Airflow データベースをクリーンアップする

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このページでは、使用中の環境で Airflow データベースを管理する方法について説明します。

時間の経過とともに、環境の Airflow データベースに保存されるデータは増えていきます。このデータには、過去の DAG の実行やタスクなどの Airflow オペレーションに関連する情報とログが含まれます。

スケジュールに従って DB メンテナンス DAG を実行する

次のメンテナンス DAG を使用して、データベースのコンテンツをプルーニングできます。

メンテナンス DAG を定期的に実行して、データベースのサイズを 16 GB 未満に維持してください。ほとんどの環境では、この DAG を毎日実行することをおすすめします。実行と実行の間でデータベース サイズの指標が大幅に増加している場合は、この DAG の実行の頻度を増やすことを検討してください。

データベースを 16 GB 未満に維持できる保持期間(DEFAULT_MAX_DB_ENTRY_AGE_IN_DAYS)を選択します。ほとんどの環境では、出発点として 30 日間をおすすめします。

この DAG は、デフォルトで jobdag_runtask_instancelogxcomsla_missdagstask_rescheduletask_failimport_error の各テーブルから古いエントリを削除します。DAG でテーブルのリストを確認し、古いエントリをテーブルから削除する必要があるかどうかを判断します。一般に、logtask_instancedag_runxcom の各テーブルをクリーンアップすることでスペースの節約をほぼ実現できます。テーブルをクリーンアップから除外するには、DAG を変更して、DATABASE_OBJECTS リストの対応する項目をコメント化します。

"""
A maintenance workflow that you can deploy into Airflow to periodically clean
out the DagRun, TaskInstance, Log, XCom, Job DB and SlaMiss entries to avoid
having too much data in your Airflow MetaStore.

## Authors

The DAG is a fork of [teamclairvoyant repository.](https://github.com/teamclairvoyant/airflow-maintenance-dags/tree/master/db-cleanup)

## Usage

1. Update the global variables (SCHEDULE_INTERVAL, DAG_OWNER_NAME,
  ALERT_EMAIL_ADDRESSES and ENABLE_DELETE) in the DAG with the desired values

2. Modify the DATABASE_OBJECTS list to add/remove objects as needed. Each
   dictionary in the list features the following parameters:
    - airflow_db_model: Model imported from airflow.models corresponding to
      a table in the airflow metadata database
    - age_check_column: Column in the model/table to use for calculating max
      date of data deletion
    - keep_last: Boolean to specify whether to preserve last run instance
        - keep_last_filters: List of filters to preserve data from deleting
          during clean-up, such as DAG runs where the external trigger is set to 0.
        - keep_last_group_by: Option to specify column by which to group the
          database entries and perform aggregate functions.

3. Create and Set the following Variables in the Airflow Web Server
  (Admin -> Variables)
    - airflow_db_cleanup__max_db_entry_age_in_days - integer - Length to retain
      the log files if not already provided in the conf. If this is set to 30,
      the job will remove those files that are 30 days old or older.

4. Put the DAG in your gcs bucket.
"""
from datetime import timedelta
import logging
import os

import airflow
from airflow import settings
from airflow.models import (
    DAG,
    DagModel,
    DagRun,
    Log,
    SlaMiss,
    TaskInstance,
    Variable,
    XCom,
)
from airflow.operators.python import PythonOperator
from airflow.utils import timezone
from airflow.version import version as airflow_version

import dateutil.parser
from sqlalchemy import and_, func, text
from sqlalchemy.exc import ProgrammingError
from sqlalchemy.orm import load_only

