Airflow kubernetes github software#
Airflow kubernetes github code#

Also, configuration information specific to the Kubernetes Executor, such as the worker namespace and image information, needs to be specified in the Airflow Configuration file.Īdditionally, the Kubernetes Executor enables specification of additional features on a per-task basis using the Executor config. One example of an Airflow deployment running on a distributed set of five nodes in a Kubernetes cluster is shown below.Ĭonsistent with the regular Airflow architecture, the Workers need access to the DAG files to execute the tasks within those DAGs and interact with the Metadata repository. The worker pod then runs the task, reports the result, and terminates. When a DAG submits a task, the KubernetesExecutor requests a worker pod from the Kubernetes API. KubernetesExecutor requires a non-sqlite database in the backend. Not necessarily need to be running on Kubernetes, but does need access to a Kubernetes cluster. KubernetesExecutor runs as a process in the Airflow Scheduler. The Kubernetes executor runs each task instance in its own pod on a Kubernetes cluster. But What About Cases Where the Scheduler Pod Crashes?.Debugging Airflow DAGs on the command line.Airflow has a broader approval, being mentioned in 172 company stacks & 523 developers stacks compared to Github Actions, which is listed in 31 company stacks and 30 developer stacks. Here's a link to Airflow's open source repository on GitHub.Īirbnb, Slack, and Robinhood are some of the popular companies that use Airflow, whereas Github Actions is used by Craftbase, Rainist, and Walls.io. On the other hand, Github Actions provides the following key features:Īirflow is an open source tool with 17K GitHub stars and 6.58K GitHub forks. Parameterizing your scripts is built in the core of Airflow using powerful Jinja templating engine. Elegant: Airflow pipelines are lean and explicit.Extensible: Easily define your own operators, executors and extend the library so that it fits the level of abstraction that suits your environment.

Airflow kubernetes github code#

This allows for writting code that instantiate pipelines dynamically. Dynamic: Airflow pipelines are configuration as code (Python), allowing for dynamic pipeline generation.Some of the features offered by Airflow are: Make code reviews, branch management, and issue triaging work the way you want.Īirflow and Github Actions can be primarily classified as 'Workflow Manager' tools. Build, test, and deploy your code right from GitHub.

Airflow kubernetes github software#

It makes it easy to automate all your software workflows, now with world-class CI/CD. The rich user interface makes it easy to visualize pipelines running in production, monitor progress and troubleshoot issues when needed Github Actions: Automate your workflow from idea to production. Rich command lines utilities makes performing complex surgeries on DAGs a snap. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Use Airflow to author workflows as directed acyclic graphs (DAGs) of tasks. Airflow vs Github Actions: What are the differences?Īirflow: A platform to programmaticaly author, schedule and monitor data pipelines, by Airbnb.