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https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/models/taskinstance.py#L458-L467
|
def clear_xcom_data(self, session=None):
"""
"""
session.query(XCom).filter(
XCom.dag_id == self.dag_id,
XCom.task_id == self.task_id,
XCom.execution_date == self.execution_date
).delete()
session.commit()
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/www/api/experimental/endpoints.py#L142-L150
|
def get_dag_code(dag_id):
""""""
try:
return get_code(dag_id)
except AirflowException as err:
_log.info(err)
response = jsonify(error="{}".format(err))
response.status_code = err.status_code
return response
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcs_hook.py#L382-L445
|
def create_bucket(self,
bucket_name,
resource=None,
storage_class='MULTI_REGIONAL',
location='US',
project_id=None,
labels=None
):
"""
"""
self.log.info('Creating Bucket: %s; Location: %s; Storage Class: %s',
bucket_name, location, storage_class)
client = self.get_conn()
bucket = client.bucket(bucket_name=bucket_name)
bucket_resource = resource or {}
for item in bucket_resource:
if item != "name":
bucket._patch_property(name=item, value=resource[item])
bucket.storage_class = storage_class
bucket.labels = labels or {}
bucket.create(project=project_id, location=location)
return bucket.id
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/opsgenie_alert_hook.py#L50-L59
|
def _get_api_key(self):
"""
"""
conn = self.get_connection(self.http_conn_id)
api_key = conn.password
if not api_key:
raise AirflowException('Opsgenie API Key is required for this hook, '
'please check your conn_id configuration.')
return api_key
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/jobs.py#L766-L894
|
def create_dag_run(self, dag, session=None):
"""
"""
if dag.schedule_interval and conf.getboolean('scheduler', 'USE_JOB_SCHEDULE'):
active_runs = DagRun.find(
dag_id=dag.dag_id,
state=State.RUNNING,
external_trigger=False,
session=session
)
# return if already reached maximum active runs and no timeout setting
if len(active_runs) >= dag.max_active_runs and not dag.dagrun_timeout:
return
timedout_runs = 0
for dr in active_runs:
if (
dr.start_date and dag.dagrun_timeout and
dr.start_date < timezone.utcnow() - dag.dagrun_timeout):
dr.state = State.FAILED
dr.end_date = timezone.utcnow()
dag.handle_callback(dr, success=False, reason='dagrun_timeout',
session=session)
timedout_runs += 1
session.commit()
if len(active_runs) - timedout_runs >= dag.max_active_runs:
return
# this query should be replaced by find dagrun
qry = (
session.query(func.max(DagRun.execution_date))
.filter_by(dag_id=dag.dag_id)
.filter(or_(
DagRun.external_trigger == False, # noqa: E712
# add % as a wildcard for the like query
DagRun.run_id.like(DagRun.ID_PREFIX + '%')
))
)
last_scheduled_run = qry.scalar()
# don't schedule @once again
if dag.schedule_interval == '@once' and last_scheduled_run:
return None
# don't do scheduler catchup for dag's that don't have dag.catchup = True
if not (dag.catchup or dag.schedule_interval == '@once'):
# The logic is that we move start_date up until
# one period before, so that timezone.utcnow() is AFTER
# the period end, and the job can be created...
now = timezone.utcnow()
next_start = dag.following_schedule(now)
last_start = dag.previous_schedule(now)
if next_start <= now:
new_start = last_start
else:
new_start = dag.previous_schedule(last_start)
if dag.start_date:
if new_start >= dag.start_date:
dag.start_date = new_start
else:
dag.start_date = new_start
next_run_date = None
if not last_scheduled_run:
# First run
task_start_dates = [t.start_date for t in dag.tasks]
if task_start_dates:
next_run_date = dag.normalize_schedule(min(task_start_dates))
self.log.debug(
"Next run date based on tasks %s",
next_run_date
)
else:
next_run_date = dag.following_schedule(last_scheduled_run)
# make sure backfills are also considered
last_run = dag.get_last_dagrun(session=session)
if last_run and next_run_date:
while next_run_date <= last_run.execution_date:
next_run_date = dag.following_schedule(next_run_date)
# don't ever schedule prior to the dag's start_date
if dag.start_date:
next_run_date = (dag.start_date if not next_run_date
else max(next_run_date, dag.start_date))
if next_run_date == dag.start_date:
next_run_date = dag.normalize_schedule(dag.start_date)
self.log.debug(
"Dag start date: %s. Next run date: %s",
dag.start_date, next_run_date
)
# don't ever schedule in the future or if next_run_date is None
if not next_run_date or next_run_date > timezone.utcnow():
return
# this structure is necessary to avoid a TypeError from concatenating
# NoneType
if dag.schedule_interval == '@once':
period_end = next_run_date
elif next_run_date:
period_end = dag.following_schedule(next_run_date)
# Don't schedule a dag beyond its end_date (as specified by the dag param)
if next_run_date and dag.end_date and next_run_date > dag.end_date:
return
# Don't schedule a dag beyond its end_date (as specified by the task params)
# Get the min task end date, which may come from the dag.default_args
min_task_end_date = []
task_end_dates = [t.end_date for t in dag.tasks if t.end_date]
if task_end_dates:
min_task_end_date = min(task_end_dates)
if next_run_date and min_task_end_date and next_run_date > min_task_end_date:
return
if next_run_date and period_end and period_end <= timezone.utcnow():
next_run = dag.create_dagrun(
run_id=DagRun.ID_PREFIX + next_run_date.isoformat(),
execution_date=next_run_date,
start_date=timezone.utcnow(),
state=State.RUNNING,
external_trigger=False
)
return next_run
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/jobs.py#L223-L298
|
def reset_state_for_orphaned_tasks(self, filter_by_dag_run=None, session=None):
"""
"""
queued_tis = self.executor.queued_tasks
# also consider running as the state might not have changed in the db yet
running_tis = self.executor.running
resettable_states = [State.SCHEDULED, State.QUEUED]
TI = models.TaskInstance
DR = models.DagRun
if filter_by_dag_run is None:
resettable_tis = (
session
.query(TI)
.join(
DR,
and_(
TI.dag_id == DR.dag_id,
TI.execution_date == DR.execution_date))
.filter(
DR.state == State.RUNNING,
DR.run_id.notlike(BackfillJob.ID_PREFIX + '%'),
TI.state.in_(resettable_states))).all()
else:
resettable_tis = filter_by_dag_run.get_task_instances(state=resettable_states,
session=session)
tis_to_reset = []
# Can't use an update here since it doesn't support joins
for ti in resettable_tis:
if ti.key not in queued_tis and ti.key not in running_tis:
tis_to_reset.append(ti)
if len(tis_to_reset) == 0:
return []
def query(result, items):
filter_for_tis = ([and_(TI.dag_id == ti.dag_id,
TI.task_id == ti.task_id,
TI.execution_date == ti.execution_date)
for ti in items])
reset_tis = (
session
.query(TI)
.filter(or_(*filter_for_tis), TI.state.in_(resettable_states))
.with_for_update()
.all())
for ti in reset_tis:
ti.state = State.NONE
session.merge(ti)
return result + reset_tis
reset_tis = helpers.reduce_in_chunks(query,
tis_to_reset,
[],
self.max_tis_per_query)
task_instance_str = '\n\t'.join(
[repr(x) for x in reset_tis])
session.commit()
self.log.info(
"Reset the following %s TaskInstances:\n\t%s",
len(reset_tis), task_instance_str
)
return reset_tis
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/jobs.py#L1442-L1484
|
def _process_executor_events(self, simple_dag_bag, session=None):
"""
"""
# TODO: this shares quite a lot of code with _manage_executor_state
TI = models.TaskInstance
for key, state in list(self.executor.get_event_buffer(simple_dag_bag.dag_ids)
.items()):
dag_id, task_id, execution_date, try_number = key
self.log.info(
"Executor reports execution of %s.%s execution_date=%s "
"exited with status %s for try_number %s",
dag_id, task_id, execution_date, state, try_number
)
if state == State.FAILED or state == State.SUCCESS:
qry = session.query(TI).filter(TI.dag_id == dag_id,
TI.task_id == task_id,
TI.execution_date == execution_date)
ti = qry.first()
if not ti:
self.log.warning("TaskInstance %s went missing from the database", ti)
