read_throughput=('total_disk_read_throughput', px.mean),\n total_disk_write_throughput=('total_disk_write_throughput', px.mean),\n avg_rss=('rss', px.mean),\n avg_vsize=('vsize', px.mean),\n )\n # Finally, calculate total (kernel + user time) percentage used over window.\n df.cpu_usage = px.Percent((df.cpu_ktime_ns + df.cpu_utime_ns) / window_ns)\n return df.drop(['cpu_ktime_ns', 'cpu_utime_ns'])\n\n\ndef nodes_for_cluster(start_time: int):\n ''' Gets a list of nodes in the current cluster since `start_time`.\n Args:\n @start_time Start time of the data to examine.\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df.node = df.ctx['node_name']\n df.pod = df.ctx['pod_name']\n agg = df.groupby(['node', 'pod']).agg()\n nodes = agg.groupby('node').agg(pod_count=('pod', px.count))\n process_stats = process_stats_by_entity(start_time, 'node')\n output = process_stats.merge(nodes, how='inner', left_on='node', right_on='node',\n suffixes=['', '_x'])\n return output[['node', 'cpu_usage', 'pod_count']]\n\n\ndef pods_for_cluster(start_time: int):\n ''' A list of pods in `namespace`.\n Args:\n @start_time: The timestamp of data to start at.\n @namespace: The name of the namespace to filter on.\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df.pod = df.ctx['pod_name']\n df.node = df.ctx['node_name']\n df.container = df.ctx['container_name']\n df = df.groupby(['pod', 'node', 'container']).agg()\n df = df.groupby(['pod', 'node']).agg(container_count=('container', px.count))\n df.start_time = px.pod_name_to_start_time(df.pod)\n df.status = px.pod_name_to_status(df.pod)\n process_stats = process_stats_by_entity(start_time, 'pod')\n output = process_stats.merge(df, how='inner', left_on='pod', right_on='pod',\n suffixes=['', '_x'])\n return output[['pod', 'cpu_usage', 'total_disk_read_throughput',\n 'total_disk_write_throughput', 'container_count',\n 'node', 'start_time', 'status']]\n\n\ndef namespaces_for_cluster(start_time: int):\n ''' Gets a overview of namespaces in the current cluster since `start_time`.\n Args:\n @start_time Start time of the data to examine.\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df.service = df.ctx['service_name']\n df.pod = df.ctx['pod_name']\n df.namespace = df.ctx['namespace']\n agg = df.groupby(['service', 'pod', 'namespace']).agg()\n pod_count = agg.groupby(['namespace', 'pod']).agg()\n pod_count = pod_count.groupby('namespace').agg(pod_count=('pod', px.count))\n svc_count = agg.groupby(['namespace', 'service']).agg()\n svc_count = svc_count.groupby('namespace').agg(service_count=('service', px.count))\n pod_and_svc_count = pod_count.merge(svc_count, how='inner',\n left_on='namespace', right_on='namespace',\n suffixes=['', '_x'])\n process_stats = process_stats_by_entity(start_time, 'namespace')\n output = process_stats.merge(pod_and_svc_count, how='inner', left_on='namespace',\n right_on='namespace', suffixes=['', '_y'])\n return output[['namespace', 'pod_count', 'service_count', 'avg_vsize', 'avg_rss']]\n\n\ndef services_for_cluster(start_time: int):\n ''' Get an overview of the services in the current cluster.\n Args:\n @start_time: The timestamp of data to start at.\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df.service = df.ctx['service']\n df = df[df.service != '']\n df.pod = df.ctx['pod']\n df = df.groupby(['service', 'pod']).agg()\n df = df.groupby('service').agg(pod_count=('pod', px.count))\n service_let = service_let_summary(start_time)\n joined = df.merge(service_let, how='left', left_on='service', right_on='service',\n suffixes=['', '_x'])\n return joined.drop('service_x')\n\n\ndef http_stats(start_time: int):\n ''' Get a dataframe of HTTP events.\n Certain traffic (like health checks) are auto removed, and some standard fields are added.\n Args:\n @start_time: The timestamp of data to start at.\n '''\n df = px.DataFrame(table='http_events', start_time=start_time)\n\n # Add K8s metadata.\n df.service = df.ctx['service']\n df.pod = df.ctx['pod']\n\n # Filter out non-k8s entities.\n df = df[df.pod != '']\n\n # Additional HTTP fields, pre-computed for convenience.\n df.failure = df.resp_status >= 400\n\n # Remove health checks, and anything with no remote address.\n health_check_req = ((df.req_path == '/healthz' or df.req_path == '/readyz') or df.req_path == '/livez')\n filter_out_conds = (health_check_req and filter_health_checks) or (df['remote_addr'] == '-')\n df = df[not filter_out_conds]\n\n return df\n\n\ndef http_stats_by_service(start_time: int):\n ''' Get a data frame of HTTP stats per service. The HTTP stats are for inbound traffic,\n and includes HTTP request count, error count and latency quantiles.\n Args:\n @start_time: The timestamp of data to start at.\n '''\n df = http_stats(start_time)\n\n # Filter only to inbound service traffic (server-side).\n # Don't include traffic initiated by this service to an external location.\n df = df[df.trace_role == 2]\n\n # Compute HTTP metrics.\n df = df.groupby(['service']).agg(\n http_req_count_in=('latency', px.count),\n http_error_count_in=('failure', px.sum),\n http_latency_in=('latency', px.quantiles)\n )\n\n return df\n\n\ndef conn_stats(start_time: int):\n ''' Get a dataframe of connection stats.\n For each client-server pair, the resulting data frame has the bytes sent and received.\n Args:\n @start_time: The timestamp of data to start at.\n '''\n df = px.DataFrame(table='conn_stats', start_time=start_time)\n\n df.pod = df.ctx['pod']\n df.service = df.ctx['service']\n\n df = df[df.service != '']\n\n # Find min/max bytes transferred over the selected time window per pod.\n df = df.groupby(['upid', 'remote_addr', 'remote_port', 'pod', 'service', 'trace_role']).agg(\n bytes_recv_min=('bytes_recv', px.min),\n bytes_recv_max=('bytes_recv', px.max),\n bytes_sent_min=('bytes_sent', px.min),\n bytes_sent_max=('bytes_sent', px.max),\n )\n\n # Calculate bytes transferred over the time window\n df.bytes_sent = df.bytes_sent_max - df.bytes_sent_min\n df.bytes_recv = df.bytes_recv_max - df.bytes_recv_min\n df = df.drop(['bytes_recv_min', 'bytes_recv_max', 'bytes_sent_min', 'bytes_sent_max'])\n\n return df\n\n\ndef conn_stats_by_service(start_time: int):\n ''' Get a dataframe of connection stats aggregated by service.\n For each service, the resulting data frame contains rx/tx stats for server-side and client-side connections.\n Args:\n @start_time: The timestamp of data to start at.\n '''\n df = conn_stats(start_time)\n\n # Group by service and trace role.\n # Do this after computing bytes sent/received by conn_stats key ({upid, remote_addr, remote_port}).\n # Keeping trace_role allows us to see which traffic was part of server duties vs client duties.\n df = df.groupby(['service', 'trace_role']).agg(\n bytes_recv=('bytes_recv', px.sum),\n bytes_sent=('bytes_sent', px.sum),\n )\n\n # Get RX/TX stats for the server side connections.\n server_df = df[df.trace_role == 2]\n server_df.rx_server = server_df.bytes_recv\n server_df.tx_server = server_df.bytes_sent\n server_df = server_df[['service', 'rx_server', 'tx_server']]\n\n # Get RX/TX stats for the client side connections.\n client_df = df[df.trace_role == 1]\n client_df.rx_client = client_df.bytes_recv\n client_df.tx_client = client_df.bytes_sent\n client_df = client_df[['service', 'rx_client', 'tx_client']]\n\n # Create a dataframe that contains both server-side and client-side RX/TX stats.\n df = server_df.merge(client_df,\n how='left',\n left_on='service',\n right_on='service',\n suffixes=['', '_x'])\n df = df['service', 'rx_server', 'tx_server', 'rx_client', 'tx_client']\n\n return df\n\n\ndef service_let_summary(start_time: int):\n ''' Compute a summary of traffic by requesting service, for requests\n on services in the current cluster..\n Args:\n @start_time: The timestamp of data to start at.\n '''\n conn_stats_df = conn_stats_by_service(start_time)\n http_stats_df = http_stats_by_service(start_time)\n\n # Merge conn_stats_df and http_stats_df.\n df = conn_stats_df.merge(http_stats_df,\n how='left',\n left_on='service',\n right_on='service',\n suffixes=['', '_x'])\n\n # Compute time window for the query and add it as a column.\n df = add_time_window_column(df, start_time)\n\n # Compute throughput values.\n df.http_req_throughput_in = df.http_req_count_in / df.window\n df.http_error_rate_in = px.Percent(\n px.select(df.http_req_count_in != 0, df.http_error_count_in / df.http_req_count_in, 0.0))\n df.inbound_conns = (df.rx_server + df.tx_server) / df.window\n df.outbound_conns = (df.tx_client + df.rx_client) / df.window\n\n return df[['service', 'http_latency_in', 'http_req_throughput_in', 'http_error_rate_in',\n 'inbound_conns', 'outbound_conns']]\n\n\ndef service_let_graph(start_time: int):\n ''' Compute a summary of traffic by requesting service, for requests on services\n in the current cluster. Similar to `service_let_summary` but also breaks down\n by pod in addition to service.\n Args:\n @start_time: The timestamp of data to start at.\n '''\n df = http_stats(start_time)\n df = df.groupby(['service', 'remote_addr', 'pod', 'trace_role']).agg(\n http_req_count_in=('latency', px.count),\n http_error_count_in=('failure', px.sum),\n latency_quantiles=('latency', px.quantiles),\n inbound_bytes_total=('req_body_size', px.sum),\n outbound_bytes_total=('resp_body_size', px.sum)\n )\n\n # Get the traced and remote pod/service/IP information.\n df.traced_pod = df.pod\n df.traced_service = df.service\n df.traced_ip = px.pod_name_to_pod_ip(df.pod)\n df.remote_pod = px.pod_id_to_pod_name(px.ip_to_pod_id(df.remote_addr))\n df.remote_service = px.service_id_to_service_name(px.ip_to_service_id(df.remote_addr))\n df.remote_ip = df.remote_addr\n # If external IPs are excluded in the service graph, then we also exclude any\n # traffic where we don't know the remote pod or remote service name.\n df = df[include_ips or (df.remote_pod != '' or df.remote_service != '')]\n\n # Associate it with Client/Server roles, based on the trace role.\n df.is_server_side_tracing = df.trace_role == 2\n df.responder_pod = px.select(df.is_server_side_tracing, df.traced_pod, df.remote_pod)\n df.requestor_pod = px.select(df.is_server_side_tracing, df.remote_pod, df.traced_pod)\n df.responder_service = px.select(df.is_server_side_tracing, df.traced_service, df.remote_service)\n df.requestor_service = px.select(df.is_server_side_tracing, df.remote_service, df.traced_service)\n df.responder_ip = px.select(df.is_server_side_tracing, df.traced_ip, df.remote_ip)\n df.requestor_ip = px.select(df.is_server_side_tracing, df.remote_ip, df.traced_ip)\n\n # Compute statistics about each edge of the service graph.\n df.latency_p50 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p50')))\n df.latency_p90 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p90')))\n df.latency_p99 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p99')))\n df = add_time_window_column(df, start_time)\n df.request_throughput = df.http_req_count_in / df.window\n df.inbound_throughput = df.inbound_bytes_total / df.window\n df.outbound_throughput = df.outbound_bytes_total / df.window\n df.error_rate = px.Percent(df.http_error_count_in / df.http_req_count_in)\n\n df = df.groupby(['responder_pod', 'requestor_pod', 'responder_service',\n 'requestor_service', 'responder_ip', 'requestor_ip']).agg(\n latency_p50=('latency_p50', px.mean),\n latency_p90=('latency_p90', px.mean),\n latency_p99=('latency_p99', px.mean),\n request_throughput=('request_throughput', px.mean),\n error_rate=('error_rate', px.mean),\n inbound_throughput=('inbound_throughput', px.mean),\n outbound_throughput=('outbound_throughput', px.mean),\n throughput_total=('http_req_count_in', px.sum)\n )\n\n return df\n \ndef graphnode_sources(start_time: int):\n df = service_let_graph(start_time)\n # Use Pod name for source node id and title. If pod name is not available,\n # use service name or IP address.\n df.source_svc_ip = px.select(df.requestor_service != '', df.requestor_service, df.requestor_ip)\n df.id = px.select(df.requestor_pod != '', df.requestor_pod, df.source_svc_ip)\n df.title = df.id\n df.arc__success = 0.1\n df.arc__failure = 0.9\n df = df.groupby(['id', 'title']).agg()\n \n return df\n\n\ndef graphnode_targets(start_time: int):\n df = service_let_graph(start_time)\n # Use Pod name for target node id and title. If pod name is not available,\n # use service name or IP address.\n df.target_svc_ip = px.select(df.responder_service != '', df.responder_service, df.responder_ip)\n df.id = px.select(df.responder_pod != '', df.responder_pod, df.target_svc_ip)\n df.title = df.id\n df = df.groupby(['id', 'title']).agg()\n \n return df\n\n\ndef nodes(start_time: int):\n node_sources = graphnode_sources(start_time)\n node_targets = graphnode_targets(start_time)\n df = node_sources.append(node_targets)\n return df\n\n\ndef edges(start_time: int):\n df = service_let_graph(start_time)\n df.source_svc_ip = px.select(df.requestor_service != '', df.requestor_service, df.requestor_ip)\n df.source = px.select(df.requestor_pod != '', df.requestor_pod, df.source_svc_ip)\n df.target_svc_ip = px.select(df.responder_service != '', df.responder_service, df.responder_ip)\n df.target = px.select(df.responder_pod != '', df.responder_pod, df.target_svc_ip)\n df.id = df.source + '-' + df.target\n df.mainStat = df.error_rate * 100\n df.secondaryStat = df.latency_p90 / ns_per_ms\n return df[['id', 'source', 'target', 'mainStat', 'secondaryStat']]\n\nstart_time = __time_from\n\nnodes_table = nodes(start_time)\nedges_table = edges(start_time)\npx.display(nodes_table, \"nodes\")\npx.display(edges_table, \"edges\")"
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"pxlScript": "import px \n\n# $pixieCluster - work around for grafana to update panel on variable change\n\ndef process_stats_by_entity(start_time: int, entity: str):\n ''' Gets the windowed process stats (CPU, memory, etc) per node or pod.\n Args:\n @start_time Starting time of the data to examine.\n @entity: Either pod or node_name.\n '''\n # Window size to use on time_ column for bucketing.\n ns_per_s = 1000 * 1000 * 1000\n window_ns = px.DurationNanos(10 * ns_per_s)\n\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df[entity] = df.ctx[entity]\n df.timestamp = px.bin(df.time_, window_ns)\n # First calculate CPU usage by process (UPID) in each k8s_object\n # over all windows.\n df = df.groupby([entity, 'upid', 'timestamp']).agg(\n rss=('rss_bytes', px.mean),\n vsize=('vsize_bytes', px.mean),\n # The fields below are counters, so we take the min and the max to subtract them.\n cpu_utime_ns_max=('cpu_utime_ns', px.max),\n cpu_utime_ns_min=('cpu_utime_ns', px.min),\n cpu_ktime_ns_max=('cpu_ktime_ns', px.max),\n cpu_ktime_ns_min=('cpu_ktime_ns', px.min),\n read_bytes_max=('read_bytes', px.max),\n read_bytes_min=('read_bytes', px.min),\n write_bytes_max=('write_bytes', px.max),\n write_bytes_min=('write_bytes', px.min),\n rchar_bytes_max=('rchar_bytes', px.max),\n rchar_bytes_min=('rchar_bytes', px.min),\n wchar_bytes_max=('wchar_bytes', px.max),\n wchar_bytes_min=('wchar_bytes', px.min),\n )\n # Next calculate cpu usage and memory stats per window.\n df.cpu_utime_ns = df.cpu_utime_ns_max - df.cpu_utime_ns_min\n df.cpu_ktime_ns = df.cpu_ktime_ns_max - df.cpu_ktime_ns_min\n df.read_bytes = df.read_bytes_max - df.read_bytes_min\n df.write_bytes = df.write_bytes_max - df.write_bytes_min\n df.rchar_bytes = df.rchar_bytes_max - df.rchar_bytes_min\n df.wchar_bytes = df.wchar_bytes_max - df.wchar_bytes_min\n # Sum by UPID.\n df = df.groupby([entity, 'timestamp']).agg(\n cpu_ktime_ns=('cpu_ktime_ns', px.sum),\n cpu_utime_ns=('cpu_utime_ns', px.sum),\n read_bytes=('read_bytes', px.sum),\n write_bytes=('write_bytes', px.sum),\n rchar_bytes=('rchar_bytes', px.sum),\n wchar_bytes=('wchar_bytes', px.sum),\n rss=('rss', px.sum),\n vsize=('vsize', px.sum),\n )\n df.actual_disk_read_throughput = df.read_bytes / window_ns\n df.actual_disk_write_throughput = df.write_bytes / window_ns\n df.total_disk_read_throughput = df.rchar_bytes / window_ns\n df.total_disk_write_throughput = df.wchar_bytes / window_ns\n # Now take the mean value over the various timestamps.\n df = df.groupby(entity).agg(\n cpu_ktime_ns=('cpu_ktime_ns', px.mean),\n cpu_utime_ns=('cpu_utime_ns', px.mean),\n actual_disk_read_throughput=('actual_disk_read_throughput', px.mean),\n actual_disk_write_throughput=('actual_disk_write_throughput', px.mean),\n total_disk_read_throughput=('total_disk_read_throughput', px.mean),\n total_disk_write_throughput=('total_disk_write_throughput', px.mean),\n avg_rss=('rss', px.mean),\n avg_vsize=('vsize', px.mean),\n )\n # Finally, calculate total (kernel + user time) percentage used over window.\n df.cpu_usage = px.Percent((df.cpu_ktime_ns + df.cpu_utime_ns) / window_ns)\n return df.drop(['cpu_ktime_ns', 'cpu_utime_ns'])\n\ndef nodes_for_cluster(start_time: int):\n ''' Gets a list of nodes in the current cluster since `start_time`.\n Args:\n @start_time Start time of the data to examine.\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df.node = df.ctx['node_name']\n df.pod = df.ctx['pod_name']\n agg = df.groupby(['node', 'pod']).agg()\n nodes = agg.groupby('node').agg(pod_count=('pod', px.count))\n process_stats = process_stats_by_entity(start_time, 'node')\n output = process_stats.merge(nodes, how='inner', left_on='node', right_on='node',\n suffixes=['', '_x'])\n \n return output[['node', 'cpu_usage', 'pod_count']]\n\noutput = nodes_for_cluster(__time_from)\noutput = output[['node', 'cpu_usage', 'pod_count']]\npx.display(output)"
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"datasource": "${DS_PIXIE_GRAFANA DATASOURCE PLUGIN}",
