在 Amazon MWAA 环境中清理 Aurora PostgreSQL 数据库 - Amazon Managed Workflows for Apache Airflow
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在 Amazon MWAA 环境中清理 Aurora PostgreSQL 数据库

Amazon Managed Workflows for Apache Airflow 使用 Aurora PostgreSQL 数据库作为 DAG 运行并存储任务实例的 Apache Airflow 元数据库。以下示例代码会定期为 Amazon MWAA 环境清除专用 Aurora PostgreSQL 数据库中的条目。

版本

你可以将本页上的代码示例与 Python 3. 10 中的 Apache Airflow v2 和 Python3.11 中的 Apac he A irflow v3 配合使用。

先决条件

要使用本页上的示例代码,您需要以下内容:

依赖项

要在 Apache Airflow v2 及更高版本中使用此代码示例,不需要额外的依赖关系。用于aws-mwaa-docker-images安装 Apache Airflow。

代码示例

以下 DAG 会清理 TABLES_TO_CLEAN 中指定表的元数据数据库。该示例将删除指定表中存在超过 30 天的数据。要调整删除条目的存续时间,请将 MAX_AGE_IN_DAYS 设置为其他值。

Apache Airflow v2.4 and later
from airflow import DAG from airflow.models.param import Param from airflow.operators.bash_operator import BashOperator from airflow.utils.dates import days_ago from datetime import datetime, timedelta # Note: Database commands might time out if running longer than 5 minutes. If this occurs, please increase the MAX_AGE_IN_DAYS (or change # timestamp parameter to an earlier date) for initial runs, then reduce on subsequent runs until the desired retention is met. MAX_AGE_IN_DAYS = 30 # To clean specific tables, please provide a comma-separated list per # https://airflow.apache.org/docs/apache-airflow/stable/cli-and-env-variables-ref.html#clean # A value of None will clean all tables TABLES_TO_CLEAN = None with DAG( dag_id="clean_db_dag", schedule_interval=None, catchup=False, start_date=days_ago(1), params={ "timestamp": Param( default=(datetime.now()-timedelta(days=MAX_AGE_IN_DAYS)).strftime("%Y-%m-%d %H:%M:%S"), type="string", minLength=1, maxLength=255, ), } ) as dag: if TABLES_TO_CLEAN: bash_command="airflow db clean --clean-before-timestamp '{{ params.timestamp }}' --tables '"+TABLES_TO_CLEAN+"' --skip-archive --yes" else: bash_command="airflow db clean --clean-before-timestamp '{{ params.timestamp }}' --skip-archive --yes" cli_command = BashOperator( task_id="bash_command", bash_command=bash_command )
Apache Airflow v2.2 and earlier
from airflow import settings from airflow.utils.dates import days_ago from airflow.models import DagTag, DagModel, DagRun, ImportError, Log, SlaMiss, RenderedTaskInstanceFields, TaskInstance, TaskReschedule, XCom from airflow.decorators import dag, task from airflow.utils.dates import days_ago from time import sleep from airflow.version import version major_version, minor_version = int(version.split('.')[0]), int(version.split('.')[1]) if major_version >= 2 and minor_version >= 6: from airflow.jobs.job import Job else: # The BaseJob class was renamed as of Apache Airflow v2.6 from airflow.jobs.base_job import BaseJob as Job # Delete entries for the past 30 days. Adjust MAX_AGE_IN_DAYS to set how far back this DAG cleans the database. MAX_AGE_IN_DAYS = 30 MIN_AGE_IN_DAYS = 0 DECREMENT = -7 # This is a list of (table, time) tuples. # table = the table to clean in the metadata database # time = the column in the table associated to the timestamp of an entry # or None if not applicable. TABLES_TO_CLEAN = [[Job, Job.latest_heartbeat], [TaskInstance, TaskInstance.execution_date], [TaskReschedule, TaskReschedule.execution_date], [DagTag, None], [DagModel, DagModel.last_parsed_time], [DagRun, DagRun.execution_date], [ImportError, ImportError.timestamp], [Log, Log.dttm], [SlaMiss, SlaMiss.execution_date], [RenderedTaskInstanceFields, RenderedTaskInstanceFields.execution_date], [XCom, XCom.execution_date], ] @task() def cleanup_db_fn(x): session = settings.Session() if x[1]: for oldest_days_ago in range(MAX_AGE_IN_DAYS, MIN_AGE_IN_DAYS, DECREMENT): earliest_days_ago = max(oldest_days_ago + DECREMENT, MIN_AGE_IN_DAYS) print(f"deleting {str(x[0])} entries between {earliest_days_ago} and {oldest_days_ago} days old...") earliest_date = days_ago(earliest_days_ago) oldest_date = days_ago(oldest_days_ago) query = session.query(x[0]).filter(x[1] >= earliest_date).filter(x[1] <= oldest_date) query.delete(synchronize_session= False) session.commit() sleep(5) else: # No time column specified for the table. Delete all entries print("deleting", str(x[0]), "...") query = session.query(x[0]) query.delete(synchronize_session= False) session.commit() session.close() @dag( dag_id="cleanup_db", schedule_interval="@weekly", start_date=days_ago(7), catchup=False, is_paused_upon_creation=False ) def clean_db_dag_fn(): t_last=None for x in TABLES_TO_CLEAN: t=cleanup_db_fn(x) if t_last: t_last >> t t_last = t clean_db_dag = clean_db_dag_fn()