continue
# TODO: should we fail RUNNING as well, as we do in Backfills?
if ti.try_number == try_number and ti.state == State.QUEUED:
msg = ("Executor reports task instance {} finished ({}) "
"although the task says its {}. Was the task "
"killed externally?".format(ti, state, ti.state))
self.log.error(msg)
try:
simple_dag = simple_dag_bag.get_dag(dag_id)
dagbag = models.DagBag(simple_dag.full_filepath)
dag = dagbag.get_dag(dag_id)
ti.task = dag.get_task(task_id)
ti.handle_failure(msg)
except Exception:
self.log.error("Cannot load the dag bag to handle failure for %s"
". Setting task to FAILED without callbacks or "
"retries. Do you have enough resources?", ti)
ti.state = State.FAILED
session.merge(ti)
session.commit()
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/www/decorators.py#L97-L127
|
def has_dag_access(**dag_kwargs):
"""
"""
def decorator(f):
@functools.wraps(f)
def wrapper(self, *args, **kwargs):
has_access = self.appbuilder.sm.has_access
dag_id = request.args.get('dag_id')
# if it is false, we need to check whether user has write access on the dag
can_dag_edit = dag_kwargs.get('can_dag_edit', False)
# 1. check whether the user has can_dag_edit permissions on all_dags
# 2. if 1 false, check whether the user
# has can_dag_edit permissions on the dag
# 3. if 2 false, check whether it is can_dag_read view,
# and whether user has the permissions
if (
has_access('can_dag_edit', 'all_dags') or
has_access('can_dag_edit', dag_id) or (not can_dag_edit and
(has_access('can_dag_read',
'all_dags') or
has_access('can_dag_read',
dag_id)))):
return f(self, *args, **kwargs)
else:
flash("Access is Denied", "danger")
return redirect(url_for(self.appbuilder.sm.auth_view.
__class__.__name__ + ".login"))
return wrapper
return decorator
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_bigtable_hook.py#L90-L168
|
def create_instance(self,
instance_id,
main_cluster_id,
main_cluster_zone,
project_id=None,
replica_cluster_id=None,
replica_cluster_zone=None,
instance_display_name=None,
instance_type=enums.Instance.Type.TYPE_UNSPECIFIED,
instance_labels=None,
cluster_nodes=None,
cluster_storage_type=enums.StorageType.STORAGE_TYPE_UNSPECIFIED,
timeout=None):
"""
"""
cluster_storage_type = enums.StorageType(cluster_storage_type)
instance_type = enums.Instance.Type(instance_type)
instance = Instance(
instance_id,
self._get_client(project_id=project_id),
instance_display_name,
instance_type,
instance_labels,
)
clusters = [
instance.cluster(
main_cluster_id,
main_cluster_zone,
cluster_nodes,
cluster_storage_type
)
]
if replica_cluster_id and replica_cluster_zone:
clusters.append(instance.cluster(
replica_cluster_id,
replica_cluster_zone,
cluster_nodes,
cluster_storage_type
))
operation = instance.create(
clusters=clusters
)
operation.result(timeout)
return instance
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/utils/log/wasb_task_handler.py#L123-L133
|
def wasb_log_exists(self, remote_log_location):
"""
"""
try:
return self.hook.check_for_blob(self.wasb_container, remote_log_location)
except Exception:
pass
return False
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/_vendor/nvd3/NVD3Chart.py#L488-L501
|
def _main():
"""
"""
# Parse arguments
usage = "usage: nvd3.py [options]"
parser = OptionParser(usage=usage,
version=("python-nvd3 - Charts generator with "
"nvd3.js and d3.js"))
parser.add_option("-q", "--quiet",
action="store_false", dest="verbose", default=True,
help="don't print messages to stdout")
(options, args) = parser.parse_args()
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_text_to_speech_hook.py#L53-L80
|
def synthesize_speech(self, input_data, voice, audio_config, retry=None, timeout=None):
"""
"""
client = self.get_conn()
self.log.info("Synthesizing input: %s" % input_data)
return client.synthesize_speech(
input_=input_data, voice=voice, audio_config=audio_config, retry=retry, timeout=timeout
)
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_compute_hook.py#L189-L220
|
def insert_instance_template(self, body, request_id=None, project_id=None):
"""
"""
response = self.get_conn().instanceTemplates().insert(
project=project_id,
body=body,
requestId=request_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)
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/utils/log/wasb_task_handler.py#L68-L95
|
def close(self):
"""
"""
# When application exit, system shuts down all handlers by
# calling close method. Here we check if logger is already
# closed to prevent uploading the log to remote storage multiple
# times when `logging.shutdown` is called.
if self.closed:
return
super().close()
if not self.upload_on_close:
return
local_loc = os.path.join(self.local_base, self.log_relative_path)
remote_loc = os.path.join(self.remote_base, self.log_relative_path)
if os.path.exists(local_loc):
# read log and remove old logs to get just the latest additions
with open(local_loc, 'r') as logfile:
log = logfile.read()
self.wasb_write(log, remote_loc, append=True)
if self.delete_local_copy:
shutil.rmtree(os.path.dirname(local_loc))
# Mark closed so we don't double write if close is called twice
self.closed = True
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/jobs.py#L139-L189
|
def heartbeat(self):
"""
"""
try:
with create_session() as session:
job = session.query(BaseJob).filter_by(id=self.id).one()
make_transient(job)
session.commit()
if job.state == State.SHUTDOWN:
self.kill()
is_unit_test = conf.getboolean('core', 'unit_test_mode')
if not is_unit_test:
# Figure out how long to sleep for
sleep_for = 0
if job.latest_heartbeat:
seconds_remaining = self.heartrate - \
(timezone.utcnow() - job.latest_heartbeat)\
.total_seconds()
sleep_for = max(0, seconds_remaining)
sleep(sleep_for)
# Update last heartbeat time
with create_session() as session:
job = session.query(BaseJob).filter(BaseJob.id == self.id).first()
job.latest_heartbeat = timezone.utcnow()
session.merge(job)
session.commit()
self.heartbeat_callback(session=session)
self.log.debug('[heartbeat]')
except OperationalError as e:
self.log.error("Scheduler heartbeat got an exception: %s", str(e))
|
https://github.com/soimort/you-get/blob/b746ac01c9f39de94cac2d56f665285b0523b974/src/you_get/extractors/wanmen.py#L37-L51
|
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)
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/azure_fileshare_hook.py#L40-L45
|
def get_conn(self):
""""""
conn = self.get_connection(self.conn_id)
service_options = conn.extra_dejson
return FileService(account_name=conn.login,
account_key=conn.password, **service_options)
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/operators/databricks_operator.py#L61-L92
|
def _handle_databricks_operator_execution(operator, hook, log, context):
"""
"""
if operator.do_xcom_push:
context['ti'].xcom_push(key=XCOM_RUN_ID_KEY, value=operator.run_id)
log.info('Run submitted with run_id: %s', operator.run_id)
run_page_url = hook.get_run_page_url(operator.run_id)
if operator.do_xcom_push:
context['ti'].xcom_push(key=XCOM_RUN_PAGE_URL_KEY, value=run_page_url)
log.info('View run status, Spark UI, and logs at %s', run_page_url)
while True:
run_state = hook.get_run_state(operator.run_id)
if run_state.is_terminal:
if run_state.is_successful:
log.info('%s completed successfully.', operator.task_id)
log.info('View run status, Spark UI, and logs at %s', run_page_url)
return
else:
error_message = '{t} failed with terminal state: {s}'.format(
t=operator.task_id,
s=run_state)
raise AirflowException(error_message)
else:
log.info('%s in run state: %s', operator.task_id, run_state)
log.info('View run status, Spark UI, and logs at %s', run_page_url)
log.info('Sleeping for %s seconds.', operator.polling_period_seconds)
time.sleep(operator.polling_period_seconds)
|
https://github.com/soimort/you-get/blob/b746ac01c9f39de94cac2d56f665285b0523b974/src/you_get/extractors/showroom.py#L11-L24
