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def get_vid_from_url(url):
return match1(url, r'youtu\.be/([^?/]+)') or \
match1(url, r'youtube\.com/embed/([^/?]+)') or \
match1(url, r'youtube\.com/v/([^/?]+)') or \
match1(url, r'youtube\.com/watch/([^/?]+)') or \
parse_query_param(url, 'v') or \
parse_query_param(parse_query_param(url, 'u'), 'v')
|
[python] Extracts video ID from URL.
|
def sina_xml_to_url_list(xml_data):
rawurl = []
dom = parseString(xml_data)
for node in dom.getElementsByTagName('durl'):
url = node.getElementsByTagName('url')[0]
rawurl.append(url.childNodes[0].data)
return rawurl
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[python] str->list
Convert XML to URL List.
From Biligrab.
|
def makeMimi(upid):
strSeed = "gGddgPfeaf_gzyr"
prehash = upid + "_" + strSeed
return md5(prehash.encode('utf-8')).hexdigest()
|
[python] From http://cdn37.atwikiimg.com/sitescript/pub/dksitescript/FC2.site.js
Also com.hps.util.fc2.FC2EncrptUtil.makeMimiLocal
L110
|
def dailymotion_download(url, output_dir='.', merge=True, info_only=False, **kwargs):
html = get_content(rebuilt_url(url))
info = json.loads(match1(html, r'qualities":({.+?}),"'))
title = match1(html, r'"video_title"\s*:\s*"([^"]+)"') or \
match1(html, r'"title"\s*:\s*"([^"]+)"')
title = unicodize(title)
for quality in ['1080','720','480','380','240','144','auto']:
try:
real_url = info[quality][1]["url"]
if real_url:
break
except KeyError:
pass
mime, ext, size = url_info(real_url)
print_info(site_info, title, mime, size)
if not info_only:
download_urls([real_url], title, ext, size, output_dir=output_dir, merge=merge)
|
[python] Downloads Dailymotion videos by URL.
|
def sina_download_by_vid(vid, title=None, output_dir='.', merge=True, info_only=False):
xml = api_req(vid)
urls, name, size = video_info(xml)
if urls is None:
log.wtf(name)
title = name
print_info(site_info, title, 'flv', size)
if not info_only:
download_urls(urls, title, 'flv', size, output_dir = output_dir, merge = merge)
|
[python] Downloads a Sina video by its unique vid.
http://video.sina.com.cn/
|
def sina_download_by_vkey(vkey, title=None, output_dir='.', merge=True, info_only=False):
url = 'http://video.sina.com/v/flvideo/%s_0.flv' % vkey
type, ext, size = url_info(url)
print_info(site_info, title, 'flv', size)
if not info_only:
download_urls([url], title, 'flv', size, output_dir = output_dir, merge = merge)
|
[python] Downloads a Sina video by its unique vkey.
http://video.sina.com/
|
def sina_download(url, output_dir='.', merge=True, info_only=False, **kwargs):
if 'news.sina.com.cn/zxt' in url:
sina_zxt(url, output_dir=output_dir, merge=merge, info_only=info_only, **kwargs)
return
vid = match1(url, r'vid=(\d+)')
if vid is None:
video_page = get_content(url)
vid = hd_vid = match1(video_page, r'hd_vid\s*:\s*\'([^\']+)\'')
if hd_vid == '0':
vids = match1(video_page, r'[^\w]vid\s*:\s*\'([^\']+)\'').split('|')
vid = vids[-1]
if vid is None:
vid = match1(video_page, r'vid:"?(\d+)"?')
if vid:
#title = match1(video_page, r'title\s*:\s*\'([^\']+)\'')
sina_download_by_vid(vid, output_dir=output_dir, merge=merge, info_only=info_only)
else:
vkey = match1(video_page, r'vkey\s*:\s*"([^"]+)"')
if vkey is None:
vid = match1(url, r'#(\d+)')
sina_download_by_vid(vid, output_dir=output_dir, merge=merge, info_only=info_only)
return
title = match1(video_page, r'title\s*:\s*"([^"]+)"')
sina_download_by_vkey(vkey, title=title, output_dir=output_dir, merge=merge, info_only=info_only)
|
[python] Downloads Sina videos by URL.
|
def download_by_id(self, vid = '', title = None, output_dir='.', merge=True, info_only=False,**kwargs):
assert vid
self.prepare(vid = vid, title = title, **kwargs)
self.extract(**kwargs)
self.download(output_dir = output_dir,
merge = merge,
info_only = info_only, **kwargs)
|
[python] self, str->None
Keyword arguments:
self: self
vid: The video ID for BokeCC cloud, something like
FE3BB999594978049C33DC5901307461
Calls the prepare() to download the video.
If no title is provided, this method shall try to find a proper title
with the information providin within the
returned content of the API.
|
def get_vid_from_url(self, url):
hit = re.search(r'live.qq.com/(\d+)', url)
if hit is not None:
return hit.group(1)
hit = re.search(r'live.qq.com/directory/match/(\d+)', url)
if hit is not None:
return self.get_room_id_from_url(hit.group(1))
html = get_content(url)
room_id = match1(html, r'room_id\":(\d+)')
if room_id is None:
log.wtf('Unknown page {}'.format(url))
return room_id
|
[python] Extracts video ID from live.qq.com.
|
def sprint(text, *colors):
return "\33[{}m{content}\33[{}m".format(";".join([str(color) for color in colors]), RESET, content=text) if IS_ANSI_TERMINAL and colors else text
|
[python] Format text with color or other effects into ANSI escaped string.
|
def print_log(text, *colors):
sys.stderr.write(sprint("{}: {}".format(script_name, text), *colors) + "\n")
|
[python] Print a log message to standard error.
|
def e(message, exit_code=None):
print_log(message, YELLOW, BOLD)
if exit_code is not None:
sys.exit(exit_code)
|
[python] Print an error log message.
|
def wtf(message, exit_code=1):
print_log(message, RED, BOLD)
if exit_code is not None:
sys.exit(exit_code)
|
[python] What a Terrible Failure!
|
def vimeo_download_by_id(id, title=None, output_dir='.', merge=True, info_only=False, **kwargs):
'''
try:
# normal Vimeo video
html = get_content('https://vimeo.com/' + id)
cfg_patt = r'clip_page_config\s*=\s*(\{.+?\});'
cfg = json.loads(match1(html, cfg_patt))
video_page = get_content(cfg['player']['config_url'], headers=fake_headers)
title = cfg['clip']['title']
info = loads(video_page)
except:
# embedded player - referer may be required
if 'referer' in kwargs:
fake_headers['Referer'] = kwargs['referer']
video_page = get_content('http://player.vimeo.com/video/%s' % id, headers=fake_headers)
title = r1(r'<title>([^<]+)</title>', video_page)
info = loads(match1(video_page, r'var t=(\{.+?\});'))
streams = info['request']['files']['progressive']
streams = sorted(streams, key=lambda i: i['height'])
url = streams[-1]['url']
type, ext, size = url_info(url, faker=True)
print_info(site_info, title, type, size)
if not info_only:
download_urls([url], title, ext, size, output_dir, merge=merge, faker=True)
'''
site = VimeoExtractor()
site.download_by_vid(id, info_only=info_only, output_dir=output_dir, merge=merge, **kwargsf vimeo_download_by_id(id, title=None, output_dir='.', merge=True, info_only=False, **kwargs):
'''
try:
# normal Vimeo video
html = get_content('https://vimeo.com/' + id)
cfg_patt = r'clip_page_config\s*=\s*(\{.+?\});'
cfg = json.loads(match1(html, cfg_patt))
video_page = get_content(cfg['player']['config_url'], headers=fake_headers)
title = cfg['clip']['title']
info = loads(video_page)
except:
# embedded player - referer may be required
if 'referer' in kwargs:
fake_headers['Referer'] = kwargs['referer']
video_page = get_content('http://player.vimeo.com/video/%s' % id, headers=fake_headers)
title = r1(r'<title>([^<]+)</title>', video_page)
info = loads(match1(video_page, r'var t=(\{.+?\});'))
streams = info['request']['files']['progressive']
streams = sorted(streams, key=lambda i: i['height'])
url = streams[-1]['url']
type, ext, size = url_info(url, faker=True)
print_info(site_info, title, type, size)
if not info_only:
download_urls([url], title, ext, size, output_dir, merge=merge, faker=True)
'''
site = VimeoExtractor()
site.download_by_vid(id, info_only=info_only, output_dir=output_dir, merge=merge, **kwargs)
|
[python] try:
# normal Vimeo video
html = get_content('https://vimeo.com/' + id)
cfg_patt = r'clip_page_config\s*=\s*(\{.+?\});'
cfg = json.loads(match1(html, cfg_patt))
video_page = get_content(cfg['player']['config_url'], headers=fake_headers)
title = cfg['clip']['title']
info = loads(video_page)
except:
# embedded player - referer may be required
if 'referer' in kwargs:
fake_headers['Referer'] = kwargs['referer']
video_page = get_content('http://player.vimeo.com/video/%s' % id, headers=fake_headers)
title = r1(r'<title>([^<]+)</title>', video_page)
info = loads(match1(video_page, r'var t=(\{.+?\});'))
streams = info['request']['files']['progressive']
streams = sorted(streams, key=lambda i: i['height'])
url = streams[-1]['url']
type, ext, size = url_info(url, faker=True)
print_info(site_info, title, type, size)
if not info_only:
download_urls([url], title, ext, size, output_dir, merge=merge, faker=True)
|
def ckplayer_get_info_by_xml(ckinfo):
e = ET.XML(ckinfo)
video_dict = {'title': '',
#'duration': 0,
'links': [],
'size': 0,
'flashvars': '',}
dictified = dictify(e)['ckplayer']
if 'info' in dictified:
if '_text' in dictified['info'][0]['title'][0]: #title
video_dict['title'] = dictified['info'][0]['title'][0]['_text'].strip()
#if dictify(e)['ckplayer']['info'][0]['title'][0]['_text'].strip(): #duration
#video_dict['title'] = dictify(e)['ckplayer']['info'][0]['title'][0]['_text'].strip()
if '_text' in dictified['video'][0]['size'][0]: #size exists for 1 piece
video_dict['size'] = sum([int(i['size'][0]['_text']) for i in dictified['video']])
if '_text' in dictified['video'][0]['file'][0]: #link exist
video_dict['links'] = [i['file'][0]['_text'].strip() for i in dictified['video']]
if '_text' in dictified['flashvars'][0]:
video_dict['flashvars'] = dictified['flashvars'][0]['_text'].strip()
return video_dict
|
[python] str->dict
Information for CKPlayer API content.
