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def sina_xml_to_url_list(xml_data):
"""str->list
Convert XML to URL List.
From Biligrab.
"""
rawurl = []
dom = parseString(xml_data)
for node in dom.getElementsByTagName('durl'):
url = node.getElementsByTagName('url')[0]
rawurl.append(url.childNodes[0].data)
return rawurl | 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 | str->list
Convert XML to URL List.
From Biligrab. |
def sprint(text, *colors):
"""Format text with color or other effects into ANSI escaped string."""
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 | 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 | Format text with color or other effects into ANSI escaped string. |
def ckplayer_get_info_by_xml(ckinfo):
"""str->dict
Information for CKPlayer API content."""
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 | 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 | str->dict
Information for CKPlayer API content. |
def get_video_url_from_video_id(video_id):
"""Splicing URLs according to video ID to get video details"""
# 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 | 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 | Splicing URLs according to video ID to get video details |
def download(self, **kwargs):
"""Override the original one
Ugly ugly dirty hack"""
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.') | 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.') | Override the original one
Ugly ugly dirty hack |
def acfun_download_by_vid(vid, title, output_dir='.', merge=True, info_only=False, **kwargs):
"""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.
"""
#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 | 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 | 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 matchall(text, patterns):
"""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.
"""
ret = []
for pattern in patterns:
match = re.findall(pattern, text)
ret += match
return ret | def matchall(text, patterns):
ret = []
for pattern in patterns:
match = re.findall(pattern, text)
ret += match
return ret | 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):
"""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.
"""
try:
return parse.parse_qs(parse.urlparse(url).query)[param][0]
except:
return None | def parse_query_param(url, param):
try:
return parse.parse_qs(parse.urlparse(url).query)[param][0]
except:
return None | 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 get_content(url, headers={}, decoded=True):
"""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.
"""
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 | 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 | 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):
"""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.
"""
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 | 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 | 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 _wanmen_get_title_by_json_topic_part(json_content, tIndex, pIndex):
"""JSON, int, int, int->str
Get a proper title with courseid+topicID+partID."""
return '_'.join([json_content[0]['name'],
json_content[0]['Topics'][tIndex]['name'],
json_content[0]['Topics'][tIndex]['Parts'][pIndex]['name']]) | 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']]) | 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):
"""int->None
Download a WHOLE course.
Reuse the API call to save time."""
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) | 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) | 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):
"""int, int, int->None
Download ONE PART of the course."""
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) | 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) | int, int, int->None
Download ONE PART of the course. |
def has_task(self, task_instance):
"""
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
"""
if task_instance.key in self.queued_tasks or task_instance.key in self.running:
return True | def has_task(self, task_instance):
if task_instance.key in self.queued_tasks or task_instance.key in self.running:
return True | 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):
"""
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
"""
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 | 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 | 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_aws_credentials(self):
"""
returns aws_access_key_id, aws_secret_access_key
from extra
intended to be used by external import and export statements
"""
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 | 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 | 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):
"""
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.
"""
full_field_name = 'extra__grpc__{}'.format(field_name)
if full_field_name in self.extras:
return self.extras[full_field_name]
else:
return default | 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 | 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):
"""
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.
"""
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() | 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() | 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 max_partition(
table, schema="default", field=None, filter_map=None,
metastore_conn_id='metastore_default'):
"""
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'
"""
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) | 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) | 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 task_state(args):
"""
Returns the state of a TaskInstance at the command line.
>>> airflow task_state tutorial sleep 2015-01-01
success
"""
dag = get_dag(args)
task = dag.get_task(task_id=args.task_id)
ti = TaskInstance(task, args.execution_date)
print(ti.current_state()) | 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()) | Returns the state of a TaskInstance at the command line.
>>> airflow task_state tutorial sleep 2015-01-01
success |
def restart_workers(gunicorn_master_proc, num_workers_expected, master_timeout):
"""
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 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) | 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) | 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):
"""
Retrieves connection to Cloud Translate
:return: Google Cloud Translate client object.
:rtype: Client
"""
if not self._client:
self._client = Client(credentials=self._get_credentials())
return self._client | def get_conn(self):
if not self._client:
self._client = Client(credentials=self._get_credentials())
return self._client | 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
):
"""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.
"""
client = self.get_conn()
return client.translate(
values=values,
target_language=target_language,
format_=format_,
source_language=source_language,
model=model,
) | 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,
) | 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 get_instance(self, instance, project_id=None):
"""
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
"""
return self.get_conn().instances().get(
project=project_id,
instance=instance
).execute(num_retries=self.num_retries) | 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) | 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):
"""
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
"""
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) | 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) | 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):
"""
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
"""
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) | 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) | 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):
"""
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
"""
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) | 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) | 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):
"""
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
"""
return self.get_conn().databases().get(
project=project_id,
instance=instance,
database=database
).execute(num_retries=self.num_retries) | 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) | 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):
"""
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
"""
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) | 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) | 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):
"""
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
"""
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) | 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) | 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):
"""
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
"""
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) | 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) | 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):
"""
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
"""
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)
) | 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)
) | 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 start_proxy(self):
"""
Starts Cloud SQL Proxy.
You have to remember to stop the proxy if you started it!
"""
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 | 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 | Starts Cloud SQL Proxy.
You have to remember to stop the proxy if you started it! |
def stop_proxy(self):
"""
Stops running proxy.
You should stop the proxy after you stop using it.
"""
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) | 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) | Stops running proxy.
You should stop the proxy after you stop using it. |
def create_connection(self, session=None):
"""
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).
"""
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() | 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() | 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):
"""
Retrieves the dynamically created connection from the Connection table.
:param session: Session of the SQL Alchemy ORM (automatically generated with
decorator).
"""
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 | 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 | 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):
"""
Delete the dynamically created connection from the Connection table.
:param session: Session of the SQL Alchemy ORM (automatically generated with
decorator).
"""
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!") | 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!") | 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):
"""
Retrieve Cloud SQL Proxy runner. It is used to manage the proxy
lifecycle per task.
:return: The Cloud SQL Proxy runner.
:rtype: CloudSqlProxyRunner
"""
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
) | 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
) | 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):
"""
Retrieve database hook. This is the actual Postgres or MySQL database hook
that uses proxy or connects directly to the Google Cloud SQL database.
"""
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 | 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 | 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 reserve_free_tcp_port(self):
"""
Reserve free TCP port to be used by Cloud SQL Proxy
"""
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] | 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] | Reserve free TCP port to be used by Cloud SQL Proxy |
def _normalize_mlengine_job_id(job_id):
"""
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.
"""
# 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 | 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 | 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 clear_dag_runs():
"""
Remove any existing DAG runs for the perf test DAGs.
"""
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) | 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) | Remove any existing DAG runs for the perf test DAGs. |
def clear_dag_task_instances():
"""
Remove any existing task instances for the perf test DAGs.
"""
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() | 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() | Remove any existing task instances for the perf test DAGs. |
def heartbeat(self):
"""
Override the scheduler heartbeat to determine when the test is complete
"""
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() | 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() | Override the scheduler heartbeat to determine when the test is complete |
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):
"""
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)
"""
# 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 | 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 | 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):
"""
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
"""
try:
o_umask = os.umask(0)
os.makedirs(path, mode)
except OSError:
if not os.path.isdir(path):
raise
finally:
os.umask(o_umask) | 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) | 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):
"""
A small helper function to convert a string to a numeric value
if appropriate
:param s: the string to be converted
:type s: str
"""
try:
ret = float(s)
except (ValueError, TypeError):
ret = s
return ret | def _convert_to_float_if_possible(s):
try:
ret = float(s)
except (ValueError, TypeError):
ret = s
return ret | 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 make_aware(value, timezone=None):
"""
Make a naive datetime.datetime in a given time zone aware.