now = timezone.utcnow

# airflow-db-cleanup
DAG_ID = os.path.basename(__file__).replace(".pyc", "").replace(".py", "")
START_DATE = airflow.utils.dates.days_ago(1)
# How often to Run. @daily - Once a day at Midnight (UTC)
SCHEDULE_INTERVAL = "@daily"
# Who is listed as the owner of this DAG in the Airflow Web Server
DAG_OWNER_NAME = "operations"
# List of email address to send email alerts to if this job fails
ALERT_EMAIL_ADDRESSES = []
# Airflow version used by the environment in list form, value stored in
# airflow_version is in format e.g "2.3.4+composer"
AIRFLOW_VERSION = airflow_version[: -len("+composer")].split(".")
# Length to retain the log files if not already provided in the conf. If this
# is set to 30, the job will remove those files that arE 30 days old or older.
DEFAULT_MAX_DB_ENTRY_AGE_IN_DAYS = int(
    Variable.get("airflow_db_cleanup__max_db_entry_age_in_days", 30)
)
# Prints the database entries which will be getting deleted; set to False
# to avoid printing large lists and slowdown process
PRINT_DELETES = False
# Whether the job should delete the db entries or not. Included if you want to
# temporarily avoid deleting the db entries.
ENABLE_DELETE = True
# List of all the objects that will be deleted. Comment out the DB objects you
# want to skip.
DATABASE_OBJECTS = [
    {
        "airflow_db_model": DagRun,
        "age_check_column": DagRun.execution_date,
        "keep_last": True,
        "keep_last_filters": [DagRun.external_trigger.is_(False)],
        "keep_last_group_by": DagRun.dag_id,
    },
    {
        "airflow_db_model": TaskInstance,
        "age_check_column": TaskInstance.start_date
        if AIRFLOW_VERSION < ["2", "2", "0"]
        else TaskInstance.start_date,
        "keep_last": False,
        "keep_last_filters": None,
        "keep_last_group_by": None,
    },
    {
        "airflow_db_model": Log,
        "age_check_column": Log.dttm,
        "keep_last": False,
        "keep_last_filters": None,
        "keep_last_group_by": None,
    },
    {
        "airflow_db_model": XCom,
        "age_check_column": XCom.execution_date
        if AIRFLOW_VERSION < ["2", "2", "5"]
        else XCom.timestamp,
        "keep_last": False,
        "keep_last_filters": None,
        "keep_last_group_by": None,
    },
    {
        "airflow_db_model": SlaMiss,
        "age_check_column": SlaMiss.execution_date,
        "keep_last": False,
        "keep_last_filters": None,
        "keep_last_group_by": None,
    },
    {
        "airflow_db_model": DagModel,
        "age_check_column": DagModel.last_parsed_time,
        "keep_last": False,
        "keep_last_filters": None,
        "keep_last_group_by": None,
    },
]

# Check for TaskReschedule model
try:
    from airflow.models import TaskReschedule

    DATABASE_OBJECTS.append(
        {
            "airflow_db_model": TaskReschedule,
            "age_check_column": TaskReschedule.execution_date
            if AIRFLOW_VERSION < ["2", "2", "0"]
            else TaskReschedule.start_date,
            "keep_last": False,
            "keep_last_filters": None,
            "keep_last_group_by": None,
        }
    )

except Exception as e:
    logging.error(e)

# Check for TaskFail model
try:
    from airflow.models import TaskFail

    DATABASE_OBJECTS.append(
        {
            "airflow_db_model": TaskFail,
            "age_check_column": TaskFail.start_date,
            "keep_last": False,
            "keep_last_filters": None,
            "keep_last_group_by": None,
        }
    )

except Exception as e:
    logging.error(e)

# Check for RenderedTaskInstanceFields model
if AIRFLOW_VERSION < ["2", "4", "0"]:
    try:
        from airflow.models import RenderedTaskInstanceFields

        DATABASE_OBJECTS.append(
            {
                "airflow_db_model": RenderedTaskInstanceFields,
                "age_check_column": RenderedTaskInstanceFields.execution_date,
                "keep_last": False,
                "keep_last_filters": None,
                "keep_last_group_by": None,
            }
        )

    except Exception as e:
        logging.error(e)

# Check for ImportError model
try:
    from airflow.models import ImportError

    DATABASE_OBJECTS.append(
        {
            "airflow_db_model": ImportError,
            "age_check_column": ImportError.timestamp,
            "keep_last": False,
            "keep_last_filters": None,
            "keep_last_group_by": None,
            "do_not_delete_by_dag_id": True,
        }
    )

except Exception as e:
    logging.error(e)

if AIRFLOW_VERSION < ["2", "6", "0"]:
    try:
        from airflow.jobs.base_job import BaseJob

        DATABASE_OBJECTS.append(
            {
                "airflow_db_model": BaseJob,
                "age_check_column": BaseJob.latest_heartbeat,
                "keep_last": False,
                "keep_last_filters": None,
                "keep_last_group_by": None,
            }
        )
    except Exception as e:
        logging.error(e)
else:
    try:
        from airflow.jobs.job import Job