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"pxlScript": "# Copyright 2018- The Pixie Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n# SPDX-License-Identifier: Apache-2.0\n\n''' Cluster Overview\n\nThis view lists the namespaces and the nodes that are available on the current cluster.\n\n'''\nimport px\n\n\n# Flag to filter out health checks from the data.\nfilter_health_checks = True\n\n# Whether or not to include traffic from IPs that don't resolve to a known pod/service.\ninclude_ips = True\n\n# $pixieCluster - work around for grafana to update panel on variable change\n\n# Hack to get the time window for the script.\n# TODO(philkuz): Replace this with a built-in.\ndef get_time_window(start_time: int):\n ''' Converts the start_time string into a table with a single column and single row.\n The approach is hacky, and will round to roughly 1 second.\n '''\n df = px.DataFrame('process_stats', start_time=start_time)\n\n df = df.agg(\n time_min=('time_', px.min),\n time_max=('time_', px.max),\n )\n\n df.window = px.DurationNanos(df.time_max - df.time_min)\n df = df[['window']]\n\n return df\n\n\ndef add_time_window_column(df, start_time):\n tw = get_time_window(start_time)\n df = df.merge(tw, how='inner', left_on=[], right_on=[])\n return df\n\n\ndef process_stats_by_entity(start_time: int, entity: str):\n ''' Gets the windowed process stats (CPU, memory, etc) per node or pod.\n Args:\n @start_time Starting time of the data to examine.\n @entity: Either pod or node_name.\n '''\n # Window size to use on time_ column for bucketing.\n ns_per_s = 1000 * 1000 * 1000\n window_ns = px.DurationNanos(10 * ns_per_s)\n\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df[entity] = df.ctx[entity]\n df.timestamp = px.bin(df.time_, window_ns)\n # First calculate CPU usage by process (UPID) in each k8s_object\n # over all windows.\n df = df.groupby([entity, 'upid', 'timestamp']).agg(\n rss=('rss_bytes', px.mean),\n vsize=('vsize_bytes', px.mean),\n # The fields below are counters, so we take the min and the max to subtract them.\n cpu_utime_ns_max=('cpu_utime_ns', px.max),\n cpu_utime_ns_min=('cpu_utime_ns', px.min),\n cpu_ktime_ns_max=('cpu_ktime_ns', px.max),\n cpu_ktime_ns_min=('cpu_ktime_ns', px.min),\n read_bytes_max=('read_bytes', px.max),\n read_bytes_min=('read_bytes', px.min),\n write_bytes_max=('write_bytes', px.max),\n write_bytes_min=('write_bytes', px.min),\n rchar_bytes_max=('rchar_bytes', px.max),\n rchar_bytes_min=('rchar_bytes', px.min),\n wchar_bytes_max=('wchar_bytes', px.max),\n wchar_bytes_min=('wchar_bytes', px.min),\n )\n # Next calculate cpu usage and memory stats per window.\n df.cpu_utime_ns = df.cpu_utime_ns_max - df.cpu_utime_ns_min\n df.cpu_ktime_ns = df.cpu_ktime_ns_max - df.cpu_ktime_ns_min\n df.read_bytes = df.read_bytes_max - df.read_bytes_min\n df.write_bytes = df.write_bytes_max - df.write_bytes_min\n df.rchar_bytes = df.rchar_bytes_max - df.rchar_bytes_min\n df.wchar_bytes = df.wchar_bytes_max - df.wchar_bytes_min\n # Sum by UPID.\n df = df.groupby([entity, 'timestamp']).agg(\n cpu_ktime_ns=('cpu_ktime_ns', px.sum),\n cpu_utime_ns=('cpu_utime_ns', px.sum),\n read_bytes=('read_bytes', px.sum),\n write_bytes=('write_bytes', px.sum),\n rchar_bytes=('rchar_bytes', px.sum),\n wchar_bytes=('wchar_bytes', px.sum),\n rss=('rss', px.sum),\n vsize=('vsize', px.sum),\n )\n df.actual_disk_read_throughput = df.read_bytes / window_ns\n df.actual_disk_write_throughput = df.write_bytes / window_ns\n df.total_disk_read_throughput = df.rchar_bytes / window_ns\n df.total_disk_write_throughput = df.wchar_bytes / window_ns\n # Now take the mean value over the various timestamps.\n df = df.groupby(entity).agg(\n cpu_ktime_ns=('cpu_ktime_ns', px.mean),\n cpu_utime_ns=('cpu_utime_ns', px.mean),\n actual_disk_read_throughput=('actual_disk_read_throughput', px.mean),\n actual_disk_write_throughput=('actual_disk_write_throughput', px.mean),\n total_disk_read_throughput=('total_disk_read_throughput', px.mean),\n total_disk_write_throughput=('total_disk_write_throughput', px.mean),\n avg_rss=('rss', px.mean),\n avg_vsize=('vsize', px.mean),\n )\n # Finally, calculate total (kernel + user time) percentage used over window.\n df.cpu_usage = px.Percent((df.cpu_ktime_ns + df.cpu_utime_ns) / window_ns)\n return df.drop(['cpu_ktime_ns', 'cpu_utime_ns'])\n\n\ndef nodes_for_cluster(start_time: int):\n ''' Gets a list of nodes in the current cluster since `start_time`.\n Args:\n @start_time Start time of the data to examine.\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df.node = df.ctx['node_name']\n df.pod = df.ctx['pod_name']\n agg = df.groupby(['node', 'pod']).agg()\n nodes = agg.groupby('node').agg(pod_count=('pod', px.count))\n process_stats = process_stats_by_entity(start_time, 'node')\n output = process_stats.merge(nodes, how='inner', left_on='node', right_on='node',\n suffixes=['', '_x'])\n return output[['node', 'cpu_usage', 'pod_count']]\n\n\ndef pods_for_cluster(start_time: int):\n ''' A list of pods in `namespace`.\n Args:\n @start_time: The timestamp of data to start at.\n @namespace: The name of the namespace to filter on.\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df.pod = df.ctx['pod_name']\n df.node = df.ctx['node_name']\n df.container = df.ctx['container_name']\n df = df.groupby(['pod', 'node', 'container']).agg()\n df = df.groupby(['pod', 'node']).agg(container_count=('container', px.count))\n df.start_time = px.pod_name_to_start_time(df.pod)\n df.status = px.pod_name_to_status(df.pod)\n process_stats = process_stats_by_entity(start_time, 'pod')\n output = process_stats.merge(df, how='inner', left_on='pod', right_on='pod',\n suffixes=['', '_x'])\n return output[['pod', 'cpu_usage', 'total_disk_read_throughput',\n 'total_disk_write_throughput', 'container_count',\n 'node', 'start_time', 'status']]\n\n\ndef namespaces_for_cluster(start_time: int):\n ''' Gets a overview of namespaces in the current cluster since `start_time`.\n Args:\n @start_time Start time of the data to examine.\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df.service = df.ctx['service_name']\n df.pod = df.ctx['pod_name']\n df.namespace = df.ctx['namespace']\n agg = df.groupby(['service', 'pod', 'namespace']).agg()\n pod_count = agg.groupby(['namespace', 'pod']).agg()\n pod_count = pod_count.groupby('namespace').agg(pod_count=('pod', px.count))\n svc_count = agg.groupby(['namespace', 'service']).agg()\n svc_count = svc_count.groupby('namespace').agg(service_count=('service', px.count))\n pod_and_svc_count = pod_count.merge(svc_count, how='inner',\n left_on='namespace', right_on='namespace',\n suffixes=['', '_x'])\n process_stats = process_stats_by_entity(start_time, 'namespace')\n output = process_stats.merge(pod_and_svc_count, how='inner', left_on='namespace',\n right_on='namespace', suffixes=['', '_y'])\n return output[['namespace', 'pod_count', 'service_count', 'avg_vsize', 'avg_rss']]\n\n\ndef services_for_cluster(start_time: int):\n ''' Get an overview of the services in the current cluster.\n Args:\n @start_time: The timestamp of data to start at.\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df.service = df.ctx['service']\n df = df[df.service != '']\n df.pod = df.ctx['pod']\n df = df.groupby(['service', 'pod']).agg()\n df = df.groupby('service').agg(pod_count=('pod', px.count))\n service_let = service_let_summary(start_time)\n joined = df.merge(service_let, how='left', left_on='service', right_on='service',\n suffixes=['', '_x'])\n return joined.drop('service_x')\n\n\ndef http_stats(start_time: int):\n ''' Get a dataframe of HTTP events.\n Certain traffic (like health checks) are auto removed, and some standard fields are added.\n Args:\n @start_time: The timestamp of data to start at.\n '''\n df = px.DataFrame(table='http_events', start_time=start_time)\n\n # Add K8s metadata.\n df.service = df.ctx['service']\n df.pod = df.ctx['pod']\n\n # Filter out non-k8s entities.\n df = df[df.pod != '']\n\n # Additional HTTP fields, pre-computed for convenience.\n df.failure = df.resp_status >= 400\n\n # Remove health checks, and anything with no remote address.\n health_check_req = ((df.req_path == '/healthz' or df.req_path == '/readyz') or df.req_path == '/livez')\n filter_out_conds = (health_check_req and filter_health_checks) or (df['remote_addr'] == '-')\n df = df[not filter_out_conds]\n\n return df\n\n\ndef http_stats_by_service(start_time: int):\n ''' Get a data frame of HTTP stats per service. The HTTP stats are for inbound traffic,\n and includes HTTP request count, error count and latency quantiles.\n Args:\n @start_time: The timestamp of data to start at.\n '''\n df = http_stats(start_time)\n\n # Filter only to inbound service traffic (server-side).\n # Don't include traffic initiated by this service to an external location.\n df = df[df.trace_role == 2]\n\n # Compute HTTP metrics.\n df = df.groupby(['service']).agg(\n http_req_count_in=('latency', px.count),\n http_error_count_in=('failure', px.sum),\n http_latency_in=('latency', px.quantiles)\n )\n\n return df\n\n\ndef conn_stats(start_time: int):\n ''' Get a dataframe of connection stats.\n For each client-server pair, the resulting data frame has the bytes sent and received.\n Args:\n @start_time: The timestamp of data to start at.\n '''\n df = px.DataFrame(table='conn_stats', start_time=start_time)\n\n df.pod = df.ctx['pod']\n df.service = df.ctx['service']\n\n df = df[df.service != '']\n\n # Find min/max bytes transferred over the selected time window per pod.\n df = df.groupby(['upid', 'remote_addr', 'remote_port', 'pod', 'service', 'trace_role']).agg(\n bytes_recv_min=('bytes_recv', px.min),\n bytes_recv_max=('bytes_recv', px.max),\n bytes_sent_min=('bytes_sent', px.min),\n bytes_sent_max=('bytes_sent', px.max),\n )\n\n # Calculate bytes transferred over the time window\n df.bytes_sent = df.bytes_sent_max - df.bytes_sent_min\n df.bytes_recv = df.bytes_recv_max - df.bytes_recv_min\n df = df.drop(['bytes_recv_min', 'bytes_recv_max', 'bytes_sent_min', 'bytes_sent_max'])\n\n return df\n\n\ndef conn_stats_by_service(start_time: int):\n ''' Get a dataframe of connection stats aggregated by service.\n For each service, the resulting data frame contains rx/tx stats for server-side and client-side connections.\n Args:\n @start_time: The timestamp of data to start at.\n '''\n df = conn_stats(start_time)\n\n # Group by service and trace role.\n # Do this after computing bytes sent/received by conn_stats key ({upid, remote_addr, remote_port}).\n # Keeping trace_role allows us to see which traffic was part of server duties vs client duties.\n df = df.groupby(['service', 'trace_role']).agg(\n bytes_recv=('bytes_recv', px.sum),\n bytes_sent=('bytes_sent', px.sum),\n )\n\n # Get RX/TX stats for the server side connections.\n server_df = df[df.trace_role == 2]\n server_df.rx_server = server_df.bytes_recv\n server_df.tx_server = server_df.bytes_sent\n server_df = server_df[['service', 'rx_server', 'tx_server']]\n\n # Get RX/TX stats for the client side connections.\n client_df = df[df.trace_role == 1]\n client_df.rx_client = client_df.bytes_recv\n client_df.tx_client = client_df.bytes_sent\n client_df = client_df[['service', 'rx_client', 'tx_client']]\n\n # Create a dataframe that contains both server-side and client-side RX/TX stats.\n df = server_df.merge(client_df,\n how='left',\n left_on='service',\n right_on='service',\n suffixes=['', '_x'])\n df = df['service', 'rx_server', 'tx_server', 'rx_client', 'tx_client']\n\n return df\n\n\ndef service_let_summary(start_time: int):\n ''' Compute a summary of traffic by requesting service, for requests\n on services in the current cluster..\n Args:\n @start_time: The timestamp of data to start at.\n '''\n conn_stats_df = conn_stats_by_service(start_time)\n http_stats_df = http_stats_by_service(start_time)\n\n # Merge conn_stats_df and http_stats_df.\n df = conn_stats_df.merge(http_stats_df,\n how='left',\n left_on='service',\n right_on='service',\n suffixes=['', '_x'])\n\n # Compute time window for the query and add it as a column.\n df = add_time_window_column(df, start_time)\n\n # Compute throughput values.\n df.http_req_throughput_in = df.http_req_count_in / df.window\n df.http_error_rate_in = px.Percent(\n px.select(df.http_req_count_in != 0, df.http_error_count_in / df.http_req_count_in, 0.0))\n df.inbound_conns = (df.rx_server + df.tx_server) / df.window\n df.outbound_conns = (df.tx_client + df.rx_client) / df.window\n\n return df[['service', 'http_latency_in', 'http_req_throughput_in', 'http_error_rate_in',\n 'inbound_conns', 'outbound_conns']]\n\n\ndef service_let_graph(start_time: int):\n ''' Compute a summary of traffic by requesting service, for requests on services\n in the current cluster. Similar to `service_let_summary` but also breaks down\n by pod in addition to service.\n Args:\n @start_time: The timestamp of data to start at.\n '''\n df = http_stats(start_time)\n df = df.groupby(['service', 'remote_addr', 'pod', 'trace_role']).agg(\n http_req_count_in=('latency', px.count),\n http_error_count_in=('failure', px.sum),\n latency_quantiles=('latency', px.quantiles),\n inbound_bytes_total=('req_body_size', px.sum),\n outbound_bytes_total=('resp_body_size', px.sum)\n )\n\n # Get the traced and remote pod/service/IP information.\n df.traced_pod = df.pod\n df.traced_service = df.service\n df.traced_ip = px.pod_name_to_pod_ip(df.pod)\n df.remote_pod = px.pod_id_to_pod_name(px.ip_to_pod_id(df.remote_addr))\n df.remote_service = px.service_id_to_service_name(px.ip_to_service_id(df.remote_addr))\n df.remote_ip = df.remote_addr\n # If external IPs are excluded in the service graph, then we also exclude any\n # traffic where we don't know the remote pod or remote service name.\n df = df[include_ips or (df.remote_pod != '' or df.remote_service != '')]\n\n # Associate it with Client/Server roles, based on the trace role.\n df.is_server_side_tracing = df.trace_role == 2\n df.responder_pod = px.select(df.is_server_side_tracing, df.traced_pod, df.remote_pod)\n df.requestor_pod = px.select(df.is_server_side_tracing, df.remote_pod, df.traced_pod)\n df.responder_service = px.select(df.is_server_side_tracing, df.traced_service, df.remote_service)\n df.requestor_service = px.select(df.is_server_side_tracing, df.remote_service, df.traced_service)\n df.responder_ip = px.select(df.is_server_side_tracing, df.traced_ip, df.remote_ip)\n df.requestor_ip = px.select(df.is_server_side_tracing, df.remote_ip, df.traced_ip)\n\n # Compute statistics about each edge of the service graph.\n df.latency_p50 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p50')))\n df.latency_p90 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p90')))\n df.latency_p99 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p99')))\n df = add_time_window_column(df, start_time)\n df.request_throughput = df.http_req_count_in / df.window\n df.inbound_throughput = df.inbound_bytes_total / df.window\n df.outbound_throughput = df.outbound_bytes_total / df.window\n df.error_rate = px.Percent(df.http_error_count_in / df.http_req_count_in)\n\n df = df.groupby(['responder_pod', 'requestor_pod', 'responder_service',\n 'requestor_service', 'responder_ip', 'requestor_ip']).agg(\n latency_p50=('latency_p50', px.mean),\n latency_p90=('latency_p90', px.mean),\n latency_p99=('latency_p99', px.mean),\n request_throughput=('request_throughput', px.mean),\n error_rate=('error_rate', px.mean),\n inbound_throughput=('inbound_throughput', px.mean),\n outbound_throughput=('outbound_throughput', px.mean),\n throughput_total=('http_req_count_in', px.sum)\n )\n\n return df\n\noutput = namespaces_for_cluster(__time_from)\noutput.avg_vsize = output.avg_vsize / px.pow(2,20)\noutput.avg_rss = output.avg_rss / px.pow(2,20)\noutput = output[['namespace', 'pod_count', 'service_count', 'avg_vsize', 'avg_rss']]\npx.display(output)"
},
"queryType": "run-script",
"refId": "A"
}
],
"title": "Namespaces",
"type": "table"
},
{
"datasource": "${DS_PIXIE_GRAFANA DATASOURCE PLUGIN}",
"fieldConfig": {
"defaults": {
"color": {
"mode": "thresholds"
},
"custom": {
"align": "auto",
"displayMode": "auto",
"inspect": false
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"decimals": 1,
"mappings": [],
"thresholds": {
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"steps": [
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"color": "green"
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{
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"overrides": [
{
"matcher": {
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},
"properties": [
{
"id": "custom.width",
"value": 296
},
{
"id": "links",
"value": [
{
"title": "",
"url": "/d/qhHtFyCnk/px-service?orgId=1&${pixieCluster:queryparam}&var-pixieService=${__data.fields.service}"
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}
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},
{
"matcher": {
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{
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{
"matcher": {
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"properties": [
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},
{
"matcher": {
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},
"properties": [
{
"id": "unit",
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{
"matcher": {
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"h": 9,
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"id": 8,
"options": {
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"reducer": [
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},
"pluginVersion": "7.5.1",
"targets": [
{
"datasource": "${DS_PIXIE_GRAFANA DATASOURCE PLUGIN}",
"queryBody": {
"pxlScript": "# Copyright 2018- The Pixie Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n# SPDX-License-Identifier: Apache-2.0\n\n''' Cluster Overview\n\nThis view lists the namespaces and the nodes that are available on the current cluster.\n\n'''\nimport px\n\n\n# Flag to filter out health checks from the data.\nfilter_health_checks = True\n\n# Whether or not to include traffic from IPs that don't resolve to a known pod/service.\ninclude_ips = True\n\n# $pixieCluster - work around for grafana to update panel on variable change\n\n# Hack to get the time window for the script.\n# TODO(philkuz): Replace this with a built-in.\ndef get_time_window(start_time: int):\n ''' Converts the start_time string into a table with a single column and single row.\n The approach is hacky, and will round to roughly 1 second.\n '''\n df = px.DataFrame('process_stats', start_time=start_time)\n\n df = df.agg(\n time_min=('time_', px.min),\n time_max=('time_', px.max),\n )\n\n df.window = px.DurationNanos(df.time_max - df.time_min)\n df = df[['window']]\n\n return df\n\n\ndef add_time_window_column(df, start_time):\n tw = get_time_window(start_time)\n df = df.merge(tw, how='inner', left_on=[], right_on=[])\n return df\n\n\ndef process_stats_by_entity(start_time: int, entity: str):\n ''' Gets the windowed process stats (CPU, memory, etc) per node or pod.\n Args:\n @start_time Starting time of the data to examine.\n @entity: Either pod or node_name.\n '''\n # Window size to use on time_ column for bucketing.\n ns_per_s = 1000 * 1000 * 1000\n window_ns = px.DurationNanos(10 * ns_per_s)\n\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df[entity] = df.ctx[entity]\n df.timestamp = px.bin(df.time_, window_ns)\n # First calculate CPU usage by process (UPID) in each k8s_object\n # over all windows.\n df = df.groupby([entity, 'upid', 'timestamp']).agg(\n rss=('rss_bytes', px.mean),\n vsize=('vsize_bytes', px.mean),\n # The fields below are counters, so we take the min and the max to subtract them.\n cpu_utime_ns_max=('cpu_utime_ns', px.max),\n cpu_utime_ns_min=('cpu_utime_ns', px.min),\n cpu_ktime_ns_max=('cpu_ktime_ns', px.max),\n cpu_ktime_ns_min=('cpu_ktime_ns', px.min),\n read_bytes_max=('read_bytes', px.max),\n read_bytes_min=('read_bytes', px.min),\n write_bytes_max=('write_bytes', px.max),\n write_bytes_min=('write_bytes', px.min),\n rchar_bytes_max=('rchar_bytes', px.max),\n rchar_bytes_min=('rchar_bytes', px.min),\n wchar_bytes_max=('wchar_bytes', px.max),\n wchar_bytes_min=('wchar_bytes', px.min),\n )\n # Next calculate cpu usage and memory stats per window.\n df.cpu_utime_ns = df.cpu_utime_ns_max - df.cpu_utime_ns_min\n df.cpu_ktime_ns = df.cpu_ktime_ns_max - df.cpu_ktime_ns_min\n df.read_bytes = df.read_bytes_max - df.read_bytes_min\n df.write_bytes = df.write_bytes_max - df.write_bytes_min\n df.rchar_bytes = df.rchar_bytes_max - df.rchar_bytes_min\n df.wchar_bytes = df.wchar_bytes_max - df.wchar_bytes_min\n # Sum by UPID.\n df = df.groupby([entity, 'timestamp']).agg(\n cpu_ktime_ns=('cpu_ktime_ns', px.sum),\n cpu_utime_ns=('cpu_utime_ns', px.sum),\n read_bytes=('read_bytes', px.sum),\n write_bytes=('write_bytes', px.sum),\n rchar_bytes=('rchar_bytes', px.sum),\n wchar_bytes=('wchar_bytes', px.sum),\n rss=('rss', px.sum),\n vsize=('vsize', px.sum),\n )\n df.actual_disk_read_throughput = df.read_bytes / window_ns\n df.actual_disk_write_throughput = df.write_bytes / window_ns\n df.total_disk_read_throughput = df.rchar_bytes / window_ns\n df.total_disk_write_throughput = df.wchar_bytes / window_ns\n # Now take the mean value over the various timestamps.\n df = df.groupby(entity).agg(\n cpu_ktime_ns=('cpu_ktime_ns', px.mean),\n cpu_utime_ns=('cpu_utime_ns', px.mean),\n actual_disk_read_throughput=('actual_disk_read_throughput', px.mean),\n actual_disk_write_throughput=('actual_disk_write_throughput', px.mean),\n total_disk_read_throughput=('total_disk_read_throughput', px.mean),\n total_disk_write_throughput=('total_disk_write_throughput', px.mean),\n avg_rss=('rss', px.mean),\n avg_vsize=('vsize', px.mean),\n )\n # Finally, calculate total (kernel + user time) percentage used over window.\n df.cpu_usage = px.Percent((df.cpu_ktime_ns + df.cpu_utime_ns) / window_ns)\n return df.drop(['cpu_ktime_ns', 'cpu_utime_ns'])\n\n\ndef nodes_for_cluster(start_time: int):\n ''' Gets a list of nodes in the current cluster since `start_time`.\n Args:\n @start_time Start time of the data to examine.\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df.node = df.ctx['node_name']\n df.pod = df.ctx['pod_name']\n agg = df.groupby(['node', 'pod']).agg()\n nodes = agg.groupby('node').agg(pod_count=('pod', px.count))\n process_stats = process_stats_by_entity(start_time, 'node')\n output = process_stats.merge(nodes, how='inner', left_on='node', right_on='node',\n suffixes=['', '_x'])\n return output[['node', 'cpu_usage', 'pod_count']]\n\n\ndef pods_for_cluster(start_time: int):\n ''' A list of pods in `namespace`.\n Args:\n @start_time: The timestamp of data to start at.\n @namespace: The name of the namespace to filter on.\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df.pod = df.ctx['pod_name']\n df.node = df.ctx['node_name']\n df.container = df.ctx['container_name']\n df = df.groupby(['pod', 'node', 'container']).agg()\n df = df.groupby(['pod', 'node']).agg(container_count=('container', px.count))\n df.start_time = px.pod_name_to_start_time(df.pod)\n df.status = px.pod_name_to_status(df.pod)\n process_stats = process_stats_by_entity(start_time, 'pod')\n output = process_stats.merge(df, how='inner', left_on='pod', right_on='pod',\n suffixes=['', '_x'])\n return output[['pod', 'cpu_usage', 'total_disk_read_throughput',\n 'total_disk_write_throughput', 'container_count',\n 'node', 'start_time', 'status']]\n\n\ndef namespaces_for_cluster(start_time: int):\n ''' Gets a overview of namespaces in the current cluster since `start_time`.