Source code for airflow.models.serialized_dag

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"""Serialized DAG table in database."""

import hashlib
import logging
from datetime import datetime, timedelta
from typing import Any, Dict, List, Optional

import sqlalchemy_jsonfield
from sqlalchemy import BigInteger, Column, Index, String, and_
from sqlalchemy.orm import Session, backref, foreign, relationship
from sqlalchemy.sql.expression import func, literal

from airflow.models.base import ID_LEN, Base
from airflow.models.dag import DAG, DagModel
from airflow.models.dagcode import DagCode
from airflow.models.dagrun import DagRun
from airflow.serialization.serialized_objects import DagDependency, SerializedDAG
from airflow.settings import MIN_SERIALIZED_DAG_UPDATE_INTERVAL, json
from airflow.utils import timezone
from airflow.utils.session import provide_session
from airflow.utils.sqlalchemy import UtcDateTime

[docs]log = logging.getLogger(__name__)
[docs]class SerializedDagModel(Base): """A table for serialized DAGs. serialized_dag table is a snapshot of DAG files synchronized by scheduler. This feature is controlled by: * ``[core] min_serialized_dag_update_interval = 30`` (s): serialized DAGs are updated in DB when a file gets processed by scheduler, to reduce DB write rate, there is a minimal interval of updating serialized DAGs. * ``[scheduler] dag_dir_list_interval = 300`` (s): interval of deleting serialized DAGs in DB when the files are deleted, suggest to use a smaller interval such as 60 It is used by webserver to load dags because reading from database is lightweight compared to importing from files, it solves the webserver scalability issue. """
[docs] __tablename__ = 'serialized_dag'
[docs] dag_id = Column(String(ID_LEN), primary_key=True)
[docs] fileloc = Column(String(2000), nullable=False)
# The max length of fileloc exceeds the limit of indexing.
[docs] fileloc_hash = Column(BigInteger, nullable=False)
[docs] data = Column(sqlalchemy_jsonfield.JSONField(json=json), nullable=False)
[docs] last_updated = Column(UtcDateTime, nullable=False)
[docs] dag_hash = Column(String(32), nullable=False)
[docs] __table_args__ = (Index('idx_fileloc_hash', fileloc_hash, unique=False),)
[docs] dag_runs = relationship( DagRun, primaryjoin=dag_id == foreign(DagRun.dag_id), backref=backref('serialized_dag', uselist=False, innerjoin=True),
[docs] dag_model = relationship( DagModel, primaryjoin=dag_id == DagModel.dag_id, # type: ignore foreign_keys=dag_id, uselist=False, innerjoin=True, backref=backref('serialized_dag', uselist=False, innerjoin=True),
) def __init__(self, dag: DAG): self.dag_id = dag.dag_id self.fileloc = dag.fileloc self.fileloc_hash = DagCode.dag_fileloc_hash(self.fileloc) = SerializedDAG.to_dict(dag) self.last_updated = timezone.utcnow() self.dag_hash = hashlib.md5(json.dumps(, sort_keys=True).encode("utf-8")).hexdigest()
[docs] def __repr__(self): return f"<SerializedDag: {self.dag_id}>"
@classmethod @provide_session
[docs] def write_dag(cls, dag: DAG, min_update_interval: Optional[int] = None, session: Session = None) -> bool: """Serializes a DAG and writes it into database. If the record already exists, it checks if the Serialized DAG changed or not. If it is changed, it updates the record, ignores otherwise. :param dag: a DAG to be written into database :param min_update_interval: minimal interval in seconds to update serialized DAG :param session: ORM Session :returns: Boolean indicating if the DAG was written to the DB """ # Checks if (Current Time - Time when the DAG was written to DB) < min_update_interval # If Yes, does nothing # If No or the DAG does not exists, updates / writes Serialized DAG to DB if min_update_interval is not None: if ( session.query(literal(True)) .filter( and_( cls.dag_id == dag.dag_id, (timezone.utcnow() - timedelta(seconds=min_update_interval)) < cls.last_updated, ) ) .first() is not None ): # TODO: .first() is not None can be changed to .scalar() once we update to sqlalchemy 1.4+ # as the associated sqlalchemy bug for MySQL was fixed # related issue : return False log.debug("Checking if DAG (%s) changed", dag.dag_id) new_serialized_dag = cls(dag) serialized_dag_hash_from_db = session.query(cls.dag_hash).filter(cls.dag_id == dag.dag_id).scalar() if serialized_dag_hash_from_db == new_serialized_dag.dag_hash: log.debug("Serialized DAG (%s) is unchanged. Skipping writing to DB", dag.dag_id) return False log.debug("Writing Serialized DAG: %s to the DB", dag.dag_id) session.merge(new_serialized_dag) log.debug("DAG: %s written to the DB", dag.dag_id) return True
@classmethod @provide_session
