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|---|---|
https://github.com/soimort/you-get/blob/b746ac01c9f39de94cac2d56f665285b0523b974/src/you_get/extractors/sina.py#L41-L52
|
def sina_download_by_vid(vid, title=None, output_dir='.', merge=True, info_only=False):
"""
"""
xml = api_req(vid)
urls, name, size = video_info(xml)
if urls is None:
log.wtf(name)
title = name
print_info(site_info, title, 'flv', size)
if not info_only:
download_urls(urls, title, 'flv', size, output_dir = output_dir, merge = merge)
|
https://github.com/soimort/you-get/blob/b746ac01c9f39de94cac2d56f665285b0523b974/src/you_get/common.py#L457-L501
|
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
|
https://github.com/soimort/you-get/blob/b746ac01c9f39de94cac2d56f665285b0523b974/src/you_get/extractors/ixigua.py#L34-L78
|
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
|
https://github.com/soimort/you-get/blob/b746ac01c9f39de94cac2d56f665285b0523b974/src/you_get/common.py#L252-L268
|
def matchall(text, patterns):
"""
"""
ret = []
for pattern in patterns:
match = re.findall(pattern, text)
ret += match
return ret
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/hooks/mysql_hook.py#L107-L117
|
def bulk_load(self, table, tmp_file):
"""
"""
conn = self.get_conn()
cur = conn.cursor()
cur.execute("""
LOAD DATA LOCAL INFILE '{tmp_file}'
INTO TABLE {table}
""".format(tmp_file=tmp_file, table=table))
conn.commit()
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_sql_hook.py#L282-L310
|
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)
)
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/hooks/postgres_hook.py#L85-L89
|
def bulk_load(self, table, tmp_file):
"""
"""
self.copy_expert("COPY {table} FROM STDIN".format(table=table), tmp_file)
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/macros/hive.py#L83-L118
|
def closest_ds_partition(
table, ds, before=True, schema="default",
metastore_conn_id='metastore_default'):
"""
"""
from airflow.hooks.hive_hooks import HiveMetastoreHook
if '.' in table:
schema, table = table.split('.')
hh = HiveMetastoreHook(metastore_conn_id=metastore_conn_id)
partitions = hh.get_partitions(schema=schema, table_name=table)
if not partitions:
return None
part_vals = [list(p.values())[0] for p in partitions]
if ds in part_vals:
return ds
else:
parts = [datetime.datetime.strptime(pv, '%Y-%m-%d')
for pv in part_vals]
target_dt = datetime.datetime.strptime(ds, '%Y-%m-%d')
closest_ds = _closest_date(target_dt, parts, before_target=before)
return closest_ds.isoformat()
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/azure_cosmos_hook.py#L181-L206
|
def upsert_document(self, document, database_name=None, collection_name=None, document_id=None):
"""
"""
# Assign unique ID if one isn't provided
if document_id is None:
document_id = str(uuid.uuid4())
if document is None:
raise AirflowBadRequest("You cannot insert a None document")
# Add document id if isn't found
if 'id' in document:
if document['id'] is None:
document['id'] = document_id
else:
document['id'] = document_id
created_document = self.get_conn().CreateItem(
get_collection_link(
self.__get_database_name(database_name),
self.__get_collection_name(collection_name)),
document)
return created_document
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/scripts/perf/scheduler_ops_metrics.py#L138-L148
|
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)
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/azure_cosmos_hook.py#L239-L253
|
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
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/operators/file_to_gcs.py#L68-L82
|
def execute(self, context):
"""
"""
hook = GoogleCloudStorageHook(
google_cloud_storage_conn_id=self.google_cloud_storage_conn_id,
delegate_to=self.delegate_to)
hook.upload(
bucket_name=self.bucket,
object_name=self.dst,
mime_type=self.mime_type,
filename=self.src,
gzip=self.gzip,
)
|
https://github.com/soimort/you-get/blob/b746ac01c9f39de94cac2d56f665285b0523b974/src/you_get/extractors/cbs.py#L9-L17
|
def cbs_download(url, output_dir='.', merge=True, info_only=False, **kwargs):
"""
"""
html = get_content(url)
pid = match1(html, r'video\.settings\.pid\s*=\s*\'([^\']+)\'')
title = match1(html, r'video\.settings\.title\s*=\s*\"([^\"]+)\"')
theplatform_download_by_pid(pid, title, output_dir=output_dir, merge=merge, info_only=info_only)
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/utils/dag_processing.py#L289-L365
|
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
|
https://github.com/soimort/you-get/blob/b746ac01c9f39de94cac2d56f665285b0523b974/src/you_get/extractors/wanmen.py#L18-L25
|
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']])
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_sql_hook.py#L961-L972
|
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
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/bin/cli.py#L527-L550
|
def task_failed_deps(args):
"""
"""
dag = get_dag(args)
task = dag.get_task(task_id=args.task_id)
ti = TaskInstance(task, args.execution_date)
dep_context = DepContext(deps=SCHEDULER_DEPS)
failed_deps = list(ti.get_failed_dep_statuses(dep_context=dep_context))
# TODO, Do we want to print or log this
if failed_deps:
print("Task instance dependencies not met:")
for dep in failed_deps:
print("{}: {}".format(dep.dep_name, dep.reason))
else:
print("Task instance dependencies are all met.")
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/scripts/perf/scheduler_ops_metrics.py#L65-L101
|
def print_stats(self):
"""
"""
session = settings.Session()
TI = TaskInstance
tis = (
session
.query(TI)
.filter(TI.dag_id.in_(DAG_IDS))
.all()
)
successful_tis = [x for x in tis if x.state == State.SUCCESS]
ti_perf = [(ti.dag_id, ti.task_id, ti.execution_date,
(ti.queued_dttm - self.start_date).total_seconds(),
(ti.start_date - self.start_date).total_seconds(),
(ti.end_date - self.start_date).total_seconds(),
ti.duration) for ti in successful_tis]
ti_perf_df = pd.DataFrame(ti_perf, columns=['dag_id', 'task_id',
'execution_date',
'queue_delay',
'start_delay', 'land_time',
'duration'])
print('Performance Results')
print('###################')
for dag_id in DAG_IDS:
print('DAG {}'.format(dag_id))
print(ti_perf_df[ti_perf_df['dag_id'] == dag_id])
print('###################')
if len(tis) > len(successful_tis):
print("WARNING!! The following task instances haven't completed")
print(pd.DataFrame([(ti.dag_id, ti.task_id, ti.execution_date, ti.state)
for ti in filter(lambda x: x.state != State.SUCCESS, tis)],
columns=['dag_id', 'task_id', 'execution_date', 'state']))
session.commit()
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/utils/timezone.py#L98-L128
|
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)
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/grpc_hook.py#L112-L123
|
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
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/aws_hook.py#L28-L77
|
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
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/bin/cli.py#L579-L600
|
def next_execution(args):
"""
"""
dag = get_dag(args)
if dag.is_paused:
print("[INFO] Please be reminded this DAG is PAUSED now.")
if dag.latest_execution_date:
next_execution_dttm = dag.following_schedule(dag.latest_execution_date)
if next_execution_dttm is None:
print("[WARN] No following schedule can be found. " +
"This DAG may have schedule interval '@once' or `None`.")
print(next_execution_dttm)
else:
print("[WARN] Only applicable when there is execution record found for the DAG.")
print(None)
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/utils/dag_processing.py#L612-L623
|
def _sync_metadata(self):
"""
"""
while not self._stat_queue.empty():
stat = self._stat_queue.get()
self._file_paths = stat.file_paths
self._all_pids = stat.all_pids
self._done = stat.done
self._all_files_processed = stat.all_files_processed
self._result_count += stat.result_count
|
https://github.com/soimort/you-get/blob/b746ac01c9f39de94cac2d56f665285b0523b974/src/you_get/util/git.py#L7-L15
|
def get_head(repo_path):
""""""
try:
ref = open(os.path.join(repo_path, '.git', 'HEAD'), 'r').read().strip()[5:].split('/')
branch = ref[-1]
commit = open(os.path.join(repo_path, '.git', *ref), 'r').read().strip()[:7]
return branch, commit
except:
return None
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_sql_hook.py#L229-L256
|
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)
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/utils/db.py#L312-L331
|
def resetdb():
"""
"""
from airflow import models
# alembic adds significant import time, so we import it lazily
from alembic.migration import MigrationContext
log.info("Dropping tables that exist")
models.base.Base.metadata.drop_all(settings.engine)
mc = MigrationContext.configure(settings.engine)
if mc._version.exists(settings.engine):
mc._version.drop(settings.engine)
from flask_appbuilder.models.sqla import Base
Base.metadata.drop_all(settings.engine)
initdb()
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/datastore_hook.py#L62-L81
|
def allocate_ids(self, partial_keys):
"""
"""
conn = self.get_conn()
resp = (conn
.projects()
.allocateIds(projectId=self.project_id, body={'keys': partial_keys})
.execute(num_retries=self.num_retries))
return resp['keys']
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_sql_hook.py#L567-L599
|
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)
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/sensors/gcs_sensor.py#L242-L310
|
def is_bucket_updated(self, current_num_objects):
"""
"""
if current_num_objects > self.previous_num_objects:
# When new objects arrived, reset the inactivity_seconds
# previous_num_objects for the next poke.
self.log.info(
'''
New objects found at {} resetting last_activity_time.
'''.format(os.path.join(self.bucket, self.prefix)))
self.last_activity_time = get_time()
self.inactivity_seconds = 0
self.previous_num_objects = current_num_objects
elif current_num_objects < self.previous_num_objects:
# During the last poke interval objects were deleted.
if self.allow_delete:
self.previous_num_objects = current_num_objects
self.last_activity_time = get_time()
self.log.warning(
'''
Objects were deleted during the last
poke interval. Updating the file counter and
resetting last_activity_time.
'''
)
else:
raise RuntimeError(
'''
Illegal behavior: objects were deleted in {} between pokes.
'''.format(os.path.join(self.bucket, self.prefix))
)
else:
if self.last_activity_time:
self.inactivity_seconds = (
get_time() - self.last_activity_time).total_seconds()
else:
# Handles the first poke where last inactivity time is None.
self.last_activity_time = get_time()
self.inactivity_seconds = 0
if self.inactivity_seconds >= self.inactivity_period:
if current_num_objects >= self.min_objects:
self.log.info(
'''
SUCCESS:
Sensor found {} objects at {}.
Waited at least {} seconds, with no new objects dropped.