:param value: datetime
:param timezone: timezone
:return: localized datetime in settings.TIMEZONE or timezone
"""
if timezone is None:
timezone = TIMEZONE
# Check that we won't overwrite the timezone of an aware datetime.
if is_localized(value):
raise ValueError(
"make_aware expects a naive datetime, got %s" % value)
if hasattr(value, 'fold'):
# In case of python 3.6 we want to do the same that pendulum does for python3.5
# i.e in case we move clock back we want to schedule the run at the time of the second
# instance of the same clock time rather than the first one.
# Fold parameter has no impact in other cases so we can safely set it to 1 here
value = value.replace(fold=1)
if hasattr(timezone, 'localize'):
# This method is available for pytz time zones.
return timezone.localize(value)
elif hasattr(timezone, 'convert'):
# For pendulum
return timezone.convert(value)
else:
# This may be wrong around DST changes!
return value.replace(tzinfo=timezone) | def make_aware(value, timezone=None):
if timezone is None:
timezone = TIMEZONE
# Check that we won't overwrite the timezone of an aware datetime.
if is_localized(value):
raise ValueError(
"make_aware expects a naive datetime, got %s" % value)
if hasattr(value, 'fold'):
# In case of python 3.6 we want to do the same that pendulum does for python3.5
# i.e in case we move clock back we want to schedule the run at the time of the second
# instance of the same clock time rather than the first one.
# Fold parameter has no impact in other cases so we can safely set it to 1 here
value = value.replace(fold=1)
if hasattr(timezone, 'localize'):
# This method is available for pytz time zones.
return timezone.localize(value)
elif hasattr(timezone, 'convert'):
# For pendulum
return timezone.convert(value)
else:
# This may be wrong around DST changes!
return value.replace(tzinfo=timezone) | Make a naive datetime.datetime in a given time zone aware.
:param value: datetime
:param timezone: timezone
:return: localized datetime in settings.TIMEZONE or timezone |
def make_naive(value, timezone=None):
"""
Make an aware datetime.datetime naive in a given time zone.
:param value: datetime
:param timezone: timezone
:return: naive datetime
"""
if timezone is None:
timezone = TIMEZONE
# Emulate the behavior of astimezone() on Python < 3.6.
if is_naive(value):
raise ValueError("make_naive() cannot be applied to a naive datetime")
o = value.astimezone(timezone)
# cross library compatibility
naive = dt.datetime(o.year,
o.month,
o.day,
o.hour,
o.minute,
o.second,
o.microsecond)
return naive | def make_naive(value, timezone=None):
if timezone is None:
timezone = TIMEZONE
# Emulate the behavior of astimezone() on Python < 3.6.
if is_naive(value):
raise ValueError("make_naive() cannot be applied to a naive datetime")
o = value.astimezone(timezone)
# cross library compatibility
naive = dt.datetime(o.year,
o.month,
o.day,
o.hour,
o.minute,
o.second,
o.microsecond)
return naive | Make an aware datetime.datetime naive in a given time zone.
:param value: datetime
:param timezone: timezone
:return: naive datetime |
def datetime(*args, **kwargs):
"""
Wrapper around datetime.datetime that adds settings.TIMEZONE if tzinfo not specified
:return: datetime.datetime
"""
if 'tzinfo' not in kwargs:
kwargs['tzinfo'] = TIMEZONE
return dt.datetime(*args, **kwargs) | def datetime(*args, **kwargs):
if 'tzinfo' not in kwargs:
kwargs['tzinfo'] = TIMEZONE
return dt.datetime(*args, **kwargs) | Wrapper around datetime.datetime that adds settings.TIMEZONE if tzinfo not specified
:return: datetime.datetime |
def get_conn(self, headers=None):
"""
Returns http session for use with requests
:param headers: additional headers to be passed through as a dictionary
:type headers: dict
"""
session = requests.Session()
if self.http_conn_id:
conn = self.get_connection(self.http_conn_id)
if "://" in conn.host:
self.base_url = conn.host
else:
# schema defaults to HTTP
schema = conn.schema if conn.schema else "http"
self.base_url = schema + "://" + conn.host
if conn.port:
self.base_url = self.base_url + ":" + str(conn.port)
if conn.login:
session.auth = (conn.login, conn.password)
if conn.extra:
try:
session.headers.update(conn.extra_dejson)
except TypeError:
self.log.warn('Connection to %s has invalid extra field.', conn.host)
if headers:
session.headers.update(headers)
return session | def get_conn(self, headers=None):
session = requests.Session()
if self.http_conn_id:
conn = self.get_connection(self.http_conn_id)
if "://" in conn.host:
self.base_url = conn.host
else:
# schema defaults to HTTP
schema = conn.schema if conn.schema else "http"
self.base_url = schema + "://" + conn.host
if conn.port:
self.base_url = self.base_url + ":" + str(conn.port)
if conn.login:
session.auth = (conn.login, conn.password)
if conn.extra:
try:
session.headers.update(conn.extra_dejson)
except TypeError:
self.log.warn('Connection to %s has invalid extra field.', conn.host)
if headers:
session.headers.update(headers)
return session | Returns http session for use with requests
:param headers: additional headers to be passed through as a dictionary
:type headers: dict |
def run(self, endpoint, data=None, headers=None, extra_options=None):
"""
Performs the request
:param endpoint: the endpoint to be called i.e. resource/v1/query?
:type endpoint: str
:param data: payload to be uploaded or request parameters
:type data: dict
:param headers: additional headers to be passed through as a dictionary
:type headers: dict
:param extra_options: additional options to be used when executing the request
i.e. {'check_response': False} to avoid checking raising exceptions on non
2XX or 3XX status codes
:type extra_options: dict
"""
extra_options = extra_options or {}
session = self.get_conn(headers)
if self.base_url and not self.base_url.endswith('/') and \
endpoint and not endpoint.startswith('/'):
url = self.base_url + '/' + endpoint
else:
url = (self.base_url or '') + (endpoint or '')
req = None
if self.method == 'GET':
# GET uses params
req = requests.Request(self.method,
url,
params=data,
headers=headers)
elif self.method == 'HEAD':
# HEAD doesn't use params
req = requests.Request(self.method,
url,
headers=headers)
else:
# Others use data
req = requests.Request(self.method,
url,
data=data,
headers=headers)
prepped_request = session.prepare_request(req)
self.log.info("Sending '%s' to url: %s", self.method, url)
return self.run_and_check(session, prepped_request, extra_options) | def run(self, endpoint, data=None, headers=None, extra_options=None):
extra_options = extra_options or {}
session = self.get_conn(headers)
if self.base_url and not self.base_url.endswith('/') and \
endpoint and not endpoint.startswith('/'):
url = self.base_url + '/' + endpoint
else:
url = (self.base_url or '') + (endpoint or '')
req = None
if self.method == 'GET':
# GET uses params
req = requests.Request(self.method,
url,
params=data,
headers=headers)
elif self.method == 'HEAD':
# HEAD doesn't use params
req = requests.Request(self.method,
url,
headers=headers)
else:
# Others use data
req = requests.Request(self.method,
url,
data=data,
headers=headers)
prepped_request = session.prepare_request(req)
self.log.info("Sending '%s' to url: %s", self.method, url)
return self.run_and_check(session, prepped_request, extra_options) | Performs the request
:param endpoint: the endpoint to be called i.e. resource/v1/query?