|
def showroom_get_roomid_by_room_url_key(room_url_key):
""""""
fake_headers_mobile = {
'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8',
'Accept-Charset': 'UTF-8,*;q=0.5',
'Accept-Encoding': 'gzip,deflate,sdch',
'Accept-Language': 'en-US,en;q=0.8',
'User-Agent': 'Mozilla/5.0 (Linux; Android 4.4.2; Nexus 4 Build/KOT49H) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/34.0.1847.114 Mobile Safari/537.36'
}
webpage_url = 'https://www.showroom-live.com/' + room_url_key
html = get_content(webpage_url, headers = fake_headers_mobile)
roomid = match1(html, r'room\?room_id\=(\d+)')
assert roomid
return roomid
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/operators/__init__.py#L21-L27
|
def _integrate_plugins():
""""""
import sys
from airflow.plugins_manager import operators_modules
for operators_module in operators_modules:
sys.modules[operators_module.__name__] = operators_module
globals()[operators_module._name] = operators_module
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/jobs.py#L2445-L2458
|
def _set_unfinished_dag_runs_to_failed(self, dag_runs, session=None):
"""
"""
for dag_run in dag_runs:
dag_run.update_state()
if dag_run.state not in State.finished():
dag_run.set_state(State.FAILED)
session.merge(dag_run)
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/hooks/hive_hooks.py#L529-L554
|
def check_for_partition(self, schema, table, partition):
"""
"""
with self.metastore as client:
partitions = client.get_partitions_by_filter(
schema, table, partition, 1)
if partitions:
return True
else:
return False
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_container_hook.py#L221-L247
|
def get_cluster(self, name, project_id=None, retry=DEFAULT, timeout=DEFAULT):
"""
"""
self.log.info(
"Fetching cluster (project_id=%s, zone=%s, cluster_name=%s)",
project_id or self.project_id, self.location, name
)
return self.get_client().get_cluster(project_id=project_id or self.project_id,
zone=self.location,
cluster_id=name,
retry=retry,
timeout=timeout).self_link
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/operators/slack_operator.py#L79-L87
|
def execute(self, **kwargs):
"""
"""
if not self.api_params:
self.construct_api_call_params()
slack = SlackHook(token=self.token, slack_conn_id=self.slack_conn_id)
slack.call(self.method, self.api_params)
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/sensors/celery_queue_sensor.py#L49-L62
|
def _check_task_id(self, context):
"""
"""
ti = context['ti']
celery_result = ti.xcom_pull(task_ids=self.target_task_id)
return celery_result.ready()
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/api/common/experimental/get_dag_run_state.py#L24-L44
|
def get_dag_run_state(dag_id, execution_date):
""""""
dagbag = DagBag()
# Check DAG exists.
if dag_id not in dagbag.dags:
error_message = "Dag id {} not found".format(dag_id)
raise DagNotFound(error_message)
# Get DAG object and check Task Exists
dag = dagbag.get_dag(dag_id)
# Get DagRun object and check that it exists
dagrun = dag.get_dagrun(execution_date=execution_date)
if not dagrun:
error_message = ('Dag Run for date {} not found in dag {}'
.format(execution_date, dag_id))
raise DagRunNotFound(error_message)
return {'state': dagrun.get_state()}
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/operators/databricks_operator.py#L34-L58
|
def _deep_string_coerce(content, json_path='json'):
"""
"""
c = _deep_string_coerce
if isinstance(content, six.string_types):
return content
elif isinstance(content, six.integer_types + (float,)):
# Databricks can tolerate either numeric or string types in the API backend.
return str(content)
elif isinstance(content, (list, tuple)):
return [c(e, '{0}[{1}]'.format(json_path, i)) for i, e in enumerate(content)]
elif isinstance(content, dict):
return {k: c(v, '{0}[{1}]'.format(json_path, k))
for k, v in list(content.items())}
else:
param_type = type(content)
msg = 'Type {0} used for parameter {1} is not a number or a string' \
.format(param_type, json_path)
raise AirflowException(msg)
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_sql_hook.py#L343-L368
|
def _wait_for_operation_to_complete(self, project_id, operation_name):
"""
"""
service = self.get_conn()
while True:
operation_response = service.operations().get(
project=project_id,
operation=operation_name,
).execute(num_retries=self.num_retries)
if operation_response.get("status") == CloudSqlOperationStatus.DONE:
error = operation_response.get("error")
if error:
# Extracting the errors list as string and trimming square braces
error_msg = str(error.get("errors"))[1:-1]
raise AirflowException(error_msg)
# No meaningful info to return from the response in case of success
return
time.sleep(TIME_TO_SLEEP_IN_SECONDS)
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/jobs.py#L1669-L1787
|
def process_file(self, file_path, zombies, pickle_dags=False, session=None):
"""
"""
self.log.info("Processing file %s for tasks to queue", file_path)
# As DAGs are parsed from this file, they will be converted into SimpleDags
simple_dags = []
try:
dagbag = models.DagBag(file_path, include_examples=False)
except Exception:
self.log.exception("Failed at reloading the DAG file %s", file_path)
Stats.incr('dag_file_refresh_error', 1, 1)
return []
if len(dagbag.dags) > 0:
self.log.info("DAG(s) %s retrieved from %s", dagbag.dags.keys(), file_path)
else:
self.log.warning("No viable dags retrieved from %s", file_path)
self.update_import_errors(session, dagbag)
return []
# Save individual DAGs in the ORM and update DagModel.last_scheduled_time
for dag in dagbag.dags.values():
dag.sync_to_db()
paused_dag_ids = [dag.dag_id for dag in dagbag.dags.values()
if dag.is_paused]
# Pickle the DAGs (if necessary) and put them into a SimpleDag
for dag_id in dagbag.dags:
# Only return DAGs that are not paused
if dag_id not in paused_dag_ids:
dag = dagbag.get_dag(dag_id)
pickle_id = None
if pickle_dags:
pickle_id = dag.pickle(session).id
simple_dags.append(SimpleDag(dag, pickle_id=pickle_id))
if len(self.dag_ids) > 0:
dags = [dag for dag in dagbag.dags.values()
if dag.dag_id in self.dag_ids and
dag.dag_id not in paused_dag_ids]
else:
dags = [dag for dag in dagbag.dags.values()
if not dag.parent_dag and
dag.dag_id not in paused_dag_ids]
# Not using multiprocessing.Queue() since it's no longer a separate
# process and due to some unusual behavior. (empty() incorrectly
# returns true?)
ti_keys_to_schedule = []
self._process_dags(dagbag, dags, ti_keys_to_schedule)
for ti_key in ti_keys_to_schedule:
dag = dagbag.dags[ti_key[0]]
task = dag.get_task(ti_key[1])
ti = models.TaskInstance(task, ti_key[2])
ti.refresh_from_db(session=session, lock_for_update=True)
# We can defer checking the task dependency checks to the worker themselves
# since they can be expensive to run in the scheduler.
dep_context = DepContext(deps=QUEUE_DEPS, ignore_task_deps=True)
# Only schedule tasks that have their dependencies met, e.g. to avoid
# a task that recently got its state changed to RUNNING from somewhere
# other than the scheduler from getting its state overwritten.
# TODO(aoen): It's not great that we have to check all the task instance
# dependencies twice; once to get the task scheduled, and again to actually
# run the task. We should try to come up with a way to only check them once.
if ti.are_dependencies_met(
dep_context=dep_context,
session=session,
verbose=True):
# Task starts out in the scheduled state. All tasks in the
# scheduled state will be sent to the executor
ti.state = State.SCHEDULED
# Also save this task instance to the DB.
self.log.info("Creating / updating %s in ORM", ti)
session.merge(ti)
# commit batch
session.commit()
# Record import errors into the ORM
try:
self.update_import_errors(session, dagbag)
except Exception:
self.log.exception("Error logging import errors!")
try:
dagbag.kill_zombies(zombies)
except Exception:
self.log.exception("Error killing zombies!")
return simple_dags
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcs_hook.py#L54-L96
|
def copy(self, source_bucket, source_object, destination_bucket=None,
destination_object=None):
"""
"""
destination_bucket = destination_bucket or source_bucket
destination_object = destination_object or source_object
if source_bucket == destination_bucket and \
source_object == destination_object:
raise ValueError(
'Either source/destination bucket or source/destination object '
'must be different, not both the same: bucket=%s, object=%s' %
(source_bucket, source_object))
if not source_bucket or not source_object:
raise ValueError('source_bucket and source_object cannot be empty.')
client = self.get_conn()
source_bucket = client.get_bucket(source_bucket)
source_object = source_bucket.blob(source_object)
destination_bucket = client.get_bucket(destination_bucket)
destination_object = source_bucket.copy_blob(
blob=source_object,
destination_bucket=destination_bucket,
new_name=destination_object)
self.log.info('Object %s in bucket %s copied to object %s in bucket %s',
source_object.name, source_bucket.name,
destination_object.name, destination_bucket.name)
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/ssh_hook.py#L199-L239
|
def get_tunnel(self, remote_port, remote_host="localhost", local_port=None):
"""
"""
if local_port:
local_bind_address = ('localhost', local_port)
else:
local_bind_address = ('localhost',)
if self.password and self.password.strip():
client = SSHTunnelForwarder(self.remote_host,
ssh_port=self.port,
ssh_username=self.username,
ssh_password=self.password,
ssh_pkey=self.key_file,
ssh_proxy=self.host_proxy,
local_bind_address=local_bind_address,
remote_bind_address=(remote_host, remote_port),
logger=self.log)
else:
client = SSHTunnelForwarder(self.remote_host,
ssh_port=self.port,
ssh_username=self.username,
ssh_pkey=self.key_file,
ssh_proxy=self.host_proxy,
local_bind_address=local_bind_address,
remote_bind_address=(remote_host, remote_port),
host_pkey_directories=[],
logger=self.log)
return client
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/hooks/postgres_hook.py#L91-L95
|
def bulk_dump(self, table, tmp_file):
"""
"""
self.copy_expert("COPY {table} TO STDOUT".format(table=table), tmp_file)
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/discord_webhook_hook.py#L102-L124
|
def _build_discord_payload(self):
"""
"""
payload = {}
if self.username:
payload['username'] = self.username
if self.avatar_url:
payload['avatar_url'] = self.avatar_url
payload['tts'] = self.tts
if len(self.message) <= 2000:
payload['content'] = self.message
else:
raise AirflowException('Discord message length must be 2000 or fewer '
'characters.')
return json.dumps(payload)
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_container_hook.py#L96-L108
|
def get_operation(self, operation_name, project_id=None):
"""
"""
return self.get_client().get_operation(project_id=project_id or self.project_id,
zone=self.location,
operation_id=operation_name)
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/spark_submit_hook.py#L215-L297
|
def _build_spark_submit_command(self, application):
"""
"""
connection_cmd = self._get_spark_binary_path()
# The url ot the spark master
connection_cmd += ["--master", self._connection['master']]
if self._conf:
for key in self._conf:
connection_cmd += ["--conf", "{}={}".format(key, str(self._conf[key]))]
if self._env_vars and (self._is_kubernetes or self._is_yarn):
if self._is_yarn:
tmpl = "spark.yarn.appMasterEnv.{}={}"
else:
tmpl = "spark.kubernetes.driverEnv.{}={}"
for key in self._env_vars:
connection_cmd += [
"--conf",
tmpl.format(key, str(self._env_vars[key]))]
elif self._env_vars and self._connection['deploy_mode'] != "cluster":
self._env = self._env_vars # Do it on Popen of the process
elif self._env_vars and self._connection['deploy_mode'] == "cluster":
raise AirflowException(
"SparkSubmitHook env_vars is not supported in standalone-cluster mode.")
if self._is_kubernetes:
connection_cmd += ["--conf", "spark.kubernetes.namespace={}".format(
self._connection['namespace'])]
if self._files:
connection_cmd += ["--files", self._files]
if self._py_files:
connection_cmd += ["--py-files", self._py_files]
if self._archives:
connection_cmd += ["--archives", self._archives]
if self._driver_class_path:
connection_cmd += ["--driver-class-path", self._driver_class_path]
if self._jars:
connection_cmd += ["--jars", self._jars]
if self._packages:
connection_cmd += ["--packages", self._packages]
if self._exclude_packages:
connection_cmd += ["--exclude-packages", self._exclude_packages]
if self._repositories:
connection_cmd += ["--repositories", self._repositories]
if self._num_executors:
connection_cmd += ["--num-executors", str(self._num_executors)]
if self._total_executor_cores:
connection_cmd += ["--total-executor-cores", str(self._total_executor_cores)]
if self._executor_cores:
connection_cmd += ["--executor-cores", str(self._executor_cores)]
if self._executor_memory:
connection_cmd += ["--executor-memory", self._executor_memory]
if self._driver_memory:
connection_cmd += ["--driver-memory", self._driver_memory]
if self._keytab:
connection_cmd += ["--keytab", self._keytab]
if self._principal:
connection_cmd += ["--principal", self._principal]
if self._name:
connection_cmd += ["--name", self._name]
if self._java_class:
connection_cmd += ["--class", self._java_class]
if self._verbose:
connection_cmd += ["--verbose"]
if self._connection['queue']:
connection_cmd += ["--queue", self._connection['queue']]
if self._connection['deploy_mode']:
connection_cmd += ["--deploy-mode", self._connection['deploy_mode']]
# The actual script to execute
connection_cmd += [application]
# Append any application arguments
if self._application_args:
connection_cmd += self._application_args
self.log.info("Spark-Submit cmd: %s", connection_cmd)
return connection_cmd
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/lineage/__init__.py#L85-L140
|
def prepare_lineage(func):
"""
"""
@wraps(func)
def wrapper(self, context, *args, **kwargs):
self.log.debug("Preparing lineage inlets and outlets")
task_ids = set(self._inlets['task_ids']).intersection(
self.get_flat_relative_ids(upstream=True)
)
if task_ids:
inlets = self.xcom_pull(context,
task_ids=task_ids,
dag_id=self.dag_id,
key=PIPELINE_OUTLETS)
inlets = [item for sublist in inlets if sublist for item in sublist]
inlets = [DataSet.map_type(i['typeName'])(data=i['attributes'])
for i in inlets]
self.inlets.extend(inlets)
if self._inlets['auto']:
# dont append twice
task_ids = set(self._inlets['task_ids']).symmetric_difference(
self.upstream_task_ids
)
inlets = self.xcom_pull(context,
task_ids=task_ids,
dag_id=self.dag_id,
key=PIPELINE_OUTLETS)
inlets = [item for sublist in inlets if sublist for item in sublist]
inlets = [DataSet.map_type(i['typeName'])(data=i['attributes'])
for i in inlets]
self.inlets.extend(inlets)
if len(self._inlets['datasets']) > 0:
self.inlets.extend(self._inlets['datasets'])
# outlets
if len(self._outlets['datasets']) > 0:
self.outlets.extend(self._outlets['datasets'])
self.log.debug("inlets: %s, outlets: %s", self.inlets, self.outlets)
for dataset in chain(self.inlets, self.outlets):
dataset.set_context(context)
return func(self, context, *args, **kwargs)
return wrapper
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/hooks/http_hook.py#L85-L131
|
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)
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/hooks/hive_hooks.py#L858-L918
|
def to_csv(
self,
hql,
csv_filepath,
schema='default',
delimiter=',',
lineterminator='\r\n',
output_header=True,
fetch_size=1000,
hive_conf=None):
"""
"""
results_iter = self._get_results(hql, schema,
fetch_size=fetch_size, hive_conf=hive_conf)
header = next(results_iter)
message = None
i = 0
with open(csv_filepath, 'wb') as f:
writer = csv.writer(f,
delimiter=delimiter,
lineterminator=lineterminator,
encoding='utf-8')
try:
if output_header:
self.log.debug('Cursor description is %s', header)
writer.writerow([c[0] for c in header])
for i, row in enumerate(results_iter, 1):
writer.writerow(row)
if i % fetch_size == 0:
self.log.info("Written %s rows so far.", i)
except ValueError as exception:
message = str(exception)
if message:
# need to clean up the file first
os.remove(csv_filepath)
raise ValueError(message)
self.log.info("Done. Loaded a total of %s rows.", i)
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/www/utils.py#L207-L219
|
def open_maybe_zipped(f, mode='r'):
"""
"""
_, archive, filename = ZIP_REGEX.search(f).groups()
if archive and zipfile.is_zipfile(archive):
return zipfile.ZipFile(archive, mode=mode).open(filename)
else:
return io.open(f, mode=mode)
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/setup.py#L102-L128
|
def git_version(version):
"""
"""
repo = None
try:
import git
repo = git.Repo('.git')
except ImportError:
logger.warning('gitpython not found: Cannot compute the git version.')