'''.format(
current_num_objects,
os.path.join(self.bucket, self.prefix),
self.inactivity_period))
return True
warn_msg = \
'''
FAILURE:
Inactivity Period passed,
not enough objects found in {}
'''.format(
os.path.join(self.bucket, self.prefix))
self.log.warning(warn_msg)
return False
return False
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/utils/db.py#L47-L70
|
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
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/utils/helpers.py#L153-L166
|
def chain(*tasks):
"""
"""
for up_task, down_task in zip(tasks[:-1], tasks[1:]):
up_task.set_downstream(down_task)
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/configuration.py#L346-L405
|
def as_dict(
self, display_source=False, display_sensitive=False, raw=False):
"""
"""
cfg = {}
configs = [
('default', self.airflow_defaults),
('airflow.cfg', self),
]
for (source_name, config) in configs:
for section in config.sections():
sect = cfg.setdefault(section, OrderedDict())
for (k, val) in config.items(section=section, raw=raw):
if display_source:
val = (val, source_name)
sect[k] = val
# add env vars and overwrite because they have priority
for ev in [ev for ev in os.environ if ev.startswith('AIRFLOW__')]:
try:
_, section, key = ev.split('__')
opt = self._get_env_var_option(section, key)
except ValueError:
continue
if not display_sensitive and ev != 'AIRFLOW__CORE__UNIT_TEST_MODE':
opt = '< hidden >'
elif raw:
opt = opt.replace('%', '%%')
if display_source:
opt = (opt, 'env var')
cfg.setdefault(section.lower(), OrderedDict()).update(
{key.lower(): opt})
# add bash commands
for (section, key) in self.as_command_stdout:
opt = self._get_cmd_option(section, key)
if opt:
if not display_sensitive:
opt = '< hidden >'
if display_source:
opt = (opt, 'cmd')
elif raw:
opt = opt.replace('%', '%%')
cfg.setdefault(section, OrderedDict()).update({key: opt})
del cfg[section][key + '_cmd']
return cfg
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/utils/dag_processing.py#L789-L808
|
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()
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_transfer_hook.py#L204-L232
|
def delete_transfer_job(self, job_name, project_id):
"""
"""
return (
self.get_conn()
.transferJobs()
.patch(
jobName=job_name,
body={
PROJECT_ID: project_id,
TRANSFER_JOB: {STATUS1: GcpTransferJobsStatus.DELETED},
TRANSFER_JOB_FIELD_MASK: STATUS1,
},
)
.execute(num_retries=self.num_retries)
)
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/executors/celery_executor.py#L90-L111
|
def fetch_celery_task_state(celery_task):
"""
"""
try:
with timeout(seconds=2):
# Accessing state property of celery task will make actual network request
# to get the current state of the task.
res = (celery_task[0], celery_task[1].state)
except Exception as e:
exception_traceback = "Celery Task ID: {}\n{}".format(celery_task[0],
traceback.format_exc())
res = ExceptionWithTraceback(e, exception_traceback)
return res
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/hooks/mysql_hook.py#L62-L105
|
def get_conn(self):
"""
"""
conn = self.get_connection(self.mysql_conn_id)
conn_config = {
"user": conn.login,
"passwd": conn.password or '',
"host": conn.host or 'localhost',
"db": self.schema or conn.schema or ''
}
if not conn.port:
conn_config["port"] = 3306
else:
conn_config["port"] = int(conn.port)
if conn.extra_dejson.get('charset', False):
conn_config["charset"] = conn.extra_dejson["charset"]
if (conn_config["charset"]).lower() == 'utf8' or\
(conn_config["charset"]).lower() == 'utf-8':
conn_config["use_unicode"] = True
if conn.extra_dejson.get('cursor', False):
if (conn.extra_dejson["cursor"]).lower() == 'sscursor':
conn_config["cursorclass"] = MySQLdb.cursors.SSCursor
elif (conn.extra_dejson["cursor"]).lower() == 'dictcursor':
conn_config["cursorclass"] = MySQLdb.cursors.DictCursor
elif (conn.extra_dejson["cursor"]).lower() == 'ssdictcursor':
conn_config["cursorclass"] = MySQLdb.cursors.SSDictCursor
local_infile = conn.extra_dejson.get('local_infile', False)
if conn.extra_dejson.get('ssl', False):
# SSL parameter for MySQL has to be a dictionary and in case
# of extra/dejson we can get string if extra is passed via
# URL parameters
dejson_ssl = conn.extra_dejson['ssl']
if isinstance(dejson_ssl, six.string_types):
dejson_ssl = json.loads(dejson_ssl)
conn_config['ssl'] = dejson_ssl
if conn.extra_dejson.get('unix_socket'):
conn_config['unix_socket'] = conn.extra_dejson['unix_socket']
if local_infile:
conn_config["local_infile"] = 1
conn = MySQLdb.connect(**conn_config)
return conn
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_pubsub_hook.py#L60-L81
|
def publish(self, project, topic, messages):
"""
"""
body = {'messages': messages}
full_topic = _format_topic(project, topic)
request = self.get_conn().projects().topics().publish(
topic=full_topic, body=body)
try:
request.execute(num_retries=self.num_retries)
except HttpError as e:
raise PubSubException(
'Error publishing to topic {}'.format(full_topic), e)
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_transfer_hook.py#L323-L348
|
def wait_for_transfer_job(self, job, expected_statuses=(GcpTransferOperationStatus.SUCCESS,), timeout=60):
"""
"""
while timeout > 0:
operations = self.list_transfer_operations(
filter={FILTER_PROJECT_ID: job[PROJECT_ID], FILTER_JOB_NAMES: [job[NAME]]}
)
if GCPTransferServiceHook.operations_contain_expected_statuses(operations, expected_statuses):
return
time.sleep(TIME_TO_SLEEP_IN_SECONDS)
timeout -= TIME_TO_SLEEP_IN_SECONDS
raise AirflowException("Timeout. The operation could not be completed within the allotted time.")
