Limpiar la base de datos de Airflow

Cloud Composer 1 | Cloud Composer 2

En esta página, se explica cómo mantener la base de datos de Airflow en tu entorno.

Con el paso del tiempo, la base de datos de Airflow de tu entorno almacena cada vez más datos. Estos datos incluyen información y registros relacionados con las ejecuciones de DAG anteriores, las tareas y otras operaciones de Airflow.

Ejecuta el DAG de mantenimiento de la base de datos según un programa

Puedes usar el siguiente DAG de mantenimiento para reducir el contenido de tu base de datos.

Asegúrate de ejecutar el DAG de mantenimiento de forma periódica para mantener el tamaño de la base de datos por debajo de 16 GB. Recomendamos ejecutar este DAG a diario para la mayoría de los entornos. Si observas que la métrica del tamaño de la base de datos aumenta significativamente entre ejecuciones, considera ejecutar este DAG con más frecuencia.

Elige un período de retención (DEFAULT_MAX_DB_ENTRY_AGE_IN_DAYS) que permita mantener la base de datos por debajo de 16 GB. Recomendamos un período de 30 días como punto de partida para la mayoría de los entornos.

Este DAG quita las entradas antiguas de las tablas job, dag_run, task_instance, log, xcom, sla_miss, dags, task_reschedule, task_fail y import_error de forma predeterminada. En el DAG, revisa la lista de tablas y decide si se deben quitar las entradas antiguas. En general, si limpias las tablas log, task_instance, dag_run y xcom, obtendrás la mayor cantidad de ahorro de espacio. Para excluir una tabla de la limpieza, modifica el DAG y comenta los elementos correspondientes en la lista DATABASE_OBJECTS.

"""
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)

Quita las entradas de los DAG sin usar

Puedes quitar entradas de bases de datos de los DAG sin usar. Para ello, quita los DAG de la interfaz web de Airflow.

¿Qué sigue?