Python DagRun.execution_date方法代碼示例

# 需要導入模塊: from airflow.models import DagRun [as 別名]
# 或者: from airflow.models.DagRun import execution_date [as 別名]
def get_xcom_entry(
    dag_id: str,
    task_id: str,
    dag_run_id: str,
    xcom_key: str,
    session: Session
) -> XComCollectionItemSchema:
    """
    Get an XCom entry
    """
    query = session.query(XCom)
    query = query.filter(and_(XCom.dag_id == dag_id,
                              XCom.task_id == task_id,
                              XCom.key == xcom_key))
    query = query.join(DR, and_(XCom.dag_id == DR.dag_id, XCom.execution_date == DR.execution_date))
    query = query.filter(DR.run_id == dag_run_id)

    query_object = query.one_or_none()
    if not query_object:
        raise NotFound("XCom entry not found")
    return xcom_collection_item_schema.dump(query_object) 

# 需要導入模塊: from airflow.models import DagRun [as 別名]
# 或者: from airflow.models.DagRun import execution_date [as 別名]
def get_dag_runs():
    dag_runs = []

    session = settings.Session()

    query = session.query(DagRun)

    if request.args.get('state') is not None:
        query = query.filter(DagRun.state == request.args.get('state'))

    if request.args.get('external_trigger') is not None:
        # query = query.filter(DagRun.external_trigger == (request.args.get('external_trigger') is True))
        query = query.filter(DagRun.external_trigger == (request.args.get('external_trigger') in ['true', 'True']))

    if request.args.get('prefix') is not None:
        query = query.filter(DagRun.run_id.ilike('{}%'.format(request.args.get('prefix'))))

    runs = query.order_by(DagRun.execution_date).all()

    for run in runs:
        dag_runs.append(format_dag_run(run))

    session.close()

    return ApiResponse.success({'dag_runs': dag_runs}) 

# 需要導入模塊: from airflow.models import DagRun [as 別名]
# 或者: from airflow.models.DagRun import execution_date [as 別名]
def get_xcom_entries(
    dag_id: str,
    dag_run_id: str,
    task_id: str,
    session: Session,
    limit: Optional[int],
    offset: Optional[int] = None
) -> XComCollectionSchema:
    """
    Get all XCom values
    """

    query = session.query(XCom)
    if dag_id != '~':
        query = query.filter(XCom.dag_id == dag_id)
        query.join(DR, and_(XCom.dag_id == DR.dag_id, XCom.execution_date == DR.execution_date))
    else:
        query.join(DR, XCom.execution_date == DR.execution_date)
    if task_id != '~':
        query = query.filter(XCom.task_id == task_id)
    if dag_run_id != '~':
        query = query.filter(DR.run_id == dag_run_id)
    query = query.order_by(
        XCom.execution_date, XCom.task_id, XCom.dag_id, XCom.key
    )
    total_entries = session.query(func.count(XCom.key)).scalar()
    query = query.offset(offset).limit(limit)
    return xcom_collection_schema.dump(XComCollection(xcom_entries=query.all(), total_entries=total_entries)) 

# 需要導入模塊: from airflow.models import DagRun [as 別名]
# 或者: from airflow.models.DagRun import execution_date [as 別名]
def get_dag_runs(session, dag_id, start_date_gte=None, start_date_lte=None,
                 execution_date_gte=None, execution_date_lte=None,
                 end_date_gte=None, end_date_lte=None, offset=None, limit=None):
    """
    Get all DAG Runs.
    """

    query = session.query(DagRun)

    #  This endpoint allows specifying ~ as the dag_id to retrieve DAG Runs for all DAGs.
    if dag_id != '~':
        query = query.filter(DagRun.dag_id == dag_id)

    # filter start date
    if start_date_gte:
        query = query.filter(DagRun.start_date >= start_date_gte)

    if start_date_lte:
        query = query.filter(DagRun.start_date <= start_date_lte)

    # filter execution date
    if execution_date_gte:
        query = query.filter(DagRun.execution_date >= execution_date_gte)

    if execution_date_lte:
        query = query.filter(DagRun.execution_date <= execution_date_lte)

    # filter end date
    if end_date_gte:
        query = query.filter(DagRun.end_date >= end_date_gte)

    if end_date_lte:
        query = query.filter(DagRun.end_date <= end_date_lte)

    # apply offset and limit
    dag_run = query.order_by(DagRun.id).offset(offset).limit(limit).all()
    total_entries = session.query(func.count(DagRun.id)).scalar()

    return dagrun_collection_schema.dump(DAGRunCollection(dag_runs=dag_run,
                                                          total_entries=total_entries)) 

# 需要導入模塊: from airflow.models import DagRun [as 別名]
# 或者: from airflow.models.DagRun import execution_date [as 別名]
def get_dag_runs(dag_id):
    """
    .. http:get:: /trigger/<dag_id>