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_mlengine_hook.py#L202-L224
|
def list_versions(self, project_id, model_name):
"""
"""
result = []
full_parent_name = 'projects/{}/models/{}'.format(
project_id, model_name)
request = self._mlengine.projects().models().versions().list(
parent=full_parent_name, pageSize=100)
response = request.execute()
next_page_token = response.get('nextPageToken', None)
result.extend(response.get('versions', []))
while next_page_token is not None:
next_request = self._mlengine.projects().models().versions().list(
parent=full_parent_name,
pageToken=next_page_token,
pageSize=100)
response = next_request.execute()
next_page_token = response.get('nextPageToken', None)
result.extend(response.get('versions', []))
time.sleep(5)
return result
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_sql_hook.py#L183-L202
|
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)
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/utils/dag_processing.py#L512-L524
|
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)
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_transfer_hook.py#L157-L179
|
def list_transfer_job(self, filter):
"""
"""
conn = self.get_conn()
filter = self._inject_project_id(filter, FILTER, FILTER_PROJECT_ID)
request = conn.transferJobs().list(filter=json.dumps(filter))
jobs = []
while request is not None:
response = request.execute(num_retries=self.num_retries)
jobs.extend(response[TRANSFER_JOBS])
request = conn.transferJobs().list_next(previous_request=request, previous_response=response)
return jobs
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/ssh_hook.py#L144-L185
|
def get_conn(self):
"""
"""
self.log.debug('Creating SSH client for conn_id: %s', self.ssh_conn_id)
client = paramiko.SSHClient()
if not self.allow_host_key_change:
self.log.warning('Remote Identification Change is not verified. '
'This wont protect against Man-In-The-Middle attacks')
client.load_system_host_keys()
if self.no_host_key_check:
self.log.warning('No Host Key Verification. This wont protect '
'against Man-In-The-Middle attacks')
# Default is RejectPolicy
client.set_missing_host_key_policy(paramiko.AutoAddPolicy())
if self.password and self.password.strip():
client.connect(hostname=self.remote_host,
username=self.username,
password=self.password,
key_filename=self.key_file,
timeout=self.timeout,
compress=self.compress,
port=self.port,
sock=self.host_proxy)
else:
client.connect(hostname=self.remote_host,
username=self.username,
key_filename=self.key_file,
timeout=self.timeout,
compress=self.compress,
port=self.port,
sock=self.host_proxy)
if self.keepalive_interval:
client.get_transport().set_keepalive(self.keepalive_interval)
self.client = client
return client
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/vertica_hook.py#L35-L53
|
def get_conn(self):
"""
"""
conn = self.get_connection(self.vertica_conn_id)
conn_config = {
"user": conn.login,
"password": conn.password or '',
"database": conn.schema,
"host": conn.host or 'localhost'
}
if not conn.port:
conn_config["port"] = 5433
else:
conn_config["port"] = int(conn.port)
conn = connect(**conn_config)
return conn
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_sql_hook.py#L895-L908
|
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()
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/datastore_hook.py#L257-L295
|
def export_to_storage_bucket(self, bucket, namespace=None, entity_filter=None, labels=None):
"""
"""
admin_conn = self.get_conn()
output_uri_prefix = 'gs://' + '/'.join(filter(None, [bucket, namespace]))
if not entity_filter:
entity_filter = {}
if not labels:
labels = {}
body = {
'outputUrlPrefix': output_uri_prefix,
'entityFilter': entity_filter,
'labels': labels,
}
resp = (admin_conn
.projects()
.export(projectId=self.project_id, body=body)
.execute(num_retries=self.num_retries))
return resp
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/snowflake_hook.py#L98-L105
|
def get_uri(self):
"""
"""
conn_config = self._get_conn_params()
uri = 'snowflake://{user}:{password}@{account}/{database}/'
uri += '{schema}?warehouse={warehouse}&role={role}'
return uri.format(**conn_config)
|
https://github.com/soimort/you-get/blob/b746ac01c9f39de94cac2d56f665285b0523b974/src/you_get/extractors/dailymotion.py#L13-L35
|
def dailymotion_download(url, output_dir='.', merge=True, info_only=False, **kwargs):
"""
"""
html = get_content(rebuilt_url(url))
info = json.loads(match1(html, r'qualities":({.+?}),"'))
title = match1(html, r'"video_title"\s*:\s*"([^"]+)"') or \
match1(html, r'"title"\s*:\s*"([^"]+)"')
title = unicodize(title)
for quality in ['1080','720','480','380','240','144','auto']:
try:
real_url = info[quality][1]["url"]
if real_url:
break
except KeyError:
pass
mime, ext, size = url_info(real_url)
print_info(site_info, title, mime, size)
if not info_only:
download_urls([real_url], title, ext, size, output_dir=output_dir, merge=merge)
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/models/dagrun.py#L95-L111
|
def refresh_from_db(self, session=None):
"""
"""
DR = DagRun
exec_date = func.cast(self.execution_date, DateTime)
dr = session.query(DR).filter(
DR.dag_id == self.dag_id,
func.cast(DR.execution_date, DateTime) == exec_date,
DR.run_id == self.run_id
).one()
self.id = dr.id
self.state = dr.state
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/utils/dag_processing.py#L1264-L1275
|
def max_runs_reached(self):
"""
"""
if self._max_runs == -1: # Unlimited runs.
return False
for file_path in self._file_paths:
if self._run_count[file_path] < self._max_runs:
return False
if self._run_count[self._heart_beat_key] < self._max_runs:
return False
return True
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/models/dagbag.py#L112-L143
|
def get_dag(self, dag_id):
"""
"""
from airflow.models.dag import DagModel # Avoid circular import
# If asking for a known subdag, we want to refresh the parent
root_dag_id = dag_id
if dag_id in self.dags:
dag = self.dags[dag_id]
if dag.is_subdag:
root_dag_id = dag.parent_dag.dag_id
# If the dag corresponding to root_dag_id is absent or expired
orm_dag = DagModel.get_current(root_dag_id)
if orm_dag and (
root_dag_id not in self.dags or
(
orm_dag.last_expired and
dag.last_loaded < orm_dag.last_expired
)
):
# Reprocess source file
found_dags = self.process_file(
filepath=orm_dag.fileloc, only_if_updated=False)
# If the source file no longer exports `dag_id`, delete it from self.dags
if found_dags and dag_id in [found_dag.dag_id for found_dag in found_dags]:
return self.dags[dag_id]
elif dag_id in self.dags:
del self.dags[dag_id]
return self.dags.get(dag_id)
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/configuration.py#L484-L491
|
def parameterized_config(template):
"""
"""
all_vars = {k: v for d in [globals(), locals()] for k, v in d.items()}
return template.format(**all_vars)
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/operators/bash_operator.py#L86-L144
|
def execute(self, context):
"""
"""
self.log.info('Tmp dir root location: \n %s', gettempdir())
