$ airflow config get-value database sql_alchemy_conn
sqlite:////tmp/airflow/airflow.db
The exact format description is described in the SQLAlchemy documentation, see Database Urls. We will also show you some examples below.
Setting up a SQLite Database
SQLite database can be used to run Airflow for development purpose as it does not require any database server
(the database is stored in a local file). There are many limitations of using the SQLite database (for example
it only works with Sequential Executor) and it should NEVER be used for production.
There is a minimum version of sqlite3 required to run Airflow 2.0+ - minimum version is 3.15.0. Some of the
older systems have an earlier version of sqlite installed by default and for those system you need to manually
upgrade SQLite to use version newer than 3.15.0. Note, that this is not a python library version, it’s the
SQLite system-level application that needs to be upgraded. There are different ways how SQLite might be
installed, you can find some information about that at the official website of SQLite and in the documentation specific to distribution of your Operating
System.
Troubleshooting
Sometimes even if you upgrade SQLite to higher version and your local python reports higher version,
the python interpreter used by Airflow might still use the older version available in the
LD_LIBRARY_PATH set for the python interpreter that is used to start Airflow.
You can make sure which version is used by the interpreter by running this check:
root@b8a8e73caa2c:/opt/airflow# python
Python 3.8.10 (default, Mar 15 2022, 12:22:08)
[GCC 8.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import sqlite3
>>> sqlite3.sqlite_version
'3.27.2'
But be aware that setting environment variables for your Airflow deployment might change which SQLite
library is found first, so you might want to make sure that the “high-enough” version of SQLite is the only
version installed in your system.
An example URI for the sqlite database:
sqlite:////home/airflow/airflow.db
Upgrading SQLite on AmazonLinux AMI or Container Image
AmazonLinux SQLite can only be upgraded to v3.7 using the source repos. Airflow requires v3.15 or higher. Use the
following instructions to setup the base image (or AMI) with latest SQLite3
Pre-requisite: You will need wget, tar, gzip, gcc, make, and expect to get the upgrade process working.
yum -y install wget tar gzip gcc make expect
Download source from https://sqlite.org/, make and install locally.
wget https://www.sqlite.org/src/tarball/sqlite.tar.gz
tar xzf sqlite.tar.gz
cd sqlite/
export CFLAGS="-DSQLITE_ENABLE_FTS3 \
-DSQLITE_ENABLE_FTS3_PARENTHESIS \
-DSQLITE_ENABLE_FTS4 \
-DSQLITE_ENABLE_FTS5 \
-DSQLITE_ENABLE_JSON1 \
-DSQLITE_ENABLE_LOAD_EXTENSION \
-DSQLITE_ENABLE_RTREE \
-DSQLITE_ENABLE_STAT4 \
-DSQLITE_ENABLE_UPDATE_DELETE_LIMIT \
-DSQLITE_SOUNDEX \
-DSQLITE_TEMP_STORE=3 \
-DSQLITE_USE_URI \
-O2 \
-fPIC"
export PREFIX="/usr/local"
LIBS="-lm" ./configure --disable-tcl --enable-shared --enable-tempstore=always --prefix="$PREFIX"
make install
Post install add /usr/local/lib to library path
export LD_LIBRARY_PATH=/usr/local/lib:$LD_LIBRARY_PATH
Setting up a PostgreSQL Database
You need to create a database and a database user that Airflow will use to access this database.
In the example below, a database airflow_db and user with username airflow_user with password airflow_pass will be created
CREATE DATABASE airflow_db;
CREATE USER airflow_user WITH PASSWORD 'airflow_pass';
GRANT ALL PRIVILEGES ON DATABASE airflow_db TO airflow_user;
-- PostgreSQL 15 requires additional privileges:
GRANT ALL ON SCHEMA public TO airflow_user;
The database must use a UTF-8 character set
You may need to update your Postgres pg_hba.conf to add the
airflow user to the database access control list; and to reload
the database configuration to load your change. See
The pg_hba.conf File
in the Postgres documentation to learn more.
Warning
When you use SQLAlchemy 1.4.0+, you need to use postgresql:// as the database in the sql_alchemy_conn.
In the previous versions of SQLAlchemy it was possible to use postgres://, but using it in
SQLAlchemy 1.4.0+ results in:
> raise exc.NoSuchModuleError(
"Can't load plugin: %s:%s" % (self.group, name)
E sqlalchemy.exc.NoSuchModuleError: Can't load plugin: sqlalchemy.dialects:postgres
If you cannot change the prefix of your URL immediately, Airflow continues to work with SQLAlchemy
1.3 and you can downgrade SQLAlchemy, but we recommend to update the prefix.
Details in the SQLAlchemy Changelog.
We recommend using the psycopg2 driver and specifying it in your SqlAlchemy connection string.
postgresql+psycopg2://<user>:<password>@<host>/<db>
Also note that since SqlAlchemy does not expose a way to target a specific schema in the database URI, you need to ensure schema public is in your Postgres user’s search_path.
