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Kafka at scale
• LinkedIn: 1.1 trillion (1.1⨯1012) events per day
peak: 18 M events/sec, 3.8 GB/sec
• Netflix: 400 billion (4⨯1011) events per day
peak: 8 M events/sec, 17 GB/sec
• Uber, Twitter, Yahoo, Spotify, etc.
• http://www.confluent.io/blog/apache-kafka-hits-1.1-trillion-messages-per-day-joins-the-4-comma-club (Sept 2015)
https://engineering.linkedin.com/kafka/running-kafka-scale (March 2015)
https://engineering.linkedin.com/blog/2016/01/whats-new-samza (January 2016)
http://www.slideshare.net/wangxia5/netflix-kafka (March 2015)
https://cwiki.apache.org/confluence/display/KAFKA/Powered+By
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References
1. Tyler Akidau, Robert Bradshaw, Craig Chambers, et al.: “The Dataflow Model: A Practical Approach to Balancing
Correctness, Latency, and Cost in Massive-Scale, Unbounded, Out-of-Order Data Processