This document describes the current stable version of Celery (5.6). For development docs, go here.

# Configuration and defaults

This document describes the configuration options available.

If you’re using the default loader, you must create the `celeryconfig.py` module and make sure it’s available on the Python path.

## Example configuration file

This is an example configuration file to get you started. It should contain all you need to run a basic Celery set-up.

    ## Broker settings.
    broker_url = 'amqp://guest:guest@localhost:5672//'

    # List of modules to import when the Celery worker starts.
    imports = ('myapp.tasks',)

    ## Using the database to store task state and results.
    result_backend = 'db+sqlite:///results.db'

    task_annotations = {'tasks.add': {'rate_limit': '10/s'}}

## New lowercase settings

Version 4.0 introduced new lower case settings and setting organization.

The major difference between previous versions, apart from the lower case names, are the renaming of some prefixes, like `celery_beat_` to `beat_`, `celeryd_` to `worker_`, and most of the top level `celery_` settings have been moved into a new `task_` prefix.

Warning

Celery will still be able to read old configuration files until Celery 6.0. Afterwards, support for the old configuration files will be removed. We provide the `celery upgrade` command that should handle plenty of cases (including Django).

Please migrate to the new configuration scheme as soon as possible.

## Configuration Directives

### General settings

#### `accept_content`

Default: `{'json'}` (set, list, or tuple).

A white-list of content-types/serializers to allow.

If a message is received that’s not in this list then the message will be discarded with an error.

By default only json is enabled but any content type can be added, including pickle and yaml; when this is the case make sure untrusted parties don’t have access to your broker. See Security for more.

Example:

    # using serializer name
    accept_content = ['json']

    # or the actual content-type (MIME)
    accept_content = ['application/json']

#### `result_accept_content`

Default: `None` (can be set, list or tuple).

Added in version 4.3.

A white-list of content-types/serializers to allow for the result backend.

If a message is received that’s not in this list then the message will be discarded with an error.

By default it is the same serializer as `accept_content`. However, a different serializer for accepted content of the result backend can be specified. Usually this is needed if signed messaging is used and the result is stored unsigned in the result backend. See Security for more.

Example:

    # using serializer name
    result_accept_content = ['json']

    # or the actual content-type (MIME)
    result_accept_content = ['application/json']

### Time and date settings

#### `enable_utc`

Added in version 2.5.

Default: Enabled by default since version 3.0.

If enabled dates and times in messages will be converted to use the UTC timezone.

Note that workers running Celery versions below 2.5 will assume a local timezone for all messages, so only enable if all workers have been upgraded.

#### `timezone`

Added in version 2.5.

Default: `"UTC"`.

Configure Celery to use a custom time zone. The timezone value can be any time zone supported by the ZoneInfo library.

If not set the UTC timezone is used. For backwards compatibility there’s also a `enable_utc` setting, and when this is set to false the system local timezone is used instead.

### Task settings

#### `task_annotations`

Added in version 2.5.

Default: `None`.

This setting can be used to rewrite any task attribute from the configuration. The setting can be a dict, or a list of annotation objects that filter for tasks and return a map of attributes to change.

This will change the `rate_limit` attribute for the `tasks.add` task:

    task_annotations = {'tasks.add': {'rate_limit': '10/s'}}

or change the same for all tasks:

    task_annotations = {'*': {'rate_limit': '10/s'}}

You can change methods too, for example the `on_failure` handler:

    def my_on_failure(self, exc, task_id, args, kwargs, einfo):
        print('Oh no! Task failed: {0!r}'.format(exc))

    task_annotations = {'*': {'on_failure': my_on_failure}}

If you need more flexibility then you can use objects instead of a dict to choose the tasks to annotate:

    class MyAnnotate:

        def annotate(self, task):
            if task.name.startswith('tasks.'):
                return {'rate_limit': '10/s'}

    task_annotations = (MyAnnotate(), {other,})

#### `task_compression`

Default: `None`

Default compression used for task messages. Can be `gzip`, `bzip2` (if available), or any custom compression schemes registered in the Kombu compression registry.

The default is to send uncompressed messages.

#### `task_protocol`

Default: 2 (since 4.0).

Set the default task message protocol version used to send tasks. Supports protocols: 1 and 2.

Protocol 2 is supported by 3.1.24 and 4.x+.

#### `task_serializer`

Default: `"json"` (since 4.0, earlier: pickle).

A string identifying the default serialization method to use. Can be json (default), pickle, yaml, msgpack, or any custom serialization methods that have been registered with `kombu.serialization.registry`.

See also

Serializers.

#### `task_publish_retry`

Added in version 2.2.

Default: Enabled.

Decides if publishing task messages will be retried in the case of connection loss or other connection errors. See also `task_publish_retry_policy`.

#### `task_publish_retry_policy`

Added in version 2.2.

Default: See Message Sending Retry.

Defines the default policy when retrying publishing a task message in the case of connection loss or other connection errors.

### Task execution settings

#### `task_always_eager`

Default: Disabled.

If this is `True`, all tasks will be executed locally by blocking until the task returns. `apply_async()` and `Task.delay()` will return an `EagerResult` instance, that emulates the API and behavior of `AsyncResult`, except the result is already evaluated.

That is, tasks will be executed locally instead of being sent to the queue.

#### `task_eager_propagates`

Default: Disabled.

If this is `True`, eagerly executed tasks (applied by task.apply(), or when the `task_always_eager` setting is enabled), will propagate exceptions.

It’s the same as always running `apply()` with `throw=True`.

#### `task_store_eager_result`

Added in version 5.1.

Default: Disabled.

If this is `True` and `task_always_eager` is `True` and `task_ignore_result` is `False`, the results of eagerly executed tasks will be saved to the backend.

By default, even with `task_always_eager` set to `True` and `task_ignore_result` set to `False`, the result will not be saved.

#### `task_remote_tracebacks`

Default: Disabled.

If enabled task results will include the workers stack when re-raising task errors.

This requires the https://pypi.org/project/tblib/ library, that can be installed using pip:

    $ pip install celery[tblib]

See Bundles for information on combining multiple extension requirements.

#### `task_ignore_result`

Default: Disabled.

Whether to store the task return values or not (tombstones). If you still want to store errors, just not successful return values, you can set `task_store_errors_even_if_ignored`.

#### `task_store_errors_even_if_ignored`

Default: Disabled.

If set, the worker stores all task errors in the result store even if `Task.ignore_result` is on.

#### `task_track_started`

Default: Disabled.

If `True` the task will report its status as ‘started’ when the task is executed by a worker. The default value is `False` as the normal behavior is to not report that level of granularity. Tasks are either pending, finished, or waiting to be retried. Having a ‘started’ state can be useful for when there are long running tasks and there’s a need to report what task is currently running.

#### `task_time_limit`

Default: No time limit.

Task hard time limit in seconds. The worker processing the task will be killed and replaced with a new one when this is exceeded.

#### `task_allow_error_cb_on_chord_header`

Added in version 5.3.

Default: Disabled.

Enabling this flag will allow linking an error callback to a chord header, which by default will not link when using `link_error()`, and preventing from the chord’s body to execute if any of the tasks in the header fails.

Consider the following canvas with the flag disabled (default behavior):

    header = group([t1, t2])
    body = t3
    c = chord(header, body)
    c.link_error(error_callback_sig)

If any of the header tasks failed (`t1` or `t2`), by default, the chord body (`t3`) would not execute, and `error_callback_sig` will be called once (for the body).

Enabling this flag will change the above behavior by:

1. `error_callback_sig` will be linked to `t1` and `t2` (as well as `t3`).

2. If any of the header tasks failed, `error_callback_sig` will be called for each failed header task and the `body` (even if the body didn’t run).

Consider now the following canvas with the flag enabled:

    header = group([failingT1, failingT2])
    body = t3
    c = chord(header, body)
    c.link_error(error_callback_sig)

If all of the header tasks failed (`failingT1` and `failingT2`), then the chord body (`t3`) would not execute, and `error_callback_sig` will be called 3 times (two times for the header and one time for the body).

Lastly, consider the following canvas with the flag enabled:

    header = group([failingT1, failingT2])
    body = t3
    upgraded_chord = chain(header, body)
    upgraded_chord.link_error(error_callback_sig)

This canvas will behave exactly the same as the previous one, since the `chain` will be upgraded to a `chord` internally.

#### `task_soft_time_limit`

Default: No soft time limit.

Task soft time limit in seconds.

The `SoftTimeLimitExceeded` exception will be raised when this is exceeded. For example, the task can catch this to clean up before the hard time limit comes:

    from celery.exceptions import SoftTimeLimitExceeded

    @app.task
    def mytask():
        try:
            return do_work()
        except SoftTimeLimitExceeded:
            cleanup_in_a_hurry()

#### `task_acks_late`

Default: Disabled.

Late ack means the task messages will be acknowledged after the task has been executed, not right before (the default behavior).

See also

FAQ: Should I use retry or acks_late?.

#### `task_acks_on_failure_or_timeout`

Default: Enabled

When enabled messages for all tasks will be acknowledged even if they fail or time out.

Configuring this setting only applies to tasks that are acknowledged after they have been executed and only if `task_acks_late` is enabled.

#### `task_reject_on_worker_lost`

Default: Disabled.

Even if `task_acks_late` is enabled, the worker will acknowledge tasks when the worker process executing them abruptly exits or is signaled (e.g., `KILL`/`INT`, etc).

Setting this to true allows the message to be re-queued instead, so that the task will execute again by the same worker, or another worker.

Warning

Enabling this can cause message loops; make sure you know what you’re doing.

#### `task_default_rate_limit`

Default: No rate limit.

The global default rate limit for tasks.

This value is used for tasks that doesn’t have a custom rate limit

See also

The `worker_disable_rate_limits` setting can disable all rate limits.

### Task result backend settings

#### `result_backend`

Default: No result backend enabled by default.

The backend used to store task results (tombstones). Can be one of the following:

- `rpc`

    Send results back as AMQP messages See RPC backend settings.

- `database`

    Use a relational database supported by SQLAlchemy. See Database backend settings.

- `redis`

    Use Redis to store the results. See Redis backend settings.

- `cache`

    Use Memcached to store the results. See Cache backend settings.

- `mongodb`

    Use MongoDB to store the results. See MongoDB backend settings.

- `cassandra`

    Use Cassandra to store the results. See Cassandra/AstraDB backend settings.

- `elasticsearch`

    Use Elasticsearch to store the results. See Elasticsearch backend settings.

- `ironcache`

    Use IronCache to store the results. See IronCache backend settings.

- `couchbase`

    Use Couchbase to store the results. See Couchbase backend settings.

- `arangodb`

    Use ArangoDB to store the results. See ArangoDB backend settings.

- `couchdb`

    Use CouchDB to store the results. See CouchDB backend settings.

- `cosmosdbsql (experimental)`

    Use the CosmosDB PaaS to store the results. See CosmosDB backend settings (experimental).

- `filesystem`

    Use a shared directory to store the results. See File-system backend settings.

- `consul`

    Use the Consul K/V store to store the results See Consul K/V store backend settings.

- `azureblockblob`

    Use the AzureBlockBlob PaaS store to store the results See Azure Block Blob backend settings.

- `s3`

    Use the S3 to store the results See S3 backend settings.

- `gcs`

    Use the GCS to store the results See GCS backend settings.

#### `result_backend_always_retry`

Default: `False`

If enable, backend will try to retry on the event of recoverable exceptions instead of propagating the exception. It will use an exponential backoff sleep time between 2 retries.

#### `result_backend_max_sleep_between_retries_ms`

Default: 10000

This specifies the maximum sleep time between two backend operation retry.

#### `result_backend_base_sleep_between_retries_ms`

Default: 10

This specifies the base amount of sleep time between two backend operation retry.

#### `result_backend_max_retries`

Default: Inf

This is the maximum of retries in case of recoverable exceptions.

#### `result_backend_thread_safe`

Default: False

If True, then the backend object is shared across threads. This may be useful for using a shared connection pool instead of creating a connection for every thread.

#### `result_backend_transport_options`

Default: `{}` (empty mapping).

A dict of additional options passed to the underlying transport.

See your transport user manual for supported options (if any).

Example setting the visibility timeout (supported by Redis and SQS transports):

    result_backend_transport_options = {'visibility_timeout': 18000}  # 5 hours

#### `result_serializer`

Default: `json` since 4.0 (earlier: pickle).

Result serialization format.

See Serializers for information about supported serialization formats.

#### `result_compression`

Default: No compression.

Optional compression method used for task results. Supports the same options as the `task_compression` setting.

#### `result_extended`

Default: `False`

Enables extended task result attributes (name, args, kwargs, worker, retries, queue, delivery_info) to be written to backend.

#### `result_expires`

Default: Expire after 1 day.

Time (in seconds, or a `timedelta` object) for when after stored task tombstones will be deleted.

A built-in periodic task will delete the results after this time (`celery.backend_cleanup`), assuming that `celery beat` is enabled. The task runs daily at 4am.

A value of `None` or 0 means results will never expire (depending on backend specifications).

Note

For the moment this only works with the AMQP, database, cache, Couchbase, filesystem and Redis backends.

When using the database or filesystem backend, `celery beat` must be running for the results to be expired.

#### `result_cache_max`

Default: Disabled by default.

Enables client caching of results.

This can be useful for the old deprecated ‘amqp’ backend where the result is unavailable as soon as one result instance consumes it.

This is the total number of results to cache before older results are evicted. A value of 0 or None means no limit, and a value of `-1` will disable the cache.

Disabled by default.

#### `result_chord_join_timeout`

Default: 3.0.

The timeout in seconds (int/float) when joining a group’s results within a chord.

#### `result_chord_retry_interval`

Default: 1.0.

Default interval for retrying chord tasks.

#### `override_backends`

Default: Disabled by default.

Path to class that implements backend.

Allows to override backend implementation. This can be useful if you need to store additional metadata about executed tasks, override retry policies, etc.

Example:

    override_backends = {"db": "custom_module.backend.class"}

### Database backend settings

#### Database URL Examples

To use the database backend you have to configure the `result_backend` setting with a connection URL and the `db+` prefix:

    result_backend = 'db+scheme://user:password@host:port/dbname'

Examples:

    # sqlite (filename)
    result_backend = 'db+sqlite:///results.sqlite'

    # mysql
    result_backend = 'db+mysql://scott:tiger@localhost/foo'

    # postgresql
    result_backend = 'db+postgresql://scott:tiger@localhost/mydatabase'

    # oracle
    result_backend = 'db+oracle://scott:tiger@127.0.0.1:1521/sidname'

Please see Supported Databases for a table of supported databases, and Connection String for more information about connection strings (this is the part of the URI that comes after the `db+` prefix).

Note

If you are upgrading from Celery 5.6 or earlier, the `date_done` column in `celery_taskmeta` and `celery_tasksetmeta` tables does not have a database index. The built-in periodic task `celery.backend_cleanup` queries on `date_done` to delete expired task results, so adding an index significantly improves cleanup performance on large tables.

Since SQLAlchemy’s `create_all()` will not alter existing tables, you will need to update your database schema. If you are using Alembic for schema migrations, you can generate an empty revision and apply the following operations:

    from alembic import op

    def upgrade():
        op.create_index('ix_celery_taskmeta_date_done', 'celery_taskmeta', ['date_done'])
        op.create_index('ix_celery_tasksetmeta_date_done', 'celery_tasksetmeta', ['date_done'])

    def downgrade():
        op.drop_index('ix_celery_tasksetmeta_date_done', table_name='celery_tasksetmeta')
        op.drop_index('ix_celery_taskmeta_date_done', table_name='celery_taskmeta')

Otherwise, you can add the indexes manually using SQL:

    CREATE INDEX ix_celery_taskmeta_date_done ON celery_taskmeta (date_done);
    CREATE INDEX ix_celery_tasksetmeta_date_done ON celery_tasksetmeta (date_done);

#### `database_create_tables_at_setup`

Added in version 5.5.0.

Default: True by default.

- If True, Celery will create the tables in the database during setup.

- If False, Celery will create the tables lazily, i.e. wait for the first task to be executed before creating the tables.

Note

Before celery 5.5, the tables were created lazily i.e. it was equivalent to database_create_tables_at_setup set to False.

#### `database_engine_options`

Default: `{'pool_pre_ping': True, 'pool_recycle': 3600}`

Changed in version 5.7: The default was changed from `{}` to include `pool_pre_ping=True` and `pool_recycle=3600` for improved connection health handling. This helps prevent stale connection errors such as `(OperationalError) (2006, 'MySQL server has gone away')`.

To specify additional SQLAlchemy database engine options you can use the `database_engine_options` setting:

    # echo enables verbose logging from SQLAlchemy.
    app.conf.database_engine_options = {'echo': True}

    # To disable the default pool health options:
    app.conf.database_engine_options = {'pool_pre_ping': False, 'pool_recycle': None}

#### `database_short_lived_sessions`

Default: Disabled by default.

Short lived sessions are disabled by default. If enabled they can drastically reduce performance, especially on systems processing lots of tasks. This option is useful on low-traffic workers that experience errors as a result of cached database connections going stale through inactivity. For example, intermittent errors like (OperationalError) (2006, ‘MySQL server has gone away’) can be fixed by enabling short lived sessions. This option only affects the database backend.

#### `database_table_schemas`

Default: `{}` (empty mapping).

When SQLAlchemy is configured as the result backend, Celery automatically creates two tables to store result meta-data for tasks. This setting allows you to customize the schema of the tables:

    # use custom schema for the database result backend.
    database_table_schemas = {
        'task': 'celery',
        'group': 'celery',
    }

#### `database_table_names`

Default: `{}` (empty mapping).

When SQLAlchemy is configured as the result backend, Celery automatically creates two tables to store result meta-data for tasks. This setting allows you to customize the table names:

    # use custom table names for the database result backend.
    database_table_names = {
        'task': 'myapp_taskmeta',
        'group': 'myapp_groupmeta',
    }

### RPC backend settings

#### `result_persistent`

Default: Disabled by default (transient messages).

If set to `True`, result messages will be persistent. This means the messages won’t be lost after a broker restart.

#### Example configuration

    result_backend = 'rpc://'
    result_persistent = False

Please note: using this backend could trigger the raise of `celery.backends.rpc.BacklogLimitExceeded` if the task tombstone is too old.

E.g.

    for i in range(10000):
        r = debug_task.delay()

    print(r.state)  # this would raise celery.backends.rpc.BacklogLimitExceeded

### Cache backend settings

Note

The cache backend supports the https://pypi.org/project/pylibmc/ and https://pypi.org/project/python-memcached/ libraries. The latter is used only if https://pypi.org/project/pylibmc/ isn’t installed.

Using a single Memcached server:

    result_backend = 'cache+memcached://127.0.0.1:11211/'

Using multiple Memcached servers:

    result_backend = """
        cache+memcached://172.19.26.240:11211;172.19.26.242:11211/
    """.strip()

The “memory” backend stores the cache in memory only:

    result_backend = 'cache'
    cache_backend = 'memory'

#### `cache_backend_options`

Default: `{}` (empty mapping).

You can set https://pypi.org/project/pylibmc/ options using the `cache_backend_options` setting:

    cache_backend_options = {
        'binary': True,
        'behaviors': {'tcp_nodelay': True},
    }

#### `cache_backend`

This setting is no longer used in celery’s builtin backends as it’s now possible to specify the cache backend directly in the `result_backend` setting.