        Get the run_ids for a dag_id, ordered by execution date

        **Example request**:

        .. sourcecode:: http

            GET /trigger/make_fit
            Host: localhost:7357

        **Example response**:

        .. sourcecode:: http

            HTTP/1.1 200 OK
            Content-Type: application/json

            {
              "dag_id": "daily_processing",
              "run_ids": ["my_special_run", "normal_run_17"]
            }
    """
    session = settings.Session()

    error_response = check_dag_exists(session, dag_id)
    if error_response:
        return error_response

    dag_runs = session.query(DagRun).filter(DagRun.dag_id == dag_id).order_by(DagRun.execution_date).all()
    run_ids = [dag_run.run_id for dag_run in dag_runs]

    return jsonify(dag_id=dag_id, run_ids=run_ids) 

# 需要導入模塊: from airflow.models import DagRun [as 別名]
# 或者: from airflow.models.DagRun import execution_date [as 別名]
def create_airflow_url(dag_id, start_date, end_date):
        """
        Creates the airflow url to redirect to. Gets the host_server based on if it's a fabio url or host:port. Then
        queries the database for the execution date, which will be in the range of the start and end date. Can have
        multiple values so will only return the earliest result. If no results are found, it'll use the start date,
        which will just take you to the most recent dagrun for that dag in the UI.
        :param dag_id: Dag id name. String.
        :param start_date: Start date. String of form %Y-%m-%d %H:%M:%S.
        :param end_date: End date. String of form %Y-%m-%d %H:%M:%S.
        :return: Airflow URL to redirect to. String.
        """
        start_date = datetime.strptime(start_date, '%Y-%m-%d %H:%M:%S')
        end_date = datetime.strptime(end_date, '%Y-%m-%d %H:%M:%S')
        host_server = conf.get('webserver', 'base_url')

        session = Session()
        try:
            dagrun_query_result = session.query(DagRun) \
                .filter(DagRun.dag_id == dag_id) \
                .filter(DagRun.execution_date >= start_date) \
                .filter(DagRun.execution_date < end_date) \
                .order_by(DagRun.execution_date.asc()) \
                .first()
            execution_date = dagrun_query_result.execution_date.isoformat()
        except:
            session.rollback()
            execution_date = start_date.isoformat()
        finally:
            session.close()

        url = '{0}/admin/airflow/graph?dag_id={1}&execution_date={2}'.format(host_server, dag_id, execution_date)
        return url 

# 需要導入模塊: from airflow.models import DagRun [as 別名]
# 或者: from airflow.models.DagRun import execution_date [as 別名]
def clear_dag_runs(dag_id, start_date, end_date):
        """
        Clears all the DagRuns and corrects the DagStats for an interval passed in the clear command because the
        clear command only clears the TaskInstances.
        :param dag_id: Dag id name. String.
        :param start_date: Start date. String of form %Y-%m-%d %H:%M:%S.
        :param end_date: End date. String of form %Y-%m-%d %H:%M:%S.
        :return: None.
        """
        start_date = datetime.strptime(start_date, '%Y-%m-%d %H:%M:%S')
        end_date = datetime.strptime(end_date, '%Y-%m-%d %H:%M:%S')
        session = Session()
        try:
            dagrun_query = session.query(DagRun) \
                .filter(DagRun.dag_id == dag_id) \
                .filter(DagRun.execution_date >= start_date) \
                .filter(DagRun.execution_date < end_date)
            dagrun_query_result = dagrun_query.all()
            # remove dagruns with this state for clear command
            for result in dagrun_query_result:
                session.delete(result)
            # fix DagStats
            for state in State.dag_states:
                removed_state_counts = dagrun_query.filter(DagRun.state == state).count()
                dagstat_query = session.query(DagStat) \
                    .filter(DagStat.dag_id == dag_id) \
                    .filter(DagStat.state == state)
                dagstat_query_result = dagstat_query.first()  # only one row every time
                dagstat_query_result.count = max(dagstat_query_result.count - removed_state_counts, 0)
            session.commit()
        except:
            session.rollback()
        finally:
            session.close() 

# 需要導入模塊: from airflow.models import DagRun [as 別名]
# 或者: from airflow.models.DagRun import execution_date [as 別名]
def format_dag_run(dag_run):
    return {
        'run_id': dag_run.run_id,
        'dag_id': dag_run.dag_id,
        'state': dag_run.get_state(),
        'start_date': (None if not dag_run.start_date else str(dag_run.start_date)),
        'end_date': (None if not dag_run.end_date else str(dag_run.end_date)),
        'external_trigger': dag_run.external_trigger,
        'execution_date': str(dag_run.execution_date)
    } 

# 需要導入模塊: from airflow.models import DagRun [as 別名]
# 或者: from airflow.models.DagRun import execution_date [as 別名]
def find_dag_runs(session, dag_id, dag_run_id, execution_date):
    qry = session.query(DagRun)
    qry = qry.filter(DagRun.dag_id == dag_id)
    qry = qry.filter(or_(DagRun.run_id == dag_run_id, DagRun.execution_date == execution_date))

    return qry.order_by(DagRun.execution_date).all() 

# 需要導入模塊: from airflow.models import DagRun [as 別名]
# 或者: from airflow.models.DagRun import execution_date [as 別名]
def check_py(session=None, **kwargs):
    key = '__SYSTEM__RATE_LIMIT_EXCEEDED__'

    obj = (session
           .query(Variable)
           .filter(Variable.key.ilike('{}%'.format(key)))
           .all())

    if obj is None:
        raise KeyError('Variable {} does not exist'.format(key))
    else:
        for _ in obj:
            _ = json.loads(_.val)

            # Clear the rate limit operator task in the specified Dag Run.
            (session
             .query(TaskInstance)
             .filter(and_(TaskInstance.task_id == _['task_id'],
                          TaskInstance.dag_id == _['dag_id'],
                          TaskInstance.execution_date == datetime.strptime(_['ts'],
                                                                           "%Y-%m-%dT%H:%M:%S")))
             .delete())

            # Clear downstream tasks in the specified Dag Run.
            for task in _['downstream_tasks']:
                (session
                 .query(TaskInstance)
                 .filter(and_(TaskInstance.task_id == task,
                              TaskInstance.dag_id == _['dag_id'],
                              TaskInstance.execution_date == datetime.strptime(_['ts'],
                                                                                "%Y-%m-%dT%H:%M:%S")))
                 .delete())

            # Set the Dag Run state to "running"
            dag_run = (session
                       .query(DagRun)
                       .filter(and_(DagRun.dag_id == _['dag_id'],
                                    DagRun.execution_date == datetime.strptime(_['ts'],
                                                                               "%Y-%m-%dT%H:%M:%S")))
                       .first())

            dag_run.set_state('running')

            # Clear the rate limit exceeded variable.
            variable_identifier = '_'.join([_['dag_id'],
                                            _['task_id'],
                                            _['ts']])

            variable_name = ''.join([key, variable_identifier])

            (session
             .query(Variable)
             .filter(Variable.key == variable_name)
             .delete()) 

# 需要導入模塊: from airflow.models import DagRun [as 別名]
# 或者: from airflow.models.DagRun import execution_date [as 別名]
def trigger_dag(dag_id):
    """
    .. http:post:: /trigger/<dag_id>/

        Triggers a defined DAG. The data  must be send in json format with
        a key "run_id" and the value a string of your choice. Passing the data
        is optional. If no data is passed the run_id will be automatically
        be generated with a timestamp and looks like
        "external_trigger_2016-01-19T02:01:49.703365".

        **Example request**:

        .. sourcecode:: http

            POST /trigger/make_fit
            Host: localhost:7357
            Content-Type: application/json

            {
              "run_id": "my_special_run"
            }

        **Example response**:

        .. sourcecode:: http

            HTTP/1.1 200 OK
            Vary: Accept
            Content-Type: application/json

            {
              "dag_id": "daily_processing",
              "run_id": "my_special_run"
            }
    """
    session = settings.Session()

    error_response = check_dag_exists(session, dag_id)
    if error_response:
        return error_response

    execution_date = datetime.now()

    run_id = None
    json_params = request.get_json()
    if json_params and 'run_id' in json_params:
        run_id = json_params['run_id']
    if not run_id:
        run_id = 'external_trigger_' + execution_date.isoformat()

    trigger = DagRun(
        dag_id=dag_id,
        run_id=run_id,
        state=State.RUNNING,
        execution_date=execution_date,
        external_trigger=True)
    session.add(trigger)
    session.commit()

    return jsonify(dag_id=dag_id, run_id=run_id) 

# 需要導入模塊: from airflow.models import DagRun [as 別名]
# 或者: from airflow.models.DagRun import execution_date [as 別名]
def dag_run_status(dag_id, run_id):
    """
    .. http:get:: /trigger/<dag_id>/<run_id>

        Gets the status of a dag run.
        Possible states are: running, success, failed

        **Example request**:

        .. sourcecode:: http

            GET /trigger/make_fit/my_special_run
            Host: localhost:7357

        **Example response**:

        .. sourcecode:: http

            HTTP/1.1 200 OK
            Content-Type: application/json

            {
              "dag_id": "daily_processing",
              "run_id": "my_special_run",
              "state": "running",
              "execution_date": "2016-06-27T15:32:57"
            }
    """
    session = settings.Session()

    error_response = check_dag_exists(session, dag_id)
    if error_response:
        return error_response

    try:
        dag_run = session.query(DagRun).filter(and_(DagRun.dag_id == dag_id, DagRun.run_id == run_id)).one()
    except NoResultFound:
        return Response('RunId {} does not exist for Dag {}'.format(run_id, dag_id), httplib.BAD_REQUEST)

    time_format = "%Y-%m-%dT%H:%M:%S"
    return jsonify(
        dag_id=dag_id,
        run_id=run_id,
        state=dag_run.state,
        execution_date=dag_run.execution_date.strftime(time_format)
    )