# Prepare env for child process.
if self.env is None:
self.env = os.environ.copy()
airflow_context_vars = context_to_airflow_vars(context, in_env_var_format=True)
self.log.info('Exporting the following env vars:\n%s',
'\n'.join(["{}={}".format(k, v)
for k, v in
airflow_context_vars.items()]))
self.env.update(airflow_context_vars)
self.lineage_data = self.bash_command
with TemporaryDirectory(prefix='airflowtmp') as tmp_dir:
with NamedTemporaryFile(dir=tmp_dir, prefix=self.task_id) as tmp_file:
tmp_file.write(bytes(self.bash_command, 'utf_8'))
tmp_file.flush()
script_location = os.path.abspath(tmp_file.name)
self.log.info('Temporary script location: %s', script_location)
def pre_exec():
# Restore default signal disposition and invoke setsid
for sig in ('SIGPIPE', 'SIGXFZ', 'SIGXFSZ'):
if hasattr(signal, sig):
signal.signal(getattr(signal, sig), signal.SIG_DFL)
os.setsid()
self.log.info('Running command: %s', self.bash_command)
sub_process = Popen(
['bash', tmp_file.name],
stdout=PIPE,
stderr=STDOUT,
cwd=tmp_dir,
env=self.env,
preexec_fn=pre_exec)
self.sub_process = sub_process
self.log.info('Output:')
line = ''
for raw_line in iter(sub_process.stdout.readline, b''):
line = raw_line.decode(self.output_encoding).rstrip()
self.log.info(line)
sub_process.wait()
self.log.info('Command exited with return code %s', sub_process.returncode)
if sub_process.returncode:
raise AirflowException('Bash command failed')
return line
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/utils/dag_processing.py#L1090-L1109
|
def set_file_paths(self, new_file_paths):
"""
"""
self._file_paths = new_file_paths
self._file_path_queue = [x for x in self._file_path_queue
if x in new_file_paths]
# Stop processors that are working on deleted files
filtered_processors = {}
for file_path, processor in self._processors.items():
if file_path in new_file_paths:
filtered_processors[file_path] = processor
else:
self.log.warning("Stopping processor for %s", file_path)
processor.terminate()
self._processors = filtered_processors
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_natural_language_hook.py#L163-L195
|
def annotate_text(self, document, features, encoding_type=None, retry=None, timeout=None, metadata=None):
"""
"""
client = self.get_conn()
return client.annotate_text(
document=document,
features=features,
encoding_type=encoding_type,
retry=retry,
timeout=timeout,
metadata=metadata,
)
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/hooks/presto_hook.py#L120-L124
|
def run(self, hql, parameters=None):
"""
"""
return super().run(self._strip_sql(hql), parameters)
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/utils/dag_processing.py#L645-L679
|
def end(self):
"""
"""
if not self._process:
self.log.warn('Ending without manager process.')
return
this_process = psutil.Process(os.getpid())
try:
manager_process = psutil.Process(self._process.pid)
except psutil.NoSuchProcess:
self.log.info("Manager process not running.")
return
# First try SIGTERM
if manager_process.is_running() \
and manager_process.pid in [x.pid for x in this_process.children()]:
self.log.info("Terminating manager process: %s", manager_process.pid)
manager_process.terminate()
# TODO: Remove magic number
timeout = 5
self.log.info("Waiting up to %ss for manager process to exit...", timeout)
try:
psutil.wait_procs({manager_process}, timeout)
except psutil.TimeoutExpired:
self.log.debug("Ran out of time while waiting for "
"processes to exit")
# Then SIGKILL
if manager_process.is_running() \
and manager_process.pid in [x.pid for x in this_process.children()]:
self.log.info("Killing manager process: %s", manager_process.pid)
manager_process.kill()
manager_process.wait()
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/executors/kubernetes_executor.py#L606-L653
|
def clear_not_launched_queued_tasks(self, session=None):
"""
"""
queued_tasks = session\
.query(TaskInstance)\
.filter(TaskInstance.state == State.QUEUED).all()
self.log.info(
'When executor started up, found %s queued task instances',
len(queued_tasks)
)
for task in queued_tasks:
dict_string = (
"dag_id={},task_id={},execution_date={},airflow-worker={}".format(
AirflowKubernetesScheduler._make_safe_label_value(task.dag_id),
AirflowKubernetesScheduler._make_safe_label_value(task.task_id),
AirflowKubernetesScheduler._datetime_to_label_safe_datestring(
task.execution_date
),
self.worker_uuid
)
)
kwargs = dict(label_selector=dict_string)
pod_list = self.kube_client.list_namespaced_pod(
self.kube_config.kube_namespace, **kwargs)
if len(pod_list.items) == 0:
self.log.info(
'TaskInstance: %s found in queued state but was not launched, '
'rescheduling', task
)
session.query(TaskInstance).filter(
TaskInstance.dag_id == task.dag_id,
TaskInstance.task_id == task.task_id,
TaskInstance.execution_date == task.execution_date
).update({TaskInstance.state: State.NONE})
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_mlengine_hook.py#L60-L121
|
def create_job(self, project_id, job, use_existing_job_fn=None):
"""
"""
request = self._mlengine.projects().jobs().create(
parent='projects/{}'.format(project_id),
body=job)
job_id = job['jobId']
try:
request.execute()
except HttpError as e:
