Clean up the Airflow metadata database using Dags

In addition to storing configurations about your Airflow environment, the Airflow metadata database stores data about past and present task runs. Airflow never automatically removes metadata, so the longer you use it, the more task run data is stored in your metadata DB. Over a long enough time, this can result in a bloated metadata DB, which can affect performance across your Airflow environment.

When a table in the metadata DB is larger than 50GiB, you might start to experience degraded scheduler performance. This can result in:

• Slow task scheduling
• Slow dag parsing
• Gunicorn timing out when using the Celery executor
• Slower Airflow UI load times

The following tables in the database are at risk of becoming too large over time:

• dag_run
• job
• log
• rendered_task_instance_fields
• task_instance
• xcom

To keep your Airflow environment running at optimal performance, you can clean the metadata DB using the Airflow CLI airflow db clean command. This command was created as a way to safely clean up your metadata DB without querying it directly.

In Airflow 3, this command cannot be called from a Dag because tasks can no longer directly access the metadata DB. Instead you can expose Airflow’s utility function used by the command via an HTTP API using an Airflow Plugin. This tutorial describes how to implement the cleanup Dag and corresponding plugin in Airflow so that you can clean your database using the command directly from the Airflow UI.

Warnings

Deleting data from the metadata database can be an extremely destructive action. If you delete data that future task runs depend on, it’s difficult to restore the database to its previous state without interrupting your data pipelines. Before implementing the Dag in this tutorial, consider the following:

• ⚠️ When specifying the clean_before_timestamp value, use as old a date as possible. The older the deleted data, the less likely it is to affect your currently running Dags.
• ⚠️ The Dag in this tutorial is not designed to keep archived tables. It drops the archived tables it created in the cleanup process by default using the skip_archive=True argument, and does not maintain any history. If the task fails (for example if it runs for longer than five minutes), the archive tables are not cleared. By calling drop_archived_tables in the second task of the Dag, we ensure all archive tables are dropped even in the event of the first task failing.

Prerequisites

• An Airflow project

  This Dag has been designed and optimized for Airflow environments running on Astro. Consider adjusting the parameters and code if you’re running the Dag in any other type of Airflow environment.