|
def get_video_url_from_video_id(video_id):
# from js
data = [""] * 256
for index, _ in enumerate(data):
t = index
for i in range(8):
t = -306674912 ^ unsigned_right_shitf(t, 1) if 1 & t else unsigned_right_shitf(t, 1)
data[index] = t
def tmp():
rand_num = random.random()
path = "/video/urls/v/1/toutiao/mp4/{video_id}?r={random_num}".format(video_id=video_id,
random_num=str(rand_num)[2:])
e = o = r = -1
i, a = 0, len(path)
while i < a:
e = ord(path[i])
i += 1
if e < 128:
r = unsigned_right_shitf(r, 8) ^ data[255 & (r ^ e)]
else:
if e < 2048:
r = unsigned_right_shitf(r, 8) ^ data[255 & (r ^ (192 | e >> 6 & 31))]
r = unsigned_right_shitf(r, 8) ^ data[255 & (r ^ (128 | 63 & e))]
else:
if 55296 <= e < 57344:
e = (1023 & e) + 64
i += 1
o = 1023 & t.url(i)
r = unsigned_right_shitf(r, 8) ^ data[255 & (r ^ (240 | e >> 8 & 7))]
r = unsigned_right_shitf(r, 8) ^ data[255 & (r ^ (128 | e >> 2 & 63))]
r = unsigned_right_shitf(r, 8) ^ data[255 & (r ^ (128 | o >> 6 & 15 | (3 & e) << 4))]
r = unsigned_right_shitf(r, 8) ^ data[255 & (r ^ (128 | 63 & o))]
else:
r = unsigned_right_shitf(r, 8) ^ data[255 & (r ^ (224 | e >> 12 & 15))]
r = unsigned_right_shitf(r, 8) ^ data[255 & (r ^ (128 | e >> 6 & 63))]
r = unsigned_right_shitf(r, 8) ^ data[255 & (r ^ (128 | 63 & e))]
return "https://ib.365yg.com{path}&s={param}".format(path=path, param=unsigned_right_shitf(r ^ -1, 0))
while 1:
url = tmp()
if url.split("=")[-1][0] != "-": # 参数s不能为负数
return url
|
[python] Splicing URLs according to video ID to get video details
|
def get_vid_from_url(url):
vid = match1(url, 'https?://www.mgtv.com/(?:b|l)/\d+/(\d+).html')
if not vid:
vid = match1(url, 'https?://www.mgtv.com/hz/bdpz/\d+/(\d+).html')
return vid
|
[python] Extracts video ID from URL.
|
def get_mgtv_real_url(url):
content = loads(get_content(url))
m3u_url = content['info']
split = urlsplit(m3u_url)
base_url = "{scheme}://{netloc}{path}/".format(scheme = split[0],
netloc = split[1],
path = dirname(split[2]))
content = get_content(content['info']) #get the REAL M3U url, maybe to be changed later?
segment_list = []
segments_size = 0
for i in content.split():
if not i.startswith('#'): #not the best way, better we use the m3u8 package
segment_list.append(base_url + i)
# use ext-info for fast size calculate
elif i.startswith('#EXT-MGTV-File-SIZE:'):
segments_size += int(i[i.rfind(':')+1:])
return m3u_url, segments_size, segment_list
|
[python] str->list of str
Give you the real URLs.
|
def get_head(repo_path):
try:
ref = open(os.path.join(repo_path, '.git', 'HEAD'), 'r').read().strip()[5:].split('/')
branch = ref[-1]
commit = open(os.path.join(repo_path, '.git', *ref), 'r').read().strip()[:7]
return branch, commit
except:
return None
|
[python] Get (branch, commit) from HEAD of a git repo.
|
def legitimize(text, os=detect_os()):
# POSIX systems
text = text.translate({
0: None,
ord('/'): '-',
ord('|'): '-',
})
# FIXME: do some filesystem detection
if os == 'windows' or os == 'cygwin' or os == 'wsl':
# Windows (non-POSIX namespace)
text = text.translate({
# Reserved in Windows VFAT and NTFS
ord(':'): '-',
ord('*'): '-',
ord('?'): '-',
ord('\\'): '-',
ord('\"'): '\'',
# Reserved in Windows VFAT
ord('+'): '-',
ord('<'): '-',
ord('>'): '-',
ord('['): '(',
ord(']'): ')',
ord('\t'): ' ',
})
else:
# *nix
if os == 'mac':
# Mac OS HFS+
text = text.translate({
ord(':'): '-',
})
# Remove leading .
if text.startswith("."):
text = text[1:]
text = text[:80] # Trim to 82 Unicode characters long
return text
|
[python] Converts a string to a valid filename.
|
def get_terminal_size():
try:
import fcntl, termios, struct # fcntl module only available on Unix
return struct.unpack('hh', fcntl.ioctl(1, termios.TIOCGWINSZ, '1234'))
except:
return (40, 80)
|
[python] Get (width, height) of the current terminal.
|
def cbs_download(url, output_dir='.', merge=True, info_only=False, **kwargs):
html = get_content(url)
pid = match1(html, r'video\.settings\.pid\s*=\s*\'([^\']+)\'')
title = match1(html, r'video\.settings\.title\s*=\s*\"([^\"]+)\"')
theplatform_download_by_pid(pid, title, output_dir=output_dir, merge=merge, info_only=info_only)
|
[python] Downloads CBS videos by URL.
|
def download(self, **kwargs):
if 'json_output' in kwargs and kwargs['json_output']:
json_output.output(self)
elif 'info_only' in kwargs and kwargs['info_only']:
if 'stream_id' in kwargs and kwargs['stream_id']:
# Display the stream
stream_id = kwargs['stream_id']
if 'index' not in kwargs:
self.p(stream_id)
else:
self.p_i(stream_id)
else:
# Display all available streams
if 'index' not in kwargs:
self.p([])
else:
stream_id = self.streams_sorted[0]['id'] if 'id' in self.streams_sorted[0] else self.streams_sorted[0]['itag']
self.p_i(stream_id)
else:
if 'stream_id' in kwargs and kwargs['stream_id']:
# Download the stream
stream_id = kwargs['stream_id']
else:
# Download stream with the best quality
stream_id = self.streams_sorted[0]['id'] if 'id' in self.streams_sorted[0] else self.streams_sorted[0]['itag']
if 'index' not in kwargs:
self.p(stream_id)
else:
self.p_i(stream_id)
if stream_id in self.streams:
urls = self.streams[stream_id]['src']
ext = self.streams[stream_id]['container']
total_size = self.streams[stream_id]['size']
else:
urls = self.dash_streams[stream_id]['src']
ext = self.dash_streams[stream_id]['container']
total_size = self.dash_streams[stream_id]['size']
if not urls:
log.wtf('[Failed] Cannot extract video source.')
# For legacy main()
#Here's the change!!
download_url_ffmpeg(urls[0], self.title, 'mp4', output_dir=kwargs['output_dir'], merge=kwargs['merge'], stream=False)
if not kwargs['caption']:
print('Skipping captions.')
return
for lang in self.caption_tracks:
filename = '%s.%s.srt' % (get_filename(self.title), lang)
print('Saving %s ... ' % filename, end="", flush=True)
srt = self.caption_tracks[lang]
with open(os.path.join(kwargs['output_dir'], filename),
'w', encoding='utf-8') as x:
x.write(srt)
print('Done.')
|
[python] Override the original one
Ugly ugly dirty hack
|
def acfun_download_by_vid(vid, title, output_dir='.', merge=True, info_only=False, **kwargs):
#first call the main parasing API
info = json.loads(get_content('http://www.acfun.cn/video/getVideo.aspx?id=' + vid))
sourceType = info['sourceType']
#decide sourceId to know which extractor to use
if 'sourceId' in info: sourceId = info['sourceId']
# danmakuId = info['danmakuId']
#call extractor decided by sourceId
if sourceType == 'sina':
sina_download_by_vid(sourceId, title, output_dir=output_dir, merge=merge, info_only=info_only)
elif sourceType == 'youku':
youku_download_by_vid(sourceId, title=title, output_dir=output_dir, merge=merge, info_only=info_only, **kwargs)
elif sourceType == 'tudou':
tudou_download_by_iid(sourceId, title, output_dir=output_dir, merge=merge, info_only=info_only)
elif sourceType == 'qq':
qq_download_by_vid(sourceId, title, True, output_dir=output_dir, merge=merge, info_only=info_only)
elif sourceType == 'letv':
letvcloud_download_by_vu(sourceId, '2d8c027396', title, output_dir=output_dir, merge=merge, info_only=info_only)
elif sourceType == 'zhuzhan':
#As in Jul.28.2016, Acfun is using embsig to anti hotlink so we need to pass this
#In Mar. 2017 there is a dedicated ``acfun_proxy'' in youku cloud player
#old code removed
url = 'http://www.acfun.cn/v/ac' + vid
yk_streams = youku_acfun_proxy(info['sourceId'], info['encode'], url)
seq = ['mp4hd3', 'mp4hd2', 'mp4hd', 'flvhd']
for t in seq:
if yk_streams.get(t):
preferred = yk_streams[t]
break
#total_size in the json could be incorrect(F.I. 0)
size = 0
for url in preferred[0]:
_, _, seg_size = url_info(url)
size += seg_size
#fallback to flvhd is not quite possible
if re.search(r'fid=[0-9A-Z\-]*.flv', preferred[0][0]):
ext = 'flv'
else:
ext = 'mp4'
print_info(site_info, title, ext, size)
if not info_only:
download_urls(preferred[0], title, ext, size, output_dir=output_dir, merge=merge)
else:
raise NotImplementedError(sourceType)
if not info_only and not dry_run:
if not kwargs['caption']:
print('Skipping danmaku.')
return
try:
title = get_filename(title)
print('Downloading %s ...\n' % (title + '.cmt.json'))
cmt = get_srt_json(vid)
with open(os.path.join(output_dir, title + '.cmt.json'), 'w', encoding='utf-8') as x:
x.write(cmt)
except:
pass
|
[python] str, str, str, bool, bool ->None
Download Acfun video by vid.
Call Acfun API, decide which site to use, and pass the job to its
extractor.
|
def main_dev(**kwargs):
# Get (branch, commit) if running from a git repo.
head = git.get_head(kwargs['repo_path'])
# Get options and arguments.
try:
opts, args = getopt.getopt(sys.argv[1:], _short_options, _options)
except getopt.GetoptError as e:
log.wtf("""
[Fatal] {}.
Try '{} --help' for more options.""".format(e, script_name))
if not opts and not args:
# Display help.
print(_help)
# Enter GUI mode.
#from .gui import gui_main
#gui_main()
else:
conf = {}
for opt, arg in opts:
if opt in ('-h', '--help'):
# Display help.
print(_help)
elif opt in ('-V', '--version'):
# Display version.
log.println("you-get:", log.BOLD)
log.println(" version: {}".format(__version__))
if head is not None:
log.println(" branch: {}\n commit: {}".format(*head))
else:
log.println(" branch: {}\n commit: {}".format("(stable)", "(tag v{})".format(__version__)))
log.println(" platform: {}".format(platform.platform()))
log.println(" python: {}".format(sys.version.split('\n')[0]))
elif opt in ('-g', '--gui'):
# Run using GUI.
conf['gui'] = True
elif opt in ('-f', '--force'):
# Force download.
conf['force'] = True
elif opt in ('-l', '--playlist', '--playlists'):
# Download playlist whenever possible.
conf['playlist'] = True
if args:
if 'gui' in conf and conf['gui']:
# Enter GUI mode.
from .gui import gui_main
gui_main(*args, **conf)
else:
# Enter console mode.
from .console import console_main
console_main(*args, **conf)
|
[python] Main entry point.
you-get-dev
|
def ffmpeg_download_stream(files, title, ext, params={}, output_dir='.', stream=True):
output = title + '.' + ext
if not (output_dir == '.'):
output = output_dir + '/' + output
print('Downloading streaming content with FFmpeg, press q to stop recording...')
if stream:
ffmpeg_params = [FFMPEG] + ['-y', '-re', '-i']
else:
ffmpeg_params = [FFMPEG] + ['-y', '-i']
ffmpeg_params.append(files) #not the same here!!!!
if FFMPEG == 'avconv': #who cares?
ffmpeg_params += ['-c', 'copy', output]
else:
ffmpeg_params += ['-c', 'copy', '-bsf:a', 'aac_adtstoasc']
if params is not None:
if len(params) > 0:
for k, v in params:
ffmpeg_params.append(k)
ffmpeg_params.append(v)
ffmpeg_params.append(output)
print(' '.join(ffmpeg_params))
try:
a = subprocess.Popen(ffmpeg_params, stdin= subprocess.PIPE)
a.communicate()
except KeyboardInterrupt:
try:
a.stdin.write('q'.encode('utf-8'))
except:
pass
return True
|
[python] str, str->True
WARNING: NOT THE SAME PARMS AS OTHER FUNCTIONS!!!!!!
You can basicly download anything with this function
but better leave it alone with
|
def match1(text, *patterns):
if len(patterns) == 1:
pattern = patterns[0]
match = re.search(pattern, text)
if match:
return match.group(1)
else:
return None
else:
ret = []
for pattern in patterns:
match = re.search(pattern, text)
if match:
ret.append(match.group(1))
return ret
|
[python] Scans through a string for substrings matched some patterns (first-subgroups only).
Args:
text: A string to be scanned.
patterns: Arbitrary number of regex patterns.
Returns:
When only one pattern is given, returns a string (None if no match found).
When more than one pattern are given, returns a list of strings ([] if no match found).
|
def matchall(text, patterns):
ret = []
for pattern in patterns:
match = re.findall(pattern, text)
ret += match
return ret
|
[python] Scans through a string for substrings matched some patterns.
Args:
text: A string to be scanned.
patterns: a list of regex pattern.
Returns:
a list if matched. empty if not.
|
def parse_query_param(url, param):
try:
return parse.parse_qs(parse.urlparse(url).query)[param][0]
except:
return None
|
[python] Parses the query string of a URL and returns the value of a parameter.
Args:
url: A URL.
param: A string representing the name of the parameter.
Returns:
The value of the parameter.
|
def ungzip(data):
from io import BytesIO
import gzip
buffer = BytesIO(data)
f = gzip.GzipFile(fileobj=buffer)
return f.read()
|
[python] Decompresses data for Content-Encoding: gzip.
|
def undeflate(data):
import zlib
decompressobj = zlib.decompressobj(-zlib.MAX_WBITS)
return decompressobj.decompress(data)+decompressobj.flush()
|
[python] Decompresses data for Content-Encoding: deflate.
(the zlib compression is used.)
|
def get_content(url, headers={}, decoded=True):
logging.debug('get_content: %s' % url)
req = request.Request(url, headers=headers)
if cookies:
cookies.add_cookie_header(req)
req.headers.update(req.unredirected_hdrs)
response = urlopen_with_retry(req)
data = response.read()
# Handle HTTP compression for gzip and deflate (zlib)
content_encoding = response.getheader('Content-Encoding')
if content_encoding == 'gzip':
data = ungzip(data)
elif content_encoding == 'deflate':
data = undeflate(data)
# Decode the response body
if decoded:
charset = match1(
response.getheader('Content-Type', ''), r'charset=([\w-]+)'
)
if charset is not None:
data = data.decode(charset, 'ignore')
else:
data = data.decode('utf-8', 'ignore')
return data
|
[python] Gets the content of a URL via sending a HTTP GET request.
Args:
url: A URL.
headers: Request headers used by the client.
decoded: Whether decode the response body using UTF-8 or the charset specified in Content-Type.
Returns:
The content as a string.
|
def post_content(url, headers={}, post_data={}, decoded=True, **kwargs):
if kwargs.get('post_data_raw'):
logging.debug('post_content: %s\npost_data_raw: %s' % (url, kwargs['post_data_raw']))
else:
logging.debug('post_content: %s\npost_data: %s' % (url, post_data))
req = request.Request(url, headers=headers)
if cookies:
cookies.add_cookie_header(req)
req.headers.update(req.unredirected_hdrs)
if kwargs.get('post_data_raw'):
post_data_enc = bytes(kwargs['post_data_raw'], 'utf-8')
else:
post_data_enc = bytes(parse.urlencode(post_data), 'utf-8')
response = urlopen_with_retry(req, data=post_data_enc)
data = response.read()
# Handle HTTP compression for gzip and deflate (zlib)
content_encoding = response.getheader('Content-Encoding')
if content_encoding == 'gzip':
data = ungzip(data)
elif content_encoding == 'deflate':
data = undeflate(data)
# Decode the response body
if decoded:
charset = match1(
response.getheader('Content-Type'), r'charset=([\w-]+)'
)
if charset is not None:
data = data.decode(charset)
else:
data = data.decode('utf-8')
return data
|
[python] Post the content of a URL via sending a HTTP POST request.
Args:
url: A URL.
headers: Request headers used by the client.
decoded: Whether decode the response body using UTF-8 or the charset specified in Content-Type.
Returns:
The content as a string.
|
def parse_host(host):
if re.match(r'^(\d+)$', host) is not None:
return ("0.0.0.0", int(host))
if re.match(r'^(\w+)://', host) is None:
host = "//" + host
o = parse.urlparse(host)
hostname = o.hostname or "0.0.0.0"
port = o.port or 0
return (hostname, port)
|
[python] Parses host name and port number from a string.
|
def print_more_compatible(*args, **kwargs):
import builtins as __builtin__
# nothing happens on py3.3 and later
if sys.version_info[:2] >= (3, 3):
return __builtin__.print(*args, **kwargs)
# in lower pyver (e.g. 3.2.x), remove 'flush' keyword and flush it as requested
doFlush = kwargs.pop('flush', False)
ret = __builtin__.print(*args, **kwargs)
if doFlush:
kwargs.get('file', sys.stdout).flush()
return ret
|
[python] Overload default print function as py (<3.3) does not support 'flush' keyword.
Although the function name can be same as print to get itself overloaded automatically,
I'd rather leave it with a different name and only overload it when importing to make less confusion.
|
def _wanmen_get_title_by_json_topic_part(json_content, tIndex, pIndex):
return '_'.join([json_content[0]['name'],
json_content[0]['Topics'][tIndex]['name'],
json_content[0]['Topics'][tIndex]['Parts'][pIndex]['name']])
|
[python] JSON, int, int, int->str
Get a proper title with courseid+topicID+partID.
|
def wanmen_download_by_course(json_api_content, output_dir='.', merge=True, info_only=False, **kwargs):
for tIndex in range(len(json_api_content[0]['Topics'])):
for pIndex in range(len(json_api_content[0]['Topics'][tIndex]['Parts'])):
wanmen_download_by_course_topic_part(json_api_content,
tIndex,
pIndex,
output_dir=output_dir,
merge=merge,
info_only=info_only,
**kwargs)
|
[python] int->None
Download a WHOLE course.
Reuse the API call to save time.
|
def wanmen_download_by_course_topic_part(json_api_content, tIndex, pIndex, output_dir='.', merge=True, info_only=False, **kwargs):
html = json_api_content
title = _wanmen_get_title_by_json_topic_part(html,
tIndex,
pIndex)
bokeccID = _wanmen_get_boke_id_by_json_topic_part(html,
tIndex,
pIndex)
bokecc_download_by_id(vid = bokeccID, title = title, output_dir=output_dir, merge=merge, info_only=info_only, **kwargs)
|
[python] int, int, int->None
Download ONE PART of the course.
|
def get_streams_by_id(account_number, video_id):
endpoint = 'https://edge.api.brightcove.com/playback/v1/accounts/{account_number}/videos/{video_id}'.format(account_number = account_number, video_id = video_id)
fake_header_id = fake_headers
#is this somehow related to the time? Magic....
fake_header_id['Accept'] ='application/json;pk=BCpkADawqM1cc6wmJQC2tvoXZt4mrB7bFfi6zGt9QnOzprPZcGLE9OMGJwspQwKfuFYuCjAAJ53JdjI8zGFx1ll4rxhYJ255AXH1BQ10rnm34weknpfG-sippyQ'
html = get_content(endpoint, headers= fake_header_id)
html_json = json.loads(html)
link_list = []
for i in html_json['sources']:
if 'src' in i: #to avoid KeyError
if i['src'].startswith('https'):
link_list.append((str(i['height']), i['src']))
return link_list
|
[python] int, int->list
Get the height of the videos.
Since brightcove is using 3 kinds of links: rtmp, http and https,
we will be using the HTTPS one to make it secure.
If somehow akamaihd.net is blocked by the Great Fucking Wall,
change the "startswith https" to http.
|
def has_task(self, task_instance):
if task_instance.key in self.queued_tasks or task_instance.key in self.running:
return True
|
[python] Checks if a task is either queued or running in this executor
:param task_instance: TaskInstance
:return: True if the task is known to this executor
|
def get_event_buffer(self, dag_ids=None):
cleared_events = dict()
if dag_ids is None:
cleared_events = self.event_buffer
self.event_buffer = dict()
else:
for key in list(self.event_buffer.keys()):
dag_id, _, _, _ = key
if dag_id in dag_ids:
cleared_events[key] = self.event_buffer.pop(key)
return cleared_events
|
[python] Returns and flush the event buffer. In case dag_ids is specified
it will only return and flush events for the given dag_ids. Otherwise
it returns and flushes all
:param dag_ids: to dag_ids to return events for, if None returns all
:return: a dict of events
|
def _get_conn_params(self):
conn = self.get_connection(self.snowflake_conn_id)
account = conn.extra_dejson.get('account', None)
warehouse = conn.extra_dejson.get('warehouse', None)
database = conn.extra_dejson.get('database', None)
region = conn.extra_dejson.get("region", None)
role = conn.extra_dejson.get('role', None)
conn_config = {
"user": conn.login,
"password": conn.password or '',
"schema": conn.schema or '',
"database": self.database or database or '',
"account": self.account or account or '',
"warehouse": self.warehouse or warehouse or '',
"region": self.region or region or '',
"role": self.role or role or '',
}
"""
If private_key_file is specified in the extra json, load the contents of the file as a private
key and specify that in the connection configuration. The connection password then becomes the
passphrase for the private key. If your private key file is not encrypted (not recommended), then
leave the password empty.
"""
private_key_file = conn.extra_dejson.get('private_key_file', None)
if private_key_file:
with open(private_key_file, "rb") as key:
passphrase = None
if conn.password:
passphrase = conn.password.strip().encode()
p_key = serialization.load_pem_private_key(
key.read(),
password=passphrase,
backend=default_backend()
)
pkb = p_key.private_bytes(encoding=serialization.Encoding.DER,
format=serialization.PrivateFormat.PKCS8,
encryption_algorithm=serialization.NoEncryption())
conn_config['private_key'] = pkb
conn_config.pop('password', None)
return conn_config
|
[python] one method to fetch connection params as a dict
used in get_uri() and get_connection()
|
def get_uri(self):
conn_config = self._get_conn_params()
uri = 'snowflake://{user}:{password}@{account}/{database}/'
uri += '{schema}?warehouse={warehouse}&role={role}'
return uri.format(**conn_config)
|
[python] override DbApiHook get_uri method for get_sqlalchemy_engine()
|
def get_conn(self):
conn_config = self._get_conn_params()
conn = snowflake.connector.connect(**conn_config)
return conn
|
[python] Returns a snowflake.connection object
|
def _get_aws_credentials(self):
if self.snowflake_conn_id:
connection_object = self.get_connection(self.snowflake_conn_id)
if 'aws_secret_access_key' in connection_object.extra_dejson:
aws_access_key_id = connection_object.extra_dejson.get(
'aws_access_key_id')
aws_secret_access_key = connection_object.extra_dejson.get(
'aws_secret_access_key')
return aws_access_key_id, aws_secret_access_key
|
[python] returns aws_access_key_id, aws_secret_access_key
from extra
intended to be used by external import and export statements
|
def _get_field(self, field_name, default=None):
full_field_name = 'extra__grpc__{}'.format(field_name)
if full_field_name in self.extras:
return self.extras[full_field_name]
else:
return default
|
[python] Fetches a field from extras, and returns it. This is some Airflow
magic. The grpc hook type adds custom UI elements
to the hook page, which allow admins to specify scopes, credential pem files, etc.
They get formatted as shown below.
|
def copy_expert(self, sql, filename, open=open):
if not os.path.isfile(filename):
with open(filename, 'w'):
pass
with open(filename, 'r+') as f:
with closing(self.get_conn()) as conn:
with closing(conn.cursor()) as cur:
cur.copy_expert(sql, f)
f.truncate(f.tell())
conn.commit()
|
[python] Executes SQL using psycopg2 copy_expert method.
Necessary to execute COPY command without access to a superuser.
Note: if this method is called with a "COPY FROM" statement and
the specified input file does not exist, it creates an empty
file and no data is loaded, but the operation succeeds.
So if users want to be aware when the input file does not exist,
they have to check its existence by themselves.
|
def bulk_load(self, table, tmp_file):
self.copy_expert("COPY {table} FROM STDIN".format(table=table), tmp_file)
|
[python] Loads a tab-delimited file into a database table
|
def bulk_dump(self, table, tmp_file):
self.copy_expert("COPY {table} TO STDOUT".format(table=table), tmp_file)
|
[python] Dumps a database table into a tab-delimited file
|
def execute(self, context):
hook = GoogleCloudStorageHook(
google_cloud_storage_conn_id=self.google_cloud_storage_conn_id,
delegate_to=self.delegate_to)
hook.upload(
bucket_name=self.bucket,
object_name=self.dst,
mime_type=self.mime_type,
filename=self.src,
gzip=self.gzip,
)
|
[python] Uploads the file to Google cloud storage
|
def max_partition(
table, schema="default", field=None, filter_map=None,
metastore_conn_id='metastore_default'):
from airflow.hooks.hive_hooks import HiveMetastoreHook
if '.' in table:
schema, table = table.split('.')
hh = HiveMetastoreHook(metastore_conn_id=metastore_conn_id)
return hh.max_partition(
schema=schema, table_name=table, field=field, filter_map=filter_map)
|
[python] Gets the max partition for a table.
:param schema: The hive schema the table lives in
:type schema: str
:param table: The hive table you are interested in, supports the dot
notation as in "my_database.my_table", if a dot is found,
the schema param is disregarded
:type table: str
:param metastore_conn_id: The hive connection you are interested in.
If your default is set you don't need to use this parameter.
:type metastore_conn_id: str
:param filter_map: partition_key:partition_value map used for partition filtering,
e.g. {'key1': 'value1', 'key2': 'value2'}.
Only partitions matching all partition_key:partition_value
pairs will be considered as candidates of max partition.
:type filter_map: map
:param field: the field to get the max value from. If there's only
one partition field, this will be inferred
:type field: str
>>> max_partition('airflow.static_babynames_partitioned')
'2015-01-01'
|
def _closest_date(target_dt, date_list, before_target=None):
fb = lambda d: target_dt - d if d <= target_dt else datetime.timedelta.max
fa = lambda d: d - target_dt if d >= target_dt else datetime.timedelta.max
fnone = lambda d: target_dt - d if d < target_dt else d - target_dt
if before_target is None:
return min(date_list, key=fnone).date()
if before_target:
return min(date_list, key=fb).date()
else:
return min(date_list, key=fa).date()
|
[python] This function finds the date in a list closest to the target date.
An optional parameter can be given to get the closest before or after.
:param target_dt: The target date
:type target_dt: datetime.date
:param date_list: The list of dates to search
:type date_list: list[datetime.date]
:param before_target: closest before or after the target
:type before_target: bool or None
:returns: The closest date
:rtype: datetime.date or None
|
def closest_ds_partition(
table, ds, before=True, schema="default",
metastore_conn_id='metastore_default'):
from airflow.hooks.hive_hooks import HiveMetastoreHook
if '.' in table:
schema, table = table.split('.')
hh = HiveMetastoreHook(metastore_conn_id=metastore_conn_id)
partitions = hh.get_partitions(schema=schema, table_name=table)
if not partitions:
return None
part_vals = [list(p.values())[0] for p in partitions]
if ds in part_vals:
return ds
else:
parts = [datetime.datetime.strptime(pv, '%Y-%m-%d')
for pv in part_vals]
target_dt = datetime.datetime.strptime(ds, '%Y-%m-%d')
closest_ds = _closest_date(target_dt, parts, before_target=before)
return closest_ds.isoformat()
|
[python] This function finds the date in a list closest to the target date.
An optional parameter can be given to get the closest before or after.
:param table: A hive table name
:type table: str
:param ds: A datestamp ``%Y-%m-%d`` e.g. ``yyyy-mm-dd``
:type ds: list[datetime.date]
:param before: closest before (True), after (False) or either side of ds
:type before: bool or None
:returns: The closest date
:rtype: str or None
>>> tbl = 'airflow.static_babynames_partitioned'
>>> closest_ds_partition(tbl, '2015-01-02')
'2015-01-01'
|
def get_conn(self):
conn = self.get_connection(self.mysql_conn_id)
conn_config = {
"user": conn.login,
"passwd": conn.password or '',
"host": conn.host or 'localhost',
"db": self.schema or conn.schema or ''
}
if not conn.port:
conn_config["port"] = 3306
else:
conn_config["port"] = int(conn.port)
if conn.extra_dejson.get('charset', False):
conn_config["charset"] = conn.extra_dejson["charset"]
if (conn_config["charset"]).lower() == 'utf8' or\
(conn_config["charset"]).lower() == 'utf-8':
conn_config["use_unicode"] = True
if conn.extra_dejson.get('cursor', False):
if (conn.extra_dejson["cursor"]).lower() == 'sscursor':
conn_config["cursorclass"] = MySQLdb.cursors.SSCursor
elif (conn.extra_dejson["cursor"]).lower() == 'dictcursor':
conn_config["cursorclass"] = MySQLdb.cursors.DictCursor
elif (conn.extra_dejson["cursor"]).lower() == 'ssdictcursor':
conn_config["cursorclass"] = MySQLdb.cursors.SSDictCursor
local_infile = conn.extra_dejson.get('local_infile', False)
if conn.extra_dejson.get('ssl', False):
# SSL parameter for MySQL has to be a dictionary and in case
# of extra/dejson we can get string if extra is passed via
# URL parameters
dejson_ssl = conn.extra_dejson['ssl']
if isinstance(dejson_ssl, six.string_types):
dejson_ssl = json.loads(dejson_ssl)
conn_config['ssl'] = dejson_ssl
if conn.extra_dejson.get('unix_socket'):
conn_config['unix_socket'] = conn.extra_dejson['unix_socket']
if local_infile:
conn_config["local_infile"] = 1
conn = MySQLdb.connect(**conn_config)
return conn
|
[python] Returns a mysql connection object
|
def bulk_load(self, table, tmp_file):
conn = self.get_conn()
cur = conn.cursor()
cur.execute("""
LOAD DATA LOCAL INFILE '{tmp_file}'
INTO TABLE {table}
""".format(tmp_file=tmp_file, table=table))
conn.commit()
|
[python] Loads a tab-delimited file into a database table
|
def is_bucket_updated(self, current_num_objects):
if current_num_objects > self.previous_num_objects:
# When new objects arrived, reset the inactivity_seconds
# previous_num_objects for the next poke.
self.log.info(
'''
New objects found at {} resetting last_activity_time.