:type endpoint: str
:param data: payload to be uploaded or request parameters
:type data: dict
:param headers: additional headers to be passed through as a dictionary
:type headers: dict
:param extra_options: additional options to be used when executing the request
i.e. {'check_response': False} to avoid checking raising exceptions on non
2XX or 3XX status codes
:type extra_options: dict |
def check_response(self, response):
"""
Checks the status code and raise an AirflowException exception on non 2XX or 3XX
status codes
:param response: A requests response object
:type response: requests.response
"""
try:
response.raise_for_status()
except requests.exceptions.HTTPError:
self.log.error("HTTP error: %s", response.reason)
if self.method not in ['GET', 'HEAD']:
self.log.error(response.text)
raise AirflowException(str(response.status_code) + ":" + response.reason) | def check_response(self, response):
try:
response.raise_for_status()
except requests.exceptions.HTTPError:
self.log.error("HTTP error: %s", response.reason)
if self.method not in ['GET', 'HEAD']:
self.log.error(response.text)
raise AirflowException(str(response.status_code) + ":" + response.reason) | Checks the status code and raise an AirflowException exception on non 2XX or 3XX
status codes
:param response: A requests response object
:type response: requests.response |
def run_and_check(self, session, prepped_request, extra_options):
"""
Grabs extra options like timeout and actually runs the request,
checking for the result
:param session: the session to be used to execute the request
:type session: requests.Session
:param prepped_request: the prepared request generated in run()
:type prepped_request: session.prepare_request
:param extra_options: additional options to be used when executing the request
i.e. {'check_response': False} to avoid checking raising exceptions on non 2XX
or 3XX status codes
:type extra_options: dict
"""
extra_options = extra_options or {}
try:
response = session.send(
prepped_request,
stream=extra_options.get("stream", False),
verify=extra_options.get("verify", True),
proxies=extra_options.get("proxies", {}),
cert=extra_options.get("cert"),
timeout=extra_options.get("timeout"),
allow_redirects=extra_options.get("allow_redirects", True))
if extra_options.get('check_response', True):
self.check_response(response)
return response
except requests.exceptions.ConnectionError as ex:
self.log.warn(str(ex) + ' Tenacity will retry to execute the operation')
raise ex | def run_and_check(self, session, prepped_request, extra_options):
extra_options = extra_options or {}
try:
response = session.send(
prepped_request,
stream=extra_options.get("stream", False),
verify=extra_options.get("verify", True),
proxies=extra_options.get("proxies", {}),
cert=extra_options.get("cert"),
timeout=extra_options.get("timeout"),
allow_redirects=extra_options.get("allow_redirects", True))
if extra_options.get('check_response', True):
self.check_response(response)
return response
except requests.exceptions.ConnectionError as ex:
self.log.warn(str(ex) + ' Tenacity will retry to execute the operation')
raise ex | Grabs extra options like timeout and actually runs the request,
checking for the result
:param session: the session to be used to execute the request
:type session: requests.Session
:param prepped_request: the prepared request generated in run()
:type prepped_request: session.prepare_request
:param extra_options: additional options to be used when executing the request
i.e. {'check_response': False} to avoid checking raising exceptions on non 2XX
or 3XX status codes
:type extra_options: dict |
def create_session():
"""
Contextmanager that will create and teardown a session.
"""
session = settings.Session()
try:
yield session
session.commit()
except Exception:
session.rollback()
raise
finally:
session.close() | def create_session():
session = settings.Session()
try:
yield session
session.commit()
except Exception:
session.rollback()
raise
finally:
session.close() | Contextmanager that will create and teardown a session. |
def provide_session(func):
"""
Function decorator that provides a session if it isn't provided.
If you want to reuse a session or run the function as part of a
database transaction, you pass it to the function, if not this wrapper
will create one and close it for you.
"""
@wraps(func)
def wrapper(*args, **kwargs):
arg_session = 'session'
func_params = func.__code__.co_varnames
session_in_args = arg_session in func_params and \
func_params.index(arg_session) < len(args)
session_in_kwargs = arg_session in kwargs
if session_in_kwargs or session_in_args:
return func(*args, **kwargs)
else:
with create_session() as session:
kwargs[arg_session] = session
return func(*args, **kwargs)
return wrapper | def provide_session(func):
@wraps(func)
def wrapper(*args, **kwargs):
arg_session = 'session'
func_params = func.__code__.co_varnames
session_in_args = arg_session in func_params and \
func_params.index(arg_session) < len(args)
session_in_kwargs = arg_session in kwargs
if session_in_kwargs or session_in_args:
return func(*args, **kwargs)
else:
with create_session() as session:
kwargs[arg_session] = session
return func(*args, **kwargs)
return wrapper | Function decorator that provides a session if it isn't provided.
If you want to reuse a session or run the function as part of a
database transaction, you pass it to the function, if not this wrapper
will create one and close it for you. |
def _get_pretty_exception_message(e):
"""
Parses some DatabaseError to provide a better error message
"""
if (hasattr(e, 'message') and
'errorName' in e.message and
'message' in e.message):
return ('{name}: {message}'.format(
name=e.message['errorName'],
message=e.message['message']))
else:
return str(e) | def _get_pretty_exception_message(e):
if (hasattr(e, 'message') and
'errorName' in e.message and
'message' in e.message):
return ('{name}: {message}'.format(
name=e.message['errorName'],
message=e.message['message']))
else:
return str(e) | Parses some DatabaseError to provide a better error message |
def run(self, hql, parameters=None):
"""
Execute the statement against Presto. Can be used to create views.
"""
return super().run(self._strip_sql(hql), parameters) | def run(self, hql, parameters=None):
return super().run(self._strip_sql(hql), parameters) | Execute the statement against Presto. Can be used to create views. |
def insert_rows(self, table, rows, target_fields=None):
"""
A generic way to insert a set of tuples into a table.
:param table: Name of the target table
:type table: str
:param rows: The rows to insert into the table
:type rows: iterable of tuples
:param target_fields: The names of the columns to fill in the table
:type target_fields: iterable of strings
"""
super().insert_rows(table, rows, target_fields, 0) | def insert_rows(self, table, rows, target_fields=None):
super().insert_rows(table, rows, target_fields, 0) | A generic way to insert a set of tuples into a table.
:param table: Name of the target table
:type table: str
:param rows: The rows to insert into the table
:type rows: iterable of tuples
:param target_fields: The names of the columns to fill in the table
:type target_fields: iterable of strings |
def create_collection(self, collection_name, database_name=None):
"""
Creates a new collection in the CosmosDB database.
"""
if collection_name is None:
raise AirflowBadRequest("Collection name cannot be None.")
# We need to check to see if this container already exists so we don't try
# to create it twice
existing_container = list(self.get_conn().QueryContainers(
get_database_link(self.__get_database_name(database_name)), {
"query": "SELECT * FROM r WHERE r.id=@id",
"parameters": [
{"name": "@id", "value": collection_name}
]
}))
# Only create if we did not find it already existing
if len(existing_container) == 0:
self.get_conn().CreateContainer(
get_database_link(self.__get_database_name(database_name)),
{"id": collection_name}) | def create_collection(self, collection_name, database_name=None):
if collection_name is None:
raise AirflowBadRequest("Collection name cannot be None.")
# We need to check to see if this container already exists so we don't try
# to create it twice
existing_container = list(self.get_conn().QueryContainers(
get_database_link(self.__get_database_name(database_name)), {
"query": "SELECT * FROM r WHERE r.id=@id",
"parameters": [
{"name": "@id", "value": collection_name}
]
}))
# Only create if we did not find it already existing
if len(existing_container) == 0:
self.get_conn().CreateContainer(
get_database_link(self.__get_database_name(database_name)),
{"id": collection_name}) | Creates a new collection in the CosmosDB database. |
def delete_collection(self, collection_name, database_name=None):
"""
Deletes an existing collection in the CosmosDB database.
