{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"Data Science Tech Brief By HackerNoon","title":"Scaling a Kafka Consumer From 4K to 25K Events per Second While Preserving Ordering","html":"","width":"100%","height":180,"duration":326,"description":"\n 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.\nA production redesign using batching, safe offset commits, and record-level fallback raised Kafka consumer throughput from 4K to 25K without adding partitions.\nCheck more stories related to data-science at: https://hackernoon.com/c/data-science.\n You can also check exclusive content about #apache-kafka, #software-architecture, #distributed-systems, #software-development, #scalability, #performance-optimization, #kafka-consumer, #event-streaming, and more.\nThis story was written by: @manjushaguntur. Learn more about this writer by checking @manjushaguntur's about page,\n and for more stories, please visit hackernoon.com.\nA 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.","thumbnail_url":"https://img.transistorcdn.com/8VxAgS1Ll3FJEERcAdhFdqqXJMnE7OfD2RUvrjauLt0/rs:fill:0:0:1/w:400/h:400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS9zaG93/LzQxMjY4LzE2ODM1/ODI1ODUtYXJ0d29y/ay5qcGc.webp","thumbnail_width":300,"thumbnail_height":300}