\n Args:\n @start_time Start time of the data to examine.\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df.service = df.ctx['service_name']\n df.pod = df.ctx['pod_name']\n df.namespace = df.ctx['namespace']\n agg = df.groupby(['service', 'pod', 'namespace']).agg()\n pod_count = agg.groupby(['namespace', 'pod']).agg()\n pod_count = pod_count.groupby('namespace').agg(pod_count=('pod', px.count))\n svc_count = agg.groupby(['namespace', 'service']).agg()\n svc_count = svc_count.groupby('namespace').agg(service_count=('service', px.count))\n pod_and_svc_count = pod_count.merge(svc_count, how='inner',\n left_on='namespace', right_on='namespace',\n suffixes=['', '_x'])\n process_stats = process_stats_by_entity(start_time, 'namespace')\n output = process_stats.merge(pod_and_svc_count, how='inner', left_on='namespace',\n right_on='namespace', suffixes=['', '_y'])\n return output[['namespace', 'pod_count', 'service_count', 'avg_vsize', 'avg_rss']]\n\n\ndef services_for_cluster(start_time: int):\n ''' Get an overview of the services in the current cluster.\n Args:\n @start_time: The timestamp of data to start at.\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df.service = df.ctx['service']\n df = df[df.service != '']\n df.pod = df.ctx['pod']\n df = df.groupby(['service', 'pod']).agg()\n df = df.groupby('service').agg(pod_count=('pod', px.count))\n service_let = service_let_summary(start_time)\n joined = df.merge(service_let, how='left', left_on='service', right_on='service',\n suffixes=['', '_x'])\n return joined.drop('service_x')\n\n\ndef http_stats(start_time: int):\n ''' Get a dataframe of HTTP events.\n Certain traffic (like health checks) are auto removed, and some standard fields are added.\n Args:\n @start_time: The timestamp of data to start at.\n '''\n df = px.DataFrame(table='http_events', start_time=start_time)\n\n # Add K8s metadata.\n df.service = df.ctx['service']\n df.pod = df.ctx['pod']\n\n # Filter out non-k8s entities.\n df = df[df.pod != '']\n\n # Additional HTTP fields, pre-computed for convenience.\n df.failure = df.resp_status >= 400\n\n # Remove health checks, and anything with no remote address.\n health_check_req = ((df.req_path == '/healthz' or df.req_path == '/readyz') or df.req_path == '/livez')\n filter_out_conds = (health_check_req and filter_health_checks) or (df['remote_addr'] == '-')\n df = df[not filter_out_conds]\n\n return df\n\n\ndef http_stats_by_service(start_time: int):\n ''' Get a data frame of HTTP stats per service. The HTTP stats are for inbound traffic,\n and includes HTTP request count, error count and latency quantiles.\n Args:\n @start_time: The timestamp of data to start at.\n '''\n df = http_stats(start_time)\n\n # Filter only to inbound service traffic (server-side).\n # Don't include traffic initiated by this service to an external location.\n df = df[df.trace_role == 2]\n\n # Compute HTTP metrics.\n df = df.groupby(['service']).agg(\n http_req_count_in=('latency', px.count),\n http_error_count_in=('failure', px.sum),\n http_latency_in=('latency', px.quantiles)\n )\n\n return df\n\n\ndef conn_stats(start_time: int):\n ''' Get a dataframe of connection stats.\n For each client-server pair, the resulting data frame has the bytes sent and received.\n Args:\n @start_time: The timestamp of data to start at.\n '''\n df = px.DataFrame(table='conn_stats', start_time=start_time)\n\n df.pod = df.ctx['pod']\n df.service = df.ctx['service']\n\n df = df[df.service != '']\n\n # Find min/max bytes transferred over the selected time window per pod.\n df = df.groupby(['upid', 'remote_addr', 'remote_port', 'pod', 'service', 'trace_role']).agg(\n bytes_recv_min=('bytes_recv', px.min),\n bytes_recv_max=('bytes_recv', px.max),\n bytes_sent_min=('bytes_sent', px.min),\n bytes_sent_max=('bytes_sent', px.max),\n )\n\n # Calculate bytes transferred over the time window\n df.bytes_sent = df.bytes_sent_max - df.bytes_sent_min\n df.bytes_recv = df.bytes_recv_max - df.bytes_recv_min\n df = df.drop(['bytes_recv_min', 'bytes_recv_max', 'bytes_sent_min', 'bytes_sent_max'])\n\n return df\n\n\ndef conn_stats_by_service(start_time: int):\n ''' Get a dataframe of connection stats aggregated by service.\n For each service, the resulting data frame contains rx/tx stats for server-side and client-side connections.\n Args:\n @start_time: The timestamp of data to start at.\n '''\n df = conn_stats(start_time)\n\n # Group by service and trace role.\n # Do this after computing bytes sent/received by conn_stats key ({upid, remote_addr, remote_port}).\n # Keeping trace_role allows us to see which traffic was part of server duties vs client duties.\n df = df.groupby(['service', 'trace_role']).agg(\n bytes_recv=('bytes_recv', px.sum),\n bytes_sent=('bytes_sent', px.sum),\n )\n\n # Get RX/TX stats for the server side connections.\n server_df = df[df.trace_role == 2]\n server_df.rx_server = server_df.bytes_recv\n server_df.tx_server = server_df.bytes_sent\n server_df = server_df[['service', 'rx_server', 'tx_server']]\n\n # Get RX/TX stats for the client side connections.\n client_df = df[df.trace_role == 1]\n client_df.rx_client = client_df.bytes_recv\n client_df.tx_client = client_df.bytes_sent\n client_df = client_df[['service', 'rx_client', 'tx_client']]\n\n # Create a dataframe that contains both server-side and client-side RX/TX stats.\n df = server_df.merge(client_df,\n how='left',\n left_on='service',\n right_on='service',\n suffixes=['', '_x'])\n df = df['service', 'rx_server', 'tx_server', 'rx_client', 'tx_client']\n\n return df\n\n\ndef service_let_summary(start_time: int):\n ''' Compute a summary of traffic by requesting service, for requests\n on services in the current cluster..\n Args:\n @start_time: The timestamp of data to start at.\n '''\n conn_stats_df = conn_stats_by_service(start_time)\n http_stats_df = http_stats_by_service(start_time)\n\n # Merge conn_stats_df and http_stats_df.\n df = conn_stats_df.merge(http_stats_df,\n how='left',\n left_on='service',\n right_on='service',\n suffixes=['', '_x'])\n\n # Compute time window for the query and add it as a column.\n df = add_time_window_column(df, start_time)\n\n # Compute throughput values.\n df.http_req_throughput_in = df.http_req_count_in / df.window\n df.http_error_rate_in = px.Percent(\n px.select(df.http_req_count_in != 0, df.http_error_count_in / df.http_req_count_in, 0.0))\n df.inbound_conns = (df.rx_server + df.tx_server) / df.window\n df.outbound_conns = (df.tx_client + df.rx_client) / df.window\n\n return df[['service', 'http_latency_in', 'http_req_throughput_in', 'http_error_rate_in',\n 'inbound_conns', 'outbound_conns']]\n\n\ndef service_let_graph(start_time: int):\n ''' Compute a summary of traffic by requesting service, for requests on services\n in the current cluster. Similar to `service_let_summary` but also breaks down\n by pod in addition to service.\n Args:\n @start_time: The timestamp of data to start at.\n '''\n df = http_stats(start_time)\n df = df.groupby(['service', 'remote_addr', 'pod', 'trace_role']).agg(\n http_req_count_in=('latency', px.count),\n http_error_count_in=('failure', px.sum),\n latency_quantiles=('latency', px.quantiles),\n inbound_bytes_total=('req_body_size', px.sum),\n outbound_bytes_total=('resp_body_size', px.sum)\n )\n\n # Get the traced and remote pod/service/IP information.\n df.traced_pod = df.pod\n df.traced_service = df.service\n df.traced_ip = px.pod_name_to_pod_ip(df.pod)\n df.remote_pod = px.pod_id_to_pod_name(px.ip_to_pod_id(df.remote_addr))\n df.remote_service = px.service_id_to_service_name(px.ip_to_service_id(df.remote_addr))\n df.remote_ip = df.remote_addr\n # If external IPs are excluded in the service graph, then we also exclude any\n # traffic where we don't know the remote pod or remote service name.\n df = df[include_ips or (df.remote_pod != '' or df.remote_service != '')]\n\n # Associate it with Client/Server roles, based on the trace role.\n df.is_server_side_tracing = df.trace_role == 2\n df.responder_pod = px.select(df.is_server_side_tracing, df.traced_pod, df.remote_pod)\n df.requestor_pod = px.select(df.is_server_side_tracing, df.remote_pod, df.traced_pod)\n df.responder_service = px.select(df.is_server_side_tracing, df.traced_service, df.remote_service)\n df.requestor_service = px.select(df.is_server_side_tracing, df.remote_service, df.traced_service)\n df.responder_ip = px.select(df.is_server_side_tracing, df.traced_ip, df.remote_ip)\n df.requestor_ip = px.select(df.is_server_side_tracing, df.remote_ip, df.traced_ip)\n\n # Compute statistics about each edge of the service graph.\n df.latency_p50 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p50')))\n df.latency_p90 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p90')))\n df.latency_p99 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p99')))\n df = add_time_window_column(df, start_time)\n df.request_throughput = df.http_req_count_in / df.window\n df.inbound_throughput = df.inbound_bytes_total / df.window\n df.outbound_throughput = df.outbound_bytes_total / df.window\n df.error_rate = px.Percent(df.http_error_count_in / df.http_req_count_in)\n\n df = df.groupby(['responder_pod', 'requestor_pod', 'responder_service',\n 'requestor_service', 'responder_ip', 'requestor_ip']).agg(\n latency_p50=('latency_p50', px.mean),\n latency_p90=('latency_p90', px.mean),\n latency_p99=('latency_p99', px.mean),\n request_throughput=('request_throughput', px.mean),\n error_rate=('error_rate', px.mean),\n inbound_throughput=('inbound_throughput', px.mean),\n outbound_throughput=('outbound_throughput', px.mean),\n throughput_total=('http_req_count_in', px.sum)\n )\n\n return df\n\noutput = services_for_cluster(__time_from)\noutput.http_latency_in_p99 = px.pluck_float64(output.http_latency_in, 'p99') / px.pow(10,6)\noutput.http_req_throughput_in = output.http_req_throughput_in * px.pow(10,9)\noutput.inbound_conns = output.inbound_conns * px.pow(10,9) / px.pow(2,10)\noutput.outbound_conns = output.outbound_conns * px.pow(10,9) / px.pow(2,10)\noutput = output[['service', 'pod_count', 'http_latency_in_p99', 'http_req_throughput_in', 'http_error_rate_in', 'inbound_conns', 'outbound_conns']]\npx.display(output)"
},
"queryType": "run-script",
"refId": "A"
}
],
"title": "Services",
"type": "table"
},
{
"datasource": "${DS_PIXIE_GRAFANA DATASOURCE PLUGIN}",
"fieldConfig": {
"defaults": {
"color": {
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"custom": {
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"decimals": 1,
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"options": {
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"Terminated": {
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"thresholds": {
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"overrides": [
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"properties": [
{
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{
"id": "links",
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{
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"url": "/d/_t3foxCnz/px-pod?orgId=1&${pixieCluster:queryparam}&var-pixiePod=${__data.fields.pod}"
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},
{
"matcher": {
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"properties": [
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"matcher": {
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"matcher": {
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{
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},
{
"matcher": {
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},
"properties": [
{
"id": "custom.width",
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},
{
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"value": "KBs"
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"matcher": {
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"options": "container_count"
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"properties": [
{
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{
"matcher": {
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{
"matcher": {
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},
"properties": [
{
"id": "links",
"value": [
{
"title": "",
"url": "/d/tcpoz_jnz/px-node?orgId=1&${pixieCluster:queryparam}&var-node=${__data.fields.node}&var-groupby=node"
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},
"gridPos": {
"h": 9,
"w": 24,
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},
"id": 10,
"options": {
"footer": {
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"reducer": [
"sum"
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"pluginVersion": "7.5.1",
"targets": [
{
"datasource": "${DS_PIXIE_GRAFANA DATASOURCE PLUGIN}",
"queryBody": {
"pxlScript": "# Copyright 2018- The Pixie Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n# SPDX-License-Identifier: Apache-2.0\n\n''' Cluster Overview\n\nThis view lists the namespaces and the nodes that are available on the current cluster.\n\n'''\nimport px\n\n\n# Flag to filter out health checks from the data.\nfilter_health_checks = True\n\n# Whether or not to include traffic from IPs that don't resolve to a known pod/service.\ninclude_ips = True\n\n# $pixieCluster - work around for grafana to update panel on variable change\n\n# Hack to get the time window for the script.\n# TODO(philkuz): Replace this with a built-in.\ndef get_time_window(start_time: int):\n ''' Converts the start_time string into a table with a single column and single row.\n The approach is hacky, and will round to roughly 1 second.\n '''\n df = px.DataFrame('process_stats', start_time=start_time)\n\n df = df.agg(\n time_min=('time_', px.min),\n time_max=('time_', px.max),\n )\n\n df.window = px.DurationNanos(df.time_max - df.time_min)\n df = df[['window']]\n\n return df\n\n\ndef add_time_window_column(df, start_time):\n tw = get_time_window(start_time)\n df = df.merge(tw, how='inner', left_on=[], right_on=[])\n return df\n\n\ndef process_stats_by_entity(start_time: int, entity: str):\n ''' Gets the windowed process stats (CPU, memory, etc) per node or pod.\n Args:\n @start_time Starting time of the data to examine.\n @entity: Either pod or node_name.\n '''\n # Window size to use on time_ column for bucketing.\n ns_per_s = 1000 * 1000 * 1000\n window_ns = px.DurationNanos(10 * ns_per_s)\n\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df[entity] = df.ctx[entity]\n df.timestamp = px.bin(df.time_, window_ns)\n # First calculate CPU usage by process (UPID) in each k8s_object\n # over all windows.\n df = df.groupby([entity, 'upid', 'timestamp']).agg(\n rss=('rss_bytes', px.mean),\n vsize=('vsize_bytes', px.mean),\n # The fields below are counters, so we take the min and the max to subtract them.\n cpu_utime_ns_max=('cpu_utime_ns', px.max),\n cpu_utime_ns_min=('cpu_utime_ns', px.min),\n cpu_ktime_ns_max=('cpu_ktime_ns', px.max),\n cpu_ktime_ns_min=('cpu_ktime_ns', px.min),\n read_bytes_max=('read_bytes', px.max),\n read_bytes_min=('read_bytes', px.min),\n write_bytes_max=('write_bytes', px.max),\n write_bytes_min=('write_bytes', px.min),\n rchar_bytes_max=('rchar_bytes', px.max),\n rchar_bytes_min=('rchar_bytes', px.min),\n wchar_bytes_max=('wchar_bytes', px.max),\n wchar_bytes_min=('wchar_bytes', px.min),\n )\n # Next calculate cpu usage and memory stats per window.\n df.cpu_utime_ns = df.cpu_utime_ns_max - df.cpu_utime_ns_min\n df.cpu_ktime_ns = df.cpu_ktime_ns_max - df.cpu_ktime_ns_min\n df.read_bytes = df.read_bytes_max - df.read_bytes_min\n df.write_bytes = df.write_bytes_max - df.write_bytes_min\n df.rchar_bytes = df.rchar_bytes_max - df.rchar_bytes_min\n df.wchar_bytes = df.wchar_bytes_max - df.wchar_bytes_min\n # Sum by UPID.\n df = df.groupby([entity, 'timestamp']).agg(\n cpu_ktime_ns=('cpu_ktime_ns', px.sum),\n cpu_utime_ns=('cpu_utime_ns', px.sum),\n read_bytes=('read_bytes', px.sum),\n write_bytes=('write_bytes', px.sum),\n rchar_bytes=('rchar_bytes', px.sum),\n wchar_bytes=('wchar_bytes', px.sum),\n rss=('rss', px.sum),\n vsize=('vsize', px.sum),\n )\n df.actual_disk_read_throughput = df.read_bytes / window_ns\n df.actual_disk_write_throughput = df.write_bytes / window_ns\n df.total_disk_read_throughput = df.rchar_bytes / window_ns\n df.total_disk_write_throughput = df.wchar_bytes / window_ns\n # Now take the mean value over the various timestamps.\n df = df.groupby(entity).agg(\n cpu_ktime_ns=('cpu_ktime_ns', px.mean),\n cpu_utime_ns=('cpu_utime_ns', px.mean),\n actual_disk_read_throughput=('actual_disk_read_throughput', px.mean),\n actual_disk_write_throughput=('actual_disk_write_throughput', px.mean),\n total_disk_read_throughput=('total_disk_read_throughput', px.mean),\n total_disk_write_throughput=('total_disk_write_throughput', px.mean),\n avg_rss=('rss', px.mean),\n avg_vsize=('vsize', px.mean),\n )\n # Finally, calculate total (kernel + user time) percentage used over window.\n df.cpu_usage = px.Percent((df.cpu_ktime_ns + df.cpu_utime_ns) / window_ns)\n return df.drop(['cpu_ktime_ns', 'cpu_utime_ns'])\n\n\ndef nodes_for_cluster(start_time: int):\n ''' Gets a list of nodes in the current cluster since `start_time`.\n Args:\n @start_time Start time of the data to examine.\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df.node = df.ctx['node_name']\n df.pod = df.ctx['pod_name']\n agg = df.groupby(['node', 'pod']).agg()\n nodes = agg.groupby('node').agg(pod_count=('pod', px.count))\n process_stats = process_stats_by_entity(start_time, 'node')\n output = process_stats.merge(nodes, how='inner', left_on='node', right_on='node',\n suffixes=['', '_x'])\n return output[['node', 'cpu_usage', 'pod_count']]\n\n\ndef pods_for_cluster(start_time: int):\n ''' A list of pods in `namespace`.\n Args:\n @start_time: The timestamp of data to start at.\n @namespace: The name of the namespace to filter on.\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df.pod = df.ctx['pod_name']\n df.node = df.ctx['node_name']\n df.container = df.ctx['container_name']\n df = df.groupby(['pod', 'node', 'container']).agg()\n df = df.groupby(['pod', 'node']).agg(container_count=('container', px.count))\n df.start_time = px.pod_name_to_start_time(df.pod)\n df.status = px.pod_name_to_status(df.pod)\n process_stats = process_stats_by_entity(start_time, 'pod')\n output = process_stats.merge(df, how='inner', left_on='pod', right_on='pod',\n suffixes=['', '_x'])\n return output[['pod', 'cpu_usage', 'total_disk_read_throughput',\n 'total_disk_write_throughput', 'container_count',\n 'node', 'start_time', 'status']]\n\n\ndef namespaces_for_cluster(start_time: int):\n ''' Gets a overview of namespaces in the current cluster since `start_time`.\n Args:\n @start_time Start time of the data to examine.\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df.service = df.ctx['service_name']\n df.pod = df.ctx['pod_name']\n df.namespace = df.ctx['namespace']\n agg = df.groupby(['service', 'pod', 'namespace']).agg()\n pod_count = agg.groupby(['namespace', 'pod']).agg()\n pod_count = pod_count.groupby('namespace').agg(pod_count=('pod', px.count))\n svc_count = agg.groupby(['namespace', 'service']).agg()\n svc_count = svc_count.groupby('namespace').agg(service_count=('service', px.count))\n pod_and_svc_count = pod_count.merge(svc_count, how='inner',\n left_on='namespace', right_on='namespace',\n suffixes=['', '_x'])\n process_stats = process_stats_by_entity(start_time, 'namespace')\n output = process_stats.merge(pod_and_svc_count, how='inner', left_on='namespace',\n right_on='namespace', suffixes=['', '_y'])\n return output[['namespace', 'pod_count', 'service_count', 'avg_vsize', 'avg_rss']]\n\n\ndef services_for_cluster(start_time: int):\n ''' Get an overview of the services in the current cluster.\n Args:\n @start_time: The timestamp of data to start at.\n '''\n df = px.DataFrame(table='process_stats', start_time=start_time)\n df.service = df.ctx['service']\n df = df[df.service != '']\n df.pod = df.ctx['pod']\n df = df.groupby(['service', 'pod']).agg()\n df = df.groupby('service').agg(pod_count=('pod', px.count))\n service_let = service_let_summary(start_time)\n joined = df.merge(service_let, how='left', left_on='service', right_on='service',\n suffixes=['', '_x'])\n return joined.drop('service_x')\n\n\ndef http_stats(start_time: int):\n ''' Get a dataframe of HTTP events.\n Certain traffic (like health checks) are auto removed, and some standard fields are added.\n Args:\n @start_time: The timestamp of data to start at.\n '''\n df = px.DataFrame(table='http_events', start_time=start_time)\n\n # Add K8s metadata.\n df.service = df.ctx['service']\n df.pod = df.ctx['pod']\n\n # Filter out non-k8s entities.\n df = df[df.pod != '']\n\n # Additional HTTP fields, pre-computed for convenience.\n df.failure = df.resp_status >= 400\n\n # Remove health checks, and anything with no remote address.\n health_check_req = ((df.req_path == '/healthz' or df.req_path == '/readyz') or df.req_path == '/livez')\n filter_out_conds = (health_check_req and filter_health_checks) or (df['remote_addr'] == '-')\n df = df[not filter_out_conds]\n\n return df\n\n\ndef http_stats_by_service(start_time: int):\n ''' Get a data frame of HTTP stats per service. The HTTP stats are for inbound traffic,\n and includes HTTP request count, error count and latency quantiles.\n Args:\n @start_time: The timestamp of data to start at.\n '''\n df = http_stats(start_time)\n\n # Filter only to inbound service traffic (server-side).\n # Don't include traffic initiated by this service to an external location.\n df = df[df.trace_role == 2]\n\n # Compute HTTP metrics.\n df = df.groupby(['service']).agg(\n http_req_count_in=('latency', px.count),\n http_error_count_in=('failure', px.sum),\n http_latency_in=('latency', px.quantiles)\n )\n\n return df\n\n\ndef conn_stats(start_time: int):\n ''' Get a dataframe of connection stats.\n For each client-server pair, the resulting data frame has the bytes sent and received.\n Args:\n @start_time: The timestamp of data to start at.\n '''\n df = px.DataFrame(table='conn_stats', start_time=start_time)\n\n df.pod = df.ctx['pod']\n df.service = df.ctx['service']\n\n df = df[df.service != '']\n\n # Find min/max bytes transferred over the selected time window per pod.\n df = df.groupby(['upid', 'remote_addr', 'remote_port', 'pod', 'service', 'trace_role']).agg(\n bytes_recv_min=('bytes_recv', px.min),\n bytes_recv_max=('bytes_recv', px.max),\n bytes_sent_min=('bytes_sent', px.min),\n bytes_sent_max=('bytes_sent', px.max),\n )\n\n # Calculate bytes transferred over the time window\n df.bytes_sent = df.bytes_sent_max - df.bytes_sent_min\n df.bytes_recv = df.bytes_recv_max - df.bytes_recv_min\n df = df.drop(['bytes_recv_min', 'bytes_recv_max', 'bytes_sent_min', 'bytes_sent_max'])\n\n return df\n\n\ndef conn_stats_by_service(start_time: int):\n ''' Get a dataframe of connection stats aggregated by service.\n For each service, the resulting data frame contains rx/tx stats for server-side and client-side connections.\n Args:\n @start_time: The timestamp of data to start at.\n '''\n df = conn_stats(start_time)\n\n # Group by service and trace role.\n # Do this after computing bytes sent/received by conn_stats key ({upid, remote_addr, remote_port}).\n # Keeping trace_role allows us to see which traffic was part of server duties vs client duties.\n df = df.groupby(['service', 'trace_role']).agg(\n bytes_recv=('bytes_recv', px.sum),\n bytes_sent=('bytes_sent', px.sum),\n )\n\n # Get RX/TX stats for the server side connections.\n server_df = df[df.trace_role == 2]\n server_df.rx_server = server_df.bytes_recv\n server_df.tx_server = server_df.bytes_sent\n server_df = server_df[['service', 'rx_server', 'tx_server']]\n\n # Get RX/TX stats for the client side connections.\n client_df = df[df.trace_role == 1]\n client_df.rx_client = client_df.bytes_recv\n client_df.tx_client = client_df.bytes_sent\n client_df = client_df[['service', 'rx_client', 'tx_client']]\n\n # Create a dataframe that contains both server-side and client-side RX/TX stats.\n df = server_df.merge(client_df,\n how='left',\n left_on='service',\n right_on='service',\n suffixes=['', '_x'])\n df = df['service', 'rx_server', 'tx_server', 'rx_client', 'tx_client']\n\n return df\n\n\ndef service_let_summary(start_time: int):\n ''' Compute a summary of traffic by requesting service, for requests\n on services in the current cluster..\n Args:\n @start_time: The timestamp of data to start at.\n '''\n conn_stats_df = conn_stats_by_service(start_time)\n http_stats_df = http_stats_by_service(start_time)\n\n # Merge conn_stats_df and http_stats_df.\n df = conn_stats_df.merge(http_stats_df,\n how='left',\n left_on='service',\n right_on='service',\n suffixes=['', '_x'])\n\n # Compute time window for the query and add it as a column.\n df = add_time_window_column(df, start_time)\n\n # Compute throughput values.\n df.http_req_throughput_in = df.http_req_count_in / df.window\n df.http_error_rate_in = px.Percent(\n px.select(df.http_req_count_in != 0, df.http_error_count_in / df.http_req_count_in, 0.0))\n df.inbound_conns = (df.rx_server + df.tx_server) / df.window\n df.outbound_conns = (df.tx_client + df.rx_client) / df.window\n\n return df[['service', 'http_latency_in', 'http_req_throughput_in', 'http_error_rate_in',\n 'inbound_conns', 'outbound_conns']]\n\n\ndef service_let_graph(start_time: int):\n ''' Compute a summary of traffic by requesting service, for requests on services\n in the current cluster. Similar to `service_let_summary` but also breaks down\n by pod in addition to service.\n Args:\n @start_time: The timestamp of data to start at.\n '''\n df = http_stats(start_time)\n df = df.groupby(['service', 'remote_addr', 'pod', 'trace_role']).agg(\n http_req_count_in=('latency', px.count),\n http_error_count_in=('failure', px.sum),\n latency_quantiles=('latency', px.quantiles),\n inbound_bytes_total=('req_body_size', px.sum),\n outbound_bytes_total=('resp_body_size', px.sum)\n )\n\n # Get the traced and remote pod/service/IP information.\n df.traced_pod = df.pod\n df.traced_service = df.service\n df.traced_ip = px.pod_name_to_pod_ip(df.pod)\n df.remote_pod = px.pod_id_to_pod_name(px.ip_to_pod_id(df.remote_addr))\n df.remote_service = px.service_id_to_service_name(px.ip_to_service_id(df.remote_addr))\n df.remote_ip = df.remote_addr\n # If external IPs are excluded in the service graph, then we also exclude any\n # traffic where we don't know the remote pod or remote service name.\n df = df[include_ips or (df.remote_pod != '' or df.remote_service != '')]\n\n # Associate it with Client/Server roles, based on the trace role.\n df.is_server_side_tracing = df.trace_role == 2\n df.responder_pod = px.select(df.is_server_side_tracing, df.traced_pod, df.remote_pod)\n df.requestor_pod = px.select(df.is_server_side_tracing, df.remote_pod, df.traced_pod)\n df.responder_service = px.select(df.is_server_side_tracing, df.traced_service, df.remote_service)\n df.requestor_service = px.select(df.is_server_side_tracing, df.remote_service, df.traced_service)\n df.responder_ip = px.select(df.is_server_side_tracing, df.traced_ip, df.remote_ip)\n df.requestor_ip = px.select(df.is_server_side_tracing, df.remote_ip, df.traced_ip)\n\n # Compute statistics about each edge of the service graph.\n df.latency_p50 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p50')))\n df.latency_p90 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p90')))\n df.latency_p99 = px.DurationNanos(px.floor(px.pluck_float64(df.latency_quantiles, 'p99')))\n df = add_time_window_column(df, start_time)\n df.request_throughput = df.http_req_count_in / df.window\n df.inbound_throughput = df.inbound_bytes_total / df.window\n df.outbound_throughput = df.outbound_bytes_total / df.window\n df.error_rate = px.Percent(df.http_error_count_in / df.http_req_count_in)\n\n df = df.groupby(['responder_pod', 'requestor_pod', 'responder_service',\n 'requestor_service', 'responder_ip', 'requestor_ip']).agg(\n latency_p50=('latency_p50', px.mean),\n latency_p90=('latency_p90', px.mean),\n latency_p99=('latency_p99', px.mean),\n request_throughput=('request_throughput', px.mean),\n error_rate=('error_rate', px.mean),\n inbound_throughput=('inbound_throughput', px.mean),\n outbound_throughput=('outbound_throughput', px.mean),\n throughput_total=('http_req_count_in', px.sum)\n )\n\n return df\n\noutput = pods_for_cluster(__time_from)\noutput.total_disk_read_throughput = output.total_disk_read_throughput * px.pow(10,9) / px.pow(2,10)\noutput.total_disk_write_throughput = output.total_disk_write_throughput * px.pow(10,9) / px.pow(2,10)\nphase = px.pluck(output.status, \"phase\") \nready = px.pluck(output.status, \"ready\") \n\noutput.phase = px.pluck(output.status, \"phase\") \noutput.ready = px.pluck(output.status, \"ready\") \n\noutput = output[['pod', 'phase', 'ready', 'cpu_usage', 'total_disk_read_throughput', 'total_disk_write_throughput', 'container_count', 'node', 'start_time']]\npx.display(output)"
},
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],
"title": "Pods",
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],
"schemaVersion": 27,
"style": "dark",
"tags": [],
"templating": {
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{
"current": {},
"datasource": "${DS_PIXIE_GRAFANA DATASOURCE PLUGIN}",
"definition": "Clusters",
"hide": 0,
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"multi": false,
"name": "pixieCluster",
"options": [],
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"time": {
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"timepicker": {},
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"title": "Pixie K8s Cluster Overview",
"description": "Dashboard includes a service map of the HTTP traffic between the services in your cluster, along with the latency, error rate, and throughput per service. It also lists the nodes, namespaces and pods available in your cluster.",
"uid": "JMSOCJj7k",
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} | 2022-09-15 23:35:39 UTC | 2022-09-15 23:40:26 UTC | 0 | 0 |
17,102 | 1 | 718,721 | 26,240 | 4 | immudb-performance-metrics | immudb Performance Metrics | Performance metrics and statistics for immudb, a lightweight, high-speed immutable database for systems and applications. | 2022-09-30 16:21:20 UTC | 2022-09-30 16:21:53 UTC | 0 | null | 0 | 0 | null | null | 0 | 0 | 26,240 | 1 | 17,102 | 1 | 4 | Performance metrics and statistics for immudb, a lightweight, high-speed immutable database for systems and applications. | {
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{
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},
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"type": "datasource",
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],
"annotations": {
"list": [
{
"$$hashKey": "object:62",
"builtIn": 1,
"datasource": "${DS_SYNPROM}",
"enable": true,
"hide": true,
"iconColor": "rgba(0, 211, 255, 1)",
"name": "Annotations & Alerts",
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},
"description": "Performance metrics and statistics for immudb, a lightweight, high-speed immutable database for systems and applications.",
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{
"content": "\n# immudb\nimmudb is a **lightweight, high-speed immutable database** for systems and applications. Written in Go.\nWith immudb you can track changes in sensitive data in your transactional databases and then record those changes permanently in a\ntamperproof immudb database. This allows you to keep an indelible history of sensitive data, for example debit/credit card transactions.\n\nTraditional transaction logs are hard to scale and are mutable. So there is no way to know for sure if your data has been compromised.\n\nAs such, immudb provides **unparalleled insights** **retroactively** of changes to your sensitive data, even\nif your perimeter has been compromised. immudb guarantees immutability by using a **Merkle tree structure** internally.\n\n## Prometheus integration\nThis dashboard uses a Prometheus datasource and expects the Prometheus job **immudbmetrics**\n\n[immudb website](https://immudb.io/)\n\n[immudb GitHub](https://github.com/codenotary/immudb)\n\n\n",
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"description": "Collected key metrics are:\n\n* no. instances\n* Current reads\n* Current writes\n* immudb functions\n* Pending operations\n* Blocked operations\n* Total numbers over time\n* Details of disk and log usage\n* immugw auditor results\n\n[immudb website](https://immudb.io/)\n\n[immudb GitHub](https://github.com/codenotary/immudb)",
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"panels": [],
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} | 2022-09-30 16:21:20 UTC | null | 0 | 0 |
16,371 | 1 | 656,432 | 24,951 | 4 | kubernetes-networking-cluster | Kubernetes / Networking / Cluster | Kubernetes | 2022-06-02 04:08:02 UTC | 2022-06-02 04:17:48 UTC | 0 | null | 0 | 0 | nodeExporter | null | 0 | 0 | 24,951 | 1 | 16,371 | 1 | 4 | null | {
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"uid": "ff635a025bcfea7bc3dd4f508990a3e9",
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"gnetId": 16371,
"description": "Kubernetes"
} | 2022-06-02 04:08:02 UTC | 2022-06-02 04:17:48 UTC | 0 | 0 |
16,445 | 1 | 658,264 | 25,081 | 4 | devops-new-releases | Devops - New Releases | Keep track of new DevOps tools releases | 2022-06-14 18:37:44 UTC | 2022-06-14 18:46:02 UTC | 0 | Built for the https://grafana.com/grafana/plugins/volkovlabs-rss-datasource/ plugin, this dashboard will aggregate mutliple data sources, merge them and disable some fields.
Add your own data sources according to your needs. | 74,075 | 74,076 | null | null | 0 | 0 | 25,081 | 1 | 16,445 | 1 | 4 | null | {
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} | 2022-06-14 18:37:44 UTC | 2022-06-14 18:44:05 UTC | 0 | 0 |
16,728 | 1 | 695,565 | 25,645 | 4 | dhtexporter | DHTExporter | Dashboard that uses a dht11 sensor board and a esp8266 along with prometheus to read and display temperature and humidity | 2022-08-18 02:30:07 UTC | 2022-08-18 02:39:45 UTC | 0 | Using An Affordable sensor and microcontroller (ESP8266 + DHT11 Sensor) this dashboard will display metric information using a custom prometheus exporter DHTexporter
Only requires to set a target in '/etc/prometheus/prometheus.yml'.
```
- targets: ['ESP8266IP:8663']
```
Available on github: https://github.com/TurlxTheHuman/dhtexporter
| 75,709 | 75,710 | Other | null | 0 | 0 | 25,645 | 1 | 16,728 | 1 | 4 | null | {
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} | 2022-08-18 02:30:07 UTC | 2022-08-18 02:37:01 UTC | 0 | 0 |
15,368 | 1 | 575,290 | 23,134 | 4 | github-default | GitHub Default | Github API data visually represented in Grafana dashboard | 2021-12-09 17:01:28 UTC | 2021-12-09 17:02:22 UTC | 0 | null | 0 | 0 | null | null | 1 | 3 | 23,134 | 1 | 15,368 | 1 | 4 | null | {
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15,710 | 1 | 351,177 | 23,738 | 5 | portaria | Portaria | windows | 2022-02-07 18:50:10 UTC | 2022-02-07 18:50:26 UTC | 0 | null | 0 | 0 | null | null | 0 | 0 | 23,738 | 1 | 15,710 | 1 | 5 | null | {
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14,653 | 1 | 451,383 | 21,747 | 5 | tupy-embaladora | TUPY - Embaladora | Dashboard módulo digital - versão final | 2021-07-01 13:50:12 UTC | 2021-07-01 13:51:00 UTC | 0 | null | 0 | 0 | null | null | 0 | 0 | 21,747 | 1 | 14,653 | 1 | 5 | Dashboard módulo digital | {
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"y": 7
},
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],
"type": "time"
},
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],
"type": "fill"
}
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"policy": "default",
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{
"params": [
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}
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"timeRegions": [],
"timeShift": null,
"title": "LINHA DO TEMPO VELOCIDADE",
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"x": 0,
"y": 13
},
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"panels": [],
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},
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"created": "2021-06-30T18:19:21Z",
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"timeFrom": "1m",
"timeShift": "2m",
"title": "EMBALADORA 01 ",
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"timeFrom": "1m",
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"style": "dark",
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"current": {},
"datasource": "${DS_INFLUXDB}",
"definition": "SHOW TAG VALUES WITH KEY = \"bus\" ",
"description": null,
"error": null,
"hide": 0,
"includeAll": false,
"label": "Bus number",
"multi": false,
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"options": [],
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"regex": "",
"skipUrlSync": false,
"sort": 3,
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},
"time": {
"from": "2021-08-18T21:00:00.000Z",
"to": "2021-09-22T20:59:59.000Z"
},
"timepicker": {},
"timezone": "",
"title": "Auve Bags",
"uid": "XC-S7bI7z",
"version": 32,
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} | 2021-09-21 05:58:47 UTC | 2021-09-21 06:00:13 UTC | 0 | 0 |
17,141 | 1 | 723,477 | 26,338 | 5 | jvm-micrometer | JVM (Micrometer) | Dashboard for Micrometer instrumented applications (Java, Spring Boot) | 2022-10-10 13:36:17 UTC | 2022-10-10 13:36:59 UTC | 0 | null | 0 | 0 | null | null | 0 | 0 | 26,338 | 1 | 17,141 | 1 | 5 | Dashboard for Micrometer instrumented applications (Java, Spring Boot) | {
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} | 2020-11-16 12:56:52 UTC | null | 0 | 0 |
15,824 | 1 | 607,855 | 23,964 | 5 | opennms-world-map-example | OpenNMS World Map example | Sample dashboard to visualize metrics for your OpenNMS nodes on a world map. | 2022-02-28 04:31:23 UTC | 2022-02-28 04:38:55 UTC | 0 | null | 71,379 | 71,380 | null | null | 0 | 0 | 23,964 | 1 | 15,824 | 1 | 5 | Sample dashboard to visualize metrics for your OpenNMS nodes on a world map. | {
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"2d",
"7d",
"30d"
]
},
"timezone": "",
"title": "OpenNMS World Map example",
"uid": "5AGHqphmk",
"version": 8,
"description": "Sample dashboard to visualize metrics for your OpenNMS nodes on a world map."
} | 2022-02-28 04:31:23 UTC | null | 0 | 0 |
16,821 | 1 | 425,872 | 25,787 | 5 | speedtest | Speedtest | Display Ookla speedtest results. | 2022-08-31 12:25:11 UTC | 2022-08-31 12:30:39 UTC | 0 | Use the exec plugin in order to run ookla speedtest in json output mode
```
[[inputs.exec]]
interval = "300s"
# Shell/commands array
# Full command line to executable with parameters, or a glob pattern to run all matching files.
commands = ["/usr/bin/speedtest -f json --accept-license"]
## Timeout for each command to complete.
timeout = "60s"
# Data format to consume.
# NOTE json only reads numerical measurements, strings and booleans are ignored.
data_format = "json"
# measurement name suffix (for separating different commands)
name_suffix = "_speedtest"
```
| 0 | 0 | Telegraf | Make sure you have the ookla speedtest cli installed:
`https://www.speedtest.net/apps/cli`
| 0 | 0 | 25,787 | 1 | 16,821 | 1 | 5 | null | {
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} | 2022-08-31 12:25:11 UTC | 2022-08-31 12:28:43 UTC | 0 | 0 |
16,844 | 1 | 680,152 | 25,818 | 5 | node-exporter-full | Node Exporter Full Customer Test | Prometheus node exporter IMRAAN | 2022-09-03 11:39:47 UTC | 2022-09-03 11:41:10 UTC | 0 | null | 0 | 0 | Other | null | 0 | 0 | 25,818 | 1 | 16,844 | 1 | 5 | Graphed values of Prometheus node exporter | {
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} | 2022-09-03 11:39:47 UTC | 2022-09-03 11:41:10 UTC | 0 | 0 |
14,036 | 1 | 471,382 | 20,571 | 6 | my-first | My First | test | 2021-03-10 12:17:53 UTC | 2021-03-10 12:18:12 UTC | 0 | null | 0 | 0 | null | null | 0 | 0 | 20,571 | 1 | 14,036 | 1 | 6 | test | {
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} | 2021-03-10 12:17:53 UTC | null | 0 | 0 |
16,900 | 1 | 707,149 | 25,931 | 6 | slos-for-apis-managed-by-tyk | SLOs for APIs managed by Tyk | Service level indicators (SLIs) and service level objectives (SLOs) for APIs managed by Tyk. | 2022-09-09 18:21:12 UTC | 2022-09-09 18:34:58 UTC | 0 | Service level indicators (SLIs) and service level objectives (SLOs) for your APIs managed by Tyk OSS Gateway or Tyk Self-Managed.
