11 Running tasks in containers

 

This chapter covers

  • Identifying some challenges involved in managing Airflow deployments
  • Examining how containerized approaches can help simplify Airflow deployments
  • Running containerized tasks in Airflow on Docker
  • Establishing a high-level overview of workflows in developing containerized DAGs

Previously, we implemented several DAGs using different Airflow operators, each specialized to perform a specific type of task. Here, we touch on some of the drawbacks of using many different operators, especially with an eye on creating Airflow DAGs that are easy to build, deploy, and maintain. In light of these issues, we look at how we can use Airflow to run tasks in containers using Docker and Kubernetes and some of the benefits this containerized approach can bring.

11.1 Challenges of many different operators

Operators are arguably one of the strong features of Airflow, as they provide great flexibility to coordinate jobs across many different types of systems. However, creating and managing DAGs with many different operators can be quite challenging due to the complexity involved.

11.1.1 Operator interfaces and implementations

11.1.2 Complex and conflicting dependencies

11.1.3 Moving toward a generic operator

11.2 Introducing containers

11.2.1 What are containers?

11.2.2 Running our first Docker container

11.2.3 Creating a Docker image

11.2.4 Persisting data using volumes

11.3 Containers and Airflow

11.3.1 Tasks in containers

11.3.2 Why use containers?

11.4 Running tasks in Docker

11.4.1 Introducing the DockerOperator

11.4.2 Creating container images for tasks

11.4.3 Building a DAG with Docker tasks

11.4.4 Docker-based workflow

11.5 Running tasks in Kubernetes

11.5.1 Introducing Kubernetes

11.5.2 Setting up Kubernetes

11.5.3 Using the KubernetesPodOperator

11.5.4 Diagnosing Kubernetes-related issues

11.5.5 Differences with Docker-based workflows

11.6 Summary