# 409 means there is an existing job with the same job ID.
if e.resp.status == 409:
if use_existing_job_fn is not None:
existing_job = self._get_job(project_id, job_id)
if not use_existing_job_fn(existing_job):
self.log.error(
'Job with job_id %s already exist, but it does '
'not match our expectation: %s',
job_id, existing_job
)
raise
self.log.info(
'Job with job_id %s already exist. Will waiting for it to finish',
job_id
)
else:
self.log.error('Failed to create MLEngine job: {}'.format(e))
raise
return self._wait_for_job_done(project_id, job_id)
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/utils/timezone.py#L52-L64
|
def utcnow():
"""
"""
# pendulum utcnow() is not used as that sets a TimezoneInfo object
# instead of a Timezone. This is not pickable and also creates issues
# when using replace()
d = dt.datetime.utcnow()
d = d.replace(tzinfo=utc)
return d
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/azure_cosmos_hook.py#L50-L60
|
def get_conn(self):
"""
"""
if self.cosmos_client is not None:
return self.cosmos_client
# Initialize the Python Azure Cosmos DB client
self.cosmos_client = cosmos_client.CosmosClient(self.endpoint_uri, {'masterKey': self.master_key})
return self.cosmos_client
|
https://github.com/soimort/you-get/blob/b746ac01c9f39de94cac2d56f665285b0523b974/src/you_get/extractors/sina.py#L94-L121
|
def sina_download(url, output_dir='.', merge=True, info_only=False, **kwargs):
"""
"""
if 'news.sina.com.cn/zxt' in url:
sina_zxt(url, output_dir=output_dir, merge=merge, info_only=info_only, **kwargs)
return
vid = match1(url, r'vid=(\d+)')
if vid is None:
video_page = get_content(url)
vid = hd_vid = match1(video_page, r'hd_vid\s*:\s*\'([^\']+)\'')
if hd_vid == '0':
vids = match1(video_page, r'[^\w]vid\s*:\s*\'([^\']+)\'').split('|')
vid = vids[-1]
if vid is None:
vid = match1(video_page, r'vid:"?(\d+)"?')
if vid:
#title = match1(video_page, r'title\s*:\s*\'([^\']+)\'')
sina_download_by_vid(vid, output_dir=output_dir, merge=merge, info_only=info_only)
else:
vkey = match1(video_page, r'vkey\s*:\s*"([^"]+)"')
if vkey is None:
vid = match1(url, r'#(\d+)')
sina_download_by_vid(vid, output_dir=output_dir, merge=merge, info_only=info_only)
return
title = match1(video_page, r'title\s*:\s*"([^"]+)"')
sina_download_by_vkey(vkey, title=title, output_dir=output_dir, merge=merge, info_only=info_only)
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_sql_hook.py#L524-L565
|
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
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_pubsub_hook.py#L196-L225
|
def delete_subscription(self, project, subscription,
fail_if_not_exists=False):
"""
"""
service = self.get_conn()
full_subscription = _format_subscription(project, subscription)
try:
service.projects().subscriptions().delete(
subscription=full_subscription).execute(num_retries=self.num_retries)
except HttpError as e:
# Status code 404 indicates that the subscription was not found
if str(e.resp['status']) == '404':
message = 'Subscription does not exist: {}'.format(
full_subscription)
self.log.warning(message)
if fail_if_not_exists:
raise PubSubException(message)
else:
raise PubSubException(
'Error deleting subscription {}'.format(full_subscription),
e)
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/configuration.py#L75-L93
|
def run_command(command):
"""
"""
process = subprocess.Popen(
shlex.split(command),
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
close_fds=True)
output, stderr = [stream.decode(sys.getdefaultencoding(), 'ignore')
for stream in process.communicate()]
if process.returncode != 0:
raise AirflowConfigException(
"Cannot execute {}. Error code is: {}. Output: {}, Stderr: {}"
.format(command, process.returncode, output, stderr)
)
return output
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/utils/log/wasb_task_handler.py#L97-L121
|
def _read(self, ti, try_number, metadata=None):
"""
"""
# Explicitly getting log relative path is necessary as the given
# task instance might be different than task instance passed in
# in set_context method.
log_relative_path = self._render_filename(ti, try_number)
remote_loc = os.path.join(self.remote_base, log_relative_path)
if self.wasb_log_exists(remote_loc):
# If Wasb remote file exists, we do not fetch logs from task instance
# local machine even if there are errors reading remote logs, as
# returned remote_log will contain error messages.
remote_log = self.wasb_read(remote_loc, return_error=True)
log = '*** Reading remote log from {}.\n{}\n'.format(
remote_loc, remote_log)
return log, {'end_of_log': True}
else:
return super()._read(ti, try_number)
|
https://github.com/soimort/you-get/blob/b746ac01c9f39de94cac2d56f665285b0523b974/src/you_get/common.py#L1216-L1226
|
def parse_host(host):
"""
"""
if re.match(r'^(\d+)$', host) is not None:
return ("0.0.0.0", int(host))
if re.match(r'^(\w+)://', host) is None:
host = "//" + host
o = parse.urlparse(host)
hostname = o.hostname or "0.0.0.0"
port = o.port or 0
return (hostname, port)
|
https://github.com/soimort/you-get/blob/b746ac01c9f39de94cac2d56f665285b0523b974/src/you_get/util/term.py#L3-L9
|
def get_terminal_size():
""""""
try:
import fcntl, termios, struct # fcntl module only available on Unix
return struct.unpack('hh', fcntl.ioctl(1, termios.TIOCGWINSZ, '1234'))
except:
return (40, 80)
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/operators/file_to_wasb.py#L56-L64
|
def execute(self, context):
""""""
hook = WasbHook(wasb_conn_id=self.wasb_conn_id)
self.log.info(
'Uploading %s to wasb://%s '
'as %s'.format(self.file_path, self.container_name, self.blob_name)
)
hook.load_file(self.file_path, self.container_name,
self.blob_name, **self.load_options)
|
https://github.com/soimort/you-get/blob/b746ac01c9f39de94cac2d56f665285b0523b974/src/you_get/extractors/iqiyi.py#L158-L218
|
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.')
