Scaling a Kafka Consumer From 4K to 25K Events per Second While Preserving Ordering

This story was originally published on HackerNoon at: https://hackernoon.com/scaling-a-kafka-consumer-from-4k-to-25k-events-per-second-while-preserving-ordering.
A production redesign using batching, safe offset commits, and record-level fallback raised Kafka consumer throughput from 4K to 25K without adding partitions.
Check more stories related to data-science at: https://hackernoon.com/c/data-science. You can also check exclusive content about #apache-kafka, #software-architecture, #distributed-systems, #software-development, #scalability, #performance-optimization, #kafka-consumer, #event-streaming, and more.

This story was written by: @manjushaguntur. Learn more about this writer by checking @manjushaguntur's about page, and for more stories, please visit hackernoon.com.

A Kafka consumer processing 4,000 events per second had to meet an 8,000 event SLO. Rather than add partitions, we redesigned the consumer around batching, safe offset commits, and record-level fallback. The solution was validated in production at approximately 25,000 events per second while preserving ordering and at-least-once delivery for recoverable events. This article explains how the design handled successful batches, transient failures, and terminal failures without silently losing events or blocking a partition.