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Kafka: Scaling producers and consumers

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Article from ADMIN 62/2021

By Jesse Yates

A guide to 10x scaling in Kafka with real-world metrics for high throughput, low latency, and cross-geographic data movement.

According to the Kafka home page [1], "Apache Kafka is an open-source distributed event streaming platform … for high-performance data pipelines, streaming analytics, data integration, and mission-critical applications." A basic Kafka ecosystem has three components – producers, brokers, and consumers. Although much has been written about tuning brokers, reliably configuring producers and consumers is something of a dark art. All systems are dependent on your local setup, but some standard metrics that you can look at and knobs that can be tuned can increase performance 10 times or more. In this article, I walk through a real-life(-ish) example of how to diagnose and then fix a bottlenecked stream-processing system.

Assume you are mirroring data from an edge Kafka cluster into a central Kafka cluster that will feed your analytics data warehouse (Figure 1). You've set up the edge with 100+ partitions for many of the topics you are consuming (because you had the forethought to expect scale and knew partitions are generally pretty cheap – go you!). That means you could easily be mirroring 1000+ partitions into your central Kafka for each edge cluster.