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Airflow vs StackStorm: 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; StackStorm: Open Source IFTTT for Ops: event-driven automation, security responses, auto-remediation with workflow engine & ChatOps. StackStorm is a platform for integration and automation across services and tools. It ties together your existing infrastructure and application environment so you can more easily automate that environment -- with a particular focus on taking actions in response to events.
Airflow and StackStorm are primarily classified as "Workflow Manager" and "Remote Server Task Execution" tools respectively.
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, StackStorm provides the following key features:
- Automations tie events to actions you’d like to take, using a rules engine and, if you want, comprehensive workflow. Automations are your operational patterns summarized as code.
- StackStorm automations work either by starting with your existing scripts – just add simple meta data – or by authoring the automations within StackStorm.
- Automations are the heart of StackStorm – they allow you to share operational patterns, boost productivity, and automate away the routine.
Airflow and StackStorm are both open source tools. Airflow with 13.3K GitHub stars and 4.91K forks on GitHub appears to be more popular than StackStorm with 3.36K GitHub stars and 445 GitHub forks.
According to the StackShare community, Airflow has a broader approval, being mentioned in 98 company stacks & 162 developers stacks; compared to StackStorm, which is listed in 6 company stacks and 20 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
- Features50
- Task Dependency Management14
- Beautiful UI12
- Cluster of workers12
- Extensibility10
- Open source6
- Complex workflows5
- Python5
- Good api3
- Apache project3
- Custom operators3
- Dashboard2
Pros of StackStorm
- Auto-remediation7
- Integrations5
- Automation4
- Complex workflows4
- Open source3
- Beautiful UI2
- ChatOps2
- Python2
- Extensibility1
- Slack1
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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 StackStorm
- Complexity3
- There are not enough sources of information1
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