"""
if collection_name is None:
raise AirflowBadRequest("Collection name cannot be None.")
self.get_conn().DeleteContainer(
get_collection_link(self.__get_database_name(database_name), collection_name)) | def delete_collection(self, collection_name, database_name=None):
if collection_name is None:
raise AirflowBadRequest("Collection name cannot be None.")
self.get_conn().DeleteContainer(
get_collection_link(self.__get_database_name(database_name), collection_name)) | Deletes an existing collection in the CosmosDB database. |
def insert_documents(self, documents, database_name=None, collection_name=None):
"""
Insert a list of new documents into an existing collection in the CosmosDB database.
"""
if documents is None:
raise AirflowBadRequest("You cannot insert empty documents")
created_documents = []
for single_document in documents:
created_documents.append(
self.get_conn().CreateItem(
get_collection_link(
self.__get_database_name(database_name),
self.__get_collection_name(collection_name)),
single_document))
return created_documents | def insert_documents(self, documents, database_name=None, collection_name=None):
if documents is None:
raise AirflowBadRequest("You cannot insert empty documents")
created_documents = []
for single_document in documents:
created_documents.append(
self.get_conn().CreateItem(
get_collection_link(
self.__get_database_name(database_name),
self.__get_collection_name(collection_name)),
single_document))
return created_documents | Insert a list of new documents into an existing collection in the CosmosDB database. |
def delete_document(self, document_id, database_name=None, collection_name=None):
"""
Delete an existing document out of a collection in the CosmosDB database.
"""
if document_id is None:
raise AirflowBadRequest("Cannot delete a document without an id")
self.get_conn().DeleteItem(
get_document_link(
self.__get_database_name(database_name),
self.__get_collection_name(collection_name),
document_id)) | def delete_document(self, document_id, database_name=None, collection_name=None):
if document_id is None:
raise AirflowBadRequest("Cannot delete a document without an id")
self.get_conn().DeleteItem(
get_document_link(
self.__get_database_name(database_name),
self.__get_collection_name(collection_name),
document_id)) | Delete an existing document out of a collection in the CosmosDB database. |
def get_document(self, document_id, database_name=None, collection_name=None):
"""
Get a document from an existing collection in the CosmosDB database.
"""
if document_id is None:
raise AirflowBadRequest("Cannot get a document without an id")
try:
return self.get_conn().ReadItem(
get_document_link(
self.__get_database_name(database_name),
self.__get_collection_name(collection_name),
document_id))
except HTTPFailure:
return None | def get_document(self, document_id, database_name=None, collection_name=None):
if document_id is None:
raise AirflowBadRequest("Cannot get a document without an id")
try:
return self.get_conn().ReadItem(
get_document_link(
self.__get_database_name(database_name),
self.__get_collection_name(collection_name),
document_id))
except HTTPFailure:
return None | Get a document from an existing collection in the CosmosDB database. |
def get_documents(self, sql_string, database_name=None, collection_name=None, partition_key=None):
"""
Get a list of documents from an existing collection in the CosmosDB database via SQL query.
"""
if sql_string is None:
raise AirflowBadRequest("SQL query string cannot be None")
# Query them in SQL
query = {'query': sql_string}
try:
result_iterable = self.get_conn().QueryItems(
get_collection_link(
self.__get_database_name(database_name),
self.__get_collection_name(collection_name)),
query,
partition_key)
return list(result_iterable)
except HTTPFailure:
return None | def get_documents(self, sql_string, database_name=None, collection_name=None, partition_key=None):
if sql_string is None:
raise AirflowBadRequest("SQL query string cannot be None")
# Query them in SQL
query = {'query': sql_string}
try:
result_iterable = self.get_conn().QueryItems(
get_collection_link(
self.__get_database_name(database_name),
self.__get_collection_name(collection_name)),
query,
partition_key)
return list(result_iterable)
except HTTPFailure:
return None | Get a list of documents from an existing collection in the CosmosDB database via SQL query. |
def get_function(self, name):
"""
Returns the Cloud Function with the given name.
:param name: Name of the function.
:type name: str
:return: A Cloud Functions object representing the function.
:rtype: dict
"""
return self.get_conn().projects().locations().functions().get(
name=name).execute(num_retries=self.num_retries) | def get_function(self, name):
return self.get_conn().projects().locations().functions().get(
name=name).execute(num_retries=self.num_retries) | Returns the Cloud Function with the given name.
:param name: Name of the function.
:type name: str
:return: A Cloud Functions object representing the function.
:rtype: dict |
def create_new_function(self, location, body, project_id=None):
"""
Creates a new function in Cloud Function in the location specified in the body.
:param location: The location of the function.
:type location: str
:param body: The body required by the Cloud Functions insert API.
:type body: dict
:param project_id: Optional, Google Cloud Project project_id where the function belongs.
If set to None or missing, the default project_id from the GCP connection is used.
:type project_id: str
:return: None
"""
response = self.get_conn().projects().locations().functions().create(
location=self._full_location(project_id, location),
body=body
).execute(num_retries=self.num_retries)
operation_name = response["name"]
self._wait_for_operation_to_complete(operation_name=operation_name) | def create_new_function(self, location, body, project_id=None):
response = self.get_conn().projects().locations().functions().create(
location=self._full_location(project_id, location),
body=body
).execute(num_retries=self.num_retries)
operation_name = response["name"]
self._wait_for_operation_to_complete(operation_name=operation_name) | Creates a new function in Cloud Function in the location specified in the body.
:param location: The location of the function.
:type location: str
:param body: The body required by the Cloud Functions insert API.
:type body: dict
:param project_id: Optional, Google Cloud Project project_id where the function belongs.
If set to None or missing, the default project_id from the GCP connection is used.
:type project_id: str
:return: None |
def update_function(self, name, body, update_mask):
"""
Updates Cloud Functions according to the specified update mask.
:param name: The name of the function.
:type name: str
:param body: The body required by the cloud function patch API.
:type body: dict
:param update_mask: The update mask - array of fields that should be patched.
:type update_mask: [str]
:return: None
"""
response = self.get_conn().projects().locations().functions().patch(
updateMask=",".join(update_mask),
name=name,
body=body
).execute(num_retries=self.num_retries)
operation_name = response["name"]
self._wait_for_operation_to_complete(operation_name=operation_name) | def update_function(self, name, body, update_mask):
response = self.get_conn().projects().locations().functions().patch(
updateMask=",".join(update_mask),
name=name,
body=body
).execute(num_retries=self.num_retries)
operation_name = response["name"]
self._wait_for_operation_to_complete(operation_name=operation_name) | Updates Cloud Functions according to the specified update mask.
:param name: The name of the function.
:type name: str
:param body: The body required by the cloud function patch API.
:type body: dict
:param update_mask: The update mask - array of fields that should be patched.
:type update_mask: [str]
:return: None |
def upload_function_zip(self, location, zip_path, project_id=None):
"""
Uploads zip file with sources.
:param location: The location where the function is created.
:type location: str
:param zip_path: The path of the valid .zip file to upload.
:type zip_path: str
:param project_id: Optional, Google Cloud Project project_id where the function belongs.
If set to None or missing, the default project_id from the GCP connection is used.
:type project_id: str
:return: The upload URL that was returned by generateUploadUrl method.
"""
response = self.get_conn().projects().locations().functions().generateUploadUrl(
parent=self._full_location(project_id, location)
).execute(num_retries=self.num_retries)
upload_url = response.get('uploadUrl')
with open(zip_path, 'rb') as fp:
requests.put(
url=upload_url,
data=fp,
# Those two headers needs to be specified according to:
# https://cloud.google.com/functions/docs/reference/rest/v1/projects.locations.functions/generateUploadUrl
# nopep8
headers={
'Content-type': 'application/zip',
'x-goog-content-length-range': '0,104857600',
}
)
return upload_url | def upload_function_zip(self, location, zip_path, project_id=None):
response = self.get_conn().projects().locations().functions().generateUploadUrl(
parent=self._full_location(project_id, location)
).execute(num_retries=self.num_retries)
upload_url = response.get('uploadUrl')
with open(zip_path, 'rb') as fp:
requests.put(
url=upload_url,
data=fp,
# Those two headers needs to be specified according to:
# https://cloud.google.com/functions/docs/reference/rest/v1/projects.locations.functions/generateUploadUrl
# nopep8
headers={
'Content-type': 'application/zip',
'x-goog-content-length-range': '0,104857600',
}
)
return upload_url | Uploads zip file with sources.
:param location: The location where the function is created.
:type location: str
:param zip_path: The path of the valid .zip file to upload.
:type zip_path: str
:param project_id: Optional, Google Cloud Project project_id where the function belongs.
If set to None or missing, the default project_id from the GCP connection is used.
:type project_id: str
:return: The upload URL that was returned by generateUploadUrl method. |
def delete_function(self, name):
"""
Deletes the specified Cloud Function.
:param name: The name of the function.
:type name: str
:return: None
"""
response = self.get_conn().projects().locations().functions().delete(
name=name).execute(num_retries=self.num_retries)
operation_name = response["name"]
self._wait_for_operation_to_complete(operation_name=operation_name) | def delete_function(self, name):
response = self.get_conn().projects().locations().functions().delete(
name=name).execute(num_retries=self.num_retries)
operation_name = response["name"]
self._wait_for_operation_to_complete(operation_name=operation_name) | Deletes the specified Cloud Function.
:param name: The name of the function.
:type name: str
:return: None |
def get_dep_statuses(self, ti, session, dep_context=None):
"""
Wrapper around the private _get_dep_statuses method that contains some global
checks for all dependencies.
:param ti: the task instance to get the dependency status for
:type ti: airflow.models.TaskInstance
:param session: database session
:type session: sqlalchemy.orm.session.Session
:param dep_context: the context for which this dependency should be evaluated for
:type dep_context: DepContext
"""
# this avoids a circular dependency
from airflow.ti_deps.dep_context import DepContext
if dep_context is None:
dep_context = DepContext()
if self.IGNOREABLE and dep_context.ignore_all_deps:
yield self._passing_status(
reason="Context specified all dependencies should be ignored.")
return
if self.IS_TASK_DEP and dep_context.ignore_task_deps:
yield self._passing_status(
reason="Context specified all task dependencies should be ignored.")
return
for dep_status in self._get_dep_statuses(ti, session, dep_context):
yield dep_status | def get_dep_statuses(self, ti, session, dep_context=None):
# this avoids a circular dependency
from airflow.ti_deps.dep_context import DepContext
if dep_context is None:
dep_context = DepContext()
if self.IGNOREABLE and dep_context.ignore_all_deps:
yield self._passing_status(
reason="Context specified all dependencies should be ignored.")
return
if self.IS_TASK_DEP and dep_context.ignore_task_deps:
yield self._passing_status(
reason="Context specified all task dependencies should be ignored.")
return
for dep_status in self._get_dep_statuses(ti, session, dep_context):
yield dep_status | Wrapper around the private _get_dep_statuses method that contains some global
checks for all dependencies.
:param ti: the task instance to get the dependency status for
:type ti: airflow.models.TaskInstance
:param session: database session
:type session: sqlalchemy.orm.session.Session
:param dep_context: the context for which this dependency should be evaluated for
:type dep_context: DepContext |
def is_met(self, ti, session, dep_context=None):
"""
Returns whether or not this dependency is met for a given task instance. A
dependency is considered met if all of the dependency statuses it reports are
passing.
:param ti: the task instance to see if this dependency is met for
:type ti: airflow.models.TaskInstance
:param session: database session
:type session: sqlalchemy.orm.session.Session
:param dep_context: The context this dependency is being checked under that stores
state that can be used by this dependency.
:type dep_context: BaseDepContext
"""
return all(status.passed for status in
self.get_dep_statuses(ti, session, dep_context)) | def is_met(self, ti, session, dep_context=None):
return all(status.passed for status in
self.get_dep_statuses(ti, session, dep_context)) | Returns whether or not this dependency is met for a given task instance. A
dependency is considered met if all of the dependency statuses it reports are
passing.
:param ti: the task instance to see if this dependency is met for
:type ti: airflow.models.TaskInstance
:param session: database session
:type session: sqlalchemy.orm.session.Session
:param dep_context: The context this dependency is being checked under that stores
state that can be used by this dependency.
:type dep_context: BaseDepContext |
def get_failure_reasons(self, ti, session, dep_context=None):
"""
Returns an iterable of strings that explain why this dependency wasn't met.
:param ti: the task instance to see if this dependency is met for
:type ti: airflow.models.TaskInstance
:param session: database session
:type session: sqlalchemy.orm.session.Session
:param dep_context: The context this dependency is being checked under that stores
state that can be used by this dependency.
:type dep_context: BaseDepContext
"""
for dep_status in self.get_dep_statuses(ti, session, dep_context):
if not dep_status.passed:
yield dep_status.reason | def get_failure_reasons(self, ti, session, dep_context=None):
for dep_status in self.get_dep_statuses(ti, session, dep_context):
if not dep_status.passed:
yield dep_status.reason | Returns an iterable of strings that explain why this dependency wasn't met.
:param ti: the task instance to see if this dependency is met for
:type ti: airflow.models.TaskInstance
:param session: database session
:type session: sqlalchemy.orm.session.Session
:param dep_context: The context this dependency is being checked under that stores
state that can be used by this dependency.
:type dep_context: BaseDepContext |
def _parse_s3_config(config_file_name, config_format='boto', profile=None):
"""
Parses a config file for s3 credentials. Can currently
parse boto, s3cmd.conf and AWS SDK config formats
:param config_file_name: path to the config file
:type config_file_name: str
:param config_format: config type. One of "boto", "s3cmd" or "aws".