Try it yourself with the demo project: https://github.com/TykTechnologies/demo-slo-prometheus-grafana | 76,643 | 76,644 | null | null | 4 | 5 | 25,931 | 1 | 16,900 | 1 | 6 | null | {
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16,089 | 1 | 310,599 | 24,411 | 6 | cs-admin | Cs-Admin集群管理 | Monitoring Linux Hosts via telegraf agent. | 2022-04-11 06:57:04 UTC | 2022-04-11 06:59:26 UTC | 0 | null | 0 | 0 | null | null | 1 | 5 | 24,411 | 1 | 16,089 | 1 | 6 | Monitoring Linux Hosts via telegraf agent. | {
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} | 2022-04-11 06:57:04 UTC | null | 1 | 5 |
16,502 | 1 | 669,105 | 25,176 | 6 | open-falcon-dashboard | Linux Overview With Open-Falcon | Linux Overview With Open-Falcon,
Metrics Include
- Cpu
- Load
- Memory
- Disk
- Network
- Fd | 2022-06-24 02:56:20 UTC | 2022-06-24 03:26:55 UTC | 0 | ### Open-Falcon-Agent ---> Open-Falcon ---> grafana
#### Include metric
- Cpu
- Load
- Memory
- Disk
- Network
- Fd
| 74,489 | 74,490 | Other | null | 0 | 0 | 25,176 | 1 | 16,502 | 1 | 6 | null | {
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15,359 | 1 | 573,829 | 23,107 | 6 | geth-server | Geth Server | Monitor your Ethereum Geth server with Prometheus and Grafana. | 2021-12-06 22:48:59 UTC | 2021-12-06 22:49:08 UTC | 0 | null | 0 | 0 | null | null | 0 | 0 | 23,107 | 1 | 15,359 | 1 | 6 | Monitor your Ethereum Geth server with Prometheus and Grafana. | {
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{
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"$interval"
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{
"params": [
"null"
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{
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{
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{
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{
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"$interval"
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"type": "time"
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{
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"null"
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{
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{
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{
"params": [
"$interval"
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"type": "time"
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{
"params": [
"null"
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"measurement": "mysql",
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"query": "select derivative(mean(\"value\"),1s) from \"mysql_value\" WHERE \"type_instance\" = 'Com_begin' AND \"type\" = 'counter' AND \"instance\" = 'status' AND \"host\" =~ /^$host$/ AND $timeFilter GROUP BY time($interval) fill(null)",
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{
"params": [
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{
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{
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"$interval"
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{
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"null"
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{
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{
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"title": "Top Command Counters 24 hours",
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{
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"$interval"
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{
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{
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{
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{
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{
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{
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{
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{
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"null"
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"refId": "E",
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"select": [
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{
"params": [
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{
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{
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{
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{
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{
"params": [
"handler_delete"
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{
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{
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{
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{
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{
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{
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{
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{
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{
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"title": "(Stacked) MySQL Handler",
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"title": "MySQL Metrics",
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} | 2017-12-18 16:59:55 UTC | 2017-12-18 17:00:10 UTC | 0 | 0 |
13,630 | 1 | 332,961 | 19,901 | 518 | azure-applictaion-gataway | Azure Applictaion Gataway | Azure Applictaion Gataway WAF v2 | 2020-12-23 01:59:40 UTC | 2020-12-23 02:23:20 UTC | 0 | null | 59,164 | 59,165 | Other | null | 0 | 0 | 19,901 | 1 | 13,630 | 1 | 518 | null | {
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4,031 | 1 | 97,658 | 5,474 | 6,662 | mysql | MySQL | MySQL Monitoring with Telegraf, InfluxDB & Grafana | 2017-12-17 11:03:46 UTC | 2017-12-17 11:05:39 UTC | 0 | [How to use](https://github.com/mlabouardy/grafana-dashboards/tree/master/mysql) | 19,889 | 19,892 | Telegraf | null | 5 | 2 | 5,474 | 1 | 4,031 | 1 | 6,662 | null | {
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"fill": 0,
"gridPos": {
"h": 10,
"w": 6,
"x": 15,
"y": 1
},
"id": 10,
"legend": {
"alignAsTable": false,
"avg": false,
"current": false,
"hideZero": true,
"max": false,
"min": false,
"rightSide": false,
"show": true,
"total": false,
"values": false
},
"lines": true,
"linewidth": 1,
"links": [],
"minSpan": 12,
"nullPointMode": "null",
"percentage": false,
"pointradius": 5,
"points": false,
"renderer": "flot",
"seriesOverrides": [
{
"alias": "/.*tx/",
"transform": "negative-Y"
}
],
"spaceLength": 10,
"stack": false,
"steppedLine": false,
"targets": [
{
"expr": "sum by (device) (\n irate(node_network_receive_bytes{instance=~\"($ceph_hosts).*\",device!=\"lo\"}[1m]) or \n irate(node_network_receive_bytes_total{instance=~\"($ceph_hosts).*\",device!=\"lo\"}[1m])\n)",
"format": "time_series",
"intervalFactor": 1,
"legendFormat": "{{device}}.rx",
"refId": "A",
"step": 10,
"textEditor": true
},
{
"expr": "sum by (device) (\n irate(node_network_transmit_bytes{instance=~\"($ceph_hosts).*\",device!=\"lo\"}[1m]) or\n irate(node_network_transmit_bytes_total{instance=~\"($ceph_hosts).*\",device!=\"lo\"}[1m])\n)",
"format": "time_series",
"intervalFactor": 1,
"legendFormat": "{{device}}.tx",
"refId": "B",
"step": 10
}
],
"thresholds": [],
"timeFrom": null,
"timeShift": null,
"title": "Network Load",
"tooltip": {
"shared": true,
"sort": 0,
"value_type": "individual"
},
"type": "graph",
"xaxis": {
"buckets": null,
"mode": "time",
"name": null,
"show": true,
"values": []
},
"yaxes": [
{
"format": "decbytes",
"label": "Send (-) / Receive (+)",
"logBase": 1,
"max": null,
"min": null,
"show": true
},
{
"format": "short",
"label": null,
"logBase": 1,
"max": null,
"min": null,
"show": false
}
],
"yaxis": {
"align": false,
"alignLevel": null
}
},
{
"aliasColors": {},
"bars": false,
"dashLength": 10,
"dashes": false,
"datasource": "$datasource",
"fill": 0,
"gridPos": {
"h": 5,
"w": 3,
"x": 21,
"y": 1
},
"hideTimeOverride": true,
"id": 18,
"legend": {
"avg": false,
"current": false,
"max": false,
"min": false,
"show": false,
"total": false,
"values": false
},
"lines": true,
"linewidth": 1,
"links": [],
"nullPointMode": "null",
"percentage": false,
"pointradius": 5,
"points": false,
"renderer": "flot",
"seriesOverrides": [
{
"alias": "/.*tx/",
"transform": "negative-Y"
}
],
"spaceLength": 10,
"stack": false,
"steppedLine": false,
"targets": [
{
"expr": "irate(node_network_receive_drop{instance=~\"[[ceph_hosts]].*\"}[1m]) or irate(node_network_receive_drop_total{instance=~\"[[ceph_hosts]].*\"}[1m])",
"format": "time_series",
"instant": false,
"intervalFactor": 1,
"legendFormat": "{{device}}.rx",
"refId": "A"
},
{
"expr": "irate(node_network_transmit_drop{instance=~\"[[ceph_hosts]].*\"}[1m]) or irate(node_network_transmit_drop_total{instance=~\"[[ceph_hosts]].*\"}[1m])",
"format": "time_series",
"intervalFactor": 1,
"legendFormat": "{{device}}.tx",
"refId": "B"
}
],
"thresholds": [],
"timeFrom": "15m",
"timeShift": null,
"title": "Network drop rate",
"tooltip": {
"shared": true,
"sort": 0,
"value_type": "individual"
},
"type": "graph",
"xaxis": {
"buckets": null,
"mode": "time",
"name": null,
"show": true,
"values": []
},
"yaxes": [
{
"format": "pps",
"label": "Send (-) / Receive (+)",
"logBase": 1,
"max": null,
"min": "0",
"show": true
},
{
"format": "short",
"label": null,
"logBase": 1,
"max": null,
"min": null,
"show": false
}
],
"yaxis": {
"align": false,
"alignLevel": null
}
},
{
"cacheTimeout": null,
"colorBackground": false,
"colorValue": false,
"colors": [
"rgba(245, 54, 54, 0.9)",
"rgba(237, 129, 40, 0.89)",
"rgba(50, 172, 45, 0.97)"
],
"datasource": "$datasource",
"decimals": 0,
"description": "Each OSD consists of a Journal/WAL partition and a data partition. The RAW Capacity shown is the sum of the data partitions across all OSDs on the selected OSD hosts.",
"format": "bytes",
"gauge": {
"maxValue": 100,
"minValue": 0,
"show": false,
"thresholdLabels": false,
"thresholdMarkers": true
},
"gridPos": {
"h": 5,
"w": 3,
"x": 0,
"y": 6
},
"height": "160",
"id": 2,
"interval": null,
"links": [],
"mappingType": 1,
"mappingTypes": [
{
"name": "value to text",
"value": 1
},
{
"name": "range to text",
"value": 2
}
],
"maxDataPoints": "",
"minSpan": 4,
"nullPointMode": "connected",
"nullText": null,
"postfix": "",
"postfixFontSize": "50%",
"prefix": "",
"prefixFontSize": "50%",
"rangeMaps": [
{
"from": "null",
"text": "N/A",
"to": "null"
}
],
"sparkline": {
"fillColor": "rgba(31, 118, 189, 0.18)",
"full": false,
"lineColor": "rgb(31, 120, 193)",
"show": false
},
"tableColumn": "",
"targets": [
{
"expr": "sum(ceph_osd_stat_bytes and on (ceph_daemon) ceph_disk_occupation{exported_instance=~\"($ceph_hosts).*\"})",
"format": "time_series",
"intervalFactor": 2,
"refId": "A",
"step": 40,
"textEditor": true
}
],
"thresholds": "",
"title": "Raw Capacity",
"type": "singlestat",
"valueFontSize": "80%",
"valueMaps": [
{
"op": "=",
"text": "N/A",
"value": "null"
}
],
"valueName": "current"
},
{
"aliasColors": {},
"bars": false,
"dashLength": 10,
"dashes": false,
"datasource": "$datasource",
"fill": 0,
"gridPos": {
"h": 5,
"w": 3,
"x": 21,
"y": 6
},
"hideTimeOverride": true,
"id": 19,
"legend": {
"avg": false,
"current": false,
"max": false,
"min": false,
"show": false,
"total": false,
"values": false
},
"lines": true,
"linewidth": 1,
"links": [],
"nullPointMode": "null",
"percentage": false,
"pointradius": 5,
"points": false,
"renderer": "flot",
"seriesOverrides": [
{
"alias": "/.*tx/",
"transform": "negative-Y"
}
],
"spaceLength": 10,
"stack": false,
"steppedLine": false,
"targets": [
{
"expr": "irate(node_network_receive_errs{instance=~\"[[ceph_hosts]].*\"}[1m]) or irate(node_network_receive_errs_total{instance=~\"[[ceph_hosts]].*\"}[1m])",
"format": "time_series",
"instant": false,
"intervalFactor": 1,
"legendFormat": "{{device}}.rx",
"refId": "A"
},
{
"expr": "irate(node_network_transmit_errs{instance=~\"[[ceph_hosts]].*\"}[1m]) or irate(node_network_transmit_errs_total{instance=~\"[[ceph_hosts]].*\"}[1m])",
"format": "time_series",
"intervalFactor": 1,
"legendFormat": "{{device}}.tx",
"refId": "B"
}
],
"thresholds": [],
"timeFrom": "15m",
"timeShift": null,
"title": "Network error rate",
"tooltip": {
"shared": true,
"sort": 0,
"value_type": "individual"
},
"type": "graph",
"xaxis": {
"buckets": null,
"mode": "time",
"name": null,
"show": true,
"values": []
},
"yaxes": [
{
"format": "pps",
"label": "Send (-) / Receive (+)",
"logBase": 1,
"max": null,
"min": "0",
"show": true
},
{
"format": "short",
"label": null,
"logBase": 1,
"max": null,
"min": null,
"show": false
}
],
"yaxis": {
"align": false,
"alignLevel": null
}
},
{
"collapsed": false,
"gridPos": {
"h": 1,
"w": 24,
"x": 0,
"y": 11
},
"id": 12,
"panels": [],
"repeat": null,
"title": "OSD Disk Performance Statistics",
"type": "row"
},
{
"aliasColors": {},
"bars": false,
"dashLength": 10,
"dashes": false,
"datasource": "$datasource",
"description": "For any OSD devices on the host, this chart shows the iops per physical device. Each device is shown by it's name and corresponding OSD id value",
"fill": 1,
"gridPos": {
"h": 9,
"w": 11,
"x": 0,
"y": 12
},
"id": 6,
"legend": {
"avg": false,
"current": false,
"max": false,
"min": false,
"show": true,
"total": false,
"values": false
},
"lines": true,
"linewidth": 1,
"links": [],
"minSpan": 12,
"nullPointMode": "connected",
"percentage": false,
"pointradius": 5,
"points": false,
"renderer": "flot",
"seriesOverrides": [
{
"alias": "/.*reads/",
"transform": "negative-Y"
}
],
"spaceLength": 10,
"stack": false,
"steppedLine": false,
"targets": [
{
"expr": "(irate(node_disk_writes_completed{instance=~\"($ceph_hosts).*\"}[5m]) or irate(node_disk_writes_completed_total{instance=~\"($ceph_hosts).*\"}[5m])) * on(instance, device) group_left(ceph_daemon) label_replace(label_replace(ceph_disk_occupation, \"device\", \"$1\", \"device\", \"/dev/(.*)\"), \"instance\", \"$1:9100\", \"exported_instance\", \"(.*)\")",
"format": "time_series",
"intervalFactor": 1,
"legendFormat": "{{device}}({{ceph_daemon}}) writes",
"refId": "A",
"step": 10,
"textEditor": true
},
{
"expr": "(irate(node_disk_reads_completed{instance=~\"($ceph_hosts).*\"}[5m]) or irate(node_disk_reads_completed_total{instance=~\"($ceph_hosts).*\"}[5m])) * on(instance, device) group_left(ceph_daemon) label_replace(label_replace(ceph_disk_occupation, \"device\", \"$1\", \"device\", \"/dev/(.*)\"), \"instance\", \"$1:9100\", \"exported_instance\", \"(.*)\")",
"format": "time_series",
"hide": false,
"intervalFactor": 1,
"legendFormat": "{{device}}({{ceph_daemon}}) reads",
"refId": "B"
}
],
"thresholds": [],
"timeFrom": null,
"timeShift": null,
"title": "$ceph_hosts Disk IOPS",
"tooltip": {
"shared": true,
"sort": 2,
"value_type": "individual"
},
"type": "graph",
"xaxis": {
"buckets": null,
"mode": "time",
"name": null,
"show": true,
"values": []
},
"yaxes": [
{
"format": "ops",
"label": "Read (-) / Write (+)",
"logBase": 1,
"max": null,
"min": "0",
"show": true
},
{
"format": "short",
"label": null,
"logBase": 1,
"max": null,
"min": null,
"show": false
}
],
"yaxis": {
"align": false,
"alignLevel": null
}
},
{
"aliasColors": {},
"bars": false,
"dashLength": 10,
"dashes": false,
"datasource": "$datasource",
"description": "For OSD hosts, this chart shows the disk bandwidth (read bytes/sec + write bytes/sec) of the physical OSD device. Each device is shown by device name, and corresponding OSD id",
"fill": 1,
"gridPos": {
"h": 9,
"w": 11,
"x": 12,
"y": 12
},
"id": 8,
"legend": {
"avg": false,
"current": false,
"max": false,
"min": false,
"show": true,
"total": false,
"values": false
},
"lines": true,
"linewidth": 1,
"links": [],
"minSpan": 12,
"nullPointMode": "connected",
"percentage": false,
"pointradius": 5,
"points": false,
"renderer": "flot",
"seriesOverrides": [
{
"alias": "/.*read/",
"transform": "negative-Y"
}
],
"spaceLength": 10,
"stack": false,
"steppedLine": false,
"targets": [
{
"expr": "(irate(node_disk_bytes_written{instance=~\"($ceph_hosts).*\"}[5m]) or irate(node_disk_written_bytes_total{instance=~\"($ceph_hosts).*\"}[5m])) * on(instance, device) group_left(ceph_daemon) label_replace(label_replace(ceph_disk_occupation, \"device\", \"$1\", \"device\", \"/dev/(.*)\"), \"instance\", \"$1:9100\", \"exported_instance\", \"(.*)\")",
"format": "time_series",
"intervalFactor": 1,
"legendFormat": "{{device}}({{ceph_daemon}}) write",
"refId": "B"
},
{
"expr": "(irate(node_disk_bytes_read{instance=~\"($ceph_hosts).*\"}[5m]) or irate(node_disk_read_bytes_total{instance=~\"($ceph_hosts).*\"}[5m])) * on(instance, device) group_left(ceph_daemon) label_replace(label_replace(ceph_disk_occupation, \"device\", \"$1\", \"device\", \"/dev/(.*)\"), \"instance\", \"$1:9100\", \"exported_instance\", \"(.*)\")",
"format": "time_series",
"intervalFactor": 1,
"legendFormat": "{{device}}({{ceph_daemon}}) read",
"refId": "C"
}
],
"thresholds": [],
"timeFrom": null,
"timeShift": null,
"title": "$ceph_hosts Throughput by Disk",
"tooltip": {
"shared": true,
"sort": 2,
"value_type": "individual"
},
"type": "graph",
"xaxis": {
"buckets": null,
"mode": "time",
"name": null,
"show": true,
"values": []
},
"yaxes": [
{
"format": "Bps",
"label": "Read (-) / Write (+)",
"logBase": 1,
"max": null,
"min": "0",
"show": true
},
{
"format": "short",
"label": null,
"logBase": 1,
"max": null,
"min": null,
"show": false
}
],
"yaxis": {
"align": false,
"alignLevel": null
}
},
{
"aliasColors": {},
"bars": false,
"dashLength": 10,
"dashes": false,
"datasource": "$datasource",
"description": "For OSD hosts, this chart shows the latency at the physical drive. Each drive is shown by device name, with it's corresponding OSD id",
"fill": 1,
"gridPos": {
"h": 9,
"w": 11,
"x": 0,
"y": 21
},
"id": 7,
"legend": {
"avg": false,
"current": false,
"max": false,
"min": false,
"show": true,
"total": false,
"values": false
},
"lines": true,
"linewidth": 1,
"links": [],
"minSpan": 12,
"nullPointMode": "null as zero",
"percentage": false,
"pointradius": 5,
"points": false,
"renderer": "flot",
"seriesOverrides": [],
"spaceLength": 10,
"stack": false,
"steppedLine": false,
"targets": [
{
"expr": "max by(instance,device) (((\n (irate(node_disk_write_time_ms{ instance=~\"($ceph_hosts).*\"}[5m]) )\n / clamp_min(irate(node_disk_writes_completed{ instance=~\"($ceph_hosts).*\"}[5m]), 0.001) or \n (irate(node_disk_read_time_ms{ instance=~\"($ceph_hosts).*\"}[5m]) )\n / clamp_min(irate(node_disk_reads_completed{ instance=~\"($ceph_hosts).*\"}[5m]), 0.001)\n ) / 1000) or\n (irate(node_disk_write_time_seconds_total{ instance=~\"($ceph_hosts).*\"}[5m]) )\n / clamp_min(irate(node_disk_writes_completed_total{ instance=~\"($ceph_hosts).*\"}[5m]), 0.001) or \n (irate(node_disk_read_time_seconds_total{ instance=~\"($ceph_hosts).*\"}[5m]) )\n / clamp_min(irate(node_disk_reads_completed_total{ instance=~\"($ceph_hosts).*\"}[5m]), 0.001)\n ) *\n on(instance,device) group_left(osd_id) label_replace(ceph_disk_occupation,\"osd_id\",\"$1\",\"ceph_daemon\",\"osd.(.*)\")",
"format": "time_series",
"hide": false,
"intervalFactor": 1,
"legendFormat": "{{device}}({{osd_id}})",
"refId": "D"
}
],
"thresholds": [],
"timeFrom": null,
"timeShift": null,
"title": "$ceph_hosts Disk Latency",
"tooltip": {
"shared": true,
"sort": 2,
"value_type": "individual"
},
"type": "graph",
"xaxis": {
"buckets": null,
"mode": "time",
"name": null,
"show": true,
"values": []
},
"yaxes": [
{
"format": "s",
"label": "",
"logBase": 1,
"max": null,
"min": "0",
"show": true
},
{
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"logBase": 1,
"max": null,
"min": null,
"show": true
}
],
"yaxis": {
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"alignLevel": null
}
},
{
"aliasColors": {},
"bars": false,
"dashLength": 10,
"dashes": false,
"datasource": "$datasource",
"description": "Show disk utilization % (util) of any OSD devices on the host by the physical device name and associated OSD id.",
"fill": 1,
"gridPos": {
"h": 9,
"w": 11,
"x": 12,
"y": 21
},
"id": 5,
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},
"lines": true,
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"pointradius": 5,
"points": false,
"renderer": "flot",
"seriesOverrides": [],
"spaceLength": 10,
"stack": false,
"steppedLine": false,
"targets": [
{
"expr": "(\n (irate(node_disk_io_time_ms[5m]) / 10 ) or\n irate(node_disk_io_time_seconds_total[5m]) * 100)\n* on(instance, device) group_left(osd_id) label_replace(ceph_disk_occupation{instance=~\"($ceph_hosts).*\"}, \"osd_id\",\"$1\",\"ceph_daemon\",\"osd.(.*)\")\n",
"format": "time_series",
"hide": false,
"intervalFactor": 1,
"legendFormat": "{{device}}({{osd_id}})",
"refId": "C",
"step": 10,
"textEditor": true
},
{
"expr": "(irate(node_disk_io_time_ms{instance=~\"($ceph_hosts).*\"}[5m] * 1000) or irate(node_disk_writes_completed_total{instance=~\"($ceph_hosts).*\"}[5m])) * on(instance, device) group_left(ceph_daemon) label_replace(label_replace(ceph_disk_occupation, \"device\", \"$1\", \"device\", \"/dev/(.*)\"), \"instance\", \"$1:9100\", \"exported_instance\", \"(.*)\")",
"format": "time_series",
"intervalFactor": 1,
"refId": "A"
}
],
"thresholds": [],
"timeFrom": null,
"timeShift": null,
"title": "$ceph_hosts Disk utilisation",
"tooltip": {
"shared": true,
"sort": 2,
"value_type": "individual"
},
"type": "graph",
"xaxis": {
"buckets": null,
"mode": "time",
"name": null,
"show": true,
"values": []
},
"yaxes": [
{
"format": "short",
"label": "%Util",
"logBase": 1,
"max": "100",
"min": "0",
"show": true
},
{
"format": "short",
"label": null,
"logBase": 1,
"max": null,
"min": null,
"show": false
}
],
"yaxis": {
"align": false,
"alignLevel": null
}
}
],
"refresh": "10s",
"schemaVersion": 16,
"style": "dark",
"tags": [
"overview"
],
"templating": {
"list": [
{
"current": {
"text": "default",
"value": "default"
},
"hide": 0,
"label": "Data Source",
"name": "datasource",
"options": [],
"query": "prometheus",
"refresh": 1,
"regex": "",
"skipUrlSync": false,
"type": "datasource"
},
{
"allValue": null,
"current": {},
"datasource": "$datasource",
"hide": 0,
"includeAll": false,
"label": "Hostname",
"multi": false,
"name": "ceph_hosts",
"options": [],
"query": "label_values(node_scrape_collector_success, instance) ",
"refresh": 1,
"regex": "([^:]*).*",
"skipUrlSync": false,
"sort": 3,
"tagValuesQuery": "",
"tags": [],
"tagsQuery": "",
"type": "query",
"useTags": false
}
]
},
"time": {
"from": "now-1h",
"to": "now"
},
"timepicker": {
"refresh_intervals": [
"5s",
"10s",
"30s",
"1m",
"5m",
"15m",
"30m",
"1h",
"2h",
"1d"
],
"time_options": [
"5m",
"15m",
"1h",
"6h",
"12h",
"24h",
"2d",
"7d",
"30d"
]
},
"timezone": "browser",
"title": "Host Details",
"uid": "rtOg0AiWz",
"version": 3,
"description": "Host Details"
} | 2021-04-07 08:28:37 UTC | 2021-04-07 08:28:46 UTC | 0 | 0 |
14,117 | 1 | 228,951 | 20,703 | 7 | slowlog-by-controller-action | Ruby on Rails Slowlog by Controller Action | A list of the slowest controller actions | 2021-03-22 17:06:45 UTC | 2021-03-22 17:34:00 UTC | 0 | # A Ruby on Rails Performance Dashboard
The datasource would be a [influxdb](https://www.influxdata.com/products/influxdb-overview/) database that is filled by the official [influxdb-rails](https://github.com/influxdata/influxdb-rails) reporter.
**PLEASE NOTE**: This dashboard is one of a collection of Ruby on Rails dashboards. They make the most sense united.
- [Ruby On Rails Performance Overview](https://grafana.com/dashboards/10428/)
- Performance insights into individual requests, see [Ruby On Rails Performance per Request](https://grafana.com/dashboards/10429/)
- Performance of individual actions, see [Ruby On Rails Performance per Action](https://grafana.com/grafana/dashboards/11031)
- [Ruby On Rails Health Overview](https://grafana.com/grafana/dashboards/14115)
- [Ruby on Rails ActiveJob Overview](https://grafana.com/grafana/dashboards/14116)
- [Ruby on Rails Slowlog by Request](https://grafana.com/grafana/dashboards/14118)
- [Ruby on Rails Slowlog by Action](https://grafana.com/grafana/dashboards/14117)
- [Ruby on Rails Slowlog by SQL](https://grafana.com/grafana/dashboards/14119) | 61,659 | 61,660 | Other | null | 0 | 0 | 20,703 | 1 | 14,117 | 1 | 7 | A list of the slowest controller actions | {
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} | 2022-05-18 07:02:43 UTC | 2022-05-18 07:05:28 UTC | 0 | 0 |
16,306 | 1 | 627,725 | 24,813 | 7 | express-prometheus-middleware | Express Prometheus Middleware | Official Grafana dashboard for npm package @matteodisabatino/express-prometheus-middleware | 2022-05-19 20:16:04 UTC | 2022-05-19 20:48:51 UTC | 0 | This is the official Grafana dashboard for npm package @matteodisabatino/express-prometheus-middleware.
ATTENTION: to have information about usage of CPU and RAM node-exporter is required.
Source code: https://github.com/matteodisabatino/express-prometheus-middleware/blob/main/grafana/Express%20Prometheus%20Middleware-1652991307983.json | 0 | 0 | Other | null | 0 | 0 | 24,813 | 1 | 16,306 | 1 | 7 | null | {
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"linewidth": 5,
"stack": "B"
}
],
"spaceLength": 10,
"stack": true,
"steppedLine": false,
"targets": [
{
"expr": "delta(pagespeed_lighthouse_audit_score{host=\"$host\",path=\"$path\",strategy=\"$strategy\"}[$diff])",
"format": "time_series",
"intervalFactor": 1,
"legendFormat": "{{audit}}",
"refId": "A"
}
],
"thresholds": [],
"timeFrom": null,
"timeShift": null,
"title": "Audit Diff $diff",
"tooltip": {
"shared": true,
"sort": 0,
"value_type": "individual"
},
"type": "graph",
"xaxis": {
"buckets": null,
"mode": "time",
"name": null,
"show": true,
"values": []
},
"yaxes": [
{
"decimals": 2,
"format": "short",
"label": null,
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"min": null,
"show": true
},
{
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"logBase": 1,
"max": null,
"min": null,
"show": true
}
],
"yaxis": {
"align": false,
"alignLevel": null
}
},
{
"aliasColors": {},
"bars": false,
"dashLength": 10,
"dashes": false,
"datasource": "${DS_PROMETHEUS}",
"fill": 1,
"gridPos": {
"h": 8,
"w": 12,
"x": 0,
"y": 27
},
"id": 57,
"legend": {
"alignAsTable": true,
"avg": true,
"current": true,
"max": true,
"min": true,
"rightSide": true,
"show": true,
"total": false,
"values": true
},
"lines": true,
"linewidth": 1,
"links": [],
"nullPointMode": "null",
"percentage": false,
"pointradius": 5,
"points": false,
"renderer": "flot",
"scopedVars": {
"strategy": {
"selected": true,
"text": "desktop",
"value": "desktop"
}
},
"seriesOverrides": [],
"spaceLength": 10,
"stack": false,
"steppedLine": false,
"targets": [
{
"expr": "pagespeed_lighthouse_category_score{host=\"$host\",path=\"$path\",strategy=\"$strategy\"}*100 ",
"format": "time_series",
"intervalFactor": 1,
"legendFormat": "{{category}}",
"refId": "A"
}
],
"thresholds": [],
"timeFrom": null,
"timeShift": null,
"title": "Score History",
"tooltip": {
"shared": true,
"sort": 0,
"value_type": "individual"
},
"type": "graph",
"xaxis": {
"buckets": null,
"mode": "time",
"name": null,
"show": true,
"values": []
},
"yaxes": [
{
"decimals": 2,
"format": "percent",
"label": null,
"logBase": 1,
"max": "100",
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"show": true
},
{
"decimals": 2,
"format": "short",
"label": null,
"logBase": 1,
"max": null,
"min": null,
"show": true
}
],
"yaxis": {
"align": false,
"alignLevel": null
}
},
{
"aliasColors": {},
"bars": false,
"dashLength": 10,
"dashes": false,
"datasource": "${DS_PROMETHEUS}",
"decimals": 2,
"fill": 1,
"gridPos": {
"h": 8,
"w": 12,
"x": 12,
"y": 27
},
"id": 64,
"legend": {
"alignAsTable": true,
"avg": true,
"current": true,
"max": false,
"min": false,
"rightSide": true,
"show": true,
"sideWidth": 400,
"total": false,
"values": true
},
"lines": true,
"linewidth": 1,
"links": [],
"nullPointMode": "null",
"percentage": false,
"pointradius": 5,
"points": false,
"renderer": "flot",
"scopedVars": {
"strategy": {
"selected": true,
"text": "desktop",
"value": "desktop"
}
},
"seriesOverrides": [],
"spaceLength": 10,
"stack": false,
"steppedLine": false,
"targets": [
{
"expr": "delta(pagespeed_lighthouse_category_score{host=\"$host\",path=\"$path\",strategy=\"$strategy\"}[$diff])*100",
"format": "time_series",
"intervalFactor": 1,
"legendFormat": "{{category}}",
"refId": "A"
}
],
"thresholds": [],
"timeFrom": null,
"timeShift": null,
"title": "Score History Diff $diff",
"tooltip": {
"shared": true,
"sort": 0,
"value_type": "individual"
},
"type": "graph",
"xaxis": {
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"mode": "time",
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"values": []
},
"yaxes": [
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"format": "percent",
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"min": "-100",
"show": true
},
{
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"label": null,
"logBase": 1,
"max": null,
"min": null,
"show": true
}
],
"yaxis": {
"align": false,
"alignLevel": null
}
}
],
"repeat": "strategy",
"title": "Timings / Scores / Audits ($strategy)",
"type": "row"
},
{
"collapsed": true,
"gridPos": {
"h": 1,
"w": 24,
"x": 0,
"y": 6
},
"id": 4,
"panels": [
{
"aliasColors": {},
"bars": false,
"dashLength": 10,
"dashes": false,
"datasource": "${DS_PROMETHEUS}",
"fill": 1,
"gridPos": {
"h": 9,
"w": 24,
"x": 0,
"y": 7
},
"id": 2,
"legend": {
"alignAsTable": true,
"avg": true,
"current": false,
"max": true,
"min": true,
"rightSide": false,
"show": true,
"total": false,
"values": true
},
"lines": true,
"linewidth": 1,
"links": [],
"nullPointMode": "connected",
"percentage": false,
"pointradius": 5,
"points": false,
"renderer": "flot",
"seriesOverrides": [],
"spaceLength": 10,
"stack": false,
"steppedLine": false,
"targets": [
{
"expr": "pagespeed_scrape_duration_seconds",
"format": "time_series",
"intervalFactor": 1,
"legendFormat": "scrape duration",
"refId": "A"
}
],
"thresholds": [],
"timeFrom": null,
"timeShift": null,
"title": "Pagespeed Scrape Duration",
"tooltip": {
"shared": true,
"sort": 0,
"value_type": "individual"
},
"type": "graph",
"xaxis": {
"buckets": null,
"mode": "time",
"name": null,
"show": true,
"values": []
},
"yaxes": [
{
"format": "s",
"label": null,
"logBase": 1,
"max": null,
"min": "0",
"show": true
},
{
"format": "short",
"label": null,
"logBase": 1,
"max": null,
"min": null,
"show": true
}
],
"yaxis": {
"align": false,
"alignLevel": null
}
}
],
"title": "Pagespeed Exporter Metrics",
"type": "row"
}
],
"refresh": false,
"schemaVersion": 16,
"style": "dark",
"tags": [],
"templating": {
"list": [
{
"allValue": null,
"current": {},
"datasource": "${DS_PROMETHEUS}",
"hide": 0,
"includeAll": false,
"label": "host",
"multi": false,
"name": "host",
"options": [],
"query": "label_values(pagespeed_lighthouse_category_score,host)",
"refresh": 1,
"regex": "",
"skipUrlSync": false,
"sort": 6,
"tagValuesQuery": "",
"tags": [],
"tagsQuery": "",
"type": "query",
"useTags": false
},
{
"allValue": null,
"current": {},
"datasource": "${DS_PROMETHEUS}",
"hide": 0,
"includeAll": false,
"label": "path",
"multi": false,
"name": "path",
"options": [],
"query": "label_values(pagespeed_lighthouse_category_score{host=\"$host\"},path)",
"refresh": 1,
"regex": "",
"skipUrlSync": false,
"sort": 6,
"tagValuesQuery": "",
"tags": [],
"tagsQuery": "",
"type": "query",
"useTags": false
},
{
"allValue": "all",
"current": {},
"datasource": "${DS_PROMETHEUS}",
"hide": 0,
"includeAll": true,
"label": "strategy",
"multi": true,
"name": "strategy",
"options": [],
"query": "label_values(pagespeed_lighthouse_category_score,strategy)",
"refresh": 1,
"regex": "",
"skipUrlSync": false,
"sort": 0,
"tagValuesQuery": "",
"tags": [],
"tagsQuery": "",
"type": "query",
"useTags": false
},
{
"allValue": null,
"current": {
"selected": true,
"text": "1w",
"value": "1w"
},
"hide": 0,
"includeAll": false,
"label": "Diff",
"multi": false,
"name": "diff",
"options": [
{
"selected": false,
"text": "1d",
"value": "1d"
},
{
"selected": false,
"text": "2d",
"value": "2d"
},
{
"selected": true,
"text": "1w",
"value": "1w"
},
{
"selected": false,
"text": "2w",
"value": "2w"
},
{
"selected": false,
"text": "1m",
"value": "1m"
}
],
"query": "1d,2d,1w, 2w, 1m",
"skipUrlSync": false,
"type": "custom"
}
]
},
"time": {
"from": "now-1h",
"to": "now"
},
"timepicker": {
"refresh_intervals": [
"5s",
"10s",
"30s",
"1m",
"5m",
"15m",
"30m",
"1h",
"2h",
"1d"
],
"time_options": [
"5m",
"15m",
"1h",
"6h",
"12h",
"24h",
"2d",
"7d",
"30d"
]
},
"timezone": "",
"title": "Pagespeed Sawary",
"uid": "q0iQ5eLik",
"version": 86,
"description": "Web-site Lighthouse Performance Audits "
} | 2022-04-19 15:34:29 UTC | 2022-04-19 15:35:27 UTC | 0 | 0 |
16,051 | 1 | 626,646 | 24,354 | 7 | crowdsec | ShiftMon Crowdsec | Display logs shipped from the crowdsec agent via telegraf to grafana loki | 2022-04-05 03:57:49 UTC | 2022-04-05 04:12:44 UTC | 0 | shows where attacks are coming from, which ASNs are attacking, which scenarios are being triggered, and a log view. This dashboard and collector is included in ShiftMon which can be found below
https://gitlab.com/shiftsystems/shift-rmm | 72,376 | 72,377 | Telegraf | ```
[[inputs.tail]]
files = ["/var/log/crowdsec*.log"]
data_format = "logfmt"
name_suffix = "_crowdsec"
[[processors.parser]]
parse_fields = ["msg"]
merge = "override"
data_format = "grok"
grok_patterns = ['''(\(%{GREEDYDATA}\)) %{GREEDYDATA:behavior} by ip %{DATA:ip} \(%{DATA:country_code}/%{DATA:asn}\) : %{DATA:ban_length} ban on Ip %{DATA:ban_ip}''','''Ip %{GREEDYDATA:ip} performed '%{GREEDYDATA:behavior}' \(%{INT:attempts} events over %{DATA:window}\) at %{GREEDYDATA}''']
[[ outputs.loki]]
domain = "$loki_url"
username = "$loki_user"
password = "$loki_password"
namepass = ["tail*","exec*","x509_cert","syslog"]
``` | 0 | 0 | 24,354 | 1 | 16,051 | 1 | 7 | null | {
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"label": "shift-logs",
"description": "",
"type": "datasource",
"pluginId": "loki",
"pluginName": "Loki"
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],
"__elements": [],
"__requires": [
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"id": "bargauge",
"name": "Bar gauge",
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{
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"annotations": {
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"datasource": "-- Grafana --",
"enable": true,
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"iconColor": "rgba(0, 211, 255, 1)",
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"matchAny": false,
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},
"editable": true,
"fiscalYearStartMonth": 0,
"graphTooltip": 0,
"id": null,
"iteration": 1649130998735,
"links": [],
"liveNow": false,
"panels": [
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"circleMinSize": 2,
"colors": [
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"rgba(50, 172, 45, 0.97)"
],
"datasource": {
"type": "loki",
"uid": "${DS_SHIFT-LOGS}"
},
"decimals": 0,
"esMetric": "Count",
"gridPos": {
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"w": 12,
"x": 0,
"y": 0
},
"hideEmpty": false,
"hideZero": false,
"id": 2,
"initialZoom": "2.1",
"locationData": "countries",
"mapCenter": "(0°, 0°)",
"mapCenterLatitude": 0,
"mapCenterLongitude": 0,
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"latitudeField": "latitude",
"longitudeField": "longitude",
"metricField": "metric",
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"targets": [
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"expr": "sum(count_over_time({path=~`.*crowdsec.log`} | logfmt | ban_length=\"4h\" [$__interval])) by (country_code)",
"legendFormat": "{{country_code}}",
"refId": "A"
}
],
"thresholds": "0,10",
"title": "Attack World Map",
"transformations": [],
"type": "grafana-worldmap-panel",
"unitPlural": "",
"unitSingle": "",
"valueName": "total"
},
{
"datasource": {
"type": "loki",
"uid": "${DS_SHIFT-LOGS}"
},
"fieldConfig": {
"defaults": {
"color": {
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},
"custom": {
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"tooltip": false,
"viz": false
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},
"mappings": []
},
"overrides": []
},
"gridPos": {
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"w": 12,
"x": 12,
"y": 0
},
"id": 3,
"maxDataPoints": 1,
"options": {
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"placement": "right",
"values": [
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]
},
"pieType": "donut",
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],
"fields": "",
"values": false
},
"tooltip": {
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"sort": "none"
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"legendFormat": "{{behavior}}",
"refId": "A"
}
],
"title": "Behaviors",
"transformations": [],
"type": "piechart"
},
{
"datasource": {
"type": "loki",
"uid": "${DS_SHIFT-LOGS}"
},
"fieldConfig": {
"defaults": {
"color": {
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"mappings": [],
"thresholds": {
"mode": "absolute",
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"gridPos": {
"h": 6,
"w": 12,
"x": 12,
"y": 10
},
"id": 6,
"maxDataPoints": 1,
"options": {
"displayMode": "lcd",
"orientation": "horizontal",
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"fields": "",
"values": false
},
"showUnfilled": true
},
"pluginVersion": "8.4.5",
"targets": [
{
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"uid": "${DS_SHIFT-LOGS}"
},
"expr": "sum(count_over_time({path=~`.*crowdsec.log`} | logfmt | ban_length=\"4h\" [$__interval])) by (asn) > 5",
"legendFormat": "{{asn}}",
"refId": "A"
}
],
"title": "Top ISPs",
"transformations": [],
"type": "bargauge"
},
{
"datasource": {
"type": "loki",
"uid": "${DS_SHIFT-LOGS}"
},
"fieldConfig": {
"defaults": {
"color": {
"mode": "thresholds"
},
"custom": {
"align": "auto",
"displayMode": "auto"
},
"mappings": [],
"thresholds": {
"mode": "absolute",
"steps": [
{
"color": "green",
"value": null
},
{
"color": "red",
"value": 80
}
]
}
},
"overrides": []
},
"gridPos": {
"h": 11,
"w": 24,
"x": 0,
"y": 16
},
"id": 5,
"options": {
"footer": {
"fields": "",
"reducer": [
"sum"
],
"show": false
},
"frameIndex": 2,
"showHeader": true
},
"pluginVersion": "8.4.5",
"targets": [
{
"datasource": {
"type": "loki",
"uid": "${DS_SHIFT-LOGS}"
},
"expr": "{path=~`.*crowdsec.log`} | logfmt | ban_length=\"4h\"",
"refId": "A"
}
],
"title": "Ban Log",
"transformations": [
{
"id": "labelsToFields",
"options": {
"keepLabels": [
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"ip",
"behavior",
"country_code",
"host"
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},
{
"id": "merge",
"options": {}
},
{
"id": "organize",
"options": {
"excludeByName": {
"id": true,
"line": true,
"traceID": true,
"tsNs": true
},
"indexByName": {
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"behavior": 3,
"country_code": 4,
"host": 1,
"id": 7,
"ip": 2,
"line": 6,
"traceID": 9,
"ts": 0,
"tsNs": 8
},
"renameByName": {}
}
}
],
"type": "table"
}
],
"schemaVersion": 35,
"style": "dark",
"tags": [],
"templating": {
"list": [
{
"current": {},
"datasource": {
"type": "loki",
"uid": "${DS_SHIFT-LOGS}"
},
"definition": "label_values({path=\"/var/log/crowdsec.log\"},host)",
"hide": 0,
"includeAll": false,
"multi": false,
"name": "host",
"options": [],
"query": "label_values({path=\"/var/log/crowdsec.log\"},host)",
"refresh": 1,
"regex": "",
"skipUrlSync": false,
"sort": 0,
"type": "query"
},
{
"current": {
"selected": false,
"text": "shift-logs",
"value": "shift-logs"
},
"hide": 0,
"includeAll": false,
"multi": false,
"name": "logs",
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"query": "loki",
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"skipUrlSync": false,
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]
},
"time": {
"from": "now-12h",
"to": "now"
},
"timepicker": {},
"timezone": "",
"title": "ShiftMon Crowdsec",
"uid": "j4KEK3L7k",
"version": 2,
"weekStart": "",
"gnetId": 16051,
"description": "Display logs shipped from the crowdsec agent via telegraf to grafana loki"
} | 2022-04-05 03:57:49 UTC | 2022-04-05 04:06:16 UTC | 0 | 0 |
4,934 | 1 | 108,839 | 6,560 | 263 | sensehat | SenseHAT | Dashboard for the Raspberry Pi | 2018-03-03 18:22:46 UTC | 2018-08-15 10:29:31 UTC | 0 | null | 0 | 0 | null | null | 0 | 0 | 6,560 | 1 | 4,934 | 1 | 263 | Dashboard for the Raspberry Pi | {
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{
"name": "DS_SENSEHAT",
"label": "SenseHAT",
"description": "",
"type": "datasource",
"pluginId": "influxdb",
"pluginName": "InfluxDB"
}
],
"__requires": [
{
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"id": "grafana",
"name": "Grafana",
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},
{
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"name": "Graph",
"version": "5.0.0"
},
{
"type": "datasource",
"id": "influxdb",
"name": "InfluxDB",
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},
{
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"id": "singlestat",
"name": "Singlestat",
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],
"annotations": {
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"name": "Annotations & Alerts",
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}
]
},
"description": "Dashboard for the Raspberry Pi",
"editable": false,
"gnetId": 4934,
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"id": null,
"links": [],
"panels": [
{
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"colorBackground": false,
"colorValue": false,
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"#299c46"
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"minValue": -20,
"show": true,
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"thresholdMarkers": false
},
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],
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"nullPointMode": "connected",
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"postfixFontSize": "50%",
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"prefixFontSize": "50%",
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{
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"sparkline": {
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"legendFormat": "ok_total",
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} | 2021-08-17 10:04:53 UTC | 2021-08-17 10:06:31 UTC | 0 | 0 |
15,411 | 1 | 533,153 | 23,232 | 8 | kusama-1kv-s-locations-overview | KUSAMA 1KV's Locations Overview | KUSAMA Thousand Validators Program Location Monitoring for easy overview on where to setup your validator in terms to avoid crowded location. | 2021-12-17 15:38:05 UTC | 2021-12-17 15:39:36 UTC | 0 | KUSAMA Thousand Validators Program Location Monitoring for easy overview on where to setup your validator in terms to avoid crowded locations. Based on endpoint: https://kusama.w3f.community/locationstats
A message from the God of the Nodes:
"The location score is based on where your node is and how many other nodes are in that region based on other candidates (the lower the amount of nodes in that region the better)."