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_dataproc_hook.py#L243-L247
|
def wait(self, operation):
""""""
submitted = _DataProcOperation(self.get_conn(), operation,
self.num_retries)
submitted.wait_for_done()
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/utils/dag_processing.py#L604-L610
|
def _heartbeat_manager(self):
"""
"""
if self._process and not self._process.is_alive() and not self.done:
self.start()
|
https://github.com/soimort/you-get/blob/b746ac01c9f39de94cac2d56f665285b0523b974/src/you_get/common.py#L224-L249
|
def match1(text, *patterns):
"""
"""
if len(patterns) == 1:
pattern = patterns[0]
match = re.search(pattern, text)
if match:
return match.group(1)
else:
return None
else:
ret = []
for pattern in patterns:
match = re.search(pattern, text)
if match:
ret.append(match.group(1))
return ret
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/hooks/S3_hook.py#L520-L543
|
def delete_objects(self,
bucket,
keys):
"""
"""
if isinstance(keys, list):
keys = keys
else:
keys = [keys]
delete_dict = {"Objects": [{"Key": k} for k in keys]}
response = self.get_conn().delete_objects(Bucket=bucket,
Delete=delete_dict)
return response
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/models/taskinstance.py#L494-L515
|
def are_dependents_done(self, session=None):
"""
"""
task = self.task
if not task.downstream_task_ids:
return True
ti = session.query(func.count(TaskInstance.task_id)).filter(
TaskInstance.dag_id == self.dag_id,
TaskInstance.task_id.in_(task.downstream_task_ids),
TaskInstance.execution_date == self.execution_date,
TaskInstance.state == State.SUCCESS,
)
count = ti[0][0]
return count == len(task.downstream_task_ids)
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_speech_to_text_hook.py#L53-L73
|
def recognize_speech(self, config, audio, retry=None, timeout=None):
"""
"""
client = self.get_conn()
response = client.recognize(config=config, audio=audio, retry=retry, timeout=timeout)
self.log.info("Recognised speech: %s" % response)
return response
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/azure_cosmos_hook.py#L162-L169
|
def delete_database(self, database_name):
"""
"""
if database_name is None:
raise AirflowBadRequest("Database name cannot be None.")
self.get_conn().DeleteDatabase(get_database_link(database_name))
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/sensors/hdfs_sensor.py#L57-L74
|
def poke(self, context):
"""
"""
sb = self.hook(self.hdfs_conn_id).get_conn()
result = [f for f in sb.ls([self.filepath], include_toplevel=True)]
result = self.filter_for_ignored_ext(result, self.ignored_ext,
self.ignore_copying)
result = self.filter_for_filesize(result, self.file_size)
if self.be_empty:
self.log.info('Poking for filepath %s to a empty directory', self.filepath)
return len(result) == 1 and result[0]['path'] == self.filepath
else:
self.log.info('Poking for filepath %s to a non empty directory', self.filepath)
result.pop(0)
return bool(result) and result[0]['file_type'] == 'f'
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/executors/__init__.py#L64-L95
|
def _get_executor(executor_name):
"""
"""
if executor_name == Executors.LocalExecutor:
return LocalExecutor()
elif executor_name == Executors.SequentialExecutor:
return SequentialExecutor()
elif executor_name == Executors.CeleryExecutor:
from airflow.executors.celery_executor import CeleryExecutor
return CeleryExecutor()
elif executor_name == Executors.DaskExecutor:
from airflow.executors.dask_executor import DaskExecutor
return DaskExecutor()
elif executor_name == Executors.KubernetesExecutor:
from airflow.contrib.executors.kubernetes_executor import KubernetesExecutor
return KubernetesExecutor()
else:
# Loading plugins
_integrate_plugins()
executor_path = executor_name.split('.')
if len(executor_path) != 2:
raise AirflowException(
"Executor {0} not supported: "
"please specify in format plugin_module.executor".format(executor_name))
if executor_path[0] in globals():
return globals()[executor_path[0]].__dict__[executor_path[1]]()
else:
raise AirflowException("Executor {0} not supported.".format(executor_name))
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/utils/log/file_task_handler.py#L170-L207
|
def _init_file(self, ti):
"""
"""
# To handle log writing when tasks are impersonated, the log files need to
# be writable by the user that runs the Airflow command and the user
# that is impersonated. This is mainly to handle corner cases with the
# SubDagOperator. When the SubDagOperator is run, all of the operators
# run under the impersonated user and create appropriate log files
# as the impersonated user. However, if the user manually runs tasks
# of the SubDagOperator through the UI, then the log files are created
# by the user that runs the Airflow command. For example, the Airflow
# run command may be run by the `airflow_sudoable` user, but the Airflow
# tasks may be run by the `airflow` user. If the log files are not
# writable by both users, then it's possible that re-running a task
# via the UI (or vice versa) results in a permission error as the task
# tries to write to a log file created by the other user.
relative_path = self._render_filename(ti, ti.try_number)
full_path = os.path.join(self.local_base, relative_path)
directory = os.path.dirname(full_path)
# Create the log file and give it group writable permissions
# TODO(aoen): Make log dirs and logs globally readable for now since the SubDag
# operator is not compatible with impersonation (e.g. if a Celery executor is used
# for a SubDag operator and the SubDag operator has a different owner than the
# parent DAG)
if not os.path.exists(directory):
# Create the directory as globally writable using custom mkdirs
# as os.makedirs doesn't set mode properly.
mkdirs(directory, 0o777)
if not os.path.exists(full_path):
open(full_path, "a").close()
