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Airflow vs Resque: What are the differences?
Introduction
In this Markdown code, we will highlight the key differences between Airflow and Resque.
Workflow Orchestration: Airflow is a workflow orchestration tool that allows users to schedule and monitor workflows, while Resque is a background job queue system for Ruby apps. Airflow supports complex workflows with dependencies and conditional logic, while Resque focuses on executing individual jobs in a distributed environment.
Language Support: Airflow supports workflows written in Python, SQL, and other scripting languages, whereas Resque is specifically designed for Ruby applications. This means that Airflow can be used for a wider range of use cases and integrate with different systems, while Resque is more limited in terms of language support.
Scalability: Airflow is designed to scale horizontally to handle large amounts of workflow executions and can be set up in a distributed environment, while Resque is more suitable for smaller-scale applications and may require additional configuration for scaling across multiple nodes.
Fault Tolerance: Airflow provides built-in fault tolerance mechanisms such as task retries, task-level retries, and dead-letter queues, ensuring that workflows can recover from failures and continue processing. On the other hand, Resque relies on external tools or plugins for implementing fault tolerance measures.
Monitoring and Logging: Airflow comes with a comprehensive user interface for monitoring workflows, viewing logs, and managing tasks, making it easier for users to track the progress of their workflows. Resque lacks a built-in dashboard for monitoring job queues and may require additional tools or integrations for logging and monitoring.
In Summary, the key differences between Airflow and Resque lie in their workflow orchestration capabilities, language support, scalability, fault tolerance mechanisms, and monitoring features.
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 Resque
- Free5
- Scalable3
- Easy to use on heroku1
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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 Resque
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What is Airflow?
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