Migrate all analytics pipelines to Airflow

[momegas]

Since whitebox workflows become more and more complex, we need a way to orchestrate them. We can use the Airflow Python API to define and execute workflows. A workflow is defined as a directed acyclic graph (DAG) in Airflow. Each node in the DAG represents a task, and the edges between nodes represent dependencies between tasks.

The architecture should become roughly as follows:

sequenceDiagram

participant API as API
participant Database as Database
participant Airflow as Airflow

API ->> Database: Store data

loop Cron Workflows
    Database ->> Airflow: Extract data
    Airflow ->> Airflow: Analyze data
    Airflow ->> Database: Store result
end

Some implementation notes:

• Airflow should be a different deployment that the API. Approach it as a different service that runs all the workflows.
• It should use the same instance of SQL but have its own database as a database backend
• All current (and future) metrics calculations should happen inside airflow.

[momegas]

To start development, You can use this compose and dockerfile that I made for some local tests.
Just place your dags in a dags folder in the root of the projects (or change the path in the volumes of webserver to ./whitebox/dags:/usr/local/airflow/dags)

Dockerfile:

FROM puckel/docker-airflow:1.10.9
RUN pip install evidently==0.2.0  

Compose

version: "3.7"

services:
  postgres:
    image: postgres:9.6
    environment:
      - POSTGRES_USER=airflow
      - POSTGRES_PASSWORD=airflow
      - POSTGRES_DB=airflow
    logging:
      options:
        max-size: 10m
        max-file: "3"

  webserver:
    build: .
    restart: always
    depends_on:
      - postgres
    environment:
      - LOAD_EX=n
      - EXECUTOR=Local
    logging:
      options:
        max-size: 10m
        max-file: "3"
    volumes:
      - ./dags:/usr/local/airflow/dags
      - ./evidently_reports:/usr/local/airflow/reports
      - ./data:/usr/local/airflow/data
      - ./logs:/usr/local/airflow/logs
    ports:
      - "8080:8080"
    command: webserver
    healthcheck:
      test: ["CMD-SHELL", "[ -f /usr/local/airflow/airflow-webserver.pid ]"]
      interval: 30s
      timeout: 30s
      retries: 3
volumes:
  evidently_reports: