Workers

A worker is used to deploy operators at scale. All the worker files can be found at src/worker/ folder.

Video Worker

This is a test worker that begins a Rabbit MQ queue, video operator is ran on the file and the output vector is stored in elasticsearch

Getting the worker running

1. Modify the docker-compose.yml file to include a container for the worker and add the venv volume.

  worker:
    container_name: feluda_worker
    build:
      context: ./src
      dockerfile: worker/vidvec/Dockerfile.video_worker
      target: production
      args:
        - "UID=${UID:-1000}"
        - "GID=${GID:-1000}"
    volumes:
      - ./src:/home/python/app/
      - venv:/home/python/app/venv/
    env_file: ./src/development.env
    command: tail -f /dev/null
    depends_on:
      store:
        condition: service_started
      queue:
        condition: service_started

volumes:
    venv: {}

For a pre-built ARM image, run the following command first and use the following docker compose file settings. Refer this

$ docker run --rm --privileged multiarch/qemu-user-static --reset -p yes

  worker:
    image: <built-arm-image>
    platform: linux/arm64
    container_name: feluda_worker
    volumes:
      - /usr/bin/qemu-aarch64-static:/usr/bin/qemu-aarch64-static
    env_file: ./src/development.env
    command: tail -f /dev/null
    depends_on:
      store:
        condition: service_started
      queue:
        condition: service_started

2. Start the docker container

docker-compose up store queue worker

3. Exec into the feluda_worker container and install relevant python libraries

docker exec --user python -it feluda_worker /bin/sh

Note

You can now run all the tests inside the worker, apart from those requiring python server.py and tests for other operators. Follow the instructions listed here.

4. Run the worker
   Make sure you are in the /app folder in the docker container. Then run the worker/vidvec/video_worker.py file using the following command :

python -m worker.vidvec.video_worker

Keep the worker running and in a new terminal run the video_payload_writer script, that sends payload(containing the media urls) to the worker

python -m worker.vidvec.video_payload_writer

Manual Dev Testing of RabbitMQ Disconnecting

• To test if the worker and try reconnecting to RabbitMQ when MQ crashes, follow the below steps.

1. Bring up the docker containers individually.

docker-compose up -d store
docker-compose up -d queue
docker-compose up -d worker

2. Run the worker

docker exec --user python -it feluda_worker /bin/sh
python -m worker.vidvec.video_worker

3. Run the writer

docker exec --user python -it feluda_worker /bin/sh
python -m worker.vidvec.video_payload_writer

4. The writer will add 15 messages to the queue, which the worker processing serially. While this processing is happening, you should bring down the queue container to stop RabbitMQ

docker-compose up -d --scale queue=0

Check the worker logs, it will show an disconnection error and try reconnecting.

5. To bring the queue docker container back up

docker-compose up -d --scale queue=1

Now the worker should reconnect to RabbitMQ and start consuming messages where it left off.

Related Links for Video Worker

1. Github actions
2. Images on Dockerhub

Audio Worker

This is a test worker that begins a Rabbit MQ queue, audio operator is ran on the file and the output vector is stored in elasticsearch

Getting the worker running

1. Modify the docker-compose.yml file to include a container for the worker.

  worker:
    container_name: feluda_worker
    build:
      context: ./src
      dockerfile: worker/audiovec/Dockerfile.audio_worker
      target: production
      args:
        - "UID=${UID:-1000}"
        - "GID=${GID:-1000}"
    volumes:
      - ./src:/home/python/app/
      - venv:/home/python/app/venv/
    env_file: ./src/development.env
    command: tail -f /dev/null
    depends_on:
      store:
        condition: service_started
      queue:
        condition: service_started

volumes:
    venv: {}

2. Start the docker container

docker-compose up store queue worker

3. Exec into the feluda_worker container and install relevant python libraries

docker exec --user python -it feluda_worker /bin/sh

Note

You can now run all the tests inside the worker, apart from those requiring python server.py and tests for other operators. Follow the instructions listed here.

4. Run the worker
   Make sure you are in the /app folder in the docker container. Then run the worker/audiovec/audio_worker.py file using the following command :

python -m worker.audiovec.audio_worker

5. Keep the worker running and in a new terminal run the audio_payload_writer script, that sends payload(containing the media urls) to the worker

python -m worker.audiovec.audio_payload_writer

Manual Dev Testing of RabbitMQ Disconnecting

Follow similar steps as the Video Wokrer listed here

Hash Worker

1. Modify the docker-compose.yml file to include a container for the worker and postgres.

  worker:
    container_name: feluda_worker
    build:
      context: ./src
      dockerfile: worker/hash/Dockerfile.hash_worker
      target: production
      args:
        - "UID=${UID:-1000}"
        - "GID=${GID:-1000}"
    volumes:
      - ./src:/home/python/app/
      - venv:/home/python/app/venv/
    env_file: ./src/development.env
    command: tail -f /dev/null
    depends_on:
      store:
        condition: service_started
      queue:
        condition: service_started

  postgres:
    container_name: postgres
    image: postgres:latest
    volumes:
      - ./data:/var/lib/postgresql/data
    environment:
      POSTGRES_USER: "tattle"
      POSTGRES_PASSWORD: "tattle_pw"
      POSTGRES_DB: "tattle_db"
    ports:
      - "5432:5432"

  pgadmin:
    container_name: pgadmin
    image: dpage/pgadmin4:latest
    environment:
      PGADMIN_DEFAULT_EMAIL: [email protected]
      PGADMIN_DEFAULT_PASSWORD: adminpassword
    ports:
      - "5050:80"
    volumes:
      - pgadmin_data:/var/lib/pgadmin
    depends_on:
      - postgres
    restart: always

volumes:
  pgadmin_data: