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, runcelery 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"
imports = (
"superset.sql_lab",
"superset.tasks.scheduler",
)
result_backend = "redis://localhost:6379/0"
worker_prefetch_multiplier = 10
task_acks_late = True
task_annotations = {
"sql_lab.get_sql_results": {
"rate_limit": "100/s",
},
}
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
from flask_caching.backends.base import 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 flask_caching.backends.rediscache 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