• The HTTP Airflow provider installed

Step 1: Create your Dag and plugin

1. In your dags folder, create a file called db_cleanup.py.

2. Copy the following code into the Dag file.

   1
   """A DB cleanup dag maintained by Astronomer."""
   2
   3
   from datetime import UTC, datetime, timedelta
   4
   5
   from airflow.cli.commands.db_command import all_tables
   6
   from airflow.providers.http.hooks.http import HttpHook
   7
   from airflow.sdk import Param, dag, task
   8
   9
   10
   def get_tables() -> list[str]:
   11
       tables = []
   12
   13
       for table in all_tables:
   14
           # can't delete dag versions which may be older than corresponding task instances
   15
           # in order to keep dag_version untouched we also need to ignore the dag table
   16
           # https://github.com/apache/airflow/issues/56192
   17
           if table in {
   18
               "dag_version",
   19
               "dag",
   20
           }:
   21
               continue
   22
           tables.append(table)
   23
   24
       return tables
   25
   26
   27
   @task
   28
   def get_chunked_timestamps(**context) -> list[datetime]:
   29
       from plugins.db_cleanup import OldestTimestampResponse
   30
   31
       http_conn_id = context["params"]["http_conn_id"]
   32
       tables = context["params"]["tables"]
   33
       batches = []
   34
   35
       response = HttpHook("GET", http_conn_id).run(
   36
           "/db_cleanup/api/oldest_timestamp",
   37
           data={"table_names": tables},
   38
       )
   39
       start_chunk_time = OldestTimestampResponse.model_validate_json(response.content).oldest_timestamp
   40
   41
       if start_chunk_time is not None:
   42
           start_ts = start_chunk_time
   43
           end_ts = datetime.fromisoformat(context["params"]["clean_before_timestamp"])
   44
           batch_size_days = context["params"]["batch_size_days"]
   45
   46
           while start_ts < end_ts:
   47
               batch_end = min(start_ts + timedelta(days=batch_size_days), end_ts)
   48
               batches.append(batch_end)
   49
               start_ts += timedelta(days=batch_size_days)
   50
       return batches
   51
   52
   53
   @task(map_index_template="ts {{ clean_before_timestamp }}")
   54
   def db_cleanup(clean_before_timestamp: datetime, **context) -> None:
   55
       context["clean_before_timestamp"] = clean_before_timestamp.isoformat()
   56
       tables = context["params"]["tables"]
   57
       http_conn_id = context["params"]["http_conn_id"]
   58
       HttpHook("DELETE", http_conn_id).run(
   59
           "/db_cleanup/api/records",
   60
           params={
   61
               "clean_before_timestamp": clean_before_timestamp.isoformat(),
   62
               "dry_run": context["params"]["dry_run"],
   63
               "skip_archive": True,
   64
               "table_names": tables,
   65
           },
   66
       )
   67
   68
   69
   @task(trigger_rule="all_done")
   70
   def clean_archive_tables(**context) -> None:
   71
       tables = context["params"]["tables"]
   72
       http_conn_id = context["params"]["http_conn_id"]
   73
       HttpHook("DELETE", http_conn_id).run(
   74
           "/db_cleanup/api/archived",
   75
           params={"table_names": tables},
   76
       )
   77
   78
   79
   @dag(
   80
       schedule=None,
   81
       catchup=False,
   82
       description=__doc__,
   83
       doc_md=__doc__,
   84
       render_template_as_native_obj=True,
   85
       max_active_tasks=1,
   86
       max_active_runs=1,
   87
       tags=["astronomer", "cleanup"],
   88
       params={
   89
           "clean_before_timestamp": Param(
   90
               default=(datetime.now(tz=UTC) - timedelta(days=90)).isoformat(),
   91
               type="string",
   92
               format="date-time",
   93
               description="Delete records older than this timestamp. Default is 90 days ago.",
   94
           ),
   95
           "tables": Param(
   96
               default=get_tables(),
   97
               type=["null", "array"],
   98
               examples=get_tables(),
   99
               description="List of tables to clean. Default is all tables.",
   100
           ),
   101
           "dry_run": Param(
   102
               default=False,
   103
               type="boolean",
   104
               description="Show a summary of which tables would be deleted in the api-server logs without actually deleting the records. Default is False.",
   105
           ),
   106
           "batch_size_days": Param(
   107
               default=7,
   108
               type="integer",
   109
               description="Number of days in each batch for the cleanup. Default is 7 days.",
   110
           ),
   111
           "http_conn_id": Param(
   112
               default="http_default",
   113
               type="string",
   114
               description="The HTTP connection ID for calling the cleanup API. Default is 'http_default'.",
   115
           ),
   116
       },
   117
   )
   118
   def astronomer_db_cleanup():
   119
   120
       db_cleanup.expand(clean_before_timestamp=get_chunked_timestamps()) >> clean_archive_tables()
   121
   122
   123
   astronomer_db_cleanup()

   Rather than running on a schedule, this Dag is triggered manually by default and includes params so that you’re in full control over how you clean the metadata DB.

   It includes three tasks:

   • get_chunked_timestamps: creates a list of timestamps to process in batches.
   • db_cleanup: calls the run_cleanup utility.
   • clean_archive_tables: calls the drop_archived_tables utility.

   These three tasks run with params you specify at runtime. The params let you specify:

   • clean_before_timestamp: What age of data to delete. Any data that was created before the specified time will be deleted. The default is to delete all data older than 90 days.
   • tables: Which tables to delete data from. By default all tables supported by the DB cleanup utilities are included except for the dag and dag_version table.
   • dry_run: Whether to run the cleanup as a dry run, meaning that no data is deleted. The dag will instead return the SQL that would be executed based on other parameters you have specified. The default is to run the deletion without a dry run.
   • batch_size_days: What batch size to use in order to cleanup data in batches.
   • http_conn_id: Which HTTP connection to use for calling the API exposing the DB cleanup utilities.

3. In your plugins folder, create a file called db_cleanup.py.

4. Copy the following code into the plugin file.

   1
   """A DB cleanup plugin maintained by Astronomer."""
   2
   3
   import logging
   4
   import os
   5
   from collections.abc import Generator
   6
   from datetime import datetime
   7
   from typing import Annotated
   8
   9
   import pendulum
   10
   from airflow.api_fastapi.common.router import AirflowRouter
   11
   from airflow.api_fastapi.core_api.security import requires_access_configuration
   12
   from airflow.plugins_manager import AirflowPlugin
   13
   from airflow.utils.db import reflect_tables
   14
   from airflow.utils.db_cleanup import _effective_table_names, drop_archived_tables, run_cleanup
   15
   from airflow.utils.session import create_session
   16
   from fastapi import Depends, FastAPI, Query
   17
   from pydantic import BaseModel
   18
   from sqlalchemy import func
   19
   from sqlalchemy.orm.session import Session
   20
   21
   22
   def _get_session() -> Generator[Session, None]:
   23
       with create_session() as session:
   24
           yield session
   25
   26
   27
   logger = logging.getLogger(__name__)
   28
   29
   30
   class TableInfo(BaseModel):
   31
       table_name: str
   32
       row_estimate: int = 0
   33
       table_bytes: int = 0
   34
       index_bytes: int = 0
   35
       toast_bytes: int = 0
   36
       total_bytes: int = 0
   37
   38
   39
   class InfoResponse(BaseModel):
   40
       tables: list[TableInfo] = []
   41
   42
   43
   class OldestTimestampResponse(BaseModel):
   44
       oldest_timestamp: datetime | None = None
   45
   46
   47
   api = AirflowRouter(
   48
       tags=["DB API"],
   49
       dependencies=[Depends(requires_access_configuration("GET"))],
   50
   )
   51
   52
   53
   @api.get("/info")
   54
   def info(
   55
       *,
   56
       order_by: str = "total_bytes",
   57
       order_desc: bool = True,
   58
       session: Annotated[Session, Depends(_get_session)],
   59
   ) -> InfoResponse:
   60
       """
   61
       Provides information about the size of tables in the metadata database.
   62
       """
   63
       if order_by not in {
   64
           "table_name",
   65
           "row_estimate",
   66
           "table_bytes",
   67
           "index_bytes",
   68
           "toast_bytes",
   69
           "total_bytes",
   70
       }:
   71
           raise ValueError(f"Invalid order_by value: {order_by}")
   72
       query = f"""
   73
       SELECT
   74
           table_name,
   75
           row_estimate,
   76
           total_bytes - index_bytes - COALESCE(toast_bytes, 0) AS table_bytes,
   77
           index_bytes,
   78
           toast_bytes,
   79
           total_bytes
   80
       FROM (
   81
           SELECT
   82
               relname AS table_name,
   83
               c.reltuples::int AS row_estimate,
   84
               pg_indexes_size(c.oid) AS index_bytes,
   85
               pg_total_relation_size(reltoastrelid) AS toast_bytes,
   86
               pg_total_relation_size(c.oid) AS total_bytes
   87
           FROM pg_class c
   88
           LEFT JOIN pg_namespace n ON n.oid = c.relnamespace
   89
           WHERE relkind = 'r'
   90
           AND nspname = :table_schema
   91
       ) a
   92
       ORDER BY {order_by} {"DESC" if order_desc else "ASC"};
   93
       """
   94
       table_schema = "public" if os.getenv("ASTRONOMER_ENVIRONMENT") == "local" else "airflow"
   95
   96
       result = session.execute(query, {"table_schema": table_schema})
   97
       response = InfoResponse()
   98
   99
       for row in result:
   100
           response.tables.append(TableInfo(**{k: v for k, v in row._mapping.items() if v is not None}))
   101
   102
       return response
   103
   104
   105
   @api.get("/oldest_timestamp")
   106
   def get_oldest_timestamp(
   107
       *,
   108
       table_names: Annotated[list[str] | None, Query()] = None,
   109
       session: Annotated[Session, Depends(_get_session)],
   110
   ) -> OldestTimestampResponse:
   111
       oldest_timestamp_list = []
   112
       existing_tables = reflect_tables(tables=None, session=session).tables
   113
       _, effective_config_dict = _effective_table_names(table_names=table_names)
   114
       for table_name, table_config in effective_config_dict.items():
   115
           if table_name in existing_tables:
   116
               orm_model = table_config.orm_model
   117
               recency_column = table_config.recency_column
   118
               oldest_execution_date = session.query(func.min(recency_column)).select_from(orm_model).scalar()
   119
               if oldest_execution_date:
   120
                   oldest_timestamp_list.append(oldest_execution_date)
   121
               else:
   122
                   logging.info("No data found for %s, skipping...", table_name)
   123
           else:
   124
               logging.warning("Table %s not found. Skipping.", table_name)
   125
   126
       response = OldestTimestampResponse()
   127
       if oldest_timestamp_list:
   128
           response.oldest_timestamp = min(oldest_timestamp_list)
   129
       return response
   130
   131
   132
   @api.delete("/records")
   133
   def delete_records(
   134
       *,
   135
       clean_before_timestamp: datetime,
   136
       table_names: Annotated[list[str] | None, Query()] = None,
   137
       dry_run: bool = False,
   138
       verbose: bool = False,
   139
       skip_archive: bool = False,
   140
       batch_size: int | None = None,
   141
       session: Annotated[Session, Depends(_get_session)],
   142
   ):
   143
       run_cleanup(
   144
           clean_before_timestamp=pendulum.instance(clean_before_timestamp),
   145
           table_names=table_names,
   146
           dry_run=dry_run,
   147
           verbose=verbose,
   148
           confirm=False,
   149
           skip_archive=skip_archive,
   150
           batch_size=batch_size,
   151
           session=session,
   152
       )
   153
   154
   155
   @api.delete("/archived")
   156
   def delete_archived(
   157
       *,
   158
       table_names: Annotated[list[str] | None, Query()] = None,
   159
       session: Annotated[Session, Depends(_get_session)],
   160
   ):
   161
       drop_archived_tables(
   162
           table_names=table_names,
   163
           needs_confirm=False,
   164
           session=session,
   165
       )
   166
   167
   168
   app = FastAPI()
   169
   app.include_router(api, prefix="/api")
   170
   171
   172
   class AstronomerDBCleanupPlugin(AirflowPlugin):
   173
       name = "AstronomerDBCleanupPlugin"
   174
       fastapi_apps = [
   175
           {
   176
               "app": app,
   177
               "url_prefix": "/db_cleanup",
   178
               "name": "Astronomer DB Cleanup Plugin",
   179
           }
   180
       ]

