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Airflow vs Amazon SWF: What are the differences?

What is 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.

What is Amazon SWF? Automate the coordination, auditing, and scaling of applications across multiple machines. Amazon Simple Workflow allows you to structure the various processing steps in an application that runs across one or more machines as a set of “tasks.” Amazon SWF manages dependencies between the tasks, schedules the tasks for execution, and runs any logic that needs to be executed in parallel. The service also stores the tasks, reliably dispatches them to application components, tracks their progress, and keeps their latest state.

Airflow belongs to "Workflow Manager" category of the tech stack, while Amazon SWF can be primarily classified under "Cloud Task Management".

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, Amazon SWF provides the following key features:

  • Maintaining application state
  • Tracking workflow executions and logging their progress
  • Holding and dispatching tasks

Airflow is an open source tool with 13K GitHub stars and 4.72K GitHub forks. Here's a link to Airflow's open source repository on GitHub.

Airbnb, Slack, and 9GAG are some of the popular companies that use Airflow, whereas Amazon SWF is used by Lookout, NASA Jet Propulsion Laboratory, and Better. Airflow has a broader approval, being mentioned in 72 company stacks & 33 developers stacks; compared to Amazon SWF, which is listed in 11 company stacks and 3 developer stacks.

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AirflowAirflow

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.

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Gilroy Gordon
Solution Architect at IGonics Limited · | 2 upvotes · 137.6K views
Recommends
CassandraCassandra

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

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Pros of Airflow
Pros of Amazon SWF
  • 45
    Features
  • 14
    Task Dependency Management
  • 12
    Beautiful UI
  • 11
    Cluster of workers
  • 10
    Extensibility
  • 5
    Open source
  • 4
    Python
  • 4
    Complex workflows
  • 3
    K
  • 2
    Custom operators
  • 2
    Apache project
  • 2
    Dashboard
  • 2
    Good api
    Be the first to leave a pro

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    Cons of Airflow
    Cons of Amazon SWF
    • 2
      Running it on kubernetes cluster relatively complex
    • 2
      Open source - provides minimum or no support
    • 1
      Logical separation of DAGs is not straight forward
    • 1
      Observability is not great when the DAGs exceed 250
      Be the first to leave a con

      Sign up to add or upvote consMake informed product decisions

      What is Airflow?

      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.

      What is Amazon SWF?

      Amazon Simple Workflow allows you to structure the various processing steps in an application that runs across one or more machines as a set of “tasks.” Amazon SWF manages dependencies between the tasks, schedules the tasks for execution, and runs any logic that needs to be executed in parallel. The service also stores the tasks, reliably dispatches them to application components, tracks their progress, and keeps their latest state.

      Need advice about which tool to choose?Ask the StackShare community!

      What companies use Airflow?
      What companies use Amazon SWF?
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      What tools integrate with Airflow?
      What tools integrate with Amazon SWF?

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      What are some alternatives to Airflow and Amazon SWF?
      Luigi
      It is a Python module that helps you build complex pipelines of batch jobs. It handles dependency resolution, workflow management, visualization etc. It also comes with Hadoop support built in.
      Apache NiFi
      An easy to use, powerful, and reliable system to process and distribute data. It supports powerful and scalable directed graphs of data routing, transformation, and system mediation logic.
      Jenkins
      In a nutshell Jenkins CI is the leading open-source continuous integration server. Built with Java, it provides over 300 plugins to support building and testing virtually any project.
      AWS Step Functions
      AWS Step Functions makes it easy to coordinate the components of distributed applications and microservices using visual workflows. Building applications from individual components that each perform a discrete function lets you scale and change applications quickly.
      Pachyderm
      Pachyderm is an open source MapReduce engine that uses Docker containers for distributed computations.
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