return ''
except Exception as e:
logger.warning('Cannot compute the git version. {}'.format(e))
return ''
if repo:
sha = repo.head.commit.hexsha
if repo.is_dirty():
return '.dev0+{sha}.dirty'.format(sha=sha)
# commit is clean
return '.release:{version}+{sha}'.format(version=version, sha=sha)
else:
return 'no_git_version'
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/datastore_hook.py#L170-L189
|
def run_query(self, body):
"""
"""
conn = self.get_conn()
resp = (conn
.projects()
.runQuery(projectId=self.project_id, body=body)
.execute(num_retries=self.num_retries))
return resp['batch']
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/azure_fileshare_hook.py#L174-L191
|
def load_string(self, string_data, share_name, directory_name, file_name, **kwargs):
"""
"""
self.connection.create_file_from_text(share_name, directory_name,
file_name, string_data, **kwargs)
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/ftp_hook.py#L264-L272
|
def rename(self, from_name, to_name):
"""
"""
conn = self.get_conn()
return conn.rename(from_name, to_name)
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/jobs.py#L432-L448
|
def terminate(self, sigkill=False):
"""
"""
if self._process is None:
raise AirflowException("Tried to call stop before starting!")
# The queue will likely get corrupted, so remove the reference
self._result_queue = None
self._process.terminate()
# Arbitrarily wait 5s for the process to die
self._process.join(5)
if sigkill and self._process.is_alive():
self.log.warning("Killing PID %s", self._process.pid)
os.kill(self._process.pid, signal.SIGKILL)
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/utils/dag_processing.py#L931-L944
|
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()
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_pubsub_hook.py#L83-L110
|
def create_topic(self, project, topic, fail_if_exists=False):
"""
"""
service = self.get_conn()
full_topic = _format_topic(project, topic)
try:
service.projects().topics().create(
name=full_topic, body={}).execute(num_retries=self.num_retries)
except HttpError as e:
# Status code 409 indicates that the topic already exists.
if str(e.resp['status']) == '409':
message = 'Topic already exists: {}'.format(full_topic)
self.log.warning(message)
if fail_if_exists:
raise PubSubException(message)
else:
raise PubSubException(
'Error creating topic {}'.format(full_topic), e)
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/models/taskinstance.py#L249-L292
|
def command_as_list(
self,
mark_success=False,
ignore_all_deps=False,
ignore_task_deps=False,
ignore_depends_on_past=False,
ignore_ti_state=False,
local=False,
pickle_id=None,
raw=False,
job_id=None,
pool=None,
cfg_path=None):
"""
"""
dag = self.task.dag
should_pass_filepath = not pickle_id and dag
if should_pass_filepath and dag.full_filepath != dag.filepath:
path = "DAGS_FOLDER/{}".format(dag.filepath)
elif should_pass_filepath and dag.full_filepath:
path = dag.full_filepath
else:
path = None
return TaskInstance.generate_command(
self.dag_id,
self.task_id,
self.execution_date,
mark_success=mark_success,
ignore_all_deps=ignore_all_deps,
ignore_task_deps=ignore_task_deps,
ignore_depends_on_past=ignore_depends_on_past,
ignore_ti_state=ignore_ti_state,
local=local,
pickle_id=pickle_id,
file_path=path,
raw=raw,
job_id=job_id,
pool=pool,
cfg_path=cfg_path)
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/executors/local_executor.py#L73-L92
|
def execute_work(self, key, command):
"""
"""
if key is None:
return
self.log.info("%s running %s", self.__class__.__name__, command)
try:
subprocess.check_call(command, close_fds=True)
state = State.SUCCESS
except subprocess.CalledProcessError as e:
state = State.FAILED
self.log.error("Failed to execute task %s.", str(e))
# TODO: Why is this commented out?
# raise e
self.result_queue.put((key, state))
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/bigquery_hook.py#L1211-L1273
|
def run_with_configuration(self, configuration):
"""
"""
jobs = self.service.jobs()
job_data = {'configuration': configuration}
# Send query and wait for reply.
query_reply = jobs \
.insert(projectId=self.project_id, body=job_data) \
.execute(num_retries=self.num_retries)
self.running_job_id = query_reply['jobReference']['jobId']
if 'location' in query_reply['jobReference']:
location = query_reply['jobReference']['location']
else:
location = self.location
# Wait for query to finish.
keep_polling_job = True
while keep_polling_job:
try:
if location:
job = jobs.get(
projectId=self.project_id,
jobId=self.running_job_id,
location=location).execute(num_retries=self.num_retries)
else:
job = jobs.get(
projectId=self.project_id,
jobId=self.running_job_id).execute(num_retries=self.num_retries)
if job['status']['state'] == 'DONE':
keep_polling_job = False
# Check if job had errors.
if 'errorResult' in job['status']:
raise Exception(
'BigQuery job failed. Final error was: {}. The job was: {}'.
format(job['status']['errorResult'], job))
else:
self.log.info('Waiting for job to complete : %s, %s',
self.project_id, self.running_job_id)
time.sleep(5)
except HttpError as err:
if err.resp.status in [500, 503]:
self.log.info(
'%s: Retryable error, waiting for job to complete: %s',
err.resp.status, self.running_job_id)
time.sleep(5)
else:
raise Exception(
'BigQuery job status check failed. Final error was: {}'.
