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Kafka Summit San Francisco 2018

Streaming platforms at massive scale.

October 16-17, 2018 | San Francisco

More Data, More Problems: Scaling Kafka-Mirroring Pipelines at LinkedIn

Session Level: Intermediate
Video & Slides

For several years, LinkedIn has been using Kafka MirrorMaker as the mirroring solution for copying data between Kafka clusters across data centers. However, as LinkedIn data continued to grow, mirroring trillions of Kafka messages per day across data centers uncovered the scale limitations and operability challenges of Kafka MirrorMaker. To address such issues, we have developed a new mirroring solution, built on top our stream ingestion service, Brooklin. Brooklin MirrorMaker aims to provide improved performance and stability, while facilitating better management through finer control of data pipelines. Through flushless Kafka produce, dynamic management of data pipelines, per-partition error handling and flow control, we are able to increase throughput, better withstand consume and produce failures and reduce overall operating costs. As a result, we have eliminated the major pain points of Kafka MirrorMaker. In this talk, we will dive deeper into the challenges LinkedIn has faced with Kafka MirrorMaker, how we tackled them with Brooklin MirrorMaker and our plans for iterating further on this new mirroring solution.


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