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Airflow vs Amazon SWF: What are the differences?

Introduction

This Markdown code provides a comparison between Airflow and Amazon SWF, highlighting key differences between the two and organizing them in a specific format.

  1. Scalability: Airflow is highly scalable and can handle a large number of tasks and workflows concurrently, making it suitable for complex data processing. On the other hand, Amazon SWF is designed to scale automatically and can handle millions of concurrent tasks, making it perfect for highly scalable applications.

  2. Workflow Definition: Airflow allows you to define workflows and task dependencies using Python code, providing flexibility and extensibility. In contrast, Amazon SWF uses a domain-specific language (DSL) to define workflows, which can be more readable for non-technical stakeholders and provide a visual representation of the workflow.

  3. Managed Service: Airflow is a self-managed open-source tool, which means you need to set up and maintain the infrastructure yourself. Amazon SWF, on the other hand, is a fully managed service by AWS, providing automatic scaling, fault tolerance, and removing the need for infrastructure management.

  4. Integration with AWS Services: Amazon SWF seamlessly integrates with other AWS services such as Lambda, Step Functions, and Simple Queue Service (SQS), enabling you to build complex workflows within the AWS ecosystem. While Airflow has some third-party integrations for AWS services, it may require additional configuration and setup.

  5. Visibility and Monitoring: Airflow provides a user-friendly web interface that allows users to monitor and visualize the status of workflows, tasks, and dependencies. It also provides detailed logging and error handling. Amazon SWF offers a comprehensive console and API for monitoring and managing workflows, including features like real-time tracking and workflow metrics.

  6. Deployment Options: Airflow can be deployed in various environments such as on-premises, cloud, or containers, providing flexibility in choosing the deployment architecture. Amazon SWF is specifically designed to run on the AWS cloud infrastructure, limiting deployment options for applications hosted outside of AWS.

In Summary, Airflow and Amazon SWF differ in terms of scalability, workflow definition, managed service offering, integration with AWS services, visibility, monitoring capabilities, and deployment options.

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Apache SparkApache Spark

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 · 279.2K views
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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
  • 53
    Features
  • 14
    Task Dependency Management
  • 12
    Beautiful UI
  • 12
    Cluster of workers
  • 10
    Extensibility
  • 6
    Open source
  • 5
    Complex workflows
  • 5
    Python
  • 3
    Good api
  • 3
    Apache project
  • 3
    Custom operators
  • 2
    Dashboard
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    Cons of Airflow
    Cons of Amazon SWF
    • 2
      Observability is not great when the DAGs exceed 250
    • 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
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      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.

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      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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      Blog Posts

      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.
      See all alternatives