Note

The django-celery-results - Using the Django ORM/Cache as a result backend library uses `cache_backend` for choosing django caches.

### MongoDB backend settings

Note

The MongoDB backend requires the `pymongo` library: http://github.com/mongodb/mongo-python-driver/tree/master

#### mongodb_backend_settings

This is a dict supporting the following keys:

- database

    The database name to connect to. Defaults to `celery`.

- taskmeta_collection

    The collection name to store task meta data. Defaults to `celery_taskmeta`.

- max_pool_size

    Passed as max_pool_size to PyMongo’s Connection or MongoClient constructor. It is the maximum number of TCP connections to keep open to MongoDB at a given time. If there are more open connections than max_pool_size, sockets will be closed when they are released. Defaults to 10.

- options

  > Additional keyword arguments to pass to the mongodb connection constructor. See the `pymongo` docs to see a list of arguments supported.

Note

With pymongo>=4.14, options are case-sensitive when they were previously case-insensitive. See `MongoClient` to determine the correct case.

#### Example configuration

    result_backend = 'mongodb://localhost:27017/'
    mongodb_backend_settings = {
        'database': 'mydb',
        'taskmeta_collection': 'my_taskmeta_collection',
    }

### Redis backend settings

#### Configuring the backend URL

Note

The Redis backend requires the https://pypi.org/project/redis/ library.

To install this package use pip:

    $ pip install celery[redis]

See Bundles for information on combining multiple extension requirements.

This backend requires the `result_backend` setting to be set to a Redis or Redis over TLS URL:

    result_backend = 'redis://username:password@host:port/db'

For example:

    result_backend = 'redis://localhost/0'

is the same as:

    result_backend = 'redis://'

Use the `rediss://` protocol to connect to redis over TLS:

    result_backend = 'rediss://username:password@host:port/db?ssl_cert_reqs=required'

Note that the `ssl_cert_reqs` string should be one of `required`, `optional`, or `none` (though, for backwards compatibility with older Celery versions, the string may also be one of `CERT_REQUIRED`, `CERT_OPTIONAL`, `CERT_NONE`, but those values only work for Celery, not for Redis directly).

If a Unix socket connection should be used, the URL needs to be in the format::

    result_backend = 'socket:///path/to/redis.sock'

The fields of the URL are defined as follows:

1. `username`

   > Added in version 5.1.0.

   > Username used to connect to the database.

   > Note that this is only supported in Redis>=6.0 and with py-redis>=3.4.0 installed.

   > If you use an older database version or an older client version you can omit the username:

   >     result_backend = 'redis://:password@host:port/db'

2. `password`

   > Password used to connect to the database.

3. `host`

   > Host name or IP address of the Redis server (e.g., localhost).

4. `port`

   > Port to the Redis server. Default is 6379.

5. `db`

   > Database number to use. Default is 0. The db can include an optional leading slash.

When using a TLS connection (protocol is `rediss://`), you may pass in all values in `broker_use_ssl` as query parameters. Paths to certificates must be URL encoded, and `ssl_cert_reqs` is required. Example:

    result_backend = 'rediss://:password@host:port/db?\
        ssl_cert_reqs=required\
        &ssl_ca_certs=%2Fvar%2Fssl%2Fmyca.pem\                  # /var/ssl/myca.pem
        &ssl_certfile=%2Fvar%2Fssl%2Fredis-server-cert.pem\     # /var/ssl/redis-server-cert.pem
        &ssl_keyfile=%2Fvar%2Fssl%2Fprivate%2Fworker-key.pem'   # /var/ssl/private/worker-key.pem

Note that the `ssl_cert_reqs` string should be one of `required`, `optional`, or `none` (though, for backwards compatibility, the string may also be one of `CERT_REQUIRED`, `CERT_OPTIONAL`, `CERT_NONE`).

Added in version 5.1.0.

#### `redis_backend_health_check_interval`

Default: Not configured

The Redis backend supports health checks. This value must be set as an integer whose value is the number of seconds between health checks. If a ConnectionError or a TimeoutError is encountered during the health check, the connection will be re-established and the command retried exactly once.

#### `redis_backend_use_ssl`

Default: Disabled.

The Redis backend supports SSL. This value must be set in the form of a dictionary. The valid key-value pairs are the same as the ones mentioned in the `redis` sub-section under `broker_use_ssl`.

Added in version 5.6.

#### `redis_backend_credential_provider`

Default: Disabled.

The Redis backend supports credential provider. This value must be set in the form of a class path string or a class instance. e.g. `mymodule.myfile.myclass` check more details in RedisCredentialProvider doc.

#### `redis_max_connections`

Default: No limit.

Maximum number of connections available in the Redis connection pool used for sending and retrieving results.

Warning

Redis will raise a ConnectionError if the number of concurrent connections exceeds the maximum.

#### `redis_socket_connect_timeout`

Added in version 4.0.1.

Default: `None`

Socket timeout for connections to Redis from the result backend in seconds (int/float)

#### `redis_socket_timeout`

Default: 120.0 seconds.

Socket timeout for reading/writing operations to the Redis server in seconds (int/float), used by the redis result backend.

#### `redis_retry_on_timeout`

Added in version 4.4.1.

Default: `False`

To retry reading/writing operations on TimeoutError to the Redis server, used by the redis result backend. Shouldn’t set this variable if using Redis connection by unix socket.

#### `redis_socket_keepalive`

Added in version 4.4.1.

Default: `False`

Socket TCP keepalive to keep connections healthy to the Redis server, used by the redis result backend.

#### `redis_client_name`

Added in version 5.6.

Default: `None`

Sets the client name for Redis connections used by the result backend. This can help identify connections in Redis monitoring tools.

### Cassandra/AstraDB backend settings

Note

This Cassandra backend driver requires https://pypi.org/project/cassandra-driver/.

This backend can refer to either a regular Cassandra installation or a managed Astra DB instance. Depending on which one, exactly one between the `cassandra_servers` and `cassandra_secure_bundle_path` settings must be provided (but not both).

To install, use pip:

    $ pip install celery[cassandra]

See Bundles for information on combining multiple extension requirements.

This backend requires the following configuration directives to be set.

#### `cassandra_servers`

Default: `[]` (empty list).

List of `host` Cassandra servers. This must be provided when connecting to a Cassandra cluster. Passing this setting is strictly exclusive to `cassandra_secure_bundle_path`. Example:

    cassandra_servers = ['localhost']

#### `cassandra_secure_bundle_path`

Default: None.

Absolute path to the secure-connect-bundle zip file to connect to an Astra DB instance. Passing this setting is strictly exclusive to `cassandra_servers`. Example:

    cassandra_secure_bundle_path = '/home/user/bundles/secure-connect.zip'

When connecting to Astra DB, it is necessary to specify the plain-text auth provider and the associated username and password, which take the value of the Client ID and the Client Secret, respectively, of a valid token generated for the Astra DB instance. See below for an Astra DB configuration example.

#### `cassandra_port`

Default: 9042.

Port to contact the Cassandra servers on.

#### `cassandra_keyspace`

Default: None.

The keyspace in which to store the results. For example:

    cassandra_keyspace = 'tasks_keyspace'

#### `cassandra_table`

Default: None.

The table (column family) in which to store the results. For example:

    cassandra_table = 'tasks'

#### `cassandra_read_consistency`

Default: None.

The read consistency used. Values can be `ONE`, `TWO`, `THREE`, `QUORUM`, `ALL`, `LOCAL_QUORUM`, `EACH_QUORUM`, `LOCAL_ONE`.

#### `cassandra_write_consistency`

Default: None.

The write consistency used. Values can be `ONE`, `TWO`, `THREE`, `QUORUM`, `ALL`, `LOCAL_QUORUM`, `EACH_QUORUM`, `LOCAL_ONE`.

#### `cassandra_entry_ttl`

Default: None.

Time-to-live for status entries. They will expire and be removed after that many seconds after adding. A value of `None` (default) means they will never expire.

#### `cassandra_auth_provider`

Default: `None`.

AuthProvider class within `cassandra.auth` module to use. Values can be `PlainTextAuthProvider` or `SaslAuthProvider`.

#### `cassandra_auth_kwargs`

Default: `{}` (empty mapping).

Named arguments to pass into the authentication provider. For example:

    cassandra_auth_kwargs = {
        username: 'cassandra',
        password: 'cassandra'
    }

#### `cassandra_options`

Default: `{}` (empty mapping).

Named arguments to pass into the `cassandra.cluster` class.

    cassandra_options = {
        'cql_version': '3.2.1'
        'protocol_version': 3
    }

#### Example configuration (Cassandra)

    result_backend = 'cassandra://'
    cassandra_servers = ['localhost']
    cassandra_keyspace = 'celery'
    cassandra_table = 'tasks'
    cassandra_read_consistency = 'QUORUM'
    cassandra_write_consistency = 'QUORUM'
    cassandra_entry_ttl = 86400

#### Example configuration (Astra DB)

    result_backend = 'cassandra://'
    cassandra_keyspace = 'celery'
    cassandra_table = 'tasks'
    cassandra_read_consistency = 'QUORUM'
    cassandra_write_consistency = 'QUORUM'
    cassandra_auth_provider = 'PlainTextAuthProvider'
    cassandra_auth_kwargs = {
      'username': '<<CLIENT_ID_FROM_ASTRA_DB_TOKEN>>',
      'password': '<<CLIENT_SECRET_FROM_ASTRA_DB_TOKEN>>'
    }
    cassandra_secure_bundle_path = '/path/to/secure-connect-bundle.zip'
    cassandra_entry_ttl = 86400

#### Additional configuration

The Cassandra driver, when establishing the connection, undergoes a stage of negotiating the protocol version with the server(s). Similarly, a load-balancing policy is automatically supplied (by default `DCAwareRoundRobinPolicy`, which in turn has a `local_dc` setting, also determined by the driver upon connection). When possible, one should explicitly provide these in the configuration: moreover, future versions of the Cassandra driver will require at least the load-balancing policy to be specified (using execution profiles, as shown below).

A full configuration for the Cassandra backend would thus have the following additional lines:

    from cassandra.policies import DCAwareRoundRobinPolicy
    from cassandra.cluster import ExecutionProfile
    from cassandra.cluster import EXEC_PROFILE_DEFAULT
    myEProfile = ExecutionProfile(
      load_balancing_policy=DCAwareRoundRobinPolicy(
        local_dc='datacenter1', # replace with your DC name
      )
    )
    cassandra_options = {
      'protocol_version': 5,    # for Cassandra 4, change if needed
      'execution_profiles': {EXEC_PROFILE_DEFAULT: myEProfile},
    }

And similarly for Astra DB:

    from cassandra.policies import DCAwareRoundRobinPolicy
    from cassandra.cluster import ExecutionProfile
    from cassandra.cluster import EXEC_PROFILE_DEFAULT
    myEProfile = ExecutionProfile(
      load_balancing_policy=DCAwareRoundRobinPolicy(
        local_dc='europe-west1',  # for Astra DB, region name = dc name
      )
    )
    cassandra_options = {
      'protocol_version': 4,      # for Astra DB
      'execution_profiles': {EXEC_PROFILE_DEFAULT: myEProfile},
    }

### S3 backend settings

Note

This s3 backend driver requires https://pypi.org/project/s3/.

To install, use s3:

    $ pip install celery[s3]

See Bundles for information on combining multiple extension requirements.

This backend requires the following configuration directives to be set.

#### `s3_access_key_id`

Default: None.

The s3 access key id. For example:

    s3_access_key_id = 'access_key_id'

#### `s3_secret_access_key`

Default: None.

The s3 secret access key. For example:

    s3_secret_access_key = 'access_secret_access_key'

#### `s3_bucket`

Default: None.

The s3 bucket name. For example:

    s3_bucket = 'bucket_name'

#### `s3_base_path`

Default: None.

A base path in the s3 bucket to use to store result keys. For example:

    s3_base_path = '/prefix'

#### `s3_endpoint_url`

Default: None.

A custom s3 endpoint url. Use it to connect to a custom self-hosted s3 compatible backend (Ceph, Scality…). For example:

    s3_endpoint_url = 'https://.s3.custom.url'

#### `s3_region`

Default: None.

The s3 aws region. For example:

    s3_region = 'us-east-1'

#### Example configuration

    s3_access_key_id = 's3-access-key-id'
    s3_secret_access_key = 's3-secret-access-key'
    s3_bucket = 'mybucket'
    s3_base_path = '/celery_result_backend'
    s3_endpoint_url = 'https://endpoint_url'

### Azure Block Blob backend settings

To use AzureBlockBlob as the result backend you simply need to configure the `result_backend` setting with the correct URL.

The required URL format is `azureblockblob://` followed by the storage connection string. You can find the storage connection string in the `Access Keys` pane of your storage account resource in the Azure Portal.

#### Example configuration

    result_backend = 'azureblockblob://DefaultEndpointsProtocol=https;AccountName=somename;AccountKey=Lou...bzg==;EndpointSuffix=core.windows.net'

#### `azureblockblob_container_name`

Default: celery.

The name for the storage container in which to store the results.

#### `azureblockblob_base_path`

Added in version 5.1.

Default: None.

A base path in the storage container to use to store result keys. For example:

    azureblockblob_base_path = 'prefix/'

#### `azureblockblob_retry_initial_backoff_sec`

Default: 2.

The initial backoff interval, in seconds, for the first retry. Subsequent retries are attempted with an exponential strategy.

#### `azureblockblob_retry_increment_base`

Default: 2.

#### `azureblockblob_retry_max_attempts`

Default: 3.

The maximum number of retry attempts.

#### `azureblockblob_connection_timeout`

Default: 20.

Timeout in seconds for establishing the azure block blob connection.

#### `azureblockblob_read_timeout`

Default: 120.

Timeout in seconds for reading of an azure block blob.

### GCS backend settings

Note

This gcs backend driver requires https://pypi.org/project/google-cloud-storage/ and https://pypi.org/project/google-cloud-firestore/.

To install, use gcs:

    $ pip install celery[gcs]

See Bundles for information on combining multiple extension requirements.

GCS could be configured via the URL provided in `result_backend`, for example:

    result_backend = 'gs://mybucket/some-prefix?gcs_project=myproject&ttl=600'
    result_backend = 'gs://mybucket/some-prefix?gcs_project=myproject?firestore_project=myproject2&ttl=600'

This backend requires the following configuration directives to be set:

#### `gcs_bucket`

Default: None.

The gcs bucket name. For example:

    gcs_bucket = 'bucket_name'

#### `gcs_project`

Default: None.

The gcs project name. For example:

    gcs_project = 'test-project'

#### `gcs_base_path`

Default: None.

A base path in the gcs bucket to use to store all result keys. For example:

    gcs_base_path = '/prefix'

#### `gcs_ttl`

Default: 0.

The time to live in seconds for the results blobs. Requires a GCS bucket with “Delete” Object Lifecycle Management action enabled. Use it to automatically delete results from Cloud Storage Buckets.

For example to auto remove results after 24 hours:

    gcs_ttl = 86400

#### `gcs_threadpool_maxsize`

Default: 10.

Threadpool size for GCS operations. Same value defines the connection pool size. Allows to control the number of concurrent operations. For example:

    gcs_threadpool_maxsize = 20

#### `firestore_project`

Default: gcs_project.

The Firestore project for Chord reference counting. Allows native chord ref counts. If not specified defaults to `gcs_project`. For example:

    firestore_project = 'test-project2'

#### Example configuration

    gcs_bucket = 'mybucket'
    gcs_project = 'myproject'
    gcs_base_path = '/celery_result_backend'
    gcs_ttl = 86400

### Elasticsearch backend settings

To use Elasticsearch as the result backend you simply need to configure the `result_backend` setting with the correct URL.

#### Example configuration

    result_backend = 'elasticsearch://example.com:9200/index_name/doc_type'

#### `elasticsearch_retry_on_timeout`

Default: `False`

Should timeout trigger a retry on different node?

#### `elasticsearch_max_retries`

Default: 3.

Maximum number of retries before an exception is propagated.

#### `elasticsearch_timeout`

Default: 10.0 seconds.

Global timeout,used by the elasticsearch result backend.

#### `elasticsearch_save_meta_as_text`

Default: `True`

Should meta saved as text or as native json. Result is always serialized as text.

### AWS DynamoDB backend settings

Note

The Dynamodb backend requires the https://pypi.org/project/boto3/ library.

To install this package use pip:

    $ pip install celery[dynamodb]

See Bundles for information on combining multiple extension requirements.

Warning

The Dynamodb backend is not compatible with tables that have a sort key defined.

If you want to query the results table based on something other than the partition key, please define a global secondary index (GSI) instead.

This backend requires the `result_backend` setting to be set to a DynamoDB URL:

    result_backend = 'dynamodb://aws_access_key_id:aws_secret_access_key@region:port/table?read=n&write=m'

For example, specifying the AWS region and the table name:

    result_backend = 'dynamodb://@us-east-1/celery_results'

or retrieving AWS configuration parameters from the environment, using the default table name (`celery`) and specifying read and write provisioned throughput:

    result_backend = 'dynamodb://@/?read=5&write=5'

or using the downloadable version of DynamoDB locally:

    result_backend = 'dynamodb://@localhost:8000'

or using downloadable version or other service with conforming API deployed on any host:

    result_backend = 'dynamodb://@us-east-1'
    dynamodb_endpoint_url = 'http://192.168.0.40:8000'

The fields of the DynamoDB URL in `result_backend` are defined as follows:

1. `aws_access_key_id & aws_secret_access_key`

   > The credentials for accessing AWS API resources. These can also be resolved by the https://pypi.org/project/boto3/ library from various sources, as described here.

2. `region`

   > The AWS region, e.g. `us-east-1` or `localhost` for the Downloadable Version. See the https://pypi.org/project/boto3/ library documentation for definition options.

3. `port`

   The listening port of the local DynamoDB instance, if you are using the downloadable version. If you have not specified the `region` parameter as `localhost`, setting this parameter has no effect.

4. `table`

   > Table name to use. Default is `celery`. See the DynamoDB Naming Rules for information on the allowed characters and length.

5. `read & write`

   > The Read & Write Capacity Units for the created DynamoDB table. Default is `1` for both read and write. More details can be found in the Provisioned Throughput documentation.

6. `ttl_seconds`

   > Time-to-live (in seconds) for results before they expire. The default is to not expire results, while also leaving the DynamoDB table’s Time to Live settings untouched. If `ttl_seconds` is set to a positive value, results will expire after the specified number of seconds. Setting `ttl_seconds` to a negative value means to not expire results, and also to actively disable the DynamoDB table’s Time to Live setting. Note that trying to change a table’s Time to Live setting multiple times in quick succession will cause a throttling error. More details can be found in the DynamoDB TTL documentation

### IronCache backend settings

Note

The IronCache backend requires the https://pypi.org/project/iron_celery/ library:

To install this package use pip:

    $ pip install iron_celery

IronCache is configured via the URL provided in `result_backend`, for example:

    result_backend = 'ironcache://project_id:token@'

Or to change the cache name:

    ironcache:://project_id:token@/awesomecache

For more information, see: https://github.com/iron-io/iron_celery

### Couchbase backend settings

Note

The Couchbase backend requires the https://pypi.org/project/couchbase/ library.

To install this package use pip:

    $ pip install celery[couchbase]

See Bundles for instructions how to combine multiple extension requirements.

This backend can be configured via the `result_backend` set to a Couchbase URL:

    result_backend = 'couchbase://username:password@host:port/bucket'

#### `couchbase_backend_settings`

Default: `{}` (empty mapping).

This is a dict supporting the following keys:

- `host`

  > Host name of the Couchbase server. Defaults to `localhost`.

- `port`

  > The port the Couchbase server is listening to. Defaults to `8091`.

- `bucket`

  > The default bucket the Couchbase server is writing to. Defaults to `default`.

- `username`

  > User name to authenticate to the Couchbase server as (optional).

- `password`

  > Password to authenticate to the Couchbase server (optional).

### ArangoDB backend settings

Note

The ArangoDB backend requires the https://pypi.org/project/pyArango/ library.

To install this package use pip:

    $ pip install celery[arangodb]

See Bundles for instructions how to combine multiple extension requirements.

This backend can be configured via the `result_backend` set to a ArangoDB URL:

    result_backend = 'arangodb://username:password@host:port/database/collection'

#### `arangodb_backend_settings`

Default: `{}` (empty mapping).

This is a dict supporting the following keys:

- `host`

  > Host name of the ArangoDB server. Defaults to `localhost`.

- `port`

  > The port the ArangoDB server is listening to. Defaults to `8529`.

- `database`

  > The default database in the ArangoDB server is writing to. Defaults to `celery`.

- `collection`

  > The default collection in the ArangoDB servers database is writing to. Defaults to `celery`.

- `username`

  > User name to authenticate to the ArangoDB server as (optional).

- `password`

  > Password to authenticate to the ArangoDB server (optional).

- `http_protocol`

  > HTTP Protocol in ArangoDB server connection. Defaults to `http`.

- `verify`

  > HTTPS Verification check while creating the ArangoDB connection. Defaults to `False`.

### CosmosDB backend settings (experimental)

To use CosmosDB as the result backend, you simply need to configure the `result_backend` setting with the correct URL.

#### Example configuration

    result_backend = 'cosmosdbsql://:{InsertAccountPrimaryKeyHere}@{InsertAccountNameHere}.documents.azure.com'

#### `cosmosdbsql_database_name`

Default: celerydb.

The name for the database in which to store the results.

#### `cosmosdbsql_collection_name`

Default: celerycol.

The name of the collection in which to store the results.

#### `cosmosdbsql_consistency_level`

Default: Session.

Represents the consistency levels supported for Azure Cosmos DB client operations.

Consistency levels by order of strength are: Strong, BoundedStaleness, Session, ConsistentPrefix and Eventual.

#### `cosmosdbsql_max_retry_attempts`

Default: 9.

Maximum number of retries to be performed for a request.

#### `cosmosdbsql_max_retry_wait_time`

Default: 30.

Maximum wait time in seconds to wait for a request while the retries are happening.

### CouchDB backend settings

Note

The CouchDB backend requires the https://pypi.org/project/pycouchdb/ library:

To install this Couchbase package use pip:

    $ pip install celery[couchdb]

See Bundles for information on combining multiple extension requirements.

This backend can be configured via the `result_backend` set to a CouchDB URL:

    result_backend = 'couchdb://username:password@host:port/container'

The URL is formed out of the following parts:

- `username`

  > User name to authenticate to the CouchDB server as (optional).

- `password`

  > Password to authenticate to the CouchDB server (optional).

- `host`

  > Host name of the CouchDB server. Defaults to `localhost`.

- `port`

  > The port the CouchDB server is listening to. Defaults to `8091`.

- `container`

  > The default container the CouchDB server is writing to. Defaults to `default`.

### File-system backend settings

This backend can be configured using a file URL, for example:

    CELERY_RESULT_BACKEND = 'file:///var/celery/results'

The configured directory needs to be shared and writable by all servers using the backend.

If you’re trying Celery on a single system you can simply use the backend without any further configuration. For larger clusters you could use NFS, GlusterFS, CIFS, HDFS (using FUSE), or any other file-system.

### Consul K/V store backend settings

Note

The Consul backend requires the https://pypi.org/project/python-consul2/ library:

To install this package use pip:

    $ pip install python-consul2

The Consul backend can be configured using a URL, for example:

    CELERY_RESULT_BACKEND = 'consul://localhost:8500/'

or:

    result_backend = 'consul://localhost:8500/'

The backend will store results in the K/V store of Consul as individual keys. The backend supports auto expire of results using TTLs in Consul. The full syntax of the URL is:

    consul://host:port[?one_client=1]

The URL is formed out of the following parts:

- `host`

  > Host name of the Consul server.

- `port`

  > The port the Consul server is listening to.

- `one_client`

  > By default, for correctness, the backend uses a separate client connection per operation. In cases of extreme load, the rate of creation of new connections can cause HTTP 429 “too many connections” error responses from the Consul server when under load. The recommended way to handle this is to enable retries in `python-consul2` using the patch at https://github.com/poppyred/python-consul2/pull/31.

  > Alternatively, if `one_client` is set, a single client connection will be used for all operations instead. This should eliminate the HTTP 429 errors, but the storage of results in the backend can become unreliable.

### Message Routing

#### `task_queues`

Default: `None` (queue taken from default queue settings).

Most users will not want to specify this setting and should rather use the automatic routing facilities.

If you really want to configure advanced routing, this setting should be a list of `kombu.Queue` objects the worker will consume from.

Note that workers can be overridden this setting via the `-Q` option, or individual queues from this list (by name) can be excluded using the `-X` option.

Also see Basics for more information.

The default is a queue/exchange/binding key of `celery`, with exchange type `direct`.

See also `task_routes`

#### `task_routes`

Default: `None`.

A list of routers, or a single router used to route tasks to queues. When deciding the final destination of a task the routers are consulted in order.

A router can be specified as either:

- A function with the signature `(name, args, kwargs, options, task=None, **kwargs)`

- A string providing the path to a router function.

- A dict containing router specification:

    Will be converted to a `celery.routes.MapRoute` instance.

- A list of `(pattern, route)` tuples:

    Will be converted to a `celery.routes.MapRoute` instance.

Examples:

    task_routes = {
        'celery.ping': 'default',
        'mytasks.add': 'cpu-bound',
        'feed.tasks.*': 'feeds',                           # <-- glob pattern
        re.compile(r'(image|video)\.tasks\..*'): 'media',  # <-- regex
        'video.encode': {
            'queue': 'video',
            'exchange': 'media',
            'routing_key': 'media.video.encode',
        },
    }

    task_routes = ('myapp.tasks.route_task', {'celery.ping': 'default'})

Where `myapp.tasks.route_task` could be:

    def route_task(self, name, args, kwargs, options, task=None, **kw):
        if task == 'celery.ping':
            return {'queue': 'default'}

`route_task` may return a string or a dict. A string then means it’s a queue name in `task_queues`, a dict means it’s a custom route.

When sending tasks, the routers are consulted in order. The first router that doesn’t return `None` is the route to use. The message options is then merged with the found route settings, where the task’s settings have priority.

Example if `apply_async()` has these arguments:

    Task.apply_async(immediate=False, exchange='video',
                     routing_key='video.compress')

and a router returns:

    {'immediate': True, 'exchange': 'urgent'}

the final message options will be:

    immediate=False, exchange='video', routing_key='video.compress'

(and any default message options defined in the `Task` class)

Values defined in `task_routes` have precedence over values defined in `task_queues` when merging the two.

With the follow settings:

    task_queues = {
        'cpubound': {
            'exchange': 'cpubound',
            'routing_key': 'cpubound',
        },
    }

    task_routes = {
        'tasks.add': {
            'queue': 'cpubound',
            'routing_key': 'tasks.add',
            'serializer': 'json',
        },
    }

The final routing options for `tasks.add` will become:

    {'exchange': 'cpubound',
     'routing_key': 'tasks.add',
     'serializer': 'json'}

See Routers for more examples.

#### `task_queue_max_priority`

brokers:

  RabbitMQ

Default: `None`.

See RabbitMQ Message Priorities.

#### `task_default_priority`

brokers:

  RabbitMQ, Redis

Default: `None`.

See RabbitMQ Message Priorities.

#### `task_inherit_parent_priority`

brokers:

  RabbitMQ

Default: `False`.

If enabled, child tasks will inherit priority of the parent task.

    # The last task in chain will also have priority set to 5.
    chain = celery.chain(add.s(2) | add.s(2).set(priority=5) | add.s(3))

Priority inheritance also works when calling child tasks from a parent task with delay or apply_async.

See RabbitMQ Message Priorities.

#### `worker_direct`

Default: Disabled.

This option enables so that every worker has a dedicated queue, so that tasks can be routed to specific workers.

The queue name for each worker is automatically generated based on the worker hostname and a `.dq` suffix, using the `C.dq2` exchange.

For example the queue name for the worker with node name `w1@example.com` becomes:

    w1@example.com.dq

Then you can route the task to the worker by specifying the hostname as the routing key and the `C.dq2` exchange:

    task_routes = {
        'tasks.add': {'exchange': 'C.dq2', 'routing_key': 'w1@example.com'}
    }

#### `task_create_missing_queues`

Default: Enabled.

If enabled (default), any queues specified that aren’t defined in `task_queues` will be automatically created. See Automatic routing.

#### `task_create_missing_queue_type`

Added in version 5.6.

Default: `"classic"`

When Celery needs to declare a queue that doesn’t exist (i.e., when `task_create_missing_queues` is enabled), this setting defines what type of RabbitMQ queue to create.

- `"classic"` (default): declares a standard classic queue.

- `"quorum"`: declares a RabbitMQ quorum queue (adds `x-queue-type: quorum`).

#### `task_create_missing_queue_exchange_type`

Added in version 5.6.

Default: `None`

If this option is None or the empty string (the default), Celery leaves the exchange exactly as returned by your `app.amqp.Queues.autoexchange` hook.

You can set this to a specific exchange type, such as `"direct"`, `"topic"`, or `"fanout"`, to create the missing queue with that exchange type.

Combine this setting with task_create_missing_queue_type = “quorum” to create quorum queues bound to a topic exchange, for example:

    app.conf.task_create_missing_queues=True
    app.conf.task_create_missing_queue_type="quorum"
    app.conf.task_create_missing_queue_exchange_type="topic"

Like the queue-type setting above, this option does not affect queues that you define explicitly in `task_queues`; it applies only to queues created implicitly at runtime.

#### `task_default_queue`

Default: `"celery"`.

The name of the default queue used by .apply_async if the message has no route or no custom queue has been specified.

This queue must be listed in `task_queues`. If `task_queues` isn’t specified then it’s automatically created containing one queue entry, where this name is used as the name of that queue.

See also

Changing the name of the default queue

#### `task_default_queue_type`

Added in version 5.5.

Default: `"classic"`.

This setting is used to allow changing the default queue type for the `task_default_queue` queue. The other viable option is `"quorum"` which is only supported by RabbitMQ and sets the queue type to `quorum` using the `x-queue-type` queue argument.

If the `worker_detect_quorum_queues` setting is enabled, the worker will automatically detect the queue type and disable the global QoS accordingly.

Warning

Quorum queues require confirm publish to be enabled. Use `broker_transport_options` to enable confirm publish by setting:

    broker_transport_options = {"confirm_publish": True}

For more information, see RabbitMQ documentation.

#### `task_default_exchange`

Default: Uses the value set for `task_default_queue`.

Name of the default exchange to use when no custom exchange is specified for a key in the `task_queues` setting.

#### `task_default_exchange_type`

Default: `"direct"`.

Default exchange type used when no custom exchange type is specified for a key in the `task_queues` setting.

#### `task_default_routing_key`

Default: Uses the value set for `task_default_queue`.

The default routing key used when no custom routing key is specified for a key in the `task_queues` setting.

#### `task_default_delivery_mode`

Default: `"persistent"`.

Can be transient (messages not written to disk) or persistent (written to disk).

### Broker Settings

#### `broker_url`

Default: `"amqp://"`

Default broker URL. This must be a URL in the form of:

    transport://userid:password@hostname:port/virtual_host

Only the scheme part (`transport://`) is required, the rest is optional, and defaults to the specific transports default values.

The transport part is the broker implementation to use, and the default is `amqp`, (uses `librabbitmq` if installed or falls back to `pyamqp`). There are also other choices available, including; `redis://`, `sqs://`, and `qpid://`.

The scheme can also be a fully qualified path to your own transport implementation:

    broker_url = 'proj.transports.MyTransport://localhost'

More than one broker URL, of the same transport, can also be specified. The broker URLs can be passed in as a single string that’s semicolon delimited:

    broker_url = 'transport://userid:password@hostname:port//;transport://userid:password@hostname:port//'

Or as a list:

    broker_url = [
        'transport://userid:password@localhost:port//',
        'transport://userid:password@hostname:port//'
    ]

The brokers will then be used in the `broker_failover_strategy`.

See kombu:connection-urls in the Kombu documentation for more information.

#### `broker_read_url` / `broker_write_url`

Default: Taken from `broker_url`.

These settings can be configured, instead of `broker_url` to specify different connection parameters for broker connections used for consuming and producing.

Example:

    broker_read_url = 'amqp://user:pass@broker.example.com:56721'
    broker_write_url = 'amqp://user:pass@broker.example.com:56722'

Both options can also be specified as a list for failover alternates, see `broker_url` for more information.

#### `broker_failover_strategy`

Default: `"round-robin"`.

Default failover strategy for the broker Connection object. If supplied, may map to a key in ‘kombu.connection.failover_strategies’, or be a reference to any method that yields a single item from a supplied list.

Example:

    # Random failover strategy
    def random_failover_strategy(servers):
        it = list(servers)  # don't modify callers list
        shuffle = random.shuffle
        for _ in repeat(None):
            shuffle(it)
            yield it[0]

    broker_failover_strategy = random_failover_strategy

#### `broker_heartbeat`

transports supported:

  `pyamqp`

Default: `120.0` (negotiated by server).

Note: This value is only used by the worker, clients do not use a heartbeat at the moment.

It’s not always possible to detect connection loss in a timely manner using TCP/IP alone, so AMQP defines something called heartbeats that’s is used both by the client and the broker to detect if a connection was closed.

If the heartbeat value is 10 seconds, then the heartbeat will be monitored at the interval specified by the `broker_heartbeat_checkrate` setting (by default this is set to double the rate of the heartbeat value, so for the 10 seconds, the heartbeat is checked every 5 seconds).

#### `broker_heartbeat_checkrate`

transports supported:

  `pyamqp`

Default: 2.0.

At intervals the worker will monitor that the broker hasn’t missed too many heartbeats. The rate at which this is checked is calculated by dividing the `broker_heartbeat` value with this value, so if the heartbeat is 10.0 and the rate is the default 2.0, the check will be performed every 5 seconds (twice the heartbeat sending rate).

#### `broker_use_ssl`

transports supported:

  `pyamqp`, `redis`

Default: Disabled.

Toggles SSL usage on broker connection and SSL settings.

The valid values for this option vary by transport.

##### `pyamqp`

If `True` the connection will use SSL with default SSL settings. If set to a dict, will configure SSL connection according to the specified policy. The format used is Python’s `ssl.wrap_socket()` options.

Note that SSL socket is generally served on a separate port by the broker.

Example providing a client cert and validating the server cert against a custom certificate authority:

    import ssl

    broker_use_ssl = {
      'keyfile': '/var/ssl/private/worker-key.pem',
      'certfile': '/var/ssl/amqp-server-cert.pem',
      'ca_certs': '/var/ssl/myca.pem',
      'cert_reqs': ssl.CERT_REQUIRED
    }

Added in version 5.1: Starting from Celery 5.1, py-amqp will always validate certificates received from the server and it is no longer required to manually set `cert_reqs` to `ssl.CERT_REQUIRED`.

The previous default, `ssl.CERT_NONE` is insecure and we its usage should be discouraged. If you’d like to revert to the previous insecure default set `cert_reqs` to `ssl.CERT_NONE`

##### `redis`

The setting must be a dict with the following keys:

- `ssl_cert_reqs` (required): one of the `SSLContext.verify_mode` values:

    - `ssl.CERT_NONE`

    - `ssl.CERT_OPTIONAL`

    - `ssl.CERT_REQUIRED`

- `ssl_ca_certs` (optional): path to the CA certificate

- `ssl_certfile` (optional): path to the client certificate

- `ssl_keyfile` (optional): path to the client key

#### `broker_pool_limit`

Added in version 2.3.

Default: 10.

The maximum number of connections that can be open in the connection pool.

The pool is enabled by default since version 2.5, with a default limit of ten connections. This number can be tweaked depending on the number of threads/green-threads (eventlet/gevent) using a connection. For example running eventlet with 1000 greenlets that use a connection to the broker, contention can arise and you should consider increasing the limit.

If set to `None` or 0 the connection pool will be disabled and connections will be established and closed for every use.

#### `broker_connection_timeout`

Default: 4.0.

The default timeout in seconds before we give up establishing a connection to the AMQP server. This setting is disabled when using gevent.

Note

The broker connection timeout only applies to a worker attempting to connect to the broker. It does not apply to producer sending a task, see `broker_transport_options` for how to provide a timeout for that situation.

#### `broker_connection_retry`

Default: Enabled.

Automatically try to re-establish the connection to the AMQP broker if lost after the initial connection is made.

The time between retries is increased for each retry, and is not exhausted before `broker_connection_max_retries` is exceeded.

Warning

The broker_connection_retry configuration setting will no longer determine whether broker connection retries are made during startup in Celery 6.0 and above. If you wish to refrain from retrying connections on startup, you should set broker_connection_retry_on_startup to False instead.

#### `broker_connection_retry_on_startup`

Default: Enabled.

Automatically try to establish the connection to the AMQP broker on Celery startup if it is unavailable.

The time between retries is increased for each retry, and is not exhausted before `broker_connection_max_retries` is exceeded.

#### `broker_connection_max_retries`

Default: 100.

Maximum number of retries before we give up re-establishing a connection to the AMQP broker.

If this is set to `None`, we’ll retry forever.

#### `broker_channel_error_retry`

Added in version 5.3.

Default: Disabled.

Automatically try to re-establish the connection to the AMQP broker if any invalid response has been returned.

The retry count and interval is the same as that of broker_connection_retry. Also, this option doesn’t work when broker_connection_retry is False.

#### `broker_login_method`

Default: `"AMQPLAIN"`.

Set custom amqp login method.

#### `broker_native_delayed_delivery_queue_type`

Added in version 5.5.

transports supported:

  `pyamqp`

Default: `"quorum"`.

This setting is used to allow changing the default queue type for the native delayed delivery queues. The other viable option is `"classic"` which is only supported by RabbitMQ and sets the queue type to `classic` using the `x-queue-type` queue argument.

#### `broker_transport_options`

Added in version 2.2.

Default: `{}` (empty mapping).

A dict of additional options passed to the underlying transport.

See your transport user manual for supported options (if any).

Example setting the visibility timeout (supported by Redis and SQS transports):

    broker_transport_options = {'visibility_timeout': 18000}  # 5 hours

Example setting the producer connection maximum number of retries (so producers won’t retry forever if the broker isn’t available at the first task execution):

    broker_transport_options = {'max_retries': 5}

Example enabling publisher confirms (supported by the `pyamqp` transport). Without this, messages can be silently dropped when the broker hits resource limits:

    broker_transport_options = {'confirm_publish': True}

### Worker

#### `imports`

Default: `[]` (empty list).

A sequence of modules to import when the worker starts.

This is used to specify the task modules to import, but also to import signal handlers and additional remote control commands, etc.

The modules will be imported in the original order.

#### `include`

Default: `[]` (empty list).

Exact same semantics as `imports`, but can be used as a means to have different import categories.

The modules in this setting are imported after the modules in `imports`.

#### `worker_deduplicate_successful_tasks`

Added in version 5.1.

Default: False

Before each task execution, instruct the worker to check if this task is a duplicate message.

Deduplication occurs only with tasks that have the same identifier, enabled late acknowledgment, were redelivered by the message broker and their state is `SUCCESS` in the result backend.

To avoid overflowing the result backend with queries, a local cache of successfully executed tasks is checked before querying the result backend in case the task was already successfully executed by the same worker that received the task.

This cache can be made persistent by setting the `worker_state_db` setting.

If the result backend is not persistent (the RPC backend, for example), this setting is ignored.

#### `worker_concurrency`

Default: Number of CPU cores.

The number of concurrent worker processes/threads/green threads executing tasks.

If you’re doing mostly I/O you can have more processes, but if mostly CPU-bound, try to keep it close to the number of CPUs on your machine. If not set, the number of CPUs/cores on the host will be used.

#### `worker_prefetch_multiplier`

Default: 4.

How many messages to prefetch at a time multiplied by the number of concurrent processes. The default is 4 (four messages for each process). The default setting is usually a good choice, however – if you have very long running tasks waiting in the queue and you have to start the workers, note that the first worker to start will receive four times the number of messages initially. Thus the tasks may not be fairly distributed to the workers.

To limit the broker to only deliver one message per process at a time, set `worker_prefetch_multiplier` to 1. Changing that setting to 0 will allow the worker to keep consuming as many messages as it wants.

If you need to completely disable broker prefetching while still using early acknowledgments, enable `worker_disable_prefetch`. When this option is enabled the worker only fetches a task from the broker when one of its processes is available.

Note

This feature is currently only supported when using Redis as the broker.

You can also enable this via the `--disable-prefetch` command line flag.

For more on prefetching, read Prefetch Limits

#### `worker_eta_task_limit`

Added in version 5.6.

Default: No limit (None).

The maximum number of ETA/countdown tasks that a worker can hold in memory at once. When this limit is reached, the worker will not receive new tasks from the broker until some of the existing ETA tasks are executed.

This setting helps prevent memory exhaustion when a queue contains a large number of tasks with ETA/countdown values, as these tasks are held in memory until their execution time. Without this limit, workers may fetch thousands of ETA tasks into memory, potentially causing out-of-memory issues.

Note

Tasks with ETA/countdown are fetched into memory and scheduled on an internal timer, so they are not constrained by the per-process prefetch window derived from `worker_prefetch_multiplier` in the same way as immediately executed tasks. This is why `--prefetch-multiplier=1` can appear to have no effect when many ETA/countdown tasks are present.

`worker_eta_task_limit` configures the maximum number of ETA/countdown tasks a worker will hold in memory and also sets an overall cap on unacknowledged messages via kombu’s QoS `max_prefetch`. If the prefetch count implied by `worker_prefetch_multiplier` would exceed this cap, the worker will stop consuming new messages until previously received tasks have been acknowledged.

#### `worker_disable_prefetch`

Added in version 5.6.

Default: `False`.

When enabled, a worker will only consume messages from the broker when it has an available process to execute them. This disables prefetching while still using early acknowledgments, ensuring that tasks are fairly distributed between workers.

Note

This feature is currently only supported when using Redis as the broker. Using this setting with other brokers will result in a warning and the setting will be ignored.

#### `worker_enable_prefetch_count_reduction`

Added in version 5.4.

Default: Enabled.

The `worker_enable_prefetch_count_reduction` setting governs the restoration behavior of the prefetch count to its maximum allowable value following a connection loss to the message broker. By default, this setting is enabled.

Upon a connection loss, Celery will attempt to reconnect to the broker automatically, provided the `broker_connection_retry_on_startup` or `broker_connection_retry` is not set to False. During the period of lost connection, the message broker does not keep track of the number of tasks already fetched. Therefore, to manage the task load effectively and prevent overloading, Celery reduces the prefetch count based on the number of tasks that are currently running.

The prefetch count is the number of messages that a worker will fetch from the broker at a time. The reduced prefetch count helps ensure that tasks are not fetched excessively during periods of reconnection.

With `worker_enable_prefetch_count_reduction` set to its default value (Enabled), the prefetch count will be gradually restored to its maximum allowed value each time a task that was running before the connection was lost is completed. This behavior helps maintain a balanced distribution of tasks among the workers while managing the load effectively.

To disable the reduction and restoration of the prefetch count to its maximum allowed value on reconnection, set `worker_enable_prefetch_count_reduction` to False. Disabling this setting might be useful in scenarios where a fixed prefetch count is desired to control the rate of task processing or manage the worker load, especially in environments with fluctuating connectivity.

The `worker_enable_prefetch_count_reduction` setting provides a way to control the restoration behavior of the prefetch count following a connection loss, aiding in maintaining a balanced task distribution and effective load management across the workers.

#### `worker_lost_wait`

Default: 10.0 seconds.

In some cases a worker may be killed without proper cleanup, and the worker may have published a result before terminating. This value specifies how long we wait for any missing results before raising a `WorkerLostError` exception.

#### `worker_max_tasks_per_child`

Maximum number of tasks a pool worker process can execute before it’s replaced with a new one. Default is no limit.

#### `worker_max_memory_per_child`

Default: No limit. Type: int (kilobytes)

Maximum amount of resident memory, in kilobytes (1024 bytes), that may be consumed by a worker before it will be replaced by a new worker. If a single task causes a worker to exceed this limit, the task will be completed, and the worker will be replaced afterwards.

Example:

    worker_max_memory_per_child = 12288  # 12 * 1024 = 12 MB

#### `worker_disable_rate_limits`

Default: Disabled (rate limits enabled).

Disable all rate limits, even if tasks has explicit rate limits set.

#### `worker_state_db`

Default: `None`.

Name of the file used to stores persistent worker state (like revoked tasks). Can be a relative or absolute path, but be aware that the suffix .db may be appended to the file name (depending on Python version).

Can also be set via the `celery worker --statedb` argument.

#### `worker_timer_precision`

Default: 1.0 seconds.

Set the maximum time in seconds that the ETA scheduler can sleep between rechecking the schedule.

Setting this value to 1 second means the schedulers precision will be 1 second. If you need near millisecond precision you can set this to 0.1.

#### `worker_enable_remote_control`

Default: Enabled by default.

Specify if remote control of the workers is enabled.

#### `worker_proc_alive_timeout`

Default: 4.0.

The timeout in seconds (int/float) when waiting for a new worker process to start up.

#### `worker_cancel_long_running_tasks_on_connection_loss`

Added in version 5.1.

Default: Disabled by default.

Kill all long-running tasks with late acknowledgment enabled on connection loss.

Tasks which have not been acknowledged before the connection loss cannot do so anymore since their channel is gone and the task is redelivered back to the queue. This is why tasks with late acknowledged enabled must be idempotent as they may be executed more than once. In this case, the task is being executed twice per connection loss (and sometimes in parallel in other workers).

When turning this option on, those tasks which have not been completed are cancelled and their execution is terminated. Tasks which have completed in any way before the connection loss are recorded as such in the result backend as long as `task_ignore_result` is not enabled.

Warning

This feature was introduced as a future breaking change. If it is turned off, Celery will emit a warning message.

In Celery 6.0, the `worker_cancel_long_running_tasks_on_connection_loss` will be set to `True` by default as the current behavior leads to more problems than it solves.

#### `worker_detect_quorum_queues`

Added in version 5.5.

Default: Enabled.

Automatically detect if any of the queues in `task_queues` are quorum queues (including the `task_default_queue`) and disable the global QoS if any quorum queue is detected.

#### `worker_soft_shutdown_timeout`

Added in version 5.5.

Default: 0.0.

The standard warm shutdown will wait for all tasks to finish before shutting down unless the cold shutdown is triggered. The soft shutdown will add a waiting time before the cold shutdown is initiated. This setting specifies how long the worker will wait before the cold shutdown is initiated and the worker is terminated.

This will apply also when the worker initiate cold shutdown without doing a warm shutdown first.

If the value is set to 0.0, the soft shutdown will be practically disabled. Regardless of the value, the soft shutdown will be disabled if there are no tasks running (unless `worker_enable_soft_shutdown_on_idle` is enabled).

Experiment with this value to find the optimal time for your tasks to finish gracefully before the worker is terminated. Recommended values can be 10, 30, 60 seconds. Too high value can lead to a long waiting time before the worker is terminated and trigger a `KILL` signal to forcefully terminate the worker by the host system.

#### `worker_enable_soft_shutdown_on_idle`

Added in version 5.5.

Default: False.

If the `worker_soft_shutdown_timeout` is set to a value greater than 0.0, the worker will skip the soft shutdown anyways if there are no tasks running. This setting will enable the soft shutdown even if there are no tasks running.

Tip

When the worker received ETA tasks, but the ETA has not been reached yet, and a shutdown is initiated, the worker will skip the soft shutdown and initiate the cold shutdown immediately if there are no tasks running. This may lead to failure in re-queueing the ETA tasks during worker teardown. To mitigate this, enable this configuration to ensure the worker waits regadless, which gives enough time for a graceful shutdown and successful re-queueing of the ETA tasks.

### Events

#### `worker_send_task_events`

Default: Disabled by default.

Send task-related events so that tasks can be monitored using tools like flower. Sets the default value for the workers `-E` argument.

#### `task_send_sent_event`

Added in version 2.2.

Default: Disabled by default.

If enabled, a `task-sent` event will be sent for every task so tasks can be tracked before they’re consumed by a worker.

#### `event_queue_ttl`

transports supported:

  `amqp`

Default: 5.0 seconds.

Message expiry time in seconds (int/float) for when messages sent to a monitor clients event queue is deleted (`x-message-ttl`)

For example, if this value is set to 10 then a message delivered to this queue will be deleted after 10 seconds.

#### `event_queue_expires`

transports supported:

  `amqp`

Default: 60.0 seconds.

Expiry time in seconds (int/float) for when after a monitor clients event queue will be deleted (`x-expires`).

#### `event_queue_durable`

transports supported:

  `amqp`

Added in version 5.6.

Default: `False`

If enabled, the event receiver’s queue will be marked as durable, meaning it will survive broker restarts.

#### `event_queue_exclusive`

transports supported:

  `amqp`

Added in version 5.6.

Default: `False`

If enabled, the event queue will be exclusive to the current connection and automatically deleted when the connection closes.

Warning

You cannot set both `event_queue_durable` and `event_queue_exclusive` to `True` at the same time. Celery will raise an `ImproperlyConfigured` error if both are set.

#### `event_queue_prefix`

Default: `"celeryev"`.

The prefix to use for event receiver queue names.

#### `event_exchange`

Default: `"celeryev"`.

Name of the event exchange.

Warning

This option is in experimental stage, please use it with caution.

#### `event_serializer`

Default: `"json"`.

Message serialization format used when sending event messages.

See also

Serializers.

#### `events_logfile`

Added in version 5.4.

Default: `None`

An optional file path for celery events to log into (defaults to stdout).

#### `events_pidfile`

Added in version 5.4.

Default: `None`

An optional file path for celery events to create/store its PID file (default to no PID file created).

#### `events_uid`

Added in version 5.4.

Default: `None`

An optional user ID to use when events celery events drops its privileges (defaults to no UID change).

#### `events_gid`

Added in version 5.4.

Default: `None`

An optional group ID to use when celery events daemon drops its privileges (defaults to no GID change).

#### `events_umask`

Added in version 5.4.

Default: `None`

An optional umask to use when celery events creates files (log, pid…) when daemonizing.

#### `events_executable`

Added in version 5.4.

Default: `None`

An optional python executable path for celery events to use when deaemonizing (defaults to `sys.executable`).

### Remote Control Commands

Note

To disable remote control commands see the `worker_enable_remote_control` setting.

#### `control_queue_ttl`

Default: 300.0

Time in seconds, before a message in a remote control command queue will expire.

If using the default of 300 seconds, this means that if a remote control command is sent and no worker picks it up within 300 seconds, the command is discarded.

This setting also applies to remote control reply queues.

#### `control_queue_expires`

Default: 10.0

Time in seconds, before an unused remote control command queue is deleted from the broker.

This setting also applies to remote control reply queues.

#### `control_exchange`

Default: `"celery"`.

Name of the control command exchange.

Warning

This option is in experimental stage, please use it with caution.

### `control_queue_durable`

- Default: `False`

- Type: `bool`

If set to `True`, the control exchange and queue will be durable — they will survive broker restarts.

### `control_queue_exclusive`

- Default: `False`

- Type: `bool`

If set to `True`, the control queue will be exclusive to a single connection. This is generally not recommended in distributed environments.

Warning

Setting both `control_queue_durable` and `control_queue_exclusive` to `True` is not supported and will raise an error.

### Logging

#### `worker_hijack_root_logger`

Added in version 2.2.

Default: Enabled by default (hijack root logger).

By default any previously configured handlers on the root logger will be removed. If you want to customize your own logging handlers, then you can disable this behavior by setting worker_hijack_root_logger = False.

Note

Logging can also be customized by connecting to the `celery.signals.setup_logging` signal.

#### `worker_log_color`

Default: Enabled if app is logging to a terminal.

Enables/disables colors in logging output by the Celery apps.

#### `worker_log_format`

Default:

    "[%(asctime)s: %(levelname)s/%(processName)s] %(message)s"

The format to use for log messages.

See the Python `logging` module for more information about log formats.

#### `worker_task_log_format`

Default:

    "[%(asctime)s: %(levelname)s/%(processName)s]
        %(task_name)s[%(task_id)s]: %(message)s"

The format to use for log messages logged in tasks.

See the Python `logging` module for more information about log formats.

#### `worker_redirect_stdouts`

Default: Enabled by default.

If enabled stdout and stderr will be redirected to the current logger.

Used by celery worker and celery beat.

#### `worker_redirect_stdouts_level`

Default: `WARNING`.

The log level output to stdout and stderr is logged as. Can be one of `DEBUG`, `INFO`, `WARNING`, `ERROR`, or `CRITICAL`.

### Security

#### `security_key`

Default: `None`.

Added in version 2.5.

The relative or absolute path to a file containing the private key used to sign messages when Message Signing is used.

#### `security_key_password`

Default: `None`.

Added in version 5.3.0.

The password used to decrypt the private key when Message Signing is used.

#### `security_certificate`

Default: `None`.

Added in version 2.5.

The relative or absolute path to an X.509 certificate file used to sign messages when Message Signing is used.

#### `security_cert_store`

Default: `None`.

Added in version 2.5.

The directory containing X.509 certificates used for Message Signing. Can be a glob with wild-cards, (for example `/etc/certs/*.pem`).

#### `security_digest`

Default: `sha256`.

Added in version 4.3.

A cryptography digest used to sign messages when Message Signing is used. https://cryptography.io/en/latest/hazmat/primitives/cryptographic-hashes/#module-cryptography.hazmat.primitives.hashes

### Custom Component Classes (advanced)

#### `worker_pool`

Default: `"prefork"` (`celery.concurrency.prefork:TaskPool`).

Name of the pool class used by the worker.

Eventlet/Gevent

Never use this option to select the eventlet or gevent pool. You must use the `-P` option to celery worker instead, to ensure the monkey patches aren’t applied too late, causing things to break in strange ways.

#### `worker_pool_restarts`

Default: Disabled by default.

If enabled the worker pool can be restarted using the `pool_restart` remote control command.

#### `worker_autoscaler`

Added in version 2.2.

Default: `"celery.worker.autoscale:Autoscaler"`.

Name of the autoscaler class to use.

#### `worker_consumer`

Default: `"celery.worker.consumer:Consumer"`.

Name of the consumer class used by the worker.

#### `worker_timer`

Default: `"kombu.asynchronous.hub.timer:Timer"`.

Name of the ETA scheduler class used by the worker. Default is or set by the pool implementation.

#### `worker_logfile`

Added in version 5.4.

Default: `None`

An optional file path for celery worker to log into (defaults to stdout).

#### `worker_pidfile`

Added in version 5.4.

Default: `None`

An optional file path for celery worker to create/store its PID file (defaults to no PID file created).

#### `worker_uid`

Added in version 5.4.

Default: `None`

An optional user ID to use when celery worker daemon drops its privileges (defaults to no UID change).

#### `worker_gid`

Added in version 5.4.

Default: `None`

An optional group ID to use when celery worker daemon drops its privileges (defaults to no GID change).

#### `worker_umask`

Added in version 5.4.

Default: `None`

An optional umask to use when celery worker creates files (log, pid…) when daemonizing.

#### `worker_executable`

Added in version 5.4.

Default: `None`

An optional python executable path for celery worker to use when deaemonizing (defaults to `sys.executable`).

### Beat Settings (celery beat)

#### `beat_schedule`

Default: `{}` (empty mapping).

The periodic task schedule used by `beat`. See Entries.

#### `beat_scheduler`

Default: `"celery.beat:PersistentScheduler"`.

The default scheduler class. May be set to `"django_celery_beat.schedulers:DatabaseScheduler"` for instance, if used alongside https://pypi.org/project/django-celery-beat/ extension.

Can also be set via the `celery beat -S` argument.

#### `beat_schedule_filename`

Default: `"celerybeat-schedule"`.

Name of the file used by PersistentScheduler to store the last run times of periodic tasks. Can be a relative or absolute path, but be aware that the suffix .db may be appended to the file name (depending on Python version).

Can also be set via the `celery beat --schedule` argument.

#### `beat_sync_every`

Default: 0.

The number of periodic tasks that can be called before another database sync is issued. A value of 0 (default) means sync based on timing - default of 3 minutes as determined by scheduler.sync_every. If set to 1, beat will call sync after every task message sent.

#### `beat_max_loop_interval`

Default: 0.

The maximum number of seconds `beat` can sleep between checking the schedule.

The default for this value is scheduler specific. For the default Celery beat scheduler the value is 300 (5 minutes), but for the https://pypi.org/project/django-celery-beat/ database scheduler it’s 5 seconds because the schedule may be changed externally, and so it must take changes to the schedule into account.

Also when running Celery beat embedded (`-B`) on Jython as a thread the max interval is overridden and set to 1 so that it’s possible to shut down in a timely manner.

#### `beat_cron_starting_deadline`

Added in version 5.3.

Default: None.

When using cron, the number of seconds `beat` can look back when deciding whether a cron schedule is due. When set to None, cronjobs that are past due will always run immediately.

Warning

Setting this higher than 3600 (1 hour) is highly discouraged.

#### `beat_logfile`

Added in version 5.4.

Default: `None`

An optional file path for celery beat to log into (defaults to stdout).

#### `beat_pidfile`

Added in version 5.4.

Default: `None`

An optional file path for celery beat to create/store it PID file (defaults to no PID file created).

#### `beat_uid`

Added in version 5.4.

Default: `None`

An optional user ID to use when beat celery beat drops its privileges (defaults to no UID change).

#### `beat_gid`

Added in version 5.4.

Default: `None`

An optional group ID to use when celery beat daemon drops its privileges (defaults to no GID change).

#### `beat_umask`

Added in version 5.4.

Default: `None`

An optional umask to use when celery beat creates files (log, pid…) when daemonizing.

#### `beat_executable`

Added in version 5.4.

Default: `None`

An optional python executable path for celery beat to use when deaemonizing (defaults to `sys.executable`).