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Async Queries via Celery

Celery

On large analytic databases, it’s common to run queries that execute for minutes or hours. To enable support for long running queries that execute beyond the typical web request’s timeout (30-60 seconds), it is necessary to configure an asynchronous backend for Superset which consists of:

  • one or many Superset workers (which is implemented as a Celery worker), and can be started with the celery worker command, run celery worker --help to view the related options.
  • a celery broker (message queue) for which we recommend using Redis or RabbitMQ
  • a results backend that defines where the worker will persist the query results

Configuring Celery requires defining a CELERY_CONFIG in your superset_config.py. Both the worker and web server processes should have the same configuration.

class CeleryConfig(object):
    BROKER_URL = 'redis://localhost:6379/0'
    CELERY_IMPORTS = (
        'superset.sql_lab',
        'superset.tasks',
    )
    CELERY_RESULT_BACKEND = 'redis://localhost:6379/0'
    CELERYD_LOG_LEVEL = 'DEBUG'
    CELERYD_PREFETCH_MULTIPLIER = 10
    CELERY_ACKS_LATE = True
    CELERY_ANNOTATIONS = {
        'sql_lab.get_sql_results': {
            'rate_limit': '100/s',
        },
        'email_reports.send': {
            'rate_limit': '1/s',
            'time_limit': 120,
            'soft_time_limit': 150,
            'ignore_result': True,
        },
    }
    CELERYBEAT_SCHEDULE = {
        'email_reports.schedule_hourly': {
            'task': 'email_reports.schedule_hourly',
            'schedule': crontab(minute=1, hour='*'),
        },
    }

CELERY_CONFIG = CeleryConfig

To start a Celery worker to leverage the configuration, run the following command:

celery --app=superset.tasks.celery_app:app worker --pool=prefork -O fair -c 4

To start a job which schedules periodic background jobs, run the following command:

celery --app=superset.tasks.celery_app:app beat

To setup a result backend, you need to pass an instance of a derivative of from cachelib.base.BaseCache to the RESULTS_BACKEND configuration key in your superset_config.py. You can use Memcached, Redis, S3 (https://pypi.python.org/pypi/s3werkzeugcache), memory or the file system (in a single server-type setup or for testing), or to write your own caching interface. Your superset_config.py may look something like:

# On S3
from s3cache.s3cache import S3Cache
S3_CACHE_BUCKET = 'foobar-superset'
S3_CACHE_KEY_PREFIX = 'sql_lab_result'
RESULTS_BACKEND = S3Cache(S3_CACHE_BUCKET, S3_CACHE_KEY_PREFIX)

# On Redis
from cachelib.redis import RedisCache
RESULTS_BACKEND = RedisCache(
    host='localhost', port=6379, key_prefix='superset_results')

For performance gains, MessagePack and PyArrow are now used for results serialization. This can be disabled by setting RESULTS_BACKEND_USE_MSGPACK = False in your superset_config.py, should any issues arise. Please clear your existing results cache store when upgrading an existing environment.

Important Notes

  • It is important that all the worker nodes and web servers in the Superset cluster share a common metadata database. This means that SQLite will not work in this context since it has limited support for concurrency and typically lives on the local file system.

  • There should only be one instance of celery beat running in your entire setup. If not, background jobs can get scheduled multiple times resulting in weird behaviors like duplicate delivery of reports, higher than expected load / traffic etc.

  • SQL Lab will only run your queries asynchronously if you enable Asynchronous Query Execution in your database settings (Sources > Databases > Edit record).

Celery Flower

Flower is a web based tool for monitoring the Celery cluster which you can install from pip:

pip install flower

You can run flower using:

celery --app=superset.tasks.celery_app:app flower