format(err.resp.status))
return self.running_job_id
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/qubole_hook.py#L212-L229
|
def get_extra_links(self, operator, dttm):
"""
"""
conn = BaseHook.get_connection(operator.kwargs['qubole_conn_id'])
if conn and conn.host:
host = re.sub(r'api$', 'v2/analyze?command_id=', conn.host)
else:
host = 'https://api.qubole.com/v2/analyze?command_id='
ti = TaskInstance(task=operator, execution_date=dttm)
qds_command_id = ti.xcom_pull(task_ids=operator.task_id, key='qbol_cmd_id')
url = host + str(qds_command_id) if qds_command_id else ''
return url
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/jobs.py#L599-L606
|
def _exit_gracefully(self, signum, frame):
"""
"""
self.log.info("Exiting gracefully upon receiving signal %s", signum)
if self.processor_agent:
self.processor_agent.end()
sys.exit(os.EX_OK)
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/operators/mysql_to_gcs.py#L313-L334
|
def type_map(cls, mysql_type):
"""
"""
d = {
FIELD_TYPE.INT24: 'INTEGER',
FIELD_TYPE.TINY: 'INTEGER',
FIELD_TYPE.BIT: 'INTEGER',
FIELD_TYPE.DATETIME: 'TIMESTAMP',
FIELD_TYPE.DATE: 'TIMESTAMP',
FIELD_TYPE.DECIMAL: 'FLOAT',
FIELD_TYPE.NEWDECIMAL: 'FLOAT',
FIELD_TYPE.DOUBLE: 'FLOAT',
FIELD_TYPE.FLOAT: 'FLOAT',
FIELD_TYPE.LONG: 'INTEGER',
FIELD_TYPE.LONGLONG: 'INTEGER',
FIELD_TYPE.SHORT: 'INTEGER',
FIELD_TYPE.TIMESTAMP: 'TIMESTAMP',
FIELD_TYPE.YEAR: 'INTEGER',
}
return d[mysql_type] if mysql_type in d else 'STRING'
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/www/security.py#L326-L342
|
def clean_perms(self):
"""
"""
self.log.debug('Cleaning faulty perms')
sesh = self.get_session
pvms = (
sesh.query(sqla_models.PermissionView)
.filter(or_(
sqla_models.PermissionView.permission == None, # NOQA
sqla_models.PermissionView.view_menu == None, # NOQA
))
)
deleted_count = pvms.delete()
sesh.commit()
if deleted_count:
self.log.info('Deleted %s faulty permissions', deleted_count)
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/bigquery_hook.py#L1533-L1598
|
def create_empty_dataset(self, dataset_id="", project_id="",
dataset_reference=None):
"""
"""
if dataset_reference:
_validate_value('dataset_reference', dataset_reference, dict)
else:
dataset_reference = {}
if "datasetReference" not in dataset_reference:
dataset_reference["datasetReference"] = {}
if not dataset_reference["datasetReference"].get("datasetId") and not dataset_id:
raise ValueError(
"{} not provided datasetId. Impossible to create dataset")
dataset_required_params = [(dataset_id, "datasetId", ""),
(project_id, "projectId", self.project_id)]
for param_tuple in dataset_required_params:
param, param_name, param_default = param_tuple
if param_name not in dataset_reference['datasetReference']:
if param_default and not param:
self.log.info(
"%s was not specified. Will be used default value %s.",
param_name, param_default
)
param = param_default
dataset_reference['datasetReference'].update(
{param_name: param})
elif param:
_api_resource_configs_duplication_check(
param_name, param,
dataset_reference['datasetReference'], 'dataset_reference')
dataset_id = dataset_reference.get("datasetReference").get("datasetId")
dataset_project_id = dataset_reference.get("datasetReference").get(
"projectId")
self.log.info('Creating Dataset: %s in project: %s ', dataset_id,
dataset_project_id)
try:
self.service.datasets().insert(
projectId=dataset_project_id,
body=dataset_reference).execute(num_retries=self.num_retries)
self.log.info('Dataset created successfully: In project %s '
'Dataset %s', dataset_project_id, dataset_id)
except HttpError as err:
raise AirflowException(
'BigQuery job failed. Error was: {}'.format(err.content)
)
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/sftp_hook.py#L145-L153
|
def list_directory(self, path):
"""
"""
conn = self.get_conn()
files = conn.listdir(path)
return files
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/api/common/experimental/mark_tasks.py#L284-L303
|
def set_dag_run_state_to_running(dag, execution_date, commit=False, session=None):
"""
"""
res = []
if not dag or not execution_date:
return res
# Mark the dag run to running.
if commit:
_set_dag_run_state(dag.dag_id, execution_date, State.RUNNING, session)
# To keep the return type consistent with the other similar functions.
return res
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/operators/jenkins_job_trigger_operator.py#L124-L147
|
def build_job(self, jenkins_server):
"""
"""
# Warning if the parameter is too long, the URL can be longer than
# the maximum allowed size
if self.parameters and isinstance(self.parameters, six.string_types):
import ast
self.parameters = ast.literal_eval(self.parameters)
if not self.parameters:
# We need a None to call the non parametrized jenkins api end point
self.parameters = None
request = Request(jenkins_server.build_job_url(self.job_name,
self.parameters, None))
return jenkins_request_with_headers(jenkins_server, request)
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/www/security.py#L440-L453
|
def update_admin_perm_view(self):
"""
"""
pvms = self.get_session.query(sqla_models.PermissionView).all()
pvms = [p for p in pvms if p.permission and p.view_menu]
admin = self.find_role('Admin')
admin.permissions = list(set(admin.permissions) | set(pvms))
self.get_session.commit()
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/bin/cli.py#L240-L257
|
def delete_dag(args):
"""
"""
log = LoggingMixin().log
if args.yes or input(
"This will drop all existing records related to the specified DAG. "
"Proceed? (y/n)").upper() == "Y":
try:
message = api_client.delete_dag(dag_id=args.dag_id)
except IOError as err:
log.error(err)
raise AirflowException(err)
log.info(message)
else:
print("Bail.")
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/hooks/webhdfs_hook.py#L92-L104
|
def check_for_path(self, hdfs_path):
"""
"""
conn = self.get_conn()
status = conn.status(hdfs_path, strict=False)
return bool(status)
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/www/security.py#L344-L363
|
def _merge_perm(self, permission_name, view_menu_name):
"""
"""
permission = self.find_permission(permission_name)
view_menu = self.find_view_menu(view_menu_name)
pv = None
if permission and view_menu:
pv = self.get_session.query(self.permissionview_model).filter_by(
permission=permission, view_menu=view_menu).first()
if not pv and permission_name and view_menu_name:
self.add_permission_view_menu(permission_name, view_menu_name)
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/_vendor/nvd3/NVD3Chart.py#L407-L417
|
def buildjschart(self):
""""""
self.jschart = ''
# add custom tooltip string in jschart
# default condition (if build_custom_tooltip is not called explicitly with date_flag=True)
if self.tooltip_condition_string == '':
self.tooltip_condition_string = 'var y = String(graph.point.y);\n'
# Include data
self.series_js = json.dumps(self.series)
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/hooks/hive_hooks.py#L171-L255
|
def run_cli(self, hql, schema=None, verbose=True, hive_conf=None):
"""
"""
conn = self.conn
schema = schema or conn.schema
if schema:
hql = "USE {schema};\n{hql}".format(schema=schema, hql=hql)
with TemporaryDirectory(prefix='airflow_hiveop_') as tmp_dir:
with NamedTemporaryFile(dir=tmp_dir) as f:
hql = hql + '\n'
f.write(hql.encode('UTF-8'))
f.flush()
hive_cmd = self._prepare_cli_cmd()
env_context = get_context_from_env_var()
# Only extend the hive_conf if it is defined.
if hive_conf:
env_context.update(hive_conf)
hive_conf_params = self._prepare_hiveconf(env_context)
if self.mapred_queue:
hive_conf_params.extend(
['-hiveconf',
'mapreduce.job.queuename={}'
.format(self.mapred_queue),
'-hiveconf',
'mapred.job.queue.name={}'
.format(self.mapred_queue),
'-hiveconf',
'tez.job.queue.name={}'
.format(self.mapred_queue)
])
if self.mapred_queue_priority:
hive_conf_params.extend(
['-hiveconf',
'mapreduce.job.priority={}'
.format(self.mapred_queue_priority)])
if self.mapred_job_name:
hive_conf_params.extend(
['-hiveconf',
'mapred.job.name={}'
.format(self.mapred_job_name)])
hive_cmd.extend(hive_conf_params)
hive_cmd.extend(['-f', f.name])
if verbose:
self.log.info("%s", " ".join(hive_cmd))
sp = subprocess.Popen(
hive_cmd,
stdout=subprocess.PIPE,
stderr=subprocess.STDOUT,
cwd=tmp_dir,
close_fds=True)
self.sp = sp
stdout = ''
while True:
line = sp.stdout.readline()
if not line:
break
stdout += line.decode('UTF-8')
if verbose:
self.log.info(line.decode('UTF-8').strip())
sp.wait()
if sp.returncode:
raise AirflowException(stdout)
return stdout
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/ti_deps/deps/ready_to_reschedule.py#L34-L69
|
def _get_dep_statuses(self, ti, session, dep_context):
"""
"""
if dep_context.ignore_in_reschedule_period:
yield self._passing_status(
reason="The context specified that being in a reschedule period was "
"permitted.")
