Cloud Composer 1 | Cloud Composer 2
本页面介绍如何在您的环境中维护 Airflow 数据库。
随着时间的推移，环境的 Airflow 数据库会存储越来越多的数据。这些数据包含与过往 DAG 运行、任务和其他 Airflow 操作相关的信息和日志。
按计划运行数据库维护 DAG
您可以使用以下维护 DAG 来剪除数据库的内容。
请务必定期运行维护 DAG，以使数据库大小保持在 16 GB 以下。对于大多数环境，我们建议每天运行此 DAG。如果您观察到每次运行之间的数据库大小指标显著增加，请考虑提高此 DAG 的运行频率。
选择一个可将数据库保持在 16 GB 以下的保留期限 (DEFAULT_MAX_DB_ENTRY_AGE_IN_DAYS)。对于大多数环境，我们建议以 30 天为起点。
默认情况下，此 DAG 会从 job、dag_run、task_instance、log、xcom、sla_miss、dags、task_reschedule、task_fail 和 import_error 表中移除旧条目。在 DAG 中，查看表列表并决定是否必须从表中移除旧条目。通常，清理 log、task_instance、dag_run 和 xcom 表可以节省大部分空间。如需从清理范围中排除某个表，请修改 DAG 并注释 DATABASE_OBJECTS 列表中的相应项。
Airflow 2
"""
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)
Airflow 1
"""
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 datetime, timedelta
import logging
import os
import airflow
from airflow import settings
from airflow.jobs import BaseJob
from airflow.models import (
DAG,
DagModel,
DagRun,
Log,
SlaMiss,
TaskInstance,
Variable,
XCom,
)
from airflow.operators.python_operator import PythonOperator
from airflow.version import version as airflow_version
import dateutil.parser
from sqlalchemy import and_, func
from sqlalchemy.exc import ProgrammingError
from sqlalchemy.orm import load_only
try:
# airflow.utils.timezone is available from v1.10 onwards
from airflow.utils import timezone
now = timezone.utcnow
except ImportError:
now = datetime.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 "1.10.15+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": BaseJob,
"age_check_column": BaseJob.latest_heartbeat,
"keep_last": False,
"keep_last_filters": None,
"keep_last_group_by": None,
},
{
"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.execution_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,
"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_scheduler_run,
"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,
"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.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)
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]
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()
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)
移除未使用的 DAG 的条目
您可以从 Airflow 网页界面中移除 DAG，以移除未使用的 DAG 的数据库条目。