Defaults to "boto"
:type config_format: str
:param profile: profile name in AWS type config file
:type profile: str
"""
config = configparser.ConfigParser()
if config.read(config_file_name): # pragma: no cover
sections = config.sections()
else:
raise AirflowException("Couldn't read {0}".format(config_file_name))
# Setting option names depending on file format
if config_format is None:
config_format = 'boto'
conf_format = config_format.lower()
if conf_format == 'boto': # pragma: no cover
if profile is not None and 'profile ' + profile in sections:
cred_section = 'profile ' + profile
else:
cred_section = 'Credentials'
elif conf_format == 'aws' and profile is not None:
cred_section = profile
else:
cred_section = 'default'
# Option names
if conf_format in ('boto', 'aws'): # pragma: no cover
key_id_option = 'aws_access_key_id'
secret_key_option = 'aws_secret_access_key'
# security_token_option = 'aws_security_token'
else:
key_id_option = 'access_key'
secret_key_option = 'secret_key'
# Actual Parsing
if cred_section not in sections:
raise AirflowException("This config file format is not recognized")
else:
try:
access_key = config.get(cred_section, key_id_option)
secret_key = config.get(cred_section, secret_key_option)
except Exception:
logging.warning("Option Error in parsing s3 config file")
raise
return access_key, secret_key | def _parse_s3_config(config_file_name, config_format='boto', profile=None):
config = configparser.ConfigParser()
if config.read(config_file_name): # pragma: no cover
sections = config.sections()
else:
raise AirflowException("Couldn't read {0}".format(config_file_name))
# Setting option names depending on file format
if config_format is None:
config_format = 'boto'
conf_format = config_format.lower()
if conf_format == 'boto': # pragma: no cover
if profile is not None and 'profile ' + profile in sections:
cred_section = 'profile ' + profile
else:
cred_section = 'Credentials'
elif conf_format == 'aws' and profile is not None:
cred_section = profile
else:
cred_section = 'default'
# Option names
if conf_format in ('boto', 'aws'): # pragma: no cover
key_id_option = 'aws_access_key_id'
secret_key_option = 'aws_secret_access_key'
# security_token_option = 'aws_security_token'
else:
key_id_option = 'access_key'
secret_key_option = 'secret_key'
# Actual Parsing
if cred_section not in sections:
raise AirflowException("This config file format is not recognized")
else:
try:
access_key = config.get(cred_section, key_id_option)
secret_key = config.get(cred_section, secret_key_option)
except Exception:
logging.warning("Option Error in parsing s3 config file")
raise
return access_key, secret_key | Parses a config file for s3 credentials. Can currently
parse boto, s3cmd.conf and AWS SDK config formats
:param config_file_name: path to the config file
:type config_file_name: str
:param config_format: config type. One of "boto", "s3cmd" or "aws".
Defaults to "boto"
:type config_format: str
:param profile: profile name in AWS type config file
:type profile: str |
def get_credentials(self, region_name=None):
"""Get the underlying `botocore.Credentials` object.
This contains the following authentication attributes: access_key, secret_key and token.
"""
session, _ = self._get_credentials(region_name)
# Credentials are refreshable, so accessing your access key and
# secret key separately can lead to a race condition.
# See https://stackoverflow.com/a/36291428/8283373
return session.get_credentials().get_frozen_credentials() | def get_credentials(self, region_name=None):
session, _ = self._get_credentials(region_name)
# Credentials are refreshable, so accessing your access key and
# secret key separately can lead to a race condition.
# See https://stackoverflow.com/a/36291428/8283373
return session.get_credentials().get_frozen_credentials() | Get the underlying `botocore.Credentials` object.
This contains the following authentication attributes: access_key, secret_key and token. |
def correct_maybe_zipped(fileloc):
"""
If the path contains a folder with a .zip suffix, then
the folder is treated as a zip archive and path to zip is returned.
"""
_, archive, filename = re.search(
r'((.*\.zip){})?(.*)'.format(re.escape(os.sep)), fileloc).groups()
if archive and zipfile.is_zipfile(archive):
return archive
else:
return fileloc | def correct_maybe_zipped(fileloc):
_, archive, filename = re.search(
r'((.*\.zip){})?(.*)'.format(re.escape(os.sep)), fileloc).groups()
if archive and zipfile.is_zipfile(archive):
return archive
else:
return fileloc | If the path contains a folder with a .zip suffix, then
the folder is treated as a zip archive and path to zip is returned. |
def list_py_file_paths(directory, safe_mode=True,
include_examples=None):
"""
Traverse a directory and look for Python files.
:param directory: the directory to traverse
:type directory: unicode
:param safe_mode: whether to use a heuristic to determine whether a file
contains Airflow DAG definitions
:return: a list of paths to Python files in the specified directory
:rtype: list[unicode]
"""
if include_examples is None:
include_examples = conf.getboolean('core', 'LOAD_EXAMPLES')
file_paths = []
if directory is None:
return []
elif os.path.isfile(directory):
return [directory]
elif os.path.isdir(directory):
patterns_by_dir = {}
for root, dirs, files in os.walk(directory, followlinks=True):
patterns = patterns_by_dir.get(root, [])
ignore_file = os.path.join(root, '.airflowignore')
if os.path.isfile(ignore_file):
with open(ignore_file, 'r') as f:
# If we have new patterns create a copy so we don't change
# the previous list (which would affect other subdirs)
patterns += [re.compile(p) for p in f.read().split('\n') if p]
# If we can ignore any subdirs entirely we should - fewer paths
# to walk is better. We have to modify the ``dirs`` array in
# place for this to affect os.walk
dirs[:] = [
d
for d in dirs
if not any(p.search(os.path.join(root, d)) for p in patterns)
]
# We want patterns defined in a parent folder's .airflowignore to
# apply to subdirs too
for d in dirs:
patterns_by_dir[os.path.join(root, d)] = patterns
for f in files:
try:
file_path = os.path.join(root, f)
if not os.path.isfile(file_path):
continue
mod_name, file_ext = os.path.splitext(
os.path.split(file_path)[-1])
if file_ext != '.py' and not zipfile.is_zipfile(file_path):
continue
if any([re.findall(p, file_path) for p in patterns]):
continue
# Heuristic that guesses whether a Python file contains an
# Airflow DAG definition.
might_contain_dag = True
if safe_mode and not zipfile.is_zipfile(file_path):
with open(file_path, 'rb') as fp:
content = fp.read()
might_contain_dag = all(
[s in content for s in (b'DAG', b'airflow')])
if not might_contain_dag:
continue
file_paths.append(file_path)
except Exception:
log = LoggingMixin().log
log.exception("Error while examining %s", f)
if include_examples:
import airflow.example_dags
example_dag_folder = airflow.example_dags.__path__[0]
file_paths.extend(list_py_file_paths(example_dag_folder, safe_mode, False))
return file_paths | def list_py_file_paths(directory, safe_mode=True,
include_examples=None):
if include_examples is None:
include_examples = conf.getboolean('core', 'LOAD_EXAMPLES')
file_paths = []
if directory is None:
return []
elif os.path.isfile(directory):
return [directory]
elif os.path.isdir(directory):
patterns_by_dir = {}
for root, dirs, files in os.walk(directory, followlinks=True):
patterns = patterns_by_dir.get(root, [])
ignore_file = os.path.join(root, '.airflowignore')
if os.path.isfile(ignore_file):
with open(ignore_file, 'r') as f:
# If we have new patterns create a copy so we don't change
# the previous list (which would affect other subdirs)
patterns += [re.compile(p) for p in f.read().split('\n') if p]
# If we can ignore any subdirs entirely we should - fewer paths
# to walk is better. We have to modify the ``dirs`` array in
# place for this to affect os.walk
dirs[:] = [
d
for d in dirs
if not any(p.search(os.path.join(root, d)) for p in patterns)
]
# We want patterns defined in a parent folder's .airflowignore to
# apply to subdirs too
for d in dirs:
patterns_by_dir[os.path.join(root, d)] = patterns
for f in files:
try:
file_path = os.path.join(root, f)
if not os.path.isfile(file_path):
continue
mod_name, file_ext = os.path.splitext(
os.path.split(file_path)[-1])
if file_ext != '.py' and not zipfile.is_zipfile(file_path):
continue
if any([re.findall(p, file_path) for p in patterns]):
continue
# Heuristic that guesses whether a Python file contains an
# Airflow DAG definition.
might_contain_dag = True
if safe_mode and not zipfile.is_zipfile(file_path):
with open(file_path, 'rb') as fp:
content = fp.read()
might_contain_dag = all(
[s in content for s in (b'DAG', b'airflow')])
if not might_contain_dag:
continue
file_paths.append(file_path)
except Exception:
log = LoggingMixin().log
log.exception("Error while examining %s", f)
if include_examples:
import airflow.example_dags
example_dag_folder = airflow.example_dags.__path__[0]
file_paths.extend(list_py_file_paths(example_dag_folder, safe_mode, False))
return file_paths | Traverse a directory and look for Python files.
