Cloud Composer 1 | Cloud Composer 2
Nesta página, explicamos como manter o banco de dados do Airflow no ambiente.
Com o passar do tempo, o banco de dados do Airflow do seu ambiente armazena cada vez mais dados. Esses dados incluem informações e registros relacionados a execuções anteriores de DAG, tarefas e outras operações do Airflow.
Executar o DAG de manutenção do banco de dados em uma programação
É possível usar o DAG de manutenção a seguir para remover o conteúdo do banco de dados.
Execute o DAG de manutenção periodicamente para manter o tamanho do banco de dados abaixo de 16 GB. Recomendamos executar esse DAG diariamente na maioria dos ambientes. Se você observar que a métrica de tamanho do banco de dados aumenta significativamente entre as execuções, considere executar esse DAG com mais frequência.
Escolha um período de armazenamento (DEFAULT_MAX_DB_ENTRY_AGE_IN_DAYS) que permita manter o banco de dados abaixo de 16 GB. Recomendamos um período de 30
dias como ponto de partida para a maioria dos ambientes.
Por padrão, esse DAG remove entradas antigas das tabelas job, dag_run, task_instance, log, xcom, sla_miss, dags, task_reschedule, task_fail e import_error. No DAG, revise a lista de tabelas e decida se as entradas antigas precisam ser removidas delas. Em
geral, a maior parte da economia de espaço é proporcionada pela limpeza das tabelas log, task_instance,
dag_run e xcom. Para excluir uma tabela da limpeza, modifique o DAG e comente os itens correspondentes na 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)
Remova entradas para DAGs não utilizados
Remova entradas de banco de dados para DAGs não utilizados removendo DAGs da interface da Web do Airflow.