   The plugin uses requires_access_configuration("GET") from Airflow’s core API security module to restrict access to users with Airflow configuration access, which is equivalent to admin access. It exposes the following API endpoints:

   • GET /db_cleanup/api/info: Provide a list of tables with their corresponding sizes and row count estimates. This endpoint is not used by the Dag, but can be useful to get insights into table sizes.
   • GET /db_cleanup/api/oldest_timestamp: Return the oldest timestamp for the tables to cleanup used for calculating batches.
   • DELETE /db_cleanup/api/records: Call the run_cleanup utility.
   • DELETE /db_cleanup/api/archived: Call the drop_archived_tables utility.

Step 2: Configure a HTTP connection

Add a HTTP connection used for calling the API endpoints.

• host: Set this to the deployment’s URL. For example on Astro this would look like something like https://cmls9yey09fpw01ncvse41m4n.4n.astronomer.run/dse41m4n. When running locally in astro dev this should be set to http://api-server:8080.
• extra: If needed, set the authorization header. On Astro with an API token this would look something like {"Authorization": "Bearer mytoken1234...abc1234"}.

Step 3: Practice running the Dag

In this step, run the Dag in a local Airflow environment to practice the workflow for cleaning metadata DB records. If you completed Step 1 in your production environment, you will need to repeat it here before starting your local Airflow project. Typically in a fresh local Airflow environment there is not much to clean up. When completing this process in a production environment which has been running for a while, there are more historic records to cleanup.

1. Run astro dev start in your Astro project to start Airflow, then open the Airflow UI at localhost:8080.

2. Ensure the Airflow connection http_default with host http://api-server:8080 is set.

   Instead of creating an Airflow connection, you can also define it as an environment variable AIRFLOW_CONN_HTTP_DEFAULT=http://api-server:8080 in your local .env file.

3. In the Airflow UI, run the astronomer_db_cleanup Dag by clicking the play button and configure the following params:

   • dry_run is enabled
   • Choose an appropriate cutoff date for clean_before_timestamp

4. Click Trigger.

5. In a local terminal run astro dev logs --api-server -f to show the api-server logs.

6. Check that the run_cleanup utility completed successfully. Note that if you created a new Astro project for this tutorial, the run will not show much data to be deleted.

You can now use this Dag to periodically clean data from the Airflow metadata DB as needed.