If you created a new Postgres account for Airflow:
The default search_path for new Postgres user is: "$user", public, no change is needed.
If you use a current Postgres user with custom search_path, search_path can be changed by the command:
ALTER USER airflow_user SET search_path = public;
For more information regarding setup of the PostgreSQL connection, see PostgreSQL dialect in SQLAlchemy documentation.
Airflow is known - especially in high-performance setup - to open many connections to metadata database. This might cause problems for
Postgres resource usage, because in Postgres, each connection creates a new process and it makes Postgres resource-hungry when a lot
of connections are opened. Therefore we recommend to use PGBouncer as database proxy for all Postgres
production installations. PGBouncer can handle connection pooling from multiple components, but also in case you have remote
database with potentially unstable connectivity, it will make your DB connectivity much more resilient to temporary network problems.
Example implementation of PGBouncer deployment can be found in the Helm Chart for Apache Airflow where you can enable pre-configured
PGBouncer instance with flipping a boolean flag. You can take a look at the approach we have taken there and use it as
an inspiration, when you prepare your own Deployment, even if you do not use the Official Helm Chart.
See also Helm Chart production guide
For managed Postgres such as Azure Postgresql, CloudSQL, Amazon RDS, you should use
keepalives_idle in the connection parameters and set it to less than the idle time because those
services will close idle connections after some time of inactivity (typically 300 seconds),
which results with error The error: psycopg2.operationalerror: SSL SYSCALL error: EOF detected.
The keepalive settings can be changed via sql_alchemy_connect_args configuration parameter
Configuration Reference in [database] section. You can configure the args for example in your
local_settings.py and the sql_alchemy_connect_args should be a full import path to the dictionary
that stores the configuration parameters. You can read about
Postgres Keepalives.
An example setup for keepalives that has been observed to fix the problem might be:
keepalive_kwargs = {
"keepalives": 1,
"keepalives_idle": 30,
"keepalives_interval": 5,
"keepalives_count": 5,
Then, if it were placed in airflow_local_settings.py, the config import path would be:
sql_alchemy_connect_args = airflow_local_settings.keepalive_kwargs
See Configuring local settings for details on how to
configure local settings.
Setting up a MySQL Database
You need to create a database and a database user that Airflow will use to access this database.
In the example below, a database airflow_db and user with username airflow_user with password airflow_pass will be created
CREATE DATABASE airflow_db CHARACTER SET utf8mb4 COLLATE utf8mb4_unicode_ci;
CREATE USER 'airflow_user' IDENTIFIED BY 'airflow_pass';
GRANT ALL PRIVILEGES ON airflow_db.* TO 'airflow_user';
The database must use a UTF-8 character set. A small caveat that you must be aware of is that utf8 in newer versions of MySQL is really utf8mb4 which
causes Airflow indexes to grow too large (see https://github.com/apache/airflow/pull/17603#issuecomment-901121618). Therefore as of Airflow 2.2
all MySQL databases have sql_engine_collation_for_ids set automatically to utf8mb3_bin (unless you override it). This might
lead to a mixture of collation ids for id fields in Airflow Database, but it has no negative consequences since all relevant IDs in Airflow use
ASCII characters only.
We rely on more strict ANSI SQL settings for MySQL in order to have sane defaults.
Make sure to have specified explicit_defaults_for_timestamp=1 option under [mysqld] section
in your my.cnf file. You can also activate these options with the --explicit-defaults-for-timestamp switch passed to mysqld executable
We recommend using the mysqlclient driver and specifying it in your SqlAlchemy connection string.
mysql+mysqldb://<user>:<password>@<host>[:<port>]/<dbname>
Important
The integration of MySQL backend has only been validated using the mysqlclient driver
during Apache Airflow’s continuous integration (CI) process.
If you want to use other drivers visit the MySQL Dialect in SQLAlchemy documentation for more information regarding download
and setup of the SqlAlchemy connection.
In addition, you also should pay particular attention to MySQL’s encoding. Although the utf8mb4 character set is more and more popular for MySQL (actually, utf8mb4 becomes default character set in MySQL8.0), using the utf8mb4 encoding requires additional setting in Airflow 2+ (See more details in #7570.). If you use utf8mb4 as character set, you should also set sql_engine_collation_for_ids=utf8mb3_bin.
In strict mode, MySQL doesn’t allow 0000-00-00 as a valid date. Then you might get errors like
"Invalid default value for 'end_date'" in some cases (some Airflow tables use 0000-00-00 00:00:00 as timestamp field default value).
To avoid this error, you could disable NO_ZERO_DATE mode on you MySQL server.
Read https://stackoverflow.com/questions/9192027/invalid-default-value-for-create-date-timestamp-field for how to disable it.
See SQL Mode - NO_ZERO_DATE for more information.