'''.format(os.path.join(self.bucket, self.prefix)))
self.last_activity_time = get_time()
self.inactivity_seconds = 0
self.previous_num_objects = current_num_objects
elif current_num_objects < self.previous_num_objects:
# During the last poke interval objects were deleted.
if self.allow_delete:
self.previous_num_objects = current_num_objects
self.last_activity_time = get_time()
self.log.warning(
'''
Objects were deleted during the last
poke interval. Updating the file counter and
resetting last_activity_time.
'''
)
else:
raise RuntimeError(
'''
Illegal behavior: objects were deleted in {} between pokes.
'''.format(os.path.join(self.bucket, self.prefix))
)
else:
if self.last_activity_time:
self.inactivity_seconds = (
get_time() - self.last_activity_time).total_seconds()
else:
# Handles the first poke where last inactivity time is None.
self.last_activity_time = get_time()
self.inactivity_seconds = 0
if self.inactivity_seconds >= self.inactivity_period:
if current_num_objects >= self.min_objects:
self.log.info(
'''
SUCCESS:
Sensor found {} objects at {}.
Waited at least {} seconds, with no new objects dropped.
'''.format(
current_num_objects,
os.path.join(self.bucket, self.prefix),
self.inactivity_period))
return True
warn_msg = \
'''
FAILURE:
Inactivity Period passed,
not enough objects found in {}
'''.format(
os.path.join(self.bucket, self.prefix))
self.log.warning(warn_msg)
return False
return False
|
[python] Checks whether new objects have been uploaded and the inactivity_period
has passed and updates the state of the sensor accordingly.
:param current_num_objects: number of objects in bucket during last poke.
:type current_num_objects: int
|
def sigquit_handler(sig, frame):
print("Dumping stack traces for all threads in PID {}".format(os.getpid()))
id_to_name = dict([(th.ident, th.name) for th in threading.enumerate()])
code = []
for thread_id, stack in sys._current_frames().items():
code.append("\n# Thread: {}({})"
.format(id_to_name.get(thread_id, ""), thread_id))
for filename, line_number, name, line in traceback.extract_stack(stack):
code.append('File: "{}", line {}, in {}'
.format(filename, line_number, name))
if line:
code.append(" {}".format(line.strip()))
print("\n".join(code))
|
[python] Helps debug deadlocks by printing stacktraces when this gets a SIGQUIT
e.g. kill -s QUIT <PID> or CTRL+\
|
def trigger_dag(args):
log = LoggingMixin().log
try:
message = api_client.trigger_dag(dag_id=args.dag_id,
run_id=args.run_id,
conf=args.conf,
execution_date=args.exec_date)
except IOError as err:
log.error(err)
raise AirflowException(err)
log.info(message)
|
[python] Creates a dag run for the specified dag
:param args:
:return:
|
def delete_dag(args):
log = LoggingMixin().log
if args.yes or input(
"This will drop all existing records related to the specified DAG. "
"Proceed? (y/n)").upper() == "Y":
try:
message = api_client.delete_dag(dag_id=args.dag_id)
except IOError as err:
log.error(err)
raise AirflowException(err)
log.info(message)
else:
print("Bail.")
|
[python] Deletes all DB records related to the specified dag
:param args:
:return:
|
def task_failed_deps(args):
dag = get_dag(args)
task = dag.get_task(task_id=args.task_id)
ti = TaskInstance(task, args.execution_date)
dep_context = DepContext(deps=SCHEDULER_DEPS)
failed_deps = list(ti.get_failed_dep_statuses(dep_context=dep_context))
# TODO, Do we want to print or log this
if failed_deps:
print("Task instance dependencies not met:")
for dep in failed_deps:
print("{}: {}".format(dep.dep_name, dep.reason))
else:
print("Task instance dependencies are all met.")
|
[python] Returns the unmet dependencies for a task instance from the perspective of the
scheduler (i.e. why a task instance doesn't get scheduled and then queued by the
scheduler, and then run by an executor).
>>> airflow task_failed_deps tutorial sleep 2015-01-01
Task instance dependencies not met:
Dagrun Running: Task instance's dagrun did not exist: Unknown reason
Trigger Rule: Task's trigger rule 'all_success' requires all upstream tasks
to have succeeded, but found 1 non-success(es).
|
def task_state(args):
dag = get_dag(args)
task = dag.get_task(task_id=args.task_id)
ti = TaskInstance(task, args.execution_date)
print(ti.current_state())
|
[python] Returns the state of a TaskInstance at the command line.
>>> airflow task_state tutorial sleep 2015-01-01
success
|
def dag_state(args):
dag = get_dag(args)
dr = DagRun.find(dag.dag_id, execution_date=args.execution_date)
print(dr[0].state if len(dr) > 0 else None)
|
[python] Returns the state of a DagRun at the command line.
>>> airflow dag_state tutorial 2015-01-01T00:00:00.000000
running
|
def next_execution(args):
dag = get_dag(args)
if dag.is_paused:
print("[INFO] Please be reminded this DAG is PAUSED now.")
if dag.latest_execution_date:
next_execution_dttm = dag.following_schedule(dag.latest_execution_date)
if next_execution_dttm is None:
print("[WARN] No following schedule can be found. " +
"This DAG may have schedule interval '@once' or `None`.")
print(next_execution_dttm)
else:
print("[WARN] Only applicable when there is execution record found for the DAG.")
print(None)
|
[python] Returns the next execution datetime of a DAG at the command line.
>>> airflow next_execution tutorial
2018-08-31 10:38:00
|
def restart_workers(gunicorn_master_proc, num_workers_expected, master_timeout):
def wait_until_true(fn, timeout=0):
"""
Sleeps until fn is true
"""
t = time.time()
while not fn():
if 0 < timeout <= time.time() - t:
raise AirflowWebServerTimeout(
"No response from gunicorn master within {0} seconds"
.format(timeout))
time.sleep(0.1)
def start_refresh(gunicorn_master_proc):
batch_size = conf.getint('webserver', 'worker_refresh_batch_size')
log.debug('%s doing a refresh of %s workers', state, batch_size)
sys.stdout.flush()
sys.stderr.flush()
excess = 0
for _ in range(batch_size):
gunicorn_master_proc.send_signal(signal.SIGTTIN)
excess += 1
wait_until_true(lambda: num_workers_expected + excess ==
get_num_workers_running(gunicorn_master_proc),
master_timeout)
try:
wait_until_true(lambda: num_workers_expected ==
get_num_workers_running(gunicorn_master_proc),
master_timeout)
while True:
num_workers_running = get_num_workers_running(gunicorn_master_proc)
num_ready_workers_running = \
get_num_ready_workers_running(gunicorn_master_proc)
state = '[{0} / {1}]'.format(num_ready_workers_running, num_workers_running)
# Whenever some workers are not ready, wait until all workers are ready
if num_ready_workers_running < num_workers_running:
log.debug('%s some workers are starting up, waiting...', state)
sys.stdout.flush()
time.sleep(1)
# Kill a worker gracefully by asking gunicorn to reduce number of workers
elif num_workers_running > num_workers_expected:
excess = num_workers_running - num_workers_expected
log.debug('%s killing %s workers', state, excess)
for _ in range(excess):
gunicorn_master_proc.send_signal(signal.SIGTTOU)
excess -= 1
wait_until_true(lambda: num_workers_expected + excess ==
get_num_workers_running(gunicorn_master_proc),
master_timeout)
# Start a new worker by asking gunicorn to increase number of workers
elif num_workers_running == num_workers_expected:
refresh_interval = conf.getint('webserver', 'worker_refresh_interval')
log.debug(
'%s sleeping for %ss starting doing a refresh...',
state, refresh_interval
)
time.sleep(refresh_interval)
start_refresh(gunicorn_master_proc)
else:
# num_ready_workers_running == num_workers_running < num_workers_expected
log.error((
"%s some workers seem to have died and gunicorn"
"did not restart them as expected"
), state)
time.sleep(10)
if len(
psutil.Process(gunicorn_master_proc.pid).children()
) < num_workers_expected:
start_refresh(gunicorn_master_proc)
except (AirflowWebServerTimeout, OSError) as err:
log.error(err)
log.error("Shutting down webserver")
try:
gunicorn_master_proc.terminate()
gunicorn_master_proc.wait()
finally:
sys.exit(1)
|
[python] Runs forever, monitoring the child processes of @gunicorn_master_proc and
restarting workers occasionally.
Each iteration of the loop traverses one edge of this state transition
diagram, where each state (node) represents
[ num_ready_workers_running / num_workers_running ]. We expect most time to
be spent in [n / n]. `bs` is the setting webserver.worker_refresh_batch_size.
The horizontal transition at ? happens after the new worker parses all the
dags (so it could take a while!)
V ────────────────────────────────────────────────────────────────────────┐
[n / n] ──TTIN──> [ [n, n+bs) / n + bs ] ────?───> [n + bs / n + bs] ──TTOU─┘
^ ^───────────────┘
│
│ ┌────────────────v
└──────┴────── [ [0, n) / n ] <─── start
We change the number of workers by sending TTIN and TTOU to the gunicorn
master process, which increases and decreases the number of child workers
respectively. Gunicorn guarantees that on TTOU workers are terminated
gracefully and that the oldest worker is terminated.
|
def get_conn(self):
if not self._client:
self._client = Client(credentials=self._get_credentials())
return self._client
|
[python] Retrieves connection to Cloud Translate
:return: Google Cloud Translate client object.
