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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.
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.
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.
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.
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.
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.
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.
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
- Features53
- Task Dependency Management14
- Beautiful UI12
- Cluster of workers12
- Extensibility10
- Open source6
- Complex workflows5
- Python5
- Good api3
- Apache project3
- Custom operators3
- Dashboard2
Pros of Amazon SWF
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Cons of Airflow
- Observability is not great when the DAGs exceed 2502
- Running it on kubernetes cluster relatively complex2
- Open source - provides minimum or no support2
- Logical separation of DAGs is not straight forward1
Cons of Amazon SWF
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What is Airflow?
What is Amazon SWF?
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