abhioncbr/docker-superset repository overview

⁠Unofficial Apache Superset Image, It's a personal work based on Apache Superset(https://superset.apache.org⁠)

An extensible, production-ready Docker image powered by Apache Superset⁠ (Debian+Python3). Easy in use for production as well as for exploration.

⁠Posts

• Running Apache Superset as a Docker Container⁠
• Apache Superset in the Production Environment⁠

⁠Repo

GitHub Repo⁠ Please give a star to the repo and raise the bug or feature request ticket if needed. Also, I welcome a contribution to the project.

⁠Supported tags and respective Dockerfile links

• 0.36.0
• 0.35.0, 0.35.1
• latest,0.32.0rc2⁠
• 0.29.0rc4, 0.29.0rc5,0.29.0rc7⁠
• 0.28.0, 0.28.1⁠

⁠Superset components stack

• GitHub Repo⁠
• Enhanced/Modified version of the docker container of apache-superset⁠.
• Superset version: Notation for representing version X.YY.ZZzzz which means either [0.29.0rc4] or [0.28.1] or [0.28.0]
• Backend database: MySQL
• SqlLabs query async mode: Celery
• Task queue & query cache: Redis

⁠Superset ports

• superset portal port: 8088
• superset celery flower: 5555

⁠Silent features of the docker image

• multiple ways to start a container, i.e. either by using docker-compose or by using docker run command.
• superset all components, i.e. web application, celery worker, celery flower UI can run in the same container or different containers.
• container first run sets required database along with examples and the Fabmanager user account with credentials username: admin & password: admin.
• superset config file i.e superset_config.py⁠ should be mounted to the container. No need to rebuild image for changing configurations.
• the default configuration uses MySQL as database and Redis as a cache & celery broker.
• starting container using docker-compose will start three containers. mysql5.7 as the database, redis3.4 as a cache & celery broker and superset container.
  • expects multiple environment variables defined in docker-compose.yml⁠ file. Among them, SUPERSET_ENV should be provided while starting the container.
  • permissible value of SUPERSET_ENV can be either local or prod.
  • in local mode one celery worker and superset flask-based superset web application run.
  • in prod mode two celery workers and Gunicorn based superset web application run.
• starting container using docker run can be a used for complete distributed setup, requires database & Redis URL for startup.
  • single or multiple server(using load balancer) container can be spawned. In the server, Gunicorn based superset web application runs.
  • multiple celery workers container running on same or different machines. In worker, celery worker & flower UI runs.

⁠How to build the image

• DockerFile⁠ uses superset-version as a build-arg, for example: 0.28.0 or 0.29.0rc4
• build image using docker build command

  docker build -t abhioncbr/docker-superset:<version-tag> --build-arg SUPERSET_VERSION=<superset-version> -f ~/docker-superset/docker-files/Dockerfile .
  

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⁠How to run

• General commands -

  • first pull a docker-superset image from docker-hub⁠

    docker pull abhioncbr/docker-superset:<tag>
    

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  • starting a superset image as a superset container in a local mode using docker-compose:

    cd docker-files/ && SUPERSET_ENV=local SUPERSET_VERSION=<version-tag> docker-compose up -d
    

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  • starting a superset image as a superset container in a prod mode using docker-compose:

    cd docker-files/ && SUPERSET_ENV=prod SUPERSET_VERSION=<version-tag> docker-compose up -d
    

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  • starting a superset image as a server container using docker run:

    docker run -p 8088:8088 -v config:/home/superset/config/ abhioncbr/docker-superset:<tag> cluster server <db_url> <redis_url>
    

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  • starting a superset image as a worker container using docker run:

    docker run -p 5555:5555 -v config:/home/superset/config/ abhioncbr/docker-superset:<tag> cluster worker <db_url> 
    <redis_url>
    

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⁠Distributed execution of superset

• As mentioned above, docker image of superset can be leveraged to run in complete distributed run
  • load-balancer in front for routing the request from the client to one server container.
  • multiple docker-superset container in server mode for serving the UI of the superset.
  • multiple docker-superset containers in worker mode for executing the SQL queries in an async mode using celery executor.
  • centralized Redis container or Redis-cluster for serving as cache layer and celery task queues for workers.
  • centralized superset metadata database.
• Image below depicts the docker-superset distributed platform: