Kafka vs NSQ: What are the differences?
Developers describe Kafka as "Distributed, fault tolerant, high throughput pub-sub messaging system". Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design. On the other hand, NSQ is detailed as "A realtime distributed messaging platform". NSQ is a realtime distributed messaging platform designed to operate at scale, handling billions of messages per day. It promotes distributed and decentralized topologies without single points of failure, enabling fault tolerance and high availability coupled with a reliable message delivery guarantee. See features & guarantees.
Kafka and NSQ belong to "Message Queue" category of the tech stack.
Some of the features offered by Kafka are:
- Written at LinkedIn in Scala
- Used by LinkedIn to offload processing of all page and other views
- Defaults to using persistence, uses OS disk cache for hot data (has higher throughput then any of the above having persistence enabled)
On the other hand, NSQ provides the following key features:
- support distributed topologies with no SPOF
- horizontally scalable (no brokers, seamlessly add more nodes to the cluster)
- low-latency push based message delivery (performance)
"High-throughput" is the primary reason why developers consider Kafka over the competitors, whereas "It's in golang" was stated as the key factor in picking NSQ.
Kafka and NSQ are both open source tools. NSQ with 15.6K GitHub stars and 2.03K forks on GitHub appears to be more popular than Kafka with 12.7K GitHub stars and 6.81K GitHub forks.
According to the StackShare community, Kafka has a broader approval, being mentioned in 509 company stacks & 470 developers stacks; compared to NSQ, which is listed in 21 company stacks and 8 developer stacks.
What is Kafka?
What is NSQ?
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Front-end messages are logged to Kafka by our API and application servers. We have batch processing (on the middle-left) and real-time processing (on the middle-right) pipelines to process the experiment data. For batch processing, after daily raw log get to s3, we start our nightly experiment workflow to figure out experiment users groups and experiment metrics. We use our in-house workflow management system Pinball to manage the dependencies of all these MapReduce jobs.
Building out real-time streaming server to present data insights to Coolfront Mobile customers and internal sales and marketing teams.
The built-in Gamification that comes with our Playbooks application uses NSQ for work queues and microservice communication.