return
if ti.state not in self.RESCHEDULEABLE_STATES:
yield self._passing_status(
reason="The task instance is not in State_UP_FOR_RESCHEDULE or NONE state.")
return
task_reschedules = TaskReschedule.find_for_task_instance(task_instance=ti)
if not task_reschedules:
yield self._passing_status(
reason="There is no reschedule request for this task instance.")
return
now = timezone.utcnow()
next_reschedule_date = task_reschedules[-1].reschedule_date
if now >= next_reschedule_date:
yield self._passing_status(
reason="Task instance id ready for reschedule.")
return
yield self._failing_status(
reason="Task is not ready for reschedule yet but will be rescheduled "
"automatically. Current date is {0} and task will be rescheduled "
"at {1}.".format(now.isoformat(), next_reschedule_date.isoformat()))
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/spark_submit_hook.py#L299-L320
|
def _build_track_driver_status_command(self):
"""
"""
connection_cmd = self._get_spark_binary_path()
# The url ot the spark master
connection_cmd += ["--master", self._connection['master']]
# The driver id so we can poll for its status
if self._driver_id:
connection_cmd += ["--status", self._driver_id]
else:
raise AirflowException(
"Invalid status: attempted to poll driver " +
"status but no driver id is known. Giving up.")
self.log.debug("Poll driver status cmd: %s", connection_cmd)
return connection_cmd
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/wasb_hook.py#L50-L64
|
def check_for_blob(self, container_name, blob_name, **kwargs):
"""
"""
return self.connection.exists(container_name, blob_name, **kwargs)
|
https://github.com/soimort/you-get/blob/b746ac01c9f39de94cac2d56f665285b0523b974/src/you_get/common.py#L1258-L1273
|
def print_more_compatible(*args, **kwargs):
import builtins as __builtin__
"""
"""
# nothing happens on py3.3 and later
if sys.version_info[:2] >= (3, 3):
return __builtin__.print(*args, **kwargs)
# in lower pyver (e.g. 3.2.x), remove 'flush' keyword and flush it as requested
doFlush = kwargs.pop('flush', False)
ret = __builtin__.print(*args, **kwargs)
if doFlush:
kwargs.get('file', sys.stdout).flush()
return ret
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/hooks/hive_hooks.py#L578-L591
|
def get_table(self, table_name, db='default'):
"""
"""
if db == 'default' and '.' in table_name:
db, table_name = table_name.split('.')[:2]
with self.metastore as client:
return client.get_table(dbname=db, tbl_name=table_name)
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/operators/cassandra_to_gcs.py#L258-L266
|
def convert_tuple_type(cls, name, value):
"""
"""
names = ['field_' + str(i) for i in range(len(value))]
values = [cls.convert_value(name, value) for name, value in zip(names, value)]
return cls.generate_data_dict(names, values)
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcs_hook.py#L223-L253
|
def is_updated_after(self, bucket_name, object_name, ts):
"""
"""
client = self.get_conn()
bucket = storage.Bucket(client=client, name=bucket_name)
blob = bucket.get_blob(blob_name=object_name)
blob.reload()
blob_update_time = blob.updated
if blob_update_time is not None:
import dateutil.tz
if not ts.tzinfo:
ts = ts.replace(tzinfo=dateutil.tz.tzutc())
self.log.info("Verify object date: %s > %s", blob_update_time, ts)
if blob_update_time > ts:
return True
return False
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/utils/dag_processing.py#L810-L854
|
def start_in_async(self):
"""
"""
while True:
loop_start_time = time.time()
if self._signal_conn.poll():
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)
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(),
max_runs_reached,
all_files_processed,
len(simple_dags))
self._stat_queue.put(dag_parsing_stat)
if max_runs_reached:
self.log.info("Exiting dag parsing loop as all files "
"have been processed %s times", self._max_runs)
break
loop_duration = time.time() - loop_start_time
if loop_duration < 1:
sleep_length = 1 - loop_duration
self.log.debug("Sleeping for %.2f seconds to prevent excessive logging", sleep_length)
time.sleep(sleep_length)
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/sqoop_hook.py#L314-L355
|
def export_table(self, table, export_dir, input_null_string,
input_null_non_string, staging_table,
clear_staging_table, enclosed_by,
escaped_by, input_fields_terminated_by,
input_lines_terminated_by,
input_optionally_enclosed_by, batch,
relaxed_isolation, extra_export_options=None):
"""
"""
cmd = self._export_cmd(table, export_dir, input_null_string,
input_null_non_string, staging_table,
clear_staging_table, enclosed_by, escaped_by,
input_fields_terminated_by,
input_lines_terminated_by,
input_optionally_enclosed_by, batch,
relaxed_isolation, extra_export_options)
self.Popen(cmd)
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/operators/mysql_to_gcs.py#L201-L208
|
def _configure_csv_file(self, file_handle, schema):
"""
"""
csv_writer = csv.writer(file_handle, encoding='utf-8',
delimiter=self.field_delimiter)
csv_writer.writerow(schema)
return csv_writer
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/salesforce_hook.py#L110-L123
|
def get_available_fields(self, obj):
"""
"""
self.get_conn()
obj_description = self.describe_object(obj)
return [field['name'] for field in obj_description['fields']]
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/plugins_manager.py#L101-L118
|
def is_valid_plugin(plugin_obj, existing_plugins):
"""
"""
if (
inspect.isclass(plugin_obj) and
issubclass(plugin_obj, AirflowPlugin) and
(plugin_obj is not AirflowPlugin)
):
plugin_obj.validate()
return plugin_obj not in existing_plugins
return False
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/executors/__init__.py#L39-L53
|
def get_default_executor():
""""""
global DEFAULT_EXECUTOR
if DEFAULT_EXECUTOR is not None:
return DEFAULT_EXECUTOR
executor_name = configuration.conf.get('core', 'EXECUTOR')
DEFAULT_EXECUTOR = _get_executor(executor_name)
log = LoggingMixin().log
log.info("Using executor %s", executor_name)
return DEFAULT_EXECUTOR
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/www/security.py#L294-L301
|
def _has_role(self, role_name_or_list):
"""
"""
if not isinstance(role_name_or_list, list):
role_name_or_list = [role_name_or_list]
return any(
[r.name in role_name_or_list for r in self.get_user_roles()])
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/utils/timezone.py#L160-L169
|
def datetime(*args, **kwargs):
"""
"""
if 'tzinfo' not in kwargs:
kwargs['tzinfo'] = TIMEZONE
return dt.datetime(*args, **kwargs)
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/api/common/experimental/pool.py#L70-L82
|
def delete_pool(name, session=None):
""""""
if not (name and name.strip()):
raise AirflowBadRequest("Pool name shouldn't be empty")
pool = session.query(Pool).filter_by(pool=name).first()
if pool is None:
raise PoolNotFound("Pool '%s' doesn't exist" % name)
session.delete(pool)
session.commit()
return pool
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/api/common/experimental/pool.py#L45-L66
|
def create_pool(name, slots, description, session=None):
""""""
if not (name and name.strip()):
raise AirflowBadRequest("Pool name shouldn't be empty")
try:
slots = int(slots)
except ValueError:
raise AirflowBadRequest("Bad value for `slots`: %s" % slots)
session.expire_on_commit = False
pool = session.query(Pool).filter_by(pool=name).first()
if pool is None:
pool = Pool(pool=name, slots=slots, description=description)
session.add(pool)
else:
pool.slots = slots
pool.description = description
session.commit()
return pool
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/models/taskinstance.py#L197-L208
|
def try_number(self):
"""
"""
# This is designed so that task logs end up in the right file.
if self.state == State.RUNNING:
return self._try_number
return self._try_number + 1
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/redshift_hook.py#L100-L113
|
def create_cluster_snapshot(self, snapshot_identifier, cluster_identifier):
"""
"""
response = self.get_conn().create_cluster_snapshot(
SnapshotIdentifier=snapshot_identifier,
ClusterIdentifier=cluster_identifier,
)
return response['Snapshot'] if response['Snapshot'] else None
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/models/taskinstance.py#L395-L411
|
def current_state(self, session=None):
"""
"""
TI = TaskInstance
ti = session.query(TI).filter(
TI.dag_id == self.dag_id,
TI.task_id == self.task_id,
TI.execution_date == self.execution_date,
).all()
if ti:
state = ti[0].state
else:
state = None
return state
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/utils/log/gcs_task_handler.py#L123-L130
|
def gcs_read(self, remote_log_location):
"""
"""
bkt, blob = self.parse_gcs_url(remote_log_location)
return self.hook.download(bkt, blob).decode('utf-8')
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_container_hook.py#L57-L70
|
def _dict_to_proto(py_dict, proto):
"""
"""
dict_json_str = json.dumps(py_dict)
return json_format.Parse(dict_json_str, proto)
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/cloudant_hook.py#L40-L59
|
def get_conn(self):
"""
"""
conn = self.get_connection(self.cloudant_conn_id)
self._validate_connection(conn)
cloudant_session = cloudant(user=conn.login, passwd=conn.password, account=conn.host)
return cloudant_session
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/models/taskinstance.py#L550-L586
|
def are_dependencies_met(
self,
dep_context=None,
session=None,
verbose=False):
"""
"""
dep_context = dep_context or DepContext()
failed = False
verbose_aware_logger = self.log.info if verbose else self.log.debug
for dep_status in self.get_failed_dep_statuses(
dep_context=dep_context,
session=session):
failed = True
verbose_aware_logger(
"Dependencies not met for %s, dependency '%s' FAILED: %s",
self, dep_status.dep_name, dep_status.reason
)
if failed:
return False
verbose_aware_logger("Dependencies all met for %s", self)
return True
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_container_hook.py#L170-L219
|
def create_cluster(self, cluster, project_id=None, retry=DEFAULT, timeout=DEFAULT):
"""
"""
if isinstance(cluster, dict):
cluster_proto = Cluster()
cluster = self._dict_to_proto(py_dict=cluster, proto=cluster_proto)
elif not isinstance(cluster, Cluster):
raise AirflowException(
"cluster is not instance of Cluster proto or python dict")
self._append_label(cluster, 'airflow-version', 'v' + version.version)
self.log.info(
"Creating (project_id=%s, zone=%s, cluster_name=%s)",
self.project_id, self.location, cluster.name
)
try:
op = self.get_client().create_cluster(project_id=project_id or self.project_id,
zone=self.location,
cluster=cluster,
retry=retry,
timeout=timeout)
op = self.wait_for_operation(op)
return op.target_link
except AlreadyExists as error:
self.log.info('Assuming Success: %s', error.message)
return self.get_cluster(name=cluster.name).self_link
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/imap_hook.py#L309-L316
|
def get_file(self):
"""
"""
return self.part.get_filename(), self.part.get_payload(decode=True)
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/operators/aws_sqs_publish_operator.py#L61-L81
|
def execute(self, context):
"""
"""
hook = SQSHook(aws_conn_id=self.aws_conn_id)
result = hook.send_message(queue_url=self.sqs_queue,
message_body=self.message_content,
delay_seconds=self.delay_seconds,
message_attributes=self.message_attributes)
self.log.info('result is send_message is %s', result)
return result
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_function_hook.py#L130-L159
|
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
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/sagemaker_hook.py#L47-L56
|
def argmin(arr, f):
""""""
m = None
i = None
for idx, item in enumerate(arr):
if item is not None:
if m is None or f(item) < m:
m = f(item)
i = idx
return i
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/operators/gcp_container_operator.py#L282-L308
|
def _set_env_from_extras(self, extras):
"""
"""
key_path = self._get_field(extras, 'key_path', False)
keyfile_json_str = self._get_field(extras, 'keyfile_dict', False)
if not key_path and not keyfile_json_str:
self.log.info('Using gcloud with application default credentials.')
elif key_path:
os.environ[G_APP_CRED] = key_path
else:
# Write service account JSON to secure file for gcloud to reference
service_key = tempfile.NamedTemporaryFile(delete=False)
service_key.write(keyfile_json_str)
os.environ[G_APP_CRED] = service_key.name
# Return file object to have a pointer to close after use,
# thus deleting from file system.
return service_key
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/bin/cli.py#L88-L103
|
def sigquit_handler(sig, frame):
"""
"""
print("Dumping stack traces for all threads in PID {}".format(os.getpid()))
id_to_name = dict([(th.ident, th.name) for th in threading.enumerate()])
code = []
for thread_id, stack in sys._current_frames().items():
code.append("\n# Thread: {}({})"
.format(id_to_name.get(thread_id, ""), thread_id))
for filename, line_number, name, line in traceback.extract_stack(stack):
code.append('File: "{}", line {}, in {}'
.format(filename, line_number, name))
if line:
code.append(" {}".format(line.strip()))
print("\n".join(code))
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/utils/cli.py#L84-L116
|
def _build_metrics(func_name, namespace):
"""
"""
metrics = {'sub_command': func_name, 'start_datetime': datetime.utcnow(),
'full_command': '{}'.format(list(sys.argv)), 'user': getpass.getuser()}
assert isinstance(namespace, Namespace)
tmp_dic = vars(namespace)
metrics['dag_id'] = tmp_dic.get('dag_id')
metrics['task_id'] = tmp_dic.get('task_id')
metrics['execution_date'] = tmp_dic.get('execution_date')
metrics['host_name'] = socket.gethostname()
extra = json.dumps(dict((k, metrics[k]) for k in ('host_name', 'full_command')))
log = Log(
event='cli_{}'.format(func_name),
task_instance=None,
owner=metrics['user'],
extra=extra,
task_id=metrics.get('task_id'),
dag_id=metrics.get('dag_id'),
execution_date=metrics.get('execution_date'))
metrics['log'] = log
return metrics
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/executors/__init__.py#L31-L36
|
def _integrate_plugins():
""""""
from airflow.plugins_manager import executors_modules
for executors_module in executors_modules:
sys.modules[executors_module.__name__] = executors_module
globals()[executors_module._name] = executors_module
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_sql_hook.py#L136-L160
|
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)
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/executors/celery_executor.py#L168-L176
|
def _num_tasks_per_fetch_process(self):
"""
"""
return max(1,
int(math.ceil(1.0 * len(self.tasks) / self._sync_parallelism)))
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/example_dags/example_trigger_controller_dag.py#L45-L52
|
def conditionally_trigger(context, dag_run_obj):
""""""
c_p = context['params']['condition_param']
print("Controller DAG : conditionally_trigger = {}".format(c_p))
if context['params']['condition_param']:
dag_run_obj.payload = {'message': context['params']['message']}
pp.pprint(dag_run_obj.payload)
return dag_run_obj
|
https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_spanner_hook.py#L163-L181
|
def delete_instance(self, instance_id, project_id=None):
"""
"""
instance = self._get_client(project_id=project_id).instance(instance_id)
try:
instance.delete()
return
except GoogleAPICallError as e:
self.log.error('An error occurred: %s. Exiting.', e.message)
raise e
|
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