:rtype: Client
|
def translate(
self, values, target_language, format_=None, source_language=None, model=None
):
client = self.get_conn()
return client.translate(
values=values,
target_language=target_language,
format_=format_,
source_language=source_language,
model=model,
)
|
[python] Translate a string or list of strings.
See https://cloud.google.com/translate/docs/translating-text
:type values: str or list
:param values: String or list of strings to translate.
:type target_language: str
:param target_language: The language to translate results into. This
is required by the API and defaults to
the target language of the current instance.
:type format_: str
:param format_: (Optional) One of ``text`` or ``html``, to specify
if the input text is plain text or HTML.
:type source_language: str or None
:param source_language: (Optional) The language of the text to
be translated.
:type model: str or None
:param model: (Optional) The model used to translate the text, such
as ``'base'`` or ``'nmt'``.
:rtype: str or list
:returns: A list of dictionaries for each queried value. Each
dictionary typically contains three keys (though not
all will be present in all cases)
* ``detectedSourceLanguage``: The detected language (as an
ISO 639-1 language code) of the text.
* ``translatedText``: The translation of the text into the
target language.
* ``input``: The corresponding input value.
* ``model``: The model used to translate the text.
If only a single value is passed, then only a single
dictionary will be returned.
:raises: :class:`~exceptions.ValueError` if the number of
values and translations differ.
|
def execute(self, context):
self.log.info('Tmp dir root location: \n %s', gettempdir())
# Prepare env for child process.
if self.env is None:
self.env = os.environ.copy()
airflow_context_vars = context_to_airflow_vars(context, in_env_var_format=True)
self.log.info('Exporting the following env vars:\n%s',
'\n'.join(["{}={}".format(k, v)
for k, v in
airflow_context_vars.items()]))
self.env.update(airflow_context_vars)
self.lineage_data = self.bash_command
with TemporaryDirectory(prefix='airflowtmp') as tmp_dir:
with NamedTemporaryFile(dir=tmp_dir, prefix=self.task_id) as tmp_file:
tmp_file.write(bytes(self.bash_command, 'utf_8'))
tmp_file.flush()
script_location = os.path.abspath(tmp_file.name)
self.log.info('Temporary script location: %s', script_location)
def pre_exec():
# Restore default signal disposition and invoke setsid
for sig in ('SIGPIPE', 'SIGXFZ', 'SIGXFSZ'):
if hasattr(signal, sig):
signal.signal(getattr(signal, sig), signal.SIG_DFL)
os.setsid()
self.log.info('Running command: %s', self.bash_command)
sub_process = Popen(
['bash', tmp_file.name],
stdout=PIPE,
stderr=STDOUT,
cwd=tmp_dir,
env=self.env,
preexec_fn=pre_exec)
self.sub_process = sub_process
self.log.info('Output:')
line = ''
for raw_line in iter(sub_process.stdout.readline, b''):
line = raw_line.decode(self.output_encoding).rstrip()
self.log.info(line)
sub_process.wait()
self.log.info('Command exited with return code %s', sub_process.returncode)
if sub_process.returncode:
raise AirflowException('Bash command failed')
return line
|
[python] Execute the bash command in a temporary directory
which will be cleaned afterwards
|
def get_instance(self, instance, project_id=None):
return self.get_conn().instances().get(
project=project_id,
instance=instance
).execute(num_retries=self.num_retries)
|
[python] Retrieves a resource containing information about a Cloud SQL instance.
:param instance: Database instance ID. This does not include the project ID.
:type instance: str
:param project_id: Project ID of the project that contains the instance. If set
to None or missing, the default project_id from the GCP connection is used.
:type project_id: str
:return: A Cloud SQL instance resource.
:rtype: dict
|
def create_instance(self, body, project_id=None):
response = self.get_conn().instances().insert(
project=project_id,
body=body
).execute(num_retries=self.num_retries)
operation_name = response["name"]
self._wait_for_operation_to_complete(project_id=project_id,
operation_name=operation_name)
|
[python] Creates a new Cloud SQL instance.
:param body: Body required by the Cloud SQL insert API, as described in
https://cloud.google.com/sql/docs/mysql/admin-api/v1beta4/instances/insert#request-body.
:type body: dict
:param project_id: Project ID of the project that contains the instance. If set
to None or missing, the default project_id from the GCP connection is used.
:type project_id: str
:return: None
|
def patch_instance(self, body, instance, project_id=None):
response = self.get_conn().instances().patch(
project=project_id,
instance=instance,
body=body
).execute(num_retries=self.num_retries)
operation_name = response["name"]
self._wait_for_operation_to_complete(project_id=project_id,
operation_name=operation_name)
|
[python] Updates settings of a Cloud SQL instance.
Caution: This is not a partial update, so you must include values for
all the settings that you want to retain.
:param body: Body required by the Cloud SQL patch API, as described in
https://cloud.google.com/sql/docs/mysql/admin-api/v1beta4/instances/patch#request-body.
:type body: dict
:param instance: Cloud SQL instance ID. This does not include the project ID.
:type instance: str
:param project_id: Project ID of the project that contains the instance. If set
to None or missing, the default project_id from the GCP connection is used.
:type project_id: str
:return: None
|
def delete_instance(self, instance, project_id=None):
response = self.get_conn().instances().delete(
project=project_id,
instance=instance,
).execute(num_retries=self.num_retries)
operation_name = response["name"]
self._wait_for_operation_to_complete(project_id=project_id,
operation_name=operation_name)
|
[python] Deletes a Cloud SQL instance.
:param project_id: Project ID of the project that contains the instance. If set
to None or missing, the default project_id from the GCP connection is used.
:type project_id: str
:param instance: Cloud SQL instance ID. This does not include the project ID.
:type instance: str
:return: None
|
def get_database(self, instance, database, project_id=None):
return self.get_conn().databases().get(
project=project_id,
instance=instance,
database=database
).execute(num_retries=self.num_retries)
|
[python] Retrieves a database resource from a Cloud SQL instance.
:param instance: Database instance ID. This does not include the project ID.
:type instance: str
:param database: Name of the database in the instance.
:type database: str
:param project_id: Project ID of the project that contains the instance. If set
to None or missing, the default project_id from the GCP connection is used.
:type project_id: str
:return: A Cloud SQL database resource, as described in
https://cloud.google.com/sql/docs/mysql/admin-api/v1beta4/databases#resource.
:rtype: dict
|
def create_database(self, instance, body, project_id=None):
response = self.get_conn().databases().insert(
project=project_id,
instance=instance,
body=body
).execute(num_retries=self.num_retries)
operation_name = response["name"]
self._wait_for_operation_to_complete(project_id=project_id,
operation_name=operation_name)
|
[python] Creates a new database inside a Cloud SQL instance.
:param instance: Database instance ID. This does not include the project ID.
:type instance: str
:param body: The request body, as described in
https://cloud.google.com/sql/docs/mysql/admin-api/v1beta4/databases/insert#request-body.
:type body: dict
:param project_id: Project ID of the project that contains the instance. If set
to None or missing, the default project_id from the GCP connection is used.
:type project_id: str
:return: None
|
def patch_database(self, instance, database, body, project_id=None):
response = self.get_conn().databases().patch(
project=project_id,
instance=instance,
database=database,
body=body
).execute(num_retries=self.num_retries)
operation_name = response["name"]
self._wait_for_operation_to_complete(project_id=project_id,
operation_name=operation_name)
|
[python] Updates a database resource inside a Cloud SQL instance.
This method supports patch semantics.
See https://cloud.google.com/sql/docs/mysql/admin-api/how-tos/performance#patch.
:param instance: Database instance ID. This does not include the project ID.
:type instance: str
:param database: Name of the database to be updated in the instance.
:type database: str
:param body: The request body, as described in
https://cloud.google.com/sql/docs/mysql/admin-api/v1beta4/databases/insert#request-body.
:type body: dict
:param project_id: Project ID of the project that contains the instance. If set
to None or missing, the default project_id from the GCP connection is used.
:type project_id: str
:return: None
|
def delete_database(self, instance, database, project_id=None):
response = self.get_conn().databases().delete(
project=project_id,
instance=instance,
database=database
).execute(num_retries=self.num_retries)
operation_name = response["name"]
self._wait_for_operation_to_complete(project_id=project_id,
operation_name=operation_name)
|
[python] Deletes a database from a Cloud SQL instance.
:param instance: Database instance ID. This does not include the project ID.
:type instance: str
:param database: Name of the database to be deleted in the instance.
:type database: str
:param project_id: Project ID of the project that contains the instance. If set
to None or missing, the default project_id from the GCP connection is used.
:type project_id: str
:return: None
|
def export_instance(self, instance, body, project_id=None):
try:
response = self.get_conn().instances().export(
project=project_id,
instance=instance,
body=body
).execute(num_retries=self.num_retries)
operation_name = response["name"]
self._wait_for_operation_to_complete(project_id=project_id,
operation_name=operation_name)
except HttpError as ex:
raise AirflowException(
'Exporting instance {} failed: {}'.format(instance, ex.content)
)
|
[python] Exports data from a Cloud SQL instance to a Cloud Storage bucket as a SQL dump
or CSV file.
:param instance: Database instance ID of the Cloud SQL instance. This does not include the
project ID.
:type instance: str
:param body: The request body, as described in
https://cloud.google.com/sql/docs/mysql/admin-api/v1beta4/instances/export#request-body
:type body: dict
:param project_id: Project ID of the project that contains the instance. If set
to None or missing, the default project_id from the GCP connection is used.
:type project_id: str
:return: None
|
def _wait_for_operation_to_complete(self, project_id, operation_name):
service = self.get_conn()
while True:
operation_response = service.operations().get(
project=project_id,
operation=operation_name,
).execute(num_retries=self.num_retries)
if operation_response.get("status") == CloudSqlOperationStatus.DONE:
error = operation_response.get("error")
if error:
# Extracting the errors list as string and trimming square braces
error_msg = str(error.get("errors"))[1:-1]
raise AirflowException(error_msg)
# No meaningful info to return from the response in case of success
return
time.sleep(TIME_TO_SLEEP_IN_SECONDS)
|
[python] Waits for the named operation to complete - checks status of the
asynchronous call.
:param project_id: Project ID of the project that contains the instance.
:type project_id: str
:param operation_name: Name of the operation.
