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To enable\nsupport for long running queries that execute beyond the typical web request’s timeout (30-60\nseconds), it is necessary to configure an asynchronous backend for Superset which consists of:`}</p>\n    <ul>\n\n      <li {...{\n        \"parentName\": \"ul\"\n      }}>{`one or many Superset workers (which is implemented as a Celery worker), and can be started with\nthe `}\n        <inlineCode {...{\n          \"parentName\": \"li\"\n        }}>{`celery worker`}</inlineCode>\n        {` command, run `}\n        <inlineCode {...{\n          \"parentName\": \"li\"\n        }}>{`celery worker --help`}</inlineCode>\n        {` to view the related options.`}</li>\n\n\n      <li {...{\n        \"parentName\": \"ul\"\n      }}>{`a celery broker (message queue) for which we recommend using Redis or RabbitMQ`}</li>\n\n\n      <li {...{\n        \"parentName\": \"ul\"\n      }}>{`a results backend that defines where the worker will persist the query results`}</li>\n\n    </ul>\n    <p>{`Configuring Celery requires defining a `}\n      <inlineCode {...{\n        \"parentName\": \"p\"\n      }}>{`CELERY_CONFIG`}</inlineCode>\n      {` in your `}\n      <inlineCode {...{\n        \"parentName\": \"p\"\n      }}>{`superset_config.py`}</inlineCode>\n      {`. Both the worker\nand web server processes should have the same configuration.`}</p>\n    <pre><code {...{\n        \"className\": \"language-python\",\n        \"parentName\": \"pre\"\n      }}>{`class CeleryConfig(object):\n    BROKER_URL = 'redis://localhost:6379/0'\n    CELERY_IMPORTS = (\n        'superset.sql_lab',\n        'superset.tasks',\n    )\n    CELERY_RESULT_BACKEND = 'redis://localhost:6379/0'\n    CELERYD_LOG_LEVEL = 'DEBUG'\n    CELERYD_PREFETCH_MULTIPLIER = 10\n    CELERY_ACKS_LATE = True\n    CELERY_ANNOTATIONS = {\n        'sql_lab.get_sql_results': {\n            'rate_limit': '100/s',\n        },\n        'email_reports.send': {\n            'rate_limit': '1/s',\n            'time_limit': 120,\n            'soft_time_limit': 150,\n            'ignore_result': True,\n        },\n    }\n    CELERYBEAT_SCHEDULE = {\n        'email_reports.schedule_hourly': {\n            'task': 'email_reports.schedule_hourly',\n            'schedule': crontab(minute=1, hour='*'),\n        },\n    }\n\nCELERY_CONFIG = CeleryConfig\n`}</code></pre>\n    <p>{`To start a Celery worker to leverage the configuration, run the following command:`}</p>\n    <pre><code {...{\n        \"parentName\": \"pre\"\n      }}>{`celery worker --app=superset.tasks.celery_app:app --pool=prefork -O fair -c 4\n`}</code></pre>\n    <p>{`To start a job which schedules periodic background jobs, run the following command:`}</p>\n    <pre><code {...{\n        \"parentName\": \"pre\"\n      }}>{`celery beat --app=superset.tasks.celery_app:app\n`}</code></pre>\n    <p>{`To setup a result backend, you need to pass an instance of a derivative of from\ncachelib.base.BaseCache to the RESULTS_BACKEND configuration key in your superset_config.py. You can\nuse Memcached, Redis, S3 (`}\n      <a {...{\n        \"href\": \"https://pypi.python.org/pypi/s3werkzeugcache\",\n        \"parentName\": \"p\"\n      }}>{`https://pypi.python.org/pypi/s3werkzeugcache`}</a>\n      {`), memory or the file system\n(in a single server-type setup or for testing), or to write your own caching interface. Your\n`}\n      <inlineCode {...{\n        \"parentName\": \"p\"\n      }}>{`superset_config.py`}</inlineCode>\n      {` may look something like:`}</p>\n    <pre><code {...{\n        \"className\": \"language-python\",\n        \"parentName\": \"pre\"\n      }}>{`# On S3\nfrom s3cache.s3cache import S3Cache\nS3_CACHE_BUCKET = 'foobar-superset'\nS3_CACHE_KEY_PREFIX = 'sql_lab_result'\nRESULTS_BACKEND = S3Cache(S3_CACHE_BUCKET, S3_CACHE_KEY_PREFIX)\n\n# On Redis\nfrom cachelib.redis import RedisCache\nRESULTS_BACKEND = RedisCache(\n    host='localhost', port=6379, key_prefix='superset_results')\n`}</code></pre>\n    <p>{`For performance gains, `}\n      <a {...{\n        \"href\": \"https://github.com/msgpack/msgpack-python\",\n        \"parentName\": \"p\"\n      }}>{`MessagePack`}</a>\n      {` and\n`}\n      <a {...{\n        \"href\": \"https://arrow.apache.org/docs/python/\",\n        \"parentName\": \"p\"\n      }}>{`PyArrow`}</a>\n      {` are now used for results serialization. This can be\ndisabled by setting `}\n      <inlineCode {...{\n        \"parentName\": \"p\"\n      }}>{`RESULTS_BACKEND_USE_MSGPACK = False`}</inlineCode>\n      {` in your `}\n      <inlineCode {...{\n        \"parentName\": \"p\"\n      }}>{`superset_config.py`}</inlineCode>\n      {`, should any\nissues arise. Please clear your existing results cache store when upgrading an existing environment.`}</p>\n    <p><strong {...{\n        \"parentName\": \"p\"\n      }}>{`Important Notes`}</strong></p>\n    <ul>\n\n      <li {...{\n        \"parentName\": \"ul\"\n      }}>\n\n        <p {...{\n          \"parentName\": \"li\"\n        }}>{`It is important that all the worker nodes and web servers in the Superset cluster `}\n          <em {...{\n            \"parentName\": \"p\"\n          }}>{`share a common\nmetadata database`}</em>\n          {`. This means that SQLite will not work in this context since it has limited\nsupport for concurrency and typically lives on the local file system.`}</p>\n\n      </li>\n\n\n      <li {...{\n        \"parentName\": \"ul\"\n      }}>\n\n        <p {...{\n          \"parentName\": \"li\"\n        }}>{`There should `}\n          <em {...{\n            \"parentName\": \"p\"\n          }}>{`only be one instance of celery beat running`}</em>\n          {` in your entire setup. If not,\nbackground jobs can get scheduled multiple times resulting in weird behaviors like duplicate\ndelivery of reports, higher than expected load / traffic etc.`}</p>\n\n      </li>\n\n\n      <li {...{\n        \"parentName\": \"ul\"\n      }}>\n\n        <p {...{\n          \"parentName\": \"li\"\n        }}>{`SQL Lab will `}\n          <em {...{\n            \"parentName\": \"p\"\n          }}>{`only run your queries asynchronously if`}</em>\n          {` you enable `}\n          <strong {...{\n            \"parentName\": \"p\"\n          }}>{`Asynchronous Query Execution`}</strong>\n          {`\nin your database settings (Sources > Databases > Edit record).`}</p>\n\n      </li>\n\n    </ul>\n    <h3 {...{\n      \"id\": \"celery-flower\"\n    }}>{`Celery Flower`}</h3>\n    <p>{`Flower is a web based tool for monitoring the Celery cluster which you can install from pip:`}</p>\n    <pre><code {...{\n        \"className\": \"language-python\",\n        \"parentName\": \"pre\"\n      }}>{`pip install flower\n`}</code></pre>\n    <p>{`You can run flower using:`}</p>\n    <pre><code {...{\n        \"parentName\": \"pre\"\n      }}>{`celery flower --app=superset.tasks.celery_app:app\n`}</code></pre>\n\n  </MDXLayout>;\n}\n;\nMDXContent.isMDXComponent = true;\n      "],"sourceRoot":""}