Pembersihan basis data Aurora PostgreSQL di lingkungan Amazon MWAA - Amazon Managed Workflows for Apache Airflow (MWAA)

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Pembersihan basis data Aurora PostgreSQL di lingkungan Amazon MWAA

Alur Kerja Terkelola Amazon untuk Apache Airflow menggunakan database Aurora PostgreSQL sebagai database metadata Apache Airflow, tempat DAG berjalan dan instance tugas disimpan. Kode contoh berikut secara berkala menghapus entri dari database Aurora PostgreSQL khusus untuk lingkungan Amazon MWAA Anda.

Versi

Prasyarat

Untuk menggunakan kode sampel di halaman ini, Anda memerlukan yang berikut:

Dependensi

Contoh kode

DAG berikut membersihkan database metadata untuk tabel yang ditentukan dalam. TABLES_TO_CLEAN Contoh menghapus data dari tabel yang ditentukan selama tujuh hari terakhir. Untuk menyesuaikan seberapa jauh entri dihapus, atur MAX_AGE_IN_DAYS ke nilai yang berbeda.

Apache Airflow v2
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 seven days. Adjust MAX_AGE_IN_DAYS to set how far back this DAG cleans the database. MAX_AGE_IN_DAYS = 7 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] >= oldest_date).filter(x[1] <= earliest_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()