:param directory: the directory to traverse
:type directory: unicode
:param safe_mode: whether to use a heuristic to determine whether a file
contains Airflow DAG definitions
:return: a list of paths to Python files in the specified directory
:rtype: list[unicode] |
def construct_task_instance(self, session=None, lock_for_update=False):
"""
Construct a TaskInstance from the database based on the primary key
:param session: DB session.
:param lock_for_update: if True, indicates that the database should
lock the TaskInstance (issuing a FOR UPDATE clause) until the
session is committed.
"""
TI = airflow.models.TaskInstance
qry = session.query(TI).filter(
TI.dag_id == self._dag_id,
TI.task_id == self._task_id,
TI.execution_date == self._execution_date)
if lock_for_update:
ti = qry.with_for_update().first()
else:
ti = qry.first()
return ti | def construct_task_instance(self, session=None, lock_for_update=False):
TI = airflow.models.TaskInstance
qry = session.query(TI).filter(
TI.dag_id == self._dag_id,
TI.task_id == self._task_id,
TI.execution_date == self._execution_date)
if lock_for_update:
ti = qry.with_for_update().first()
else:
ti = qry.first()
return ti | Construct a TaskInstance from the database based on the primary key
:param session: DB session.
:param lock_for_update: if True, indicates that the database should
lock the TaskInstance (issuing a FOR UPDATE clause) until the
session is committed. |
def start(self):
"""
Launch DagFileProcessorManager processor and start DAG parsing loop in manager.
"""
self._process = self._launch_process(self._dag_directory,
self._file_paths,
self._max_runs,
self._processor_factory,
self._child_signal_conn,
self._stat_queue,
self._result_queue,
self._async_mode)
self.log.info("Launched DagFileProcessorManager with pid: %s", self._process.pid) | def start(self):
self._process = self._launch_process(self._dag_directory,
self._file_paths,
self._max_runs,
self._processor_factory,
self._child_signal_conn,
self._stat_queue,
self._result_queue,
self._async_mode)
self.log.info("Launched DagFileProcessorManager with pid: %s", self._process.pid) | Launch DagFileProcessorManager processor and start DAG parsing loop in manager. |
def terminate(self):
"""
Send termination signal to DAG parsing processor manager
and expect it to terminate all DAG file processors.
"""
self.log.info("Sending termination message to manager.")
self._child_signal_conn.send(DagParsingSignal.TERMINATE_MANAGER) | def terminate(self):
self.log.info("Sending termination message to manager.")
self._child_signal_conn.send(DagParsingSignal.TERMINATE_MANAGER) | Send termination signal to DAG parsing processor manager
and expect it to terminate all DAG file processors. |
def _exit_gracefully(self, signum, frame):
"""
Helper method to clean up DAG file processors to avoid leaving orphan processes.
"""
self.log.info("Exiting gracefully upon receiving signal %s", signum)
self.terminate()
self.end()
self.log.debug("Finished terminating DAG processors.")
sys.exit(os.EX_OK) | def _exit_gracefully(self, signum, frame):
self.log.info("Exiting gracefully upon receiving signal %s", signum)
self.terminate()
self.end()
self.log.debug("Finished terminating DAG processors.")
sys.exit(os.EX_OK) | Helper method to clean up DAG file processors to avoid leaving orphan processes. |
def start(self):
"""
Use multiple processes to parse and generate tasks for the
DAGs in parallel. By processing them in separate processes,
we can get parallelism and isolation from potentially harmful
user code.
"""
self.log.info("Processing files using up to %s processes at a time ", self._parallelism)
self.log.info("Process each file at most once every %s seconds", self._file_process_interval)
self.log.info(
"Checking for new files in %s every %s seconds", self._dag_directory, self.dag_dir_list_interval
)
if self._async_mode:
self.log.debug("Starting DagFileProcessorManager in async mode")
self.start_in_async()
else:
self.log.debug("Starting DagFileProcessorManager in sync mode")
self.start_in_sync() | def start(self):
self.log.info("Processing files using up to %s processes at a time ", self._parallelism)
self.log.info("Process each file at most once every %s seconds", self._file_process_interval)
self.log.info(
"Checking for new files in %s every %s seconds", self._dag_directory, self.dag_dir_list_interval
)
if self._async_mode:
self.log.debug("Starting DagFileProcessorManager in async mode")
self.start_in_async()
else:
self.log.debug("Starting DagFileProcessorManager in sync mode")
self.start_in_sync() | Use multiple processes to parse and generate tasks for the
DAGs in parallel. By processing them in separate processes,
we can get parallelism and isolation from potentially harmful
user code. |
def start_in_sync(self):
"""
Parse DAG files in a loop controlled by DagParsingSignal.
Actual DAG parsing loop will run once upon receiving one
agent heartbeat message and will report done when finished the loop.
"""
while True:
agent_signal = self._signal_conn.recv()
if agent_signal == DagParsingSignal.TERMINATE_MANAGER:
self.terminate()
break
elif agent_signal == DagParsingSignal.END_MANAGER:
self.end()
sys.exit(os.EX_OK)
elif agent_signal == DagParsingSignal.AGENT_HEARTBEAT:
self._refresh_dag_dir()
simple_dags = self.heartbeat()
for simple_dag in simple_dags:
self._result_queue.put(simple_dag)
self._print_stat()
all_files_processed = all(self.get_last_finish_time(x) is not None
for x in self.file_paths)
max_runs_reached = self.max_runs_reached()
dag_parsing_stat = DagParsingStat(self._file_paths,
self.get_all_pids(),
self.max_runs_reached(),
all_files_processed,
len(simple_dags))
self._stat_queue.put(dag_parsing_stat)
self.wait_until_finished()
self._signal_conn.send(DagParsingSignal.MANAGER_DONE)
if max_runs_reached:
self.log.info("Exiting dag parsing loop as all files "
"have been processed %s times", self._max_runs)
self._signal_conn.send(DagParsingSignal.MANAGER_DONE)
break | def start_in_sync(self):
while True:
agent_signal = self._signal_conn.recv()
if agent_signal == DagParsingSignal.TERMINATE_MANAGER:
self.terminate()
break
elif agent_signal == DagParsingSignal.END_MANAGER:
self.end()
sys.exit(os.EX_OK)
elif agent_signal == DagParsingSignal.AGENT_HEARTBEAT:
self._refresh_dag_dir()
simple_dags = self.heartbeat()
for simple_dag in simple_dags:
self._result_queue.put(simple_dag)
self._print_stat()
all_files_processed = all(self.get_last_finish_time(x) is not None
for x in self.file_paths)
max_runs_reached = self.max_runs_reached()
dag_parsing_stat = DagParsingStat(self._file_paths,
self.get_all_pids(),
self.max_runs_reached(),
all_files_processed,
len(simple_dags))
self._stat_queue.put(dag_parsing_stat)
self.wait_until_finished()
self._signal_conn.send(DagParsingSignal.MANAGER_DONE)
if max_runs_reached:
self.log.info("Exiting dag parsing loop as all files "
"have been processed %s times", self._max_runs)
self._signal_conn.send(DagParsingSignal.MANAGER_DONE)
break | Parse DAG files in a loop controlled by DagParsingSignal.