        DATABASE_OBJECTS.append(
            {
                "airflow_db_model": Job,
                "age_check_column": Job.latest_heartbeat,
                "keep_last": False,
                "keep_last_filters": None,
                "keep_last_group_by": None,
            }
        )
    except Exception as e:
        logging.error(e)

default_args = {
    "owner": DAG_OWNER_NAME,
    "depends_on_past": False,
    "email": ALERT_EMAIL_ADDRESSES,
    "email_on_failure": True,
    "email_on_retry": False,
    "start_date": START_DATE,
    "retries": 1,
    "retry_delay": timedelta(minutes=1),
}

dag = DAG(
    DAG_ID,
    default_args=default_args,
    schedule_interval=SCHEDULE_INTERVAL,
    start_date=START_DATE,
)
if hasattr(dag, "doc_md"):
    dag.doc_md = __doc__
if hasattr(dag, "catchup"):
    dag.catchup = False

def print_configuration_function(**context):
    logging.info("Loading Configurations...")
    dag_run_conf = context.get("dag_run").conf
    logging.info("dag_run.conf: " + str(dag_run_conf))
    max_db_entry_age_in_days = None
    if dag_run_conf:
        max_db_entry_age_in_days = dag_run_conf.get("maxDBEntryAgeInDays", None)
    logging.info("maxDBEntryAgeInDays from dag_run.conf: " + str(dag_run_conf))
    if max_db_entry_age_in_days is None or max_db_entry_age_in_days < 1:
        logging.info(
            "maxDBEntryAgeInDays conf variable isn't included or Variable "
            + "value is less than 1. Using Default '"
            + str(DEFAULT_MAX_DB_ENTRY_AGE_IN_DAYS)
            + "'"
        )
        max_db_entry_age_in_days = DEFAULT_MAX_DB_ENTRY_AGE_IN_DAYS
    max_date = now() + timedelta(-max_db_entry_age_in_days)
    logging.info("Finished Loading Configurations")
    logging.info("")

    logging.info("Configurations:")
    logging.info("max_db_entry_age_in_days: " + str(max_db_entry_age_in_days))
    logging.info("max_date:                 " + str(max_date))
    logging.info("enable_delete:            " + str(ENABLE_DELETE))
    logging.info("")

    logging.info("Setting max_execution_date to XCom for Downstream Processes")
    context["ti"].xcom_push(key="max_date", value=max_date.isoformat())

print_configuration = PythonOperator(
    task_id="print_configuration",
    python_callable=print_configuration_function,
    provide_context=True,
    dag=dag,
)

def build_query(
    session,
    airflow_db_model,
    age_check_column,
    max_date,
    keep_last,
    keep_last_filters=None,
    keep_last_group_by=None,
):
    query = session.query(airflow_db_model).options(load_only(age_check_column))

    logging.info("INITIAL QUERY : " + str(query))

    if not keep_last:
        query = query.filter(
            age_check_column <= max_date,
        )
    else:
        subquery = session.query(func.max(DagRun.execution_date))
        # workaround for MySQL "table specified twice" issue
        # https://github.com/teamclairvoyant/airflow-maintenance-dags/issues/41
        if keep_last_filters is not None:
            for entry in keep_last_filters:
                subquery = subquery.filter(entry)

            logging.info("SUB QUERY [keep_last_filters]: " + str(subquery))

        if keep_last_group_by is not None:
            subquery = subquery.group_by(keep_last_group_by)
            logging.info("SUB QUERY [keep_last_group_by]: " + str(subquery))

        subquery = subquery.from_self()

        query = query.filter(
            and_(age_check_column.notin_(subquery)), and_(age_check_column <= max_date)
        )

    return query

def print_query(query, airflow_db_model, age_check_column):
    entries_to_delete = query.all()

    logging.info("Query: " + str(query))
    logging.info(
        "Process will be Deleting the following "
        + str(airflow_db_model.__name__)
        + "(s):"
    )
    for entry in entries_to_delete:
        date = str(entry.__dict__[str(age_check_column).split(".")[1]])
        logging.info("\tEntry: " + str(entry) + ", Date: " + date)

    logging.info(
        "Process will be Deleting "
        + str(len(entries_to_delete))
        + " "
        + str(airflow_db_model.__name__)
        + "(s)"
    )

def cleanup_function(**context):
    session = settings.Session()