MsSQL Database
Warning
After discussion
and a voting process,
the Airflow’s PMC members and Committers have reached a resolution to no longer maintain MsSQL as a supported Database Backend.
As of Airflow 2.9.0 support of MsSQL has been removed for Airflow Database Backend.
This does not affect the existing provider packages (operators and hooks), DAGs can still access and process data from MsSQL.
However, further usage may throw errors making Airflow’s core functionality unusable.
Migrating off MsSQL Server
As with Airflow 2.9.0 the support of MSSQL has ended, a migration script can help with
Airflow version 2.7.x or 2.8.x to migrate off SQL-Server. The migration script is available in
airflow-mssql-migration repo on Github.
Note that the migration script is provided without support and warranty.
Other configuration options
There are more configuration options for configuring SQLAlchemy behavior. For details, see reference documentation for sqlalchemy_* option in [database] section.
For instance, you can specify a database schema where Airflow will create its required tables. If you want Airflow to install its tables in the airflow schema of a PostgreSQL database, specify these environment variables:
export AIRFLOW__DATABASE__SQL_ALCHEMY_CONN="postgresql://postgres@localhost:5432/my_database?options=-csearch_path%3Dairflow"
export AIRFLOW__DATABASE__SQL_ALCHEMY_SCHEMA="airflow"
Note the search_path at the end of the SQL_ALCHEMY_CONN database URL.
Initialize the database
After configuring the database and connecting to it in Airflow configuration, you should create the database schema.
airflow db migrate
Database Monitoring and Maintenance in Airflow
Airflow extensively utilizes a relational metadata database for task scheduling and execution.
Monitoring and proper configuration of this database are crucial for optimal Airflow performance.
Key Concerns
Performance Impact: Long or excessive queries can significantly affect Airflow’s functionality.
These may arise due to workflow specifics, lack of optimizations, or code bugs.
Database Statistics: Incorrect optimization decisions by the database engine,
often due to outdated data statistics, can degrade performance.
Responsibilities
The responsibilities for database monitoring and maintenance in Airflow environments vary depending on
whether you’re using self-managed databases and Airflow instances or opting for managed services.
Self-Managed Environments:
In the setups where both the database and Airflow are self-managed, the Deployment Manager
is responsible for setting up, configuring, and maintaining the database. This includes monitoring
its performance, managing backups, periodic cleanups and ensuring its optimal operation with Airflow.
Managed Services:
Managed Database Services: When using managed DB services, many maintenance tasks (like backups,
patching, and basic monitoring) are handled by the provider. However, the Deployment Manager still
needs to oversee the configuration of Airflow and optimize performance settings specific to their
workflows, manages periodic cleanups and monitor their DB to ensure optimal operations with Airflow.
Managed Airflow Services: With managed Airflow services, those service provider take responsibility
for the configuration and maintenance of Airflow and its database. However, the Deployment Manager
needs to collaborate with the service configuration to ensure that the sizing and workflow requirements
are matching the sizing and configuration of the managed service.
Monitoring Aspects
Regular monitoring should include:
CPU, I/O, and memory usage.
Query frequency and number.
Identification and logging of slow or long-running queries.
Detection of inefficient query execution plans.
Analysis of disk swap versus memory usage and cache swapping frequency.
Tools and Strategies
Airflow doesn’t provide direct tooling for database monitoring.
Use server-side monitoring and logging to obtain metrics.
Enable tracking of long-running queries based on defined thresholds.
Regularly run house-keeping tasks (like ANALYZE SQL command) for maintenance.
Database Cleaning Tools
Airflow DB Clean Command: Utilize the airflow db clean command to help manage and clean
up your database.
Python Methods in ``airflow.utils.db_cleanup``: This module provides additional Python methods for
database cleanup and maintenance, offering more fine-grained control and customization for specific needs.
Recommendations
Proactive Monitoring: Implement monitoring and logging in production without significantly
impacting performance.
Database-Specific Guidance: Consult the chosen database’s documentation for specific monitoring
setup instructions.
Managed Database Services: Check if automatic maintenance tasks are available with your
database provider.
SQLAlchemy Logging
For detailed query analysis, enable SQLAlchemy client logging (echo=True in SQLAlchemy
engine configuration).
This method is more intrusive and can affect Airflow’s client-side performance.
It generates a lot of logs, especially in a busy Airflow environment.
Suitable for non-production environments like staging systems.
You can do it with echo=True as sqlalchemy engine configuration as explained in the
SQLAlchemy logging documentation.
Use sql_alchemy_engine_args configuration parameter to set echo arg to True.
Caution
Be mindful of the impact on Airflow’s performance and system resources when enabling extensive logging.
Prefer server-side monitoring over client-side logging for production environments to minimize
performance interference.
What’s next?
By default, Airflow uses SequentialExecutor, which does not provide parallelism. You should consider
configuring a different executor for better performance.
Other configuration options
Initialize the database
Database Monitoring and Maintenance in Airflow