Actual DAG parsing loop will run once upon receiving one
agent heartbeat message and will report done when finished the loop. |
def _refresh_dag_dir(self):
"""
Refresh file paths from dag dir if we haven't done it for too long.
"""
elapsed_time_since_refresh = (timezone.utcnow() -
self.last_dag_dir_refresh_time).total_seconds()
if elapsed_time_since_refresh > self.dag_dir_list_interval:
# Build up a list of Python files that could contain DAGs
self.log.info("Searching for files in %s", self._dag_directory)
self._file_paths = list_py_file_paths(self._dag_directory)
self.last_dag_dir_refresh_time = timezone.utcnow()
self.log.info("There are %s files in %s", len(self._file_paths), self._dag_directory)
self.set_file_paths(self._file_paths)
try:
self.log.debug("Removing old import errors")
self.clear_nonexistent_import_errors()
except Exception:
self.log.exception("Error removing old import errors") | def _refresh_dag_dir(self):
elapsed_time_since_refresh = (timezone.utcnow() -
self.last_dag_dir_refresh_time).total_seconds()
if elapsed_time_since_refresh > self.dag_dir_list_interval:
# Build up a list of Python files that could contain DAGs
self.log.info("Searching for files in %s", self._dag_directory)
self._file_paths = list_py_file_paths(self._dag_directory)
self.last_dag_dir_refresh_time = timezone.utcnow()
self.log.info("There are %s files in %s", len(self._file_paths), self._dag_directory)
self.set_file_paths(self._file_paths)
try:
self.log.debug("Removing old import errors")
self.clear_nonexistent_import_errors()
except Exception:
self.log.exception("Error removing old import errors") | Refresh file paths from dag dir if we haven't done it for too long. |
def _print_stat(self):
"""
Occasionally print out stats about how fast the files are getting processed
"""
if ((timezone.utcnow() - self.last_stat_print_time).total_seconds() >
self.print_stats_interval):
if len(self._file_paths) > 0:
self._log_file_processing_stats(self._file_paths)
self.last_stat_print_time = timezone.utcnow() | def _print_stat(self):
if ((timezone.utcnow() - self.last_stat_print_time).total_seconds() >
self.print_stats_interval):
if len(self._file_paths) > 0:
self._log_file_processing_stats(self._file_paths)
self.last_stat_print_time = timezone.utcnow() | Occasionally print out stats about how fast the files are getting processed |
def clear_nonexistent_import_errors(self, session):
"""
Clears import errors for files that no longer exist.
:param session: session for ORM operations
:type session: sqlalchemy.orm.session.Session
"""
query = session.query(errors.ImportError)
if self._file_paths:
query = query.filter(
~errors.ImportError.filename.in_(self._file_paths)
)
query.delete(synchronize_session='fetch')
session.commit() | def clear_nonexistent_import_errors(self, session):
query = session.query(errors.ImportError)
if self._file_paths:
query = query.filter(
~errors.ImportError.filename.in_(self._file_paths)
)
query.delete(synchronize_session='fetch')
session.commit() | Clears import errors for files that no longer exist.
:param session: session for ORM operations
:type session: sqlalchemy.orm.session.Session |
def _log_file_processing_stats(self, known_file_paths):
"""
Print out stats about how files are getting processed.
:param known_file_paths: a list of file paths that may contain Airflow
DAG definitions
:type known_file_paths: list[unicode]
:return: None
"""
# File Path: Path to the file containing the DAG definition
# PID: PID associated with the process that's processing the file. May
# be empty.
# Runtime: If the process is currently running, how long it's been
# running for in seconds.
# Last Runtime: If the process ran before, how long did it take to
# finish in seconds
# Last Run: When the file finished processing in the previous run.
headers = ["File Path",
"PID",
"Runtime",
"Last Runtime",
"Last Run"]
rows = []
for file_path in known_file_paths:
last_runtime = self.get_last_runtime(file_path)
file_name = os.path.basename(file_path)
file_name = os.path.splitext(file_name)[0].replace(os.sep, '.')
if last_runtime:
Stats.gauge(
'dag_processing.last_runtime.{}'.format(file_name),
last_runtime
)
processor_pid = self.get_pid(file_path)
processor_start_time = self.get_start_time(file_path)
runtime = ((timezone.utcnow() - processor_start_time).total_seconds()
if processor_start_time else None)
last_run = self.get_last_finish_time(file_path)
if last_run:
seconds_ago = (timezone.utcnow() - last_run).total_seconds()
Stats.gauge(
'dag_processing.last_run.seconds_ago.{}'.format(file_name),
seconds_ago
)
rows.append((file_path,
processor_pid,
runtime,
last_runtime,
last_run))
# Sort by longest last runtime. (Can't sort None values in python3)
rows = sorted(rows, key=lambda x: x[3] or 0.0)
formatted_rows = []
for file_path, pid, runtime, last_runtime, last_run in rows:
formatted_rows.append((file_path,
pid,
"{:.2f}s".format(runtime)
if runtime else None,
"{:.2f}s".format(last_runtime)
if last_runtime else None,
last_run.strftime("%Y-%m-%dT%H:%M:%S")
if last_run else None))
log_str = ("\n" +
"=" * 80 +
"\n" +
"DAG File Processing Stats\n\n" +
tabulate(formatted_rows, headers=headers) +
"\n" +
"=" * 80)
self.log.info(log_str) | def _log_file_processing_stats(self, known_file_paths):
# File Path: Path to the file containing the DAG definition
# PID: PID associated with the process that's processing the file. May
# be empty.
# Runtime: If the process is currently running, how long it's been
# running for in seconds.
# Last Runtime: If the process ran before, how long did it take to
# finish in seconds
# Last Run: When the file finished processing in the previous run.
headers = ["File Path",
"PID",
"Runtime",
"Last Runtime",
"Last Run"]
rows = []
for file_path in known_file_paths:
last_runtime = self.get_last_runtime(file_path)
file_name = os.path.basename(file_path)
file_name = os.path.splitext(file_name)[0].replace(os.sep, '.')
if last_runtime:
Stats.gauge(
'dag_processing.last_runtime.{}'.format(file_name),
last_runtime
)
processor_pid = self.get_pid(file_path)
processor_start_time = self.get_start_time(file_path)
runtime = ((timezone.utcnow() - processor_start_time).total_seconds()
if processor_start_time else None)
last_run = self.get_last_finish_time(file_path)
if last_run:
seconds_ago = (timezone.utcnow() - last_run).total_seconds()
Stats.gauge(
'dag_processing.last_run.seconds_ago.{}'.format(file_name),
seconds_ago
)
rows.append((file_path,
processor_pid,
runtime,
last_runtime,
last_run))
# Sort by longest last runtime. (Can't sort None values in python3)
rows = sorted(rows, key=lambda x: x[3] or 0.0)
formatted_rows = []
for file_path, pid, runtime, last_runtime, last_run in rows:
formatted_rows.append((file_path,
pid,
"{:.2f}s".format(runtime)
if runtime else None,
"{:.2f}s".format(last_runtime)
if last_runtime else None,
last_run.strftime("%Y-%m-%dT%H:%M:%S")
if last_run else None))
log_str = ("\n" +
"=" * 80 +
"\n" +
"DAG File Processing Stats\n\n" +
tabulate(formatted_rows, headers=headers) +
"\n" +
"=" * 80)
self.log.info(log_str) | Print out stats about how files are getting processed.
:param known_file_paths: a list of file paths that may contain Airflow
DAG definitions
:type known_file_paths: list[unicode]
:return: None |
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