:type operation_name: str
:return: None
|
def start_proxy(self):
self._download_sql_proxy_if_needed()
if self.sql_proxy_process:
raise AirflowException("The sql proxy is already running: {}".format(
self.sql_proxy_process))
else:
command_to_run = [self.sql_proxy_path]
command_to_run.extend(self.command_line_parameters)
try:
self.log.info("Creating directory %s",
self.cloud_sql_proxy_socket_directory)
os.makedirs(self.cloud_sql_proxy_socket_directory)
except OSError:
# Needed for python 2 compatibility (exists_ok missing)
pass
command_to_run.extend(self._get_credential_parameters())
self.log.info("Running the command: `%s`", " ".join(command_to_run))
self.sql_proxy_process = Popen(command_to_run,
stdin=PIPE, stdout=PIPE, stderr=PIPE)
self.log.info("The pid of cloud_sql_proxy: %s", self.sql_proxy_process.pid)
while True:
line = self.sql_proxy_process.stderr.readline().decode('utf-8')
return_code = self.sql_proxy_process.poll()
if line == '' and return_code is not None:
self.sql_proxy_process = None
raise AirflowException(
"The cloud_sql_proxy finished early with return code {}!".format(
return_code))
if line != '':
self.log.info(line)
if "googleapi: Error" in line or "invalid instance name:" in line:
self.stop_proxy()
raise AirflowException(
"Error when starting the cloud_sql_proxy {}!".format(
line))
if "Ready for new connections" in line:
return
|
[python] Starts Cloud SQL Proxy.
You have to remember to stop the proxy if you started it!
|
def stop_proxy(self):
if not self.sql_proxy_process:
raise AirflowException("The sql proxy is not started yet")
else:
self.log.info("Stopping the cloud_sql_proxy pid: %s",
self.sql_proxy_process.pid)
self.sql_proxy_process.kill()
self.sql_proxy_process = None
# Cleanup!
self.log.info("Removing the socket directory: %s",
self.cloud_sql_proxy_socket_directory)
shutil.rmtree(self.cloud_sql_proxy_socket_directory, ignore_errors=True)
if self.sql_proxy_was_downloaded:
self.log.info("Removing downloaded proxy: %s", self.sql_proxy_path)
# Silently ignore if the file has already been removed (concurrency)
try:
os.remove(self.sql_proxy_path)
except OSError as e:
if not e.errno == errno.ENOENT:
raise
else:
self.log.info("Skipped removing proxy - it was not downloaded: %s",
self.sql_proxy_path)
if os.path.isfile(self.credentials_path):
self.log.info("Removing generated credentials file %s",
self.credentials_path)
# Here file cannot be delete by concurrent task (each task has its own copy)
os.remove(self.credentials_path)
|
[python] Stops running proxy.
You should stop the proxy after you stop using it.
|
def get_proxy_version(self):
self._download_sql_proxy_if_needed()
command_to_run = [self.sql_proxy_path]
command_to_run.extend(['--version'])
command_to_run.extend(self._get_credential_parameters())
result = subprocess.check_output(command_to_run).decode('utf-8')
pattern = re.compile("^.*[V|v]ersion ([^;]*);.*$")
m = pattern.match(result)
if m:
return m.group(1)
else:
return None
|
[python] Returns version of the Cloud SQL Proxy.
|
def create_connection(self, session=None):
connection = Connection(conn_id=self.db_conn_id)
uri = self._generate_connection_uri()
self.log.info("Creating connection %s", self.db_conn_id)
connection.parse_from_uri(uri)
session.add(connection)
session.commit()
|
[python] Create connection in the Connection table, according to whether it uses
proxy, TCP, UNIX sockets, SSL. Connection ID will be randomly generated.
:param session: Session of the SQL Alchemy ORM (automatically generated with
decorator).
|
def retrieve_connection(self, session=None):
self.log.info("Retrieving connection %s", self.db_conn_id)
connections = session.query(Connection).filter(
Connection.conn_id == self.db_conn_id)
if connections.count():
return connections[0]
return None
|
[python] Retrieves the dynamically created connection from the Connection table.
:param session: Session of the SQL Alchemy ORM (automatically generated with
decorator).
|
def delete_connection(self, session=None):
self.log.info("Deleting connection %s", self.db_conn_id)
connections = session.query(Connection).filter(
Connection.conn_id == self.db_conn_id)
if connections.count():
connection = connections[0]
session.delete(connection)
session.commit()
else:
self.log.info("Connection was already deleted!")
|
[python] Delete the dynamically created connection from the Connection table.
:param session: Session of the SQL Alchemy ORM (automatically generated with
decorator).
|
def get_sqlproxy_runner(self):
if not self.use_proxy:
raise AirflowException("Proxy runner can only be retrieved in case of use_proxy = True")
return CloudSqlProxyRunner(
path_prefix=self.sql_proxy_unique_path,
instance_specification=self._get_sqlproxy_instance_specification(),
project_id=self.project_id,
sql_proxy_version=self.sql_proxy_version,
sql_proxy_binary_path=self.sql_proxy_binary_path
)
|
[python] Retrieve Cloud SQL Proxy runner. It is used to manage the proxy
lifecycle per task.
:return: The Cloud SQL Proxy runner.
:rtype: CloudSqlProxyRunner
|
def get_database_hook(self):
if self.database_type == 'postgres':
self.db_hook = PostgresHook(postgres_conn_id=self.db_conn_id,
schema=self.database)
else:
self.db_hook = MySqlHook(mysql_conn_id=self.db_conn_id,
schema=self.database)
return self.db_hook
|
[python] Retrieve database hook. This is the actual Postgres or MySQL database hook
that uses proxy or connects directly to the Google Cloud SQL database.
|
def cleanup_database_hook(self):
if self.database_type == 'postgres':
if hasattr(self.db_hook,
'conn') and self.db_hook.conn and self.db_hook.conn.notices:
for output in self.db_hook.conn.notices:
self.log.info(output)
|
[python] Clean up database hook after it was used.
|
def reserve_free_tcp_port(self):
self.reserved_tcp_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
self.reserved_tcp_socket.bind(('127.0.0.1', 0))
self.sql_proxy_tcp_port = self.reserved_tcp_socket.getsockname()[1]
|
[python] Reserve free TCP port to be used by Cloud SQL Proxy
|
def _normalize_mlengine_job_id(job_id):
# Add a prefix when a job_id starts with a digit or a template
match = re.search(r'\d|\{{2}', job_id)
if match and match.start() == 0:
job = 'z_{}'.format(job_id)
else:
job = job_id
# Clean up 'bad' characters except templates
tracker = 0
cleansed_job_id = ''
for m in re.finditer(r'\{{2}.+?\}{2}', job):
cleansed_job_id += re.sub(r'[^0-9a-zA-Z]+', '_',
job[tracker:m.start()])
cleansed_job_id += job[m.start():m.end()]
tracker = m.end()
# Clean up last substring or the full string if no templates
cleansed_job_id += re.sub(r'[^0-9a-zA-Z]+', '_', job[tracker:])
return cleansed_job_id
|
[python] Replaces invalid MLEngine job_id characters with '_'.
This also adds a leading 'z' in case job_id starts with an invalid
character.
Args:
job_id: A job_id str that may have invalid characters.
Returns:
A valid job_id representation.
|
def _get_error_code(self, e):
try:
matches = self.error_code_pattern.match(str(e))
code = int(matches.group(0))
return code
except ValueError:
return e
|
[python] Extract error code from ftp exception
|
def _integrate_plugins():
import sys
from airflow.plugins_manager import sensors_modules
for sensors_module in sensors_modules:
sys.modules[sensors_module.__name__] = sensors_module
globals()[sensors_module._name] = sensors_module
|
[python] Integrate plugins to the context
|
def clear_dag_runs():
session = settings.Session()
drs = session.query(DagRun).filter(
DagRun.dag_id.in_(DAG_IDS),
).all()
for dr in drs:
logging.info('Deleting DagRun :: {}'.format(dr))
session.delete(dr)
|
[python] Remove any existing DAG runs for the perf test DAGs.
|
def clear_dag_task_instances():
session = settings.Session()
TI = TaskInstance
tis = (
session
.query(TI)
.filter(TI.dag_id.in_(DAG_IDS))
.all()
)
for ti in tis:
logging.info('Deleting TaskInstance :: {}'.format(ti))
session.delete(ti)
session.commit()
|
[python] Remove any existing task instances for the perf test DAGs.
|
def set_dags_paused_state(is_paused):
session = settings.Session()
dms = session.query(DagModel).filter(
DagModel.dag_id.in_(DAG_IDS))
for dm in dms:
logging.info('Setting DAG :: {} is_paused={}'.format(dm, is_paused))
dm.is_paused = is_paused
session.commit()
|
[python] Toggle the pause state of the DAGs in the test.
|
def print_stats(self):
session = settings.Session()
TI = TaskInstance
tis = (
session
.query(TI)
.filter(TI.dag_id.in_(DAG_IDS))
.all()
)
successful_tis = [x for x in tis if x.state == State.SUCCESS]
ti_perf = [(ti.dag_id, ti.task_id, ti.execution_date,
(ti.queued_dttm - self.start_date).total_seconds(),
(ti.start_date - self.start_date).total_seconds(),
(ti.end_date - self.start_date).total_seconds(),
ti.duration) for ti in successful_tis]
ti_perf_df = pd.DataFrame(ti_perf, columns=['dag_id', 'task_id',
'execution_date',
'queue_delay',
'start_delay', 'land_time',
'duration'])
print('Performance Results')
print('###################')
for dag_id in DAG_IDS:
print('DAG {}'.format(dag_id))
print(ti_perf_df[ti_perf_df['dag_id'] == dag_id])
print('###################')
if len(tis) > len(successful_tis):
print("WARNING!! The following task instances haven't completed")
print(pd.DataFrame([(ti.dag_id, ti.task_id, ti.execution_date, ti.state)
for ti in filter(lambda x: x.state != State.SUCCESS, tis)],
columns=['dag_id', 'task_id', 'execution_date', 'state']))
session.commit()
|
[python] Print operational metrics for the scheduler test.