# TODO: Investigate using 444 instead of 666.
os.chmod(full_path, 0o666)
return full_path
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/datastore_hook.py#L191-L211
|
def get_operation(self, name):
"""
"""
conn = self.get_conn()
resp = (conn
.projects()
.operations()
.get(name=name)
.execute(num_retries=self.num_retries))
return resp
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/bin/cli.py#L567-L575
|
def dag_state(args):
"""
"""
dag = get_dag(args)
dr = DagRun.find(dag.dag_id, execution_date=args.execution_date)
print(dr[0].state if len(dr) > 0 else None)
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/operators/spark_submit_operator.py#L147-L176
|
def execute(self, context):
"""
"""
self._hook = SparkSubmitHook(
conf=self._conf,
conn_id=self._conn_id,
files=self._files,
py_files=self._py_files,
archives=self._archives,
driver_class_path=self._driver_class_path,
jars=self._jars,
java_class=self._java_class,
packages=self._packages,
exclude_packages=self._exclude_packages,
repositories=self._repositories,
total_executor_cores=self._total_executor_cores,
executor_cores=self._executor_cores,
executor_memory=self._executor_memory,
driver_memory=self._driver_memory,
keytab=self._keytab,
principal=self._principal,
name=self._name,
num_executors=self._num_executors,
application_args=self._application_args,
env_vars=self._env_vars,
verbose=self._verbose,
spark_binary=self._spark_binary
)
self._hook.submit(self._application)
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/utils/mlengine_operator_utils.py#L32-L246
|
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
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_sql_hook.py#L115-L133
|
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)
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/plugins_manager.py#L79-L98
|
def load_entrypoint_plugins(entry_points, airflow_plugins):
"""
"""
for entry_point in entry_points:
log.debug('Importing entry_point plugin %s', entry_point.name)
plugin_obj = entry_point.load()
if is_valid_plugin(plugin_obj, airflow_plugins):
if callable(getattr(plugin_obj, 'on_load', None)):
plugin_obj.on_load()
airflow_plugins.append(plugin_obj)
return airflow_plugins
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/azure_cosmos_hook.py#L226-L237
|
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))
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/www/api/experimental/endpoints.py#L325-L336
|
def create_pool():
""""""
params = request.get_json(force=True)
try:
pool = pool_api.create_pool(**params)
except AirflowException as err:
_log.error(err)
response = jsonify(error="{}".format(err))
response.status_code = err.status_code
return response
else:
return jsonify(pool.to_json())
|
https://github.com/soimort/you-get/blob/b746ac01c9f39de94cac2d56f665285b0523b974/src/you_get/util/log.py#L94-L98
|
def wtf(message, exit_code=1):
""""""
print_log(message, RED, BOLD)
if exit_code is not None:
sys.exit(exit_code)
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_compute_hook.py#L69-L97
|
def start_instance(self, zone, resource_id, project_id=None):
"""
"""
response = self.get_conn().instances().start(
project=project_id,
zone=zone,
instance=resource_id
).execute(num_retries=self.num_retries)
try:
operation_name = response["name"]
except KeyError:
raise AirflowException(
"Wrong response '{}' returned - it should contain "
"'name' field".format(response))
self._wait_for_operation_to_complete(project_id=project_id,
operation_name=operation_name,
zone=zone)
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_compute_hook.py#L55-L66
|
def get_conn(self):
"""
"""
if not self._conn:
http_authorized = self._authorize()
self._conn = build('compute', self.api_version,
http=http_authorized, cache_discovery=False)
return self._conn
|
https://github.com/soimort/you-get/blob/b746ac01c9f39de94cac2d56f665285b0523b974/src/you_get/common.py#L329-L335
|
def undeflate(data):
"""
"""
import zlib
decompressobj = zlib.decompressobj(-zlib.MAX_WBITS)
return decompressobj.decompress(data)+decompressobj.flush()
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/hooks/presto_hook.py#L101-L118
|
def get_pandas_df(self, hql, parameters=None):
"""
"""
import pandas
cursor = self.get_cursor()
try:
cursor.execute(self._strip_sql(hql), parameters)
data = cursor.fetchall()
except DatabaseError as e:
raise PrestoException(self._get_pretty_exception_message(e))
column_descriptions = cursor.description
if data:
df = pandas.DataFrame(data)
df.columns = [c[0] for c in column_descriptions]
else:
df = pandas.DataFrame()
return df
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/executors/celery_executor.py#L158-L166
|
def _num_tasks_per_send_process(self, to_send_count):
"""
"""
return max(1,
int(math.ceil(1.0 * to_send_count / self._sync_parallelism)))
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/azure_data_lake_hook.py#L55-L68
|
def check_for_file(self, file_path):
"""
"""
try:
files = self.connection.glob(file_path, details=False, invalidate_cache=True)
return len(files) == 1
except FileNotFoundError:
return False
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_mlengine_hook.py#L185-L200
|
def set_default_version(self, project_id, model_name, version_name):
"""
"""
full_version_name = 'projects/{}/models/{}/versions/{}'.format(
project_id, model_name, version_name)
request = self._mlengine.projects().models().versions().setDefault(
name=full_version_name, body={})
try:
response = request.execute()
self.log.info('Successfully set version: %s to default', response)
return response
except HttpError as e:
self.log.error('Something went wrong: %s', e)
raise
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/www/api/experimental/endpoints.py#L198-L232
|
def task_instance_info(dag_id, execution_date, task_id):
"""
"""
# Convert string datetime into actual datetime
try:
execution_date = timezone.parse(execution_date)
except ValueError:
error_message = (
'Given execution date, {}, could not be identified '
'as a date. Example date format: 2015-11-16T14:34:15+00:00'
.format(execution_date))
_log.info(error_message)
response = jsonify({'error': error_message})
response.status_code = 400
return response
try:
info = get_task_instance(dag_id, task_id, execution_date)
except AirflowException as err:
_log.info(err)
response = jsonify(error="{}".format(err))
response.status_code = err.status_code
return response
# JSONify and return.
fields = {k: str(v)
for k, v in vars(info).items()
if not k.startswith('_')}
return jsonify(fields)
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/azure_cosmos_hook.py#L171-L179
|
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))
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_translate_hook.py#L45-L97
|
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,
)
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/hooks/S3_hook.py#L384-L422
|
def load_bytes(self,
bytes_data,
key,
bucket_name=None,
replace=False,
encrypt=False):
"""
"""
if not bucket_name:
(bucket_name, key) = self.parse_s3_url(key)
if not replace and self.check_for_key(key, bucket_name):
raise ValueError("The key {key} already exists.".format(key=key))
extra_args = {}
if encrypt:
extra_args['ServerSideEncryption'] = "AES256"
filelike_buffer = BytesIO(bytes_data)
client = self.get_conn()
client.upload_fileobj(filelike_buffer, bucket_name, key, ExtraArgs=extra_args)
|
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