    logging.info("Retrieving max_execution_date from XCom")
    max_date = context["ti"].xcom_pull(
        task_ids=print_configuration.task_id, key="max_date"
    )
    max_date = dateutil.parser.parse(max_date)  # stored as iso8601 str in xcom

    airflow_db_model = context["params"].get("airflow_db_model")
    state = context["params"].get("state")
    age_check_column = context["params"].get("age_check_column")
    keep_last = context["params"].get("keep_last")
    keep_last_filters = context["params"].get("keep_last_filters")
    keep_last_group_by = context["params"].get("keep_last_group_by")

    logging.info("Configurations:")
    logging.info("max_date:                 " + str(max_date))
    logging.info("enable_delete:            " + str(ENABLE_DELETE))
    logging.info("session:                  " + str(session))
    logging.info("airflow_db_model:         " + str(airflow_db_model))
    logging.info("state:                    " + str(state))
    logging.info("age_check_column:         " + str(age_check_column))
    logging.info("keep_last:                " + str(keep_last))
    logging.info("keep_last_filters:        " + str(keep_last_filters))
    logging.info("keep_last_group_by:       " + str(keep_last_group_by))

    logging.info("")

    logging.info("Running Cleanup Process...")

    try:
        if context["params"].get("do_not_delete_by_dag_id"):
            query = build_query(
                session,
                airflow_db_model,
                age_check_column,
                max_date,
                keep_last,
                keep_last_filters,
                keep_last_group_by,
            )
            if PRINT_DELETES:
                print_query(query, airflow_db_model, age_check_column)
            if ENABLE_DELETE:
                logging.info("Performing Delete...")
                query.delete(synchronize_session=False)
            session.commit()
        else:
            dags = session.query(airflow_db_model.dag_id).distinct()
            session.commit()

            list_dags = [str(list(dag)[0]) for dag in dags] + [None]
            for dag in list_dags:
                query = build_query(
                    session,
                    airflow_db_model,
                    age_check_column,
                    max_date,
                    keep_last,
                    keep_last_filters,
                    keep_last_group_by,
                )
                query = query.filter(airflow_db_model.dag_id == dag)
                if PRINT_DELETES:
                    print_query(query, airflow_db_model, age_check_column)
                if ENABLE_DELETE:
                    logging.info("Performing Delete...")
                    query.delete(synchronize_session=False)
                session.commit()

        if not ENABLE_DELETE:
            logging.warn(
                "You've opted to skip deleting the db entries. "
                "Set ENABLE_DELETE to True to delete entries!!!"
            )

        logging.info("Finished Running Cleanup Process")

    except ProgrammingError as e:
        logging.error(e)
        logging.error(
            str(airflow_db_model) + " is not present in the metadata. " "Skipping..."
        )

    finally:
        session.close()

def cleanup_sessions():
    session = settings.Session()

    try:
        logging.info("Deleting sessions...")
        count_statement = "SELECT COUNT(*) AS cnt FROM session WHERE expiry < now()::timestamp(0);"
        before = session.execute(text(count_statement)).one_or_none()["cnt"]
        session.execute(text("DELETE FROM session WHERE expiry < now()::timestamp(0);"))
        after = session.execute(text(count_statement)).one_or_none()["cnt"]
        logging.info("Deleted %s expired sessions.", (before - after))
    except Exception as err:
        logging.exception(err)

    session.commit()
    session.close()

def analyze_db():
    session = settings.Session()
    session.execute("ANALYZE")
    session.commit()
    session.close()

analyze_op = PythonOperator(
    task_id="analyze_query", python_callable=analyze_db, provide_context=True, dag=dag
)

cleanup_session_op = PythonOperator(
    task_id="cleanup_sessions",
    python_callable=cleanup_sessions,
    provide_context=True,
    dag=dag,
)

cleanup_session_op.set_downstream(analyze_op)

for db_object in DATABASE_OBJECTS:
    cleanup_op = PythonOperator(
        task_id="cleanup_" + str(db_object["airflow_db_model"].__name__),
        python_callable=cleanup_function,
        params=db_object,
        provide_context=True,
        dag=dag,
    )

    print_configuration.set_downstream(cleanup_op)
    cleanup_op.set_downstream(analyze_op)

未使用の DAG のエントリを削除する

Airflow ウェブ インターフェースから DAG を削除することで、未使用の DAG のデータベース エントリを削除できます。

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