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Airflow vs Github Actions: What are the differences?
Airflow: A platform to programmaticaly author, schedule and monitor data pipelines, by Airbnb. Use Airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command lines utilities makes performing complex surgeries on DAGs a snap. 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. It makes it easy to automate all your software workflows, now with world-class CI/CD. Build, test, and deploy your code right from GitHub. Make code reviews, branch management, and issue triaging work the way you want.
Airflow and Github Actions can be primarily classified as "Workflow Manager" tools.
Some of the features offered by Airflow are:
- Dynamic: Airflow pipelines are configuration as code (Python), allowing for dynamic pipeline generation. This allows for writting code that instantiate pipelines dynamically.
- Extensible: Easily define your own operators, executors and extend the library so that it fits the level of abstraction that suits your environment.
- Elegant: Airflow pipelines are lean and explicit. Parameterizing your scripts is built in the core of Airflow using powerful Jinja templating engine.
On the other hand, Github Actions provides the following key features:
- Multiple workflow files support
- Free and open source
- Workflow run interface
Airflow is an open source tool with 17K GitHub stars and 6.58K GitHub forks. Here's a link to Airflow's open source repository on GitHub.
Airbnb, Slack, and Robinhood are some of the popular companies that use Airflow, whereas Github Actions is used by Craftbase, Rainist, and Walls.io. 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.
I am so confused. I need a tool that will allow me to go to about 10 different URLs to get a list of objects. Those object lists will be hundreds or thousands in length. I then need to get detailed data lists about each object. Those detailed data lists can have hundreds of elements that could be map/reduced somehow. My batch process dies sometimes halfway through which means hours of processing gone, i.e. time wasted. I need something like a directed graph that will keep results of successful data collection and allow me either pragmatically or manually to retry the failed ones some way (0 - forever) times. I want it to then process all the ones that have succeeded or been effectively ignored and load the data store with the aggregation of some couple thousand data-points. I know hitting this many endpoints is not a good practice but I can't put collectors on all the endpoints or anything like that. It is pretty much the only way to get the data.
For a non-streaming approach:
You could consider using more checkpoints throughout your spark jobs. Furthermore, you could consider separating your workload into multiple jobs with an intermittent data store (suggesting cassandra or you may choose based on your choice and availability) to store results , perform aggregations and store results of those.
Spark Job 1 - Fetch Data From 10 URLs and store data and metadata in a data store (cassandra) Spark Job 2..n - Check data store for unprocessed items and continue the aggregation
Alternatively for a streaming approach: Treating your data as stream might be useful also. Spark Streaming allows you to utilize a checkpoint interval - https://spark.apache.org/docs/latest/streaming-programming-guide.html#checkpointing
Pros of Airflow
- Task Dependency Management14
- Beautiful UI12
- Cluster of workers12
- Open source6
- Complex workflows5
- Good api3
- Apache project3
- Custom operators3
Pros of GitHub Actions
- Integration with GitHub7
- Easy to duplicate a workflow3
- Ready actions in Marketplace3
- Configs stored in .github2
- Docker Support2
- Read actions in Marketplace2
- Active Development Roadmap1
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Cons of Airflow
- Running it on kubernetes cluster relatively complex2
- Open source - provides minimum or no support2
- Logical separation of DAGs is not straight forward1
- Observability is not great when the DAGs exceed 2501
Cons of GitHub Actions
- Lacking [skip ci]5
- Lacking allow failure4
- Lacking job specific badges3
- No ssh login to servers2
- No Deployment Projects1
- No manual launch1