[docs] def read_all_dags(cls, session: Session = None) -> Dict[str, 'SerializedDAG']: """Reads all DAGs in serialized_dag table. :param session: ORM Session :returns: a dict of DAGs read from database """ serialized_dags = session.query(cls) dags = {} for row in serialized_dags: log.debug("Deserializing DAG: %s", row.dag_id) dag = row.dag # Coherence check if dag.dag_id == row.dag_id: dags[row.dag_id] = dag else: log.warning( "dag_id Mismatch in DB: Row with dag_id '%s' has Serialised DAG with '%s' dag_id", row.dag_id, dag.dag_id, ) return dags
[docs] def dag(self): """The DAG deserialized from the ``data`` column""" SerializedDAG._load_operator_extra_links = self.load_op_links if isinstance(, dict): dag = SerializedDAG.from_dict( # type: Any else: dag = SerializedDAG.from_json( return dag
@classmethod @provide_session
[docs] def remove_dag(cls, dag_id: str, session: Session = None): """Deletes a DAG with given dag_id. :param dag_id: dag_id to be deleted :param session: ORM Session """ session.execute(cls.__table__.delete().where(cls.dag_id == dag_id))
@classmethod @provide_session
[docs] def remove_deleted_dags(cls, alive_dag_filelocs: List[str], session=None): """Deletes DAGs not included in alive_dag_filelocs. :param alive_dag_filelocs: file paths of alive DAGs :param session: ORM Session """ alive_fileloc_hashes = [DagCode.dag_fileloc_hash(fileloc) for fileloc in alive_dag_filelocs] log.debug( "Deleting Serialized DAGs (for which DAG files are deleted) from %s table ", cls.__tablename__ ) session.execute( cls.__table__.delete().where( and_(cls.fileloc_hash.notin_(alive_fileloc_hashes), cls.fileloc.notin_(alive_dag_filelocs))
) ) @classmethod @provide_session
[docs] def has_dag(cls, dag_id: str, session: Session = None) -> bool: """Checks a DAG exist in serialized_dag table. :param dag_id: the DAG to check :param session: ORM Session """ return session.query(literal(True)).filter(cls.dag_id == dag_id).first() is not None
@classmethod @provide_session
[docs] def get(cls, dag_id: str, session: Session = None) -> Optional['SerializedDagModel']: """ Get the SerializedDAG for the given dag ID. It will cope with being passed the ID of a subdag by looking up the root dag_id from the DAG table. :param dag_id: the DAG to fetch :param session: ORM Session """ row = session.query(cls).filter(cls.dag_id == dag_id).one_or_none() if row: return row # If we didn't find a matching DAG id then ask the DAG table to find # out the root dag root_dag_id = session.query(DagModel.root_dag_id).filter(DagModel.dag_id == dag_id).scalar() return session.query(cls).filter(cls.dag_id == root_dag_id).one_or_none()
@staticmethod @provide_session
[docs] def bulk_sync_to_db(dags: List[DAG], session: Session = None): """ Saves DAGs as Serialized DAG objects in the database. Each DAG is saved in a separate database query. :param dags: the DAG objects to save to the DB :type dags: List[airflow.models.dag.DAG] :param session: ORM Session :type session: Session :return: None """ for dag in dags: if not dag.is_subdag: SerializedDagModel.write_dag( dag, min_update_interval=MIN_SERIALIZED_DAG_UPDATE_INTERVAL, session=session
) @classmethod @provide_session
[docs] def get_last_updated_datetime(cls, dag_id: str, session: Session = None) -> Optional[datetime]: """ Get the date when the Serialized DAG associated to DAG was last updated in serialized_dag table :param dag_id: DAG ID :type dag_id: str :param session: ORM Session :type session: Session """ return session.query(cls.last_updated).filter(cls.dag_id == dag_id).scalar()
@classmethod @provide_session
[docs] def get_max_last_updated_datetime(cls, session: Session = None) -> Optional[datetime]: """ Get the maximum date when any DAG was last updated in serialized_dag table :param session: ORM Session :type session: Session """ return session.query(func.max(cls.last_updated)).scalar()
@classmethod @provide_session
[docs] def get_latest_version_hash(cls, dag_id: str, session: Session = None) -> Optional[str]: """ Get the latest DAG version for a given DAG ID. :param dag_id: DAG ID :type dag_id: str :param session: ORM Session :type session: Session :return: DAG Hash, or None if the DAG is not found :rtype: str | None """ return session.query(cls.dag_hash).filter(cls.dag_id == dag_id).scalar()
@classmethod @provide_session
[docs] def get_dag_dependencies(cls, session: Session = None) -> Dict[str, List['DagDependency']]: """ Get the dependencies between DAGs :param session: ORM Session :type session: Session """ if in ["sqlite", "mysql"]: query = session.query(cls.dag_id, func.json_extract(, "$.dag.dag_dependencies")) iterator = ((dag_id, json.loads(deps_data) if deps_data else []) for dag_id, deps_data in query) elif == "mssql": query = session.query(cls.dag_id, func.json_query(, "$.dag.dag_dependencies")) iterator = ((dag_id, json.loads(deps_data) if deps_data else []) for dag_id, deps_data in query) else: iterator = session.query(cls.dag_id, func.json_extract_path(, "dag", "dag_dependencies")) return {dag_id: [DagDependency(**d) for d in (deps_data or [])] for dag_id, deps_data in iterator}

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