What is Storm?
Who uses Storm?
Why developers like Storm?
Here are some stack decisions, common use cases and reviews by companies and developers who chose Storm in their tech stack.
Lumosity is home to the world's largest cognitive training database, a responsibility we take seriously. For most of the company's history, our analysis of user behavior and training data has been powered by an event stream--first a simple Node.js pub/sub app, then a heavyweight Ruby app with stronger durability. Both supported decent throughput and latency, but they lacked some major features supported by existing open-source alternatives: replaying existing messages (also lacking in most message queue-based solutions), scaling out many different readers for the same stream, the ability to leverage existing solutions for reading and writing, and possibly most importantly: the ability to hire someone externally who already had expertise.
We ultimately migrated to Kafka in early- to mid-2016, citing both industry trends in companies we'd talked to with similar durability and throughput needs, the extremely strong documentation and community. We pored over Kyle Kingsbury's Jepsen post (https://aphyr.com/posts/293-jepsen-Kafka), as well as Jay Kreps' follow-up (http://blog.empathybox.com/post/62279088548/a-few-notes-on-kafka-and-jepsen), talked at length with Confluent folks and community members, and still wound up running parallel systems for quite a long time, but ultimately, we've been very, very happy. Understanding the internals and proper levers takes some commitment, but it's taken very little maintenance once configured. Since then, the Confluent Platform community has grown and grown; we've gone from doing most development using custom Scala consumers and producers to being 60/40 Kafka Streams/Connects.
We originally looked into Storm / Heron , and we'd moved on from Redis pub/sub. Heron looks great, but we already had a programming model across services that was more akin to consuming a message consumers than required a topology of bolts, etc. Heron also had just come out while we were starting to migrate things, and the community momentum and direction of Kafka felt more substantial than the older Storm. If we were to start the process over again today, we might check out Pulsar , although the ecosystem is much younger.
To find out more, read our 2017 engineering blog post about the migration!
Real-time analytics are much better than periodically run batch jobs, so recently we open sourced Pyleus which allows anyone to write Storm topologies using Python. Storm
In addition to batch processing, we also wanted to achieve real-time data processing. For example, to improve the success rate of experiments, we needed to figure out experiment group allocations in real-time once the experiment configuration was pushed out to production. We used Storm to tail Kafka and compute aggregated metrics in real-time to provide crucial stats. Storm
- Storm integrates with the queueing and database technologies you already use
- Simple API
- Fault tolerant
- Guarantees data processing
- Use with any language
- Easy to deploy and operate
- Free and open source