|
def heartbeat(self):
super(SchedulerMetricsJob, self).heartbeat()
session = settings.Session()
# Get all the relevant task instances
TI = TaskInstance
successful_tis = (
session
.query(TI)
.filter(TI.dag_id.in_(DAG_IDS))
.filter(TI.state.in_([State.SUCCESS]))
.all()
)
session.commit()
dagbag = DagBag(SUBDIR)
dags = [dagbag.dags[dag_id] for dag_id in DAG_IDS]
# the tasks in perf_dag_1 and per_dag_2 have a daily schedule interval.
num_task_instances = sum([(timezone.utcnow() - task.start_date).days
for dag in dags for task in dag.tasks])
if (len(successful_tis) == num_task_instances or
(timezone.utcnow() - self.start_date).total_seconds() >
MAX_RUNTIME_SECS):
if len(successful_tis) == num_task_instances:
self.log.info("All tasks processed! Printing stats.")
else:
self.log.info("Test timeout reached. Printing available stats.")
self.print_stats()
set_dags_paused_state(True)
sys.exit()
|
[python] Override the scheduler heartbeat to determine when the test is complete
|
def get_dag_run_state(dag_id, execution_date):
dagbag = DagBag()
# Check DAG exists.
if dag_id not in dagbag.dags:
error_message = "Dag id {} not found".format(dag_id)
raise DagNotFound(error_message)
# Get DAG object and check Task Exists
dag = dagbag.get_dag(dag_id)
# Get DagRun object and check that it exists
dagrun = dag.get_dagrun(execution_date=execution_date)
if not dagrun:
error_message = ('Dag Run for date {} not found in dag {}'
.format(execution_date, dag_id))
raise DagRunNotFound(error_message)
return {'state': dagrun.get_state()}
|
[python] Return the task object identified by the given dag_id and task_id.
|
def create_evaluate_ops(task_prefix,
data_format,
input_paths,
prediction_path,
metric_fn_and_keys,
validate_fn,
batch_prediction_job_id=None,
project_id=None,
region=None,
dataflow_options=None,
model_uri=None,
model_name=None,
version_name=None,
dag=None):
# Verify that task_prefix doesn't have any special characters except hyphen
# '-', which is the only allowed non-alphanumeric character by Dataflow.
if not re.match(r"^[a-zA-Z][-A-Za-z0-9]*$", task_prefix):
raise AirflowException(
"Malformed task_id for DataFlowPythonOperator (only alphanumeric "
"and hyphens are allowed but got: " + task_prefix)
metric_fn, metric_keys = metric_fn_and_keys
if not callable(metric_fn):
raise AirflowException("`metric_fn` param must be callable.")
if not callable(validate_fn):
raise AirflowException("`validate_fn` param must be callable.")
if dag is not None and dag.default_args is not None:
default_args = dag.default_args
project_id = project_id or default_args.get('project_id')
region = region or default_args.get('region')
model_name = model_name or default_args.get('model_name')
version_name = version_name or default_args.get('version_name')
dataflow_options = dataflow_options or \
default_args.get('dataflow_default_options')
evaluate_prediction = MLEngineBatchPredictionOperator(
task_id=(task_prefix + "-prediction"),
project_id=project_id,
job_id=batch_prediction_job_id,
region=region,
data_format=data_format,
input_paths=input_paths,
output_path=prediction_path,
uri=model_uri,
model_name=model_name,
version_name=version_name,
dag=dag)
metric_fn_encoded = base64.b64encode(dill.dumps(metric_fn, recurse=True))
evaluate_summary = DataFlowPythonOperator(
task_id=(task_prefix + "-summary"),
py_options=["-m"],
py_file="airflow.contrib.utils.mlengine_prediction_summary",
dataflow_default_options=dataflow_options,
options={
"prediction_path": prediction_path,
"metric_fn_encoded": metric_fn_encoded,
"metric_keys": ','.join(metric_keys)
},
dag=dag)
evaluate_summary.set_upstream(evaluate_prediction)
def apply_validate_fn(*args, **kwargs):
prediction_path = kwargs["templates_dict"]["prediction_path"]
scheme, bucket, obj, _, _ = urlsplit(prediction_path)
if scheme != "gs" or not bucket or not obj:
raise ValueError("Wrong format prediction_path: %s",
prediction_path)
summary = os.path.join(obj.strip("/"),
"prediction.summary.json")
gcs_hook = GoogleCloudStorageHook()
summary = json.loads(gcs_hook.download(bucket, summary))
return validate_fn(summary)
evaluate_validation = PythonOperator(
task_id=(task_prefix + "-validation"),
python_callable=apply_validate_fn,
provide_context=True,
templates_dict={"prediction_path": prediction_path},
dag=dag)
evaluate_validation.set_upstream(evaluate_summary)
return evaluate_prediction, evaluate_summary, evaluate_validation
|
[python] Creates Operators needed for model evaluation and returns.
It gets prediction over inputs via Cloud ML Engine BatchPrediction API by
calling MLEngineBatchPredictionOperator, then summarize and validate
the result via Cloud Dataflow using DataFlowPythonOperator.
For details and pricing about Batch prediction, please refer to the website
https://cloud.google.com/ml-engine/docs/how-tos/batch-predict
and for Cloud Dataflow, https://cloud.google.com/dataflow/docs/
It returns three chained operators for prediction, summary, and validation,
named as <prefix>-prediction, <prefix>-summary, and <prefix>-validation,
respectively.
(<prefix> should contain only alphanumeric characters or hyphen.)
The upstream and downstream can be set accordingly like:
pred, _, val = create_evaluate_ops(...)
pred.set_upstream(upstream_op)
...
downstream_op.set_upstream(val)
Callers will provide two python callables, metric_fn and validate_fn, in
order to customize the evaluation behavior as they wish.
- metric_fn receives a dictionary per instance derived from json in the
batch prediction result. The keys might vary depending on the model.
It should return a tuple of metrics.
- validation_fn receives a dictionary of the averaged metrics that metric_fn
generated over all instances.
The key/value of the dictionary matches to what's given by
metric_fn_and_keys arg.
The dictionary contains an additional metric, 'count' to represent the
total number of instances received for evaluation.
The function would raise an exception to mark the task as failed, in a
case the validation result is not okay to proceed (i.e. to set the trained
version as default).
Typical examples are like this:
def get_metric_fn_and_keys():
import math # imports should be outside of the metric_fn below.
def error_and_squared_error(inst):
label = float(inst['input_label'])
classes = float(inst['classes']) # 0 or 1
err = abs(classes-label)
squared_err = math.pow(classes-label, 2)
return (err, squared_err) # returns a tuple.
return error_and_squared_error, ['err', 'mse'] # key order must match.
def validate_err_and_count(summary):
if summary['err'] > 0.2:
raise ValueError('Too high err>0.2; summary=%s' % summary)
if summary['mse'] > 0.05:
raise ValueError('Too high mse>0.05; summary=%s' % summary)
if summary['count'] < 1000:
raise ValueError('Too few instances<1000; summary=%s' % summary)
return summary
For the details on the other BatchPrediction-related arguments (project_id,
job_id, region, data_format, input_paths, prediction_path, model_uri),
please refer to MLEngineBatchPredictionOperator too.
:param task_prefix: a prefix for the tasks. Only alphanumeric characters and
hyphen are allowed (no underscores), since this will be used as dataflow
job name, which doesn't allow other characters.
:type task_prefix: str
:param data_format: either of 'TEXT', 'TF_RECORD', 'TF_RECORD_GZIP'
:type data_format: str
:param input_paths: a list of input paths to be sent to BatchPrediction.
:type input_paths: list[str]
:param prediction_path: GCS path to put the prediction results in.
:type prediction_path: str
:param metric_fn_and_keys: a tuple of metric_fn and metric_keys:
- metric_fn is a function that accepts a dictionary (for an instance),
and returns a tuple of metric(s) that it calculates.
- metric_keys is a list of strings to denote the key of each metric.
:type metric_fn_and_keys: tuple of a function and a list[str]
:param validate_fn: a function to validate whether the averaged metric(s) is
good enough to push the model.
:type validate_fn: function
:param batch_prediction_job_id: the id to use for the Cloud ML Batch
prediction job. Passed directly to the MLEngineBatchPredictionOperator as
the job_id argument.
:type batch_prediction_job_id: str
:param project_id: the Google Cloud Platform project id in which to execute
Cloud ML Batch Prediction and Dataflow jobs. If None, then the `dag`'s
`default_args['project_id']` will be used.
:type project_id: str
:param region: the Google Cloud Platform region in which to execute Cloud ML
Batch Prediction and Dataflow jobs. If None, then the `dag`'s
`default_args['region']` will be used.
:type region: str
:param dataflow_options: options to run Dataflow jobs. If None, then the
`dag`'s `default_args['dataflow_default_options']` will be used.
:type dataflow_options: dictionary
:param model_uri: GCS path of the model exported by Tensorflow using
tensorflow.estimator.export_savedmodel(). It cannot be used with
model_name or version_name below. See MLEngineBatchPredictionOperator for
more detail.
:type model_uri: str
:param model_name: Used to indicate a model to use for prediction. Can be
used in combination with version_name, but cannot be used together with
model_uri. See MLEngineBatchPredictionOperator for more detail. If None,
then the `dag`'s `default_args['model_name']` will be used.
:type model_name: str
:param version_name: Used to indicate a model version to use for prediction,
in combination with model_name. Cannot be used together with model_uri.
See MLEngineBatchPredictionOperator for more detail. If None, then the
`dag`'s `default_args['version_name']` will be used.
:type version_name: str
:param dag: The `DAG` to use for all Operators.
:type dag: airflow.models.DAG
:returns: a tuple of three operators, (prediction, summary, validation)
:rtype: tuple(DataFlowPythonOperator, DataFlowPythonOperator,
PythonOperator)
|
def mkdirs(path, mode):
try:
o_umask = os.umask(0)
os.makedirs(path, mode)
except OSError:
if not os.path.isdir(path):
raise
finally:
os.umask(o_umask)
|
[python] Creates the directory specified by path, creating intermediate directories
as necessary. If directory already exists, this is a no-op.
:param path: The directory to create
:type path: str
:param mode: The mode to give to the directory e.g. 0o755, ignores umask
:type mode: int
|
def _convert_to_float_if_possible(s):
try:
ret = float(s)
except (ValueError, TypeError):
ret = s
return ret
|
[python] A small helper function to convert a string to a numeric value
if appropriate
:param s: the string to be converted
:type s: str
|
def utcnow():
# pendulum utcnow() is not used as that sets a TimezoneInfo object
# instead of a Timezone. This is not pickable and also creates issues
# when using replace()
d = dt.datetime.utcnow()
d = d.replace(tzinfo=utc)
return d
|
[python] Get the current date and time in UTC
:return:
|
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