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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 import exists
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 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.full_filepath
self.fileloc_hash = DagCode.dag_fileloc_hash(self.fileloc)
self.data = SerializedDAG.to_dict(dag)
self.last_updated = timezone.utcnow()
self.dag_hash = hashlib.md5(json.dumps(self.data, 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):
"""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
"""
# 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(
exists().where(
and_(
cls.dag_id == dag.dag_id,
(timezone.utcnow() - timedelta(seconds=min_update_interval)) < cls.last_updated,
)
)
).scalar():
return
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
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)
@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
# Sanity 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
@property
[docs] def dag(self):
"""The DAG deserialized from the ``data`` column"""
SerializedDAG._load_operator_extra_links = self.load_op_links # pylint: disable=protected-access
if isinstance(self.data, dict):
dag = SerializedDAG.from_dict(self.data) # type: Any
else:
dag = SerializedDAG.from_json(self.data) # noqa
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
"""
# pylint: disable=no-member
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__
)
# pylint: disable=no-member
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(exists().where(cls.dag_id == dag_id)).scalar()
@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) -> 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_latest_version_hash(cls, dag_id: str, session: Session = None) -> 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
:rtype: str
"""
return session.query(cls.dag_hash).filter(cls.dag_id == dag_id).scalar()