Steps:
Install JSON API Plugin
Add the endpoint
Enjoy the dashboard
For Support && Nominations in KUSAMA NETWORK: Validators Display name. KSMNETWORK
KUSAMA (KSM) Address H1bSKJxoxzxYRCdGQutVqFGeW7xU3AcN6vyEdZBU7Qb1rsZ
OR
PolkaDOT (DOT) Address: 15FxvBFDd3X7H9qcMGqsiuvFYEg4D3mBoTA2LQufreysTHKA | 69,632 | 69,633 | null | null | 0 | 0 | 23,232 | 1 | 15,411 | 1 | 8 | KUSAMA Thousand Validators Program Location Monitoring for easy overview on where to setup your validator in terms to avoid crowded location. | {
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} | 2021-12-17 15:38:05 UTC | 2021-12-17 15:39:36 UTC | 0 | 0 |
15,916 | 1 | 483,055 | 24,128 | 8 | playtime | Playnite | Your gaming time across all libraries as exported from Playnite Exporter extension. | 2022-03-15 17:49:40 UTC | 2022-03-15 17:58:12 UTC | 0 | 1. import and play your games using [Playnite](https://playnite.link/) frontend
2. install [Playnite Exporter](https://github.com/lapo-luchini/PlayniteExporter) extension
3. install this Dashboard | 71,747 | 71,748 | nodeExporter | null | 0 | 0 | 24,128 | 1 | 15,916 | 1 | 8 | null | {
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} | 2022-03-15 17:49:40 UTC | 2022-03-15 17:57:49 UTC | 0 | 0 |
16,541 | 1 | 222,389 | 25,254 | 8 | speedtest-exporter-dashboard | Speedtest-Exporter Dashboard | Dashboard for Speedtest Exporter https://github.com/MiguelNdeCarvalho/speedtest-exporter | 2022-07-05 20:29:06 UTC | 2022-07-05 20:35:11 UTC | 0 | Dashboard for Speedtest Exporter : github.com/miguelndecarvalho/speedtest-exporter | 74,692 | 74,693 | null | null | 0 | 0 | 25,254 | 1 | 16,541 | 1 | 8 | Dashboard for Speedtest Exporter https://github.com/MiguelNdeCarvalho/speedtest-exporter | {
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} | 2022-07-05 20:29:06 UTC | null | 0 | 0 |
12,905 | 1 | 412,437 | 18,657 | 8 | gpsti | GPSTI | Twitter Dashboard | 2020-08-27 21:37:38 UTC | 2020-08-27 21:38:18 UTC | 0 | null | 0 | 0 | null | null | 0 | 0 | 18,657 | 1 | 12,905 | 1 | 8 | Twitter Dashboard | {
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} | 2020-08-27 21:37:38 UTC | null | 0 | 0 |
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} | 2022-02-23 15:53:40 UTC | 2022-02-23 15:53:58 UTC | 0 | 0 |
14,901 | 1 | 533,153 | 22,203 | 8 | kusama-validator-monitoring-mobile-version | Kusama Validator Monitoring Mobile Version | A mobile version for monitoring of KUSAMA & PolkaDOT Validators
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For Support && Nominations in KUSAMA NETWORK: Validators Display name. KSMNETWORK
KUSAMA (KSM) Address H1bSKJxoxzxYRCdGQutVqFGeW7xU3AcN6vyEdZBU7Qb1rsZ
OR
PolkaDOT (DOT) Address: 15FxvBFDd3X7H9qcMGqsiuvFYEg4D3mBoTA2LQufreysTHKA | 66,125 | 66,126 | Other | null | 0 | 0 | 22,203 | 1 | 14,901 | 1 | 8 | A basic monitoring based on the Pokkadot log level='info' | {
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]
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},
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"title": "NGINX Ingress controller",
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} | 2018-12-08 10:28:49 UTC | 2018-12-08 10:30:29 UTC | 0 | 0 |
9,991 | 1 | 231,237 | 13,962 | 1,032 | switch-overview | Switch Overview | General information for any switch | 2019-04-01 19:45:16 UTC | 2019-04-19 16:18:43 UTC | 0 | null | 0 | 0 | Telegraf | [[inputs.snmp]]
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},
{
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},
{
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}
]
],
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"operator": "=~",
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},
{
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"value": "1000000000"
}
]
},
{
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{
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],
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},
{
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],
"type": "fill"
}
],
"measurement": "switch",
"orderByTime": "ASC",
"policy": "default",
"query": "SELECT count(\"ifLastChange\") FROM \"switch\" WHERE (\"agent_host\" =~ /^$ip$/ AND(\"ifLastChange\" / 100) > 604800 AND (\"ifLastChange\" / 100) < 18144000) AND $timeFilter GROUP BY time(1m) fill(none)",
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{
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{
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{
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]
},
{
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{
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]
}
],
"timeFrom": "1m",
"timeShift": null,
"title": "Port Statistics",
"transform": "timeseries_to_rows",
"type": "table"
},
{
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},
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},
"styles": [
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"dateFormat": "YYYY-MM-DD HH:mm:ss",
"pattern": "Time",
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{
"alias": "Description",
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"pattern": "ifDescr",
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{
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{
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{
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{
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"pattern": "Speed",
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{
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"pattern": "OutErrors",
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"dateFormat": "YYYY-MM-DD HH:mm:ss",
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"pattern": "Transmitted",
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"type": "number",
"unit": "decbytes"
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{
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"dateFormat": "YYYY-MM-DD HH:mm:ss",
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{
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{
"params": [
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],
"measurement": "switch",
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],
"timeFrom": null,
"timeShift": null,
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"transform": "table",
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}
],
"repeat": "ip",
"title": "Summary - $ip",
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"orderByTime": "ASC",
"policy": "default",
"query": "SELECT mean(\"ifInErrors\") FROM \"switch\" WHERE (\"agent_host\" =~ /^$ip$/ AND \"ifIndex\" =~ /^$port$/) AND $timeFilter AND \"ifInErrors\" > 0 GROUP BY time($__interval), \"ifIndex\" fill(null)",
"rawQuery": true,
"refId": "A",
"resultFormat": "time_series",
"select": [
[
{
"params": [
"ifInErrors"
],
"type": "field"
},
{
"params": [],
"type": "mean"
}
]
],
"tags": [
{
"key": "agent_host",
"operator": "=~",
"value": "/^$ip$/"
},
{
"condition": "AND",
"key": "ifIndex",
"operator": "=~",
"value": "/^$port$/"
}
]
},
{
"alias": "OUT $tag_ifIndex",
"groupBy": [
{
"params": [
"$__interval"
],
"type": "time"
},
{
"params": [
"ifIndex"
],
"type": "tag"
},
{
"params": [
"null"
],
"type": "fill"
}
],
"measurement": "switch",
"orderByTime": "ASC",
"policy": "default",
"query": "SELECT mean(\"ifOutErrors\") FROM \"switch\" WHERE (\"agent_host\" =~ /^$ip$/ AND \"ifIndex\" =~ /^$port$/) AND $timeFilter AND \"ifOutErrors\" > 0 GROUP BY time($__interval), \"ifIndex\" fill(null)",
"rawQuery": true,
"refId": "B",
"resultFormat": "time_series",
"select": [
[
{
"params": [
"ifOutErrors"
],
"type": "field"
},
{
"params": [],
"type": "mean"
}
]
],
"tags": [
{
"key": "agent_host",
"operator": "=~",
"value": "/^$ip$/"
},
{
"condition": "AND",
"key": "ifIndex",
"operator": "=~",
"value": "/^$port$/"
}
]
}
],
"thresholds": [],
"timeFrom": null,
"timeRegions": [],
"timeShift": null,
"title": "I/O Errors",
"tooltip": {
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"sort": 0,
"value_type": "individual"
},
"type": "graph",
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"buckets": null,
"mode": "series",
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"max": null,
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"show": false
}
],
"yaxis": {
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"alignLevel": null
}
}
],
"repeat": "ip",
"title": "I/O Errors - $ip",
"type": "row"
},
{
"collapsed": true,
"gridPos": {
"h": 1,
"w": 24,
"x": 0,
"y": 5
},
"id": 505,
"panels": [
{
"aliasColors": {},
"bars": false,
"dashLength": 10,
"dashes": false,
"datasource": "${DS_SNMP}",
"fill": 1,
"gridPos": {
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"w": 24,
"x": 0,
"y": 6
},
"id": 507,
"legend": {
"alignAsTable": true,
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"nullPointMode": "null",
"paceLength": 10,
"percentage": false,
"pointradius": 2,
"points": false,
"renderer": "flot",
"scopedVars": {
"ip": {
"selected": false,
"text": "192.168.5.208",
"value": "192.168.5.208"
}
},
"seriesOverrides": [],
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"steppedLine": false,
"targets": [
{
"alias": "IN $tag_ifIndex",
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{
"params": [
"$__interval"
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],
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{
"params": [
"null"
],
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}
],
"measurement": "switch",
"orderByTime": "ASC",
"policy": "default",
"query": "SELECT abs(derivative(mean(\"ifInOctets\"), 1s)) *8 FROM \"switch\" WHERE (\"agent_host\" =~ /^$ip$/ AND \"ifIndex\" =~ /^$port$/) AND $timeFilter GROUP BY time($__interval), \"ifIndex\" fill(null)",
"rawQuery": true,
"refId": "A",
"resultFormat": "time_series",
"select": [
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{
"params": [
"ifInOctets"
],
"type": "field"
},
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}
]
],
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{
"key": "agent_host",
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},
{
"condition": "AND",
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"value": "/^$port$/"
}
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},
{
"alias": "OUT $tag_ifIndex",
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{
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],
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{
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],
"type": "tag"
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{
"params": [
"null"
],
"type": "fill"
}
],
"measurement": "switch",
"orderByTime": "ASC",
"policy": "default",
"query": "SELECT abs(derivative(mean(\"ifOutOctets\"), 1s)) *-8 FROM \"switch\" WHERE (\"agent_host\" =~ /^$ip$/ AND \"ifIndex\" =~ /^$port$/) AND $timeFilter GROUP BY time($__interval), \"ifIndex\" fill(null)",
"rawQuery": true,
"refId": "B",
"resultFormat": "time_series",
"select": [
[
{
"params": [
"ifOutOctets"
],
"type": "field"
},
{
"params": [],
"type": "mean"
}
]
],
"tags": [
{
"key": "agent_host",
"operator": "=~",
"value": "/^$ip$/"
},
{
"condition": "AND",
"key": "ifIndex",
"operator": "=~",
"value": "/^$port$/"
}
]
}
],
"thresholds": [],
"timeFrom": null,
"timeRegions": [],
"timeShift": null,
"title": "I/O Data Rate",
"tooltip": {
"shared": false,
"sort": 0,
"value_type": "individual"
},
"type": "graph",
"xaxis": {
"buckets": null,
"mode": "time",
"name": null,
"show": true,
"values": []
},
"yaxes": [
{
"format": "bps",
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"logBase": 1,
"max": null,
"min": null,
"show": true
},
{
"format": "short",
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"max": null,
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"show": false
}
],
"yaxis": {
"align": false,
"alignLevel": null
}
}
],
"repeat": "ip",
"title": "I/O Octets - $ip",
"type": "row"
}
],
"schemaVersion": 18,
"style": "dark",
"tags": [],
"templating": {
"list": [
{
"allValue": null,
"current": {},
"datasource": "${DS_SNMP}",
"definition": "SHOW TAG VALUES FROM \"switch\" WITH KEY = \"ifIndex\"",
"hide": 0,
"includeAll": true,
"label": "Port",
"multi": true,
"name": "port",
"options": [],
"query": "SHOW TAG VALUES FROM \"switch\" WITH KEY = \"ifIndex\"",
"refresh": 1,
"regex": "",
"skipUrlSync": false,
"sort": 0,
"tagValuesQuery": "",
"tags": [],
"tagsQuery": "",
"type": "query",
"useTags": false
},
{
"allValue": null,
"current": {},
"datasource": "${DS_SNMP}",
"definition": "SHOW TAG VALUES FROM \"switch\" WITH KEY = \"agent_host\"",
"hide": 0,
"includeAll": true,
"label": "IP Switch",
"multi": true,
"name": "ip",
"options": [],
"query": "SHOW TAG VALUES FROM \"switch\" WITH KEY = \"agent_host\"",
"refresh": 1,
"regex": "",
"skipUrlSync": false,
"sort": 0,
"tagValuesQuery": "SHOW TAG VALUES FROM \"switch\" WITH KEY = \"agent_host\" WHERE entPhysicalName= '$tag'",
"tags": [
"HP V1910-48G Switch JE009A"
],
"tagsQuery": "SHOW TAG VALUES FROM \"switch\" WITH KEY = \"entPhysicalName\"",
"type": "query",
"useTags": true
}
]
},
"time": {
"from": "now-6h",
"to": "now"
},
"timepicker": {
"refresh_intervals": [
"5s",
"10s",
"30s",
"1m",
"5m",
"15m",
"30m",
"1h",
"2h",
"1d"
],
"time_options": [
"5m",
"15m",
"1h",
"6h",
"12h",
"24h",
"2d",
"7d",
"30d"
]
},
"timezone": "",
"title": "Switch Overview",
"uid": "w5SNqE3mk",
"version": 3
} | 2019-04-01 19:45:16 UTC | 2019-04-19 16:18:43 UTC | 0 | 0 |
9,632 | 1 | 228,648 | 13,349 | 1,544 | nextcloud | Nextcloud | Export Nextcloud metrics | 2019-01-12 21:06:17 UTC | 2019-01-12 21:09:52 UTC | 0 | Based on this exporter: https://github.com/xperimental/nextcloud-exporter
Exports:
* User count
* Shares
* Files count
* Auth errors | 41,802 | 41,803 | nodeExporter | null | 0 | 0 | 13,349 | 1 | 9,632 | 1 | 1,544 | null | {
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"name": "DS_PROMETHEUS",
"label": "Prometheus",
"description": "https://github.com/xperimental/nextcloud-exporter",
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"pluginName": "Prometheus"
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],
"annotations": {
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"datasource": "-- Grafana --",
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"iconColor": "rgba(0, 211, 255, 1)",
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]
},
"editable": true,
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{
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"colors": [
"#d44a3a",
"rgba(237, 129, 40, 0.89)",
"#299c46"
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"datasource": "${DS_PROMETHEUS}",
"format": "percentunit",
"gauge": {
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"minValue": 0,
"show": true,
"thresholdLabels": false,
"thresholdMarkers": true
},
"gridPos": {
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"name": "value to text",
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{
"name": "range to text",
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"nullPointMode": "connected",
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"postfix": "",
"postfixFontSize": "50%",
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"prefixFontSize": "50%",
"rangeMaps": [
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"sparkline": {
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"lineColor": "rgb(31, 120, 193)",
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},
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"targets": [
{
"expr": "avg(nextcloud_up)",
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"intervalFactor": 2,
"legendFormat": "",
"refId": "A"
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"thresholds": ".95,.99",
"title": "Nextcloud Up ?",
"type": "singlestat",
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"valueName": "avg"
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"aliasColors": {},
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"datasource": "${DS_PROMETHEUS}",
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"points": false,
"renderer": "flot",
"seriesOverrides": [],
"spaceLength": 10,
"stack": false,
"steppedLine": true,
"targets": [
{
"expr": "sum(nextcloud_shares_federated_total)",
"format": "time_series",
"interval": "",
"intervalFactor": 1,
"legendFormat": "Federated Total",
"refId": "A"
},
{
"expr": "sum(nextcloud_shares_total)",
"format": "time_series",
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],
"thresholds": [],
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"title": "Shares",
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"expr": "nextcloud_files_total",
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"expr": "nextcloud_users_total",
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{
"expr": "nextcloud_active_users_total",
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"legendFormat": "Users Active",
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],
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"targets": [
{
"expr": "nextcloud_scrape_errors_total",
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],
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"timeRegions": [],
"timeShift": null,
"title": "Scrape Errors",
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"logBase": 1,
"max": null,
"min": null,
"show": true
}
],
"yaxis": {
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"alignLevel": null
}
}
],
"refresh": false,
"schemaVersion": 16,
"style": "dark",
"tags": [
"cloud",
"storage"
],
"templating": {
"list": []
},
"time": {
"from": "now-6h",
"to": "now"
},
"timepicker": {
"refresh_intervals": [
"5s",
"10s",
"30s",
"1m",
"5m",
"15m",
"30m",
"1h",
"2h",
"1d"
],
"time_options": [
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"1h",
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"30d"
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},
"timezone": "",
"title": "Nextcloud",
"uid": "Hcp20_8mz",
"version": 19,
"description": "Export Nextcloud metrics"
} | 2019-01-12 21:06:17 UTC | 2019-01-12 21:09:52 UTC | 0 | 0 |
16,122 | 1 | 193,429 | 24,458 | 9 | users-view | Users View - Aerospike Monitoring Stack | Users View dashboard for Aerospike Monitoring Stack | 2022-04-17 09:53:20 UTC | 2022-04-17 17:03:57 UTC | 0 | Users View dashboard for [Aerospike Monitoring Stack](https://docs.aerospike.com/monitorstack)
Aerospike's Monitoring Stack extracts operational metrics from Aerospike database clusters for visualization and analysis in Prometheus and Grafana.
See [Aerospike Monitoring Stack Architecture](https://docs.aerospike.com/monitorstack/new/components-of-monitoring-stack) and [Aerospike Monitoring Stack Installation](https://docs.aerospike.com/monitorstack/new/installing-components) for more details | 72,667 | 72,668 | Other | null | 0 | 0 | 24,458 | 1 | 16,122 | 1 | 9 | null | {
"__requires": [
{
"type": "grafana",
"id": "grafana",
"name": "Grafana",
"version": "7.5.7"
},
{
"type": "datasource",
"id": "prometheus",
"name": "Prometheus",
"version": "1.0.0"
},
{
"type": "panel",
"id": "stat",
"name": "Stat",
"version": ""
}
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"refId": "J",
"resultFormat": "time_series",
"select": [
[
{
"params": [
"timers_<HTTP> GET /menu/categories_mean"
],
"type": "field"
},
{
"params": [
95
],
"type": "percentile"
}
]
],
"tags": []
},
{
"alias": "GET /sessions/current_mean",
"groupBy": [
{
"params": [
"$__interval"
],
"type": "time"
},
{
"params": [
"null"
],
"type": "fill"
}
],
"measurement": "httpjson_kis_stats",
"orderByTime": "ASC",
"policy": "default",
"refId": "K",
"resultFormat": "time_series",
"select": [
[
{
"params": [
"timers_<HTTP> GET /sessions/current_mean"
],
"type": "field"
},
{
"params": [
95
],
"type": "percentile"
}
]
],
"tags": []
},
{
"alias": "GET Unknown API_mean",
"groupBy": [
{
"params": [
"$__interval"
],
"type": "time"
},
{
"params": [
"null"
],
"type": "fill"
}
],
"measurement": "httpjson_kis_stats",
"orderByTime": "ASC",
"policy": "default",
"refId": "L",
"resultFormat": "time_series",
"select": [
[
{
"params": [
"timers_<HTTP> GET Unknown API_mean"
],
"type": "field"
},
{
"params": [
95
],
"type": "percentile"
}
]
],
"tags": []
},
{
"alias": "GET /notifications_mean",
"groupBy": [
{
"params": [
"$__interval"
],
"type": "time"
},
{
"params": [
"null"
],
"type": "fill"
}
],
"measurement": "httpjson_kis_stats",
"orderByTime": "ASC",
"policy": "default",
"refId": "O",
"resultFormat": "time_series",
"select": [
[
{
"params": [
"timers_<HTTP> GET /notifications_mean"
],
"type": "field"
},
{
"params": [
95
],
"type": "percentile"
}
]
],
"tags": []
},
{
"alias": "DELETE /sessions/{sessionKey}_mean",
"groupBy": [
{
"params": [
"$__interval"
],
"type": "time"
},
{
"params": [
"null"
],
"type": "fill"
}
],
"measurement": "httpjson_kis_stats",
"orderByTime": "ASC",
"policy": "default",
"refId": "Q",
"resultFormat": "time_series",
"select": [
[
{
"params": [
"timers_<HTTP> DELETE /sessions/{sessionKey}_mean"
],
"type": "field"
},
{
"params": [
95
],
"type": "percentile"
}
]
],
"tags": []
},
{
"alias": "GET /announcement_mean",
"groupBy": [
{
"params": [
"$__interval"
],
"type": "time"
},
{
"params": [
"null"
],
"type": "fill"
}
],
"measurement": "httpjson_kis_stats",
"orderByTime": "ASC",
"policy": "default",
"refId": "R",
"resultFormat": "time_series",
"select": [
[
{
"params": [
"timers_<HTTP> GET /announcement_mean"
],
"type": "field"
},
{
"params": [
95
],
"type": "percentile"
}
]
],
"tags": []
},
{
"alias": "GET /by-genre",
"groupBy": [
{
"params": [
"$__interval"
],
"type": "time"
},
{
"params": [
"null"
],
"type": "fill"
}
],
"measurement": "httpjson_kis_stats",
"orderByTime": "ASC",
"policy": "default",
"refId": "S",
"resultFormat": "time_series",
"select": [
[
{
"params": [
"timers_<HTTP> GET /data-filters/by-genre_mean"
],
"type": "field"
},
{
"params": [
95
],
"type": "percentile"
}
]
],
"tags": []
},
{
"alias": "GET /data-filters/genres_mean",
"groupBy": [
{
"params": [
"$__interval"
],
"type": "time"
},
{
"params": [
"null"
],
"type": "fill"
}
],
"measurement": "httpjson_kis_stats",
"orderByTime": "ASC",
"policy": "default",
"refId": "T",
"resultFormat": "time_series",
"select": [
[
{
"params": [
"timers_<HTTP> GET /data-filters/genres_mean"
],
"type": "field"
},
{
"params": [
95
],
"type": "percentile"
}
]
],
"tags": []
},
{
"alias": "GET /enums/values_mean",
"groupBy": [
{
"params": [
"$__interval"
],
"type": "time"
},
{
"params": [
"null"
],
"type": "fill"
}
],
"measurement": "httpjson_kis_stats",
"orderByTime": "ASC",
"policy": "default",
"refId": "U",
"resultFormat": "time_series",
"select": [
[
{
"params": [
"timers_<HTTP> GET /enums/values_mean"
],
"type": "field"
},
{
"params": [
95
],
"type": "percentile"
}
]
],
"tags": []
},
{
"alias": "GET /favicon_mean",
"groupBy": [
{
"params": [
"$__interval"
],
"type": "time"
},
{
"params": [
"null"
],
"type": "fill"
}
],
"measurement": "httpjson_kis_stats",
"orderByTime": "ASC",
"policy": "default",
"refId": "V",
"resultFormat": "time_series",
"select": [
[
{
"params": [
"timers_<HTTP> GET /favicon_mean"
],
"type": "field"
},
{
"params": [
95
],
"type": "percentile"
}
]
],
"tags": []
},
{
"alias": "GET /security/users/current_mean",
"groupBy": [
{
"params": [
"$__interval"
],
"type": "time"
},
{
"params": [
"null"
],
"type": "fill"
}
],
"measurement": "httpjson_kis_stats",
"orderByTime": "ASC",
"policy": "default",
"refId": "X",
"resultFormat": "time_series",
"select": [
[
{
"params": [
"timers_<HTTP> GET /security/users/current_mean"
],
"type": "field"
},
{
"params": [
95
],
"type": "percentile"
}
]
],
"tags": []
},
{
"alias": "GET /user/preferences/{key}",
"groupBy": [
{
"params": [
"$__interval"
],
"type": "time"
},
{
"params": [
"null"
],
"type": "fill"
}
],
"measurement": "httpjson_kis_stats",
"orderByTime": "ASC",
"policy": "default",
"refId": "Y",
"resultFormat": "time_series",
"select": [
[
{
"params": [
"timers_<HTTP> GET /user/preferences/{key}_mean"
],
"type": "field"
},
{
"params": [
95
],
"type": "percentile"
}
]
],
"tags": []
}
],
"timeFrom": null,
"timeShift": null,
"title": "Mean time Rest Ressources",
"type": "grafana-piechart-panel",
"valueName": "current"
},
{
"aliasColors": {},
"bars": false,
"dashLength": 10,
"dashes": false,
"datasource": "${DS_INFLUXDB}",
"fill": 1,
"fillGradient": 0,
"gridPos": {
"h": 7,
"w": 23,
"x": 0,
"y": 14
},
"hiddenSeries": false,
"id": 14,
"legend": {
"alignAsTable": true,
"avg": true,
"current": false,
"max": false,
"min": true,
"rightSide": false,
"show": true,
"total": true,
"values": true
},
"lines": true,
"linewidth": 1,
"nullPointMode": "null",
"options": {
"dataLinks": []
},
"percentage": false,
"pointradius": 2,
"points": false,
"renderer": "flot",
"seriesOverrides": [
{
"alias": "Memory used",
"yaxis": 2
}
],
"spaceLength": 10,
"stack": false,
"steppedLine": false,
"targets": [
{
"alias": "jvm.memory.heap.usage_value",
"groupBy": [
{
"params": [
"$__interval"
],
"type": "time"
},
{
"params": [
"null"
],
"type": "fill"
}
],
"measurement": "httpjson_kis_stats",
"orderByTime": "ASC",
"policy": "default",
"refId": "A",
"resultFormat": "time_series",
"select": [
[
{
"params": [
"gauges_jvm.memory.heap.usage_value"
],
"type": "field"
},
{
"params": [],
"type": "mean"
}
]
],
"tags": []
},
{
"alias": "gaiuges_jvm.memory.total.used_value",
"groupBy": [
{
"params": [
"$__interval"
],
"type": "time"
},
{
"params": [
"null"
],
"type": "fill"
}
],
"measurement": "httpjson_kis_stats",
"orderByTime": "ASC",
"policy": "default",
"refId": "B",
"resultFormat": "time_series",
"select": [
[
{
"params": [
"gauges_jvm.memory.total.used_value"
],
"type": "field"
},
{
"params": [],
"type": "mean"
}
]
],
"tags": []
}
],
"thresholds": [],
"timeFrom": null,
"timeRegions": [],
"timeShift": null,
"title": "JVM Heap ",
"tooltip": {
"shared": true,
"sort": 0,
"value_type": "individual"
},
"type": "graph",
"xaxis": {
"buckets": null,
"mode": "time",
"name": null,
"show": true,
"values": []
},
"yaxes": [
{
"format": "short",
"label": null,
"logBase": 1,
"max": null,
"min": null,
"show": true
},
{
"format": "decbytes",
"label": null,
"logBase": 1,
"max": null,
"min": null,
"show": true
}
],
"yaxis": {
"align": false,
"alignLevel": null
}
},
{
"aliasColors": {},
"bars": false,
"dashLength": 10,
"dashes": false,
"datasource": "${DS_INFLUXDB}",
"description": "Compteurs threads : running, waiting, abandon, optimal size nb (90% CPU), deadlocks JVM ",
"fill": 1,
"fillGradient": 0,
"gridPos": {
"h": 15,
"w": 11,
"x": 0,
"y": 21
},
"hiddenSeries": false,
"id": 12,
"legend": {
"alignAsTable": true,
"avg": true,
"current": true,
"max": true,
"min": true,
"rightSide": false,
"show": true,
"total": false,
"values": true
},
"lines": true,
"linewidth": 1,
"nullPointMode": "null",
"options": {
"dataLinks": []
},
"percentage": false,
"pointradius": 2,
"points": false,
"renderer": "flot",
"seriesOverrides": [
{
"alias": "Treads latency mean",
"yaxis": 2
},
{
"alias": "Kis wait tiime",
"yaxis": 2
},
{
"alias": "Threads latency mean",
"yaxis": 2
},
{
"alias": "Kis wait time",
"yaxis": 2
},
{
"alias": "kis.threads.global.waitTime_mean",
"yaxis": 2
},
{
"alias": "kis.threads.global.latency_mean",
"yaxis": 2
}
],
"spaceLength": 10,
"stack": false,
"steppedLine": false,
"targets": [
{
"alias": "kis.threads.global.running_count",
"groupBy": [
{
"params": [
"$__interval"
],
"type": "time"
},
{
"params": [
"none"
],
"type": "fill"
}
],
"hide": false,
"measurement": "httpjson_kis_stats",
"orderByTime": "ASC",
"policy": "default",
"refId": "A",
"resultFormat": "time_series",
"select": [
[
{
"params": [
"counters_kis.threads.global.running_count"
],
"type": "field"
},
{
"params": [],
"type": "mean"
}
]
],
"tags": []
},
{
"alias": "kis.threads.global.waiting_count",
"groupBy": [
{
"params": [
"$__interval"
],
"type": "time"
},
{
"params": [
"none"
],
"type": "fill"
}
],
"hide": false,
"measurement": "httpjson_kis_stats",
"orderByTime": "ASC",
"policy": "default",
"refId": "B",
"resultFormat": "time_series",
"select": [
[
{
"params": [
"counters_kis.threads.global.waiting_count"
],
"type": "field"
},
{
"params": [],
"type": "mean"
}
]
],
"tags": []
},
{
"alias": "kis.threads.global..abandonRate.count",
"groupBy": [
{
"params": [
"$__interval"
],
"type": "time"
},
{
"params": [
"null"
],
"type": "fill"
}
],
"measurement": "httpjson_kis_stats",
"orderByTime": "ASC",
"policy": "default",
"refId": "E",
"resultFormat": "time_series",
"select": [
[
{
"params": [
"meters_kis.threads.global.abandonRate_count"
],
"type": "field"
},
{
"params": [],
"type": "mean"
}
]
],
"tags": []
},
{
"alias": "kis.threads.global.optimalSize_value",
"groupBy": [
{
"params": [
"$__interval"
],
"type": "time"
},
{
"params": [
"none"
],
"type": "fill"
}
],
"measurement": "httpjson_kis_stats",
"orderByTime": "ASC",
"policy": "default",
"refId": "C",
"resultFormat": "time_series",
"select": [
[
{
"params": [
"gauges_kis.threads.global.optimalSize_value"
],
"type": "field"
},
{
"params": [],
"type": "mean"
}
]
],
"tags": []
},
{
"alias": "jvm.threads.deadlock.count_value",
"groupBy": [
{
"params": [
"$__interval"
],
"type": "time"
},
{
"params": [
"none"
],
"type": "fill"
}
],
"measurement": "httpjson_kis_stats",
"orderByTime": "ASC",
"policy": "default",
"refId": "D",
"resultFormat": "time_series",
"select": [
[
{
"params": [
"gauges_jvm.threads.deadlock.count_value"
],
"type": "field"
},
{
"params": [],
"type": "mean"
}
]
],
"tags": []
},
{
"alias": "kis-engine-async-executor.running_count",
"groupBy": [
{
"params": [
"$__interval"
],
"type": "time"
},
{
"params": [
"null"
],
"type": "fill"
}
],
"measurement": "httpjson_kis_stats",
"orderByTime": "ASC",
"policy": "default",
"refId": "G",
"resultFormat": "time_series",
"select": [
[
{
"params": [
"counters_kis-engine-async-executor.running_count"
],
"type": "field"
},
{
"params": [],
"type": "mean"
}
]
],
"tags": []
},
{
"alias": "kis.threads.global.latency_mean",
"groupBy": [
{
"params": [
"$__interval"
],
"type": "time"
},
{
"params": [
"null"
],
"type": "fill"
}
],
"measurement": "httpjson_kis_stats",
"orderByTime": "ASC",
"policy": "default",
"refId": "F",
"resultFormat": "time_series",
"select": [
[
{
"params": [
"timers_kis.threads.global.latency_mean"
],
"type": "field"
},
{
"params": [],
"type": "mean"
}
]
],
"tags": []
},
{
"alias": "kis.threads.global.waitTime_mean",
"groupBy": [
{
"params": [
"$__interval"
],
"type": "time"
},
{
"params": [
"null"
],
"type": "fill"
}
],
"measurement": "httpjson_kis_stats",
"orderByTime": "ASC",
"policy": "default",
"refId": "H",
"resultFormat": "time_series",
"select": [
[
{
"params": [
"timers_kis.threads.global.waitTime_mean"
],
"type": "field"
},
{
"params": [],
"type": "mean"
}
]
],
"tags": []
}
],
"thresholds": [],
"timeFrom": null,
"timeRegions": [],
"timeShift": null,
"title": "Threads",
"tooltip": {
"shared": true,
"sort": 0,
"value_type": "individual"
},
"transparent": true,
"type": "graph",
"xaxis": {
"buckets": null,
"mode": "time",
"name": null,
"show": true,
"values": []
},
"yaxes": [
{
"format": "short",
"label": null,
"logBase": 1,
"max": null,
"min": null,
"show": true
},
{
"format": "ns",
"label": null,
"logBase": 1,
"max": null,
"min": null,
"show": true
}
],
"yaxis": {
"align": false,
"alignLevel": null
}
},
{
"aliasColors": {},
"bars": false,
"cacheTimeout": null,
"dashLength": 10,
"dashes": false,
"datasource": "${DS_INFLUXDB}",
"fill": 1,
"fillGradient": 0,
"gridPos": {
"h": 15,
"w": 12,
"x": 11,
"y": 21
},
"hiddenSeries": false,
"id": 4,
"legend": {
"alignAsTable": true,
"avg": true,
"current": true,
"max": true,
"min": true,
"rightSide": false,
"show": true,
"total": false,
"values": true
},
"lines": true,
"linewidth": 1,
"links": [],
"nullPointMode": "null",
"options": {
"dataLinks": []
},
"percentage": false,
"pluginVersion": "6.6.2",
"pointradius": 2,
"points": false,
"renderer": "flot",
"seriesOverrides": [
{
"alias": "Message bus mean latency",
"yaxis": 2
},
{
"alias": "KIS async latency mean",
"yaxis": 2
},
{
"alias": "transaction duration",
"yaxis": 2
},
{
"alias": "kis-message-bus-local.latency_mean",
"yaxis": 2
},
{
"alias": "kis-engine-async-executor.latency_mean",
"yaxis": 2
},
{
"alias": "kis.persistence.transaction.duration_mean",
"yaxis": 2
}
],
"spaceLength": 10,
"stack": false,
"steppedLine": false,
"targets": [
{
"alias": "kis.persistence.transaction.commit_count",
"groupBy": [
{
"params": [
"$__interval"
],
"type": "time"
},
{
"params": [
"null"
],
"type": "fill"
}
],
"measurement": "httpjson_kis_stats",
"orderByTime": "ASC",
"policy": "default",
"query": "SELECT count(\"meters_kis.persistence.transaction.commit_count\") FROM \"httpjson_kis_stats\" WHERE $timeFilter GROUP BY time($__interval) fill(null)",
"rawQuery": false,
"refId": "A",
"resultFormat": "time_series",
"select": [
[
{
"params": [
"meters_kis.persistence.transaction.commit_count"
],
"type": "field"
},
{
"params": [],
"type": "count"
}
]
],
"tags": []
},
{
"alias": "kis-message-bus-local.running_count",
"groupBy": [
{
"params": [
"$__interval"
],
"type": "time"
},
{
"params": [
"null"
],
"type": "fill"
}
],
"measurement": "httpjson_kis_stats",
"orderByTime": "ASC",
"policy": "default",
"refId": "B",
"resultFormat": "time_series",
"select": [
[
{
"params": [
"counters_kis-message-bus-local.running_count"
],
"type": "field"
},
{
"params": [],
"type": "count"
}
]
],
"tags": []
},
{
"alias": "kis-messafe-bus-local.optimalSize_value",
"groupBy": [
{
"params": [
"$__interval"
],
"type": "time"
},
{
"params": [
"null"
],
"type": "fill"
}
],
"measurement": "httpjson_kis_stats",
"orderByTime": "ASC",
"policy": "default",
"refId": "D",
"resultFormat": "time_series",
"select": [
[
{
"params": [
"gauges_kis-message-bus-local.optimalSize_value"
],
"type": "field"
},
{
"params": [],
"type": "mean"
}
]
],
"tags": []
},
{
"alias": "kis-engine-async-executor.running_count",
"groupBy": [
{
"params": [
"$__interval"
],
"type": "time"
},
{
"params": [
"null"
],
"type": "fill"
}
],
"measurement": "httpjson_kis_stats",
"orderByTime": "ASC",
"policy": "default",
"refId": "E",
"resultFormat": "time_series",
"select": [
[
{
"params": [
"async"
],
"type": "field"
},
{
"params": [],
"type": "mean"
}
]
],
"tags": []
},
{
"alias": "kis.persistence.transaction.rollback_count",
"groupBy": [
{
"params": [
"$__interval"
],
"type": "time"
},
{
"params": [
"null"
],
"type": "fill"
}
],
"measurement": "httpjson_kis_stats",
"orderByTime": "ASC",
"policy": "default",
"refId": "G",
"resultFormat": "time_series",
"select": [
[
{
"params": [
"meters_kis.persistence.transaction.rollback_count"
],
"type": "field"
},
{
"params": [],
"type": "mean"
}
]
],
"tags": []
},
{
"alias": "kis-message-bus-local.latency_mean",
"groupBy": [
{
"params": [
"$__interval"
],
"type": "time"
},
{
"params": [
"null"
],
"type": "fill"
}
],
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15,424 | 1 | 575,927 | 23,246 | 9 | zeitgeist-node-monitor | Zeitgeist Node Monitor | Zeitgeist Node Operators can check their Zeitgeist Node Metrics by this | 2021-12-21 01:09:36 UTC | 2021-12-21 01:24:21 UTC | 0 | Zeitgeist - an evolving blockchain for prediction markets and futarchy. Zeitgeist is built for the Polkadot and Kusama ecosystem and envisions itself as a hub and ecosystem for prediction markets on Web3. | 69,701 | 69,702 | nodeExporter | null | 0 | 0 | 23,246 | 1 | 15,424 | 1 | 9 | Zeitgeist Node Metrics | {
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16,240 | 1 | 642,927 | 24,672 | 9 | influxdb-metrics | InfluxDB Metrics | metrics on influxdb from telegraf | 2022-05-07 07:36:30 UTC | 2022-05-07 07:37:49 UTC | 0 | null | 0 | 0 | Other | null | 0 | 0 | 24,672 | 1 | 16,240 | 1 | 9 | metrics on influxdb from telegraf | {
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"show": true
},
"yaxes": [
{
"format": "short",
"label": null,
"logBase": 1,
"max": null,
"min": null,
"show": true
},
{
"format": "short",
"label": null,
"logBase": 1,
"max": null,
"min": null,
"show": true
}
]
}
],
"showTitle": true,
"title": "Database"
},
{
"collapse": false,
"editable": true,
"height": "250px",
"panels": [
{
"aliasColors": {},
"bars": false,
"datasource": "${DS_INTERNAL_INFLUXDB}",
"editable": true,
"error": false,
"fill": 1,
"grid": {
"threshold1": null,
"threshold1Color": "rgba(216, 200, 27, 0.27)",
"threshold2": null,
"threshold2Color": "rgba(234, 112, 112, 0.22)"
},
"id": 6,
"isNew": true,
"legend": {
"avg": false,
"current": false,
"max": false,
"min": false,
"show": true,
"total": false,
"values": false
},
"lines": true,
"linewidth": 2,
"links": [],
"nullPointMode": "connected",
"percentage": false,
"pointradius": 5,
"points": false,
"renderer": "flot",
"seriesOverrides": [],
"span": 12,
"stack": false,
"steppedLine": false,
"targets": [
{
"alias": "sys",
"dsType": "influxdb",
"groupBy": [
{
"params": [
"$interval"
],
"type": "time"
},
{
"params": [
"null"
],
"type": "fill"
}
],
"measurement": "runtime",
"policy": "default",
"query": "SELECT mean(\"TotalAlloc\")/1024 FROM \"runtime\" WHERE $timeFilter GROUP BY time($interval) fill(null)",
"rawQuery": false,
"refId": "A",
"resultFormat": "time_series",
"select": [
[
{
"params": [
"Sys"
],
"type": "field"
},
{
"params": [],
"type": "mean"
}
]
],
"tags": []
}
],
"timeFrom": null,
"timeShift": null,
"title": "Memory",
"tooltip": {
"msResolution": true,
"shared": true,
"sort": 0,
"value_type": "cumulative"
},
"type": "graph",
"xaxis": {
"show": true
},
"yaxes": [
{
"format": "bytes",
"label": null,
"logBase": 1,
"max": null,
"min": null,
"show": true
},
{
"format": "short",
"label": null,
"logBase": 1,
"max": null,
"min": null,
"show": true
}
]
}
],
"showTitle": true,
"title": "Memory"
}
],
"time": {
"from": "now-7d",
"to": "now"
},
"timepicker": {
"now": true,
"refresh_intervals": [
"5s",
"10s",
"30s",
"1m",
"5m",
"15m",
"30m",
"1h",
"2h",
"1d"
],
"time_options": [
"5m",
"15m",
"1h",
"6h",
"12h",
"24h",
"2d",
"7d",
"30d"
]
},
"templating": {
"list": [
{
"type": "datasource",
"datasource": null,
"refresh": 1,
"name": "datasource",
"hide": 0,
"options": [
{
"text": "internal influxdb",
"value": "internal influxdb",
"selected": true
}
],
"includeAll": false,
"multi": false,
"query": "influxdb",
"regex": "/.*internal.*/",
"current": {
"text": "internal influxdb",
"value": "internal influxdb"
}
}
]
},
"annotations": {
"list": []
},
"schemaVersion": 12,
"version": 3,
"links": [
{
"icon": "external link",
"tags": [],
"title": "Docs",
"tooltip": "documentation for metrics",
"type": "link",
"url": "https://github.com/influxdata/telegraf/tree/master/plugins/inputs/influxdb"
},
{
"icon": "external link",
"tags": [],
"title": "Dashboard",
"tooltip": "grafana.net dashboard",
"type": "link",
"url": "https://grafana.net/users/sstarcher/dashboards"
}
],
"gnetId": 16240
} | 2022-05-07 07:36:30 UTC | null | 0 | 0 |
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