gRPC vs Kafka: What are the differences?
gRPC: A high performance, open-source universal RPC framework. gRPC is a modern open source high performance RPC framework that can run in any environment. It can efficiently connect services in and across data centers with pluggable support for load balancing, tracing, health checking..; Kafka: 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.
gRPC can be classified as a tool in the "Remote Procedure Call (RPC)" category, while Kafka is grouped under "Message Queue".
Some of the features offered by gRPC are:
- Simple service definition
- Works across languages and platforms
- Start quickly and scale
On the other hand, Kafka provides the following key features:
- 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)
gRPC and Kafka are both open source tools. gRPC with 22K GitHub stars and 5.12K forks on GitHub appears to be more popular than Kafka with 12.7K GitHub stars and 6.81K GitHub forks.
Uber Technologies, Spotify, and Slack are some of the popular companies that use Kafka, whereas gRPC is used by Slack, 9GAG, and Policygenius. Kafka has a broader approval, being mentioned in 509 company stacks & 470 developers stacks; compared to gRPC, which is listed in 53 company stacks and 48 developer stacks.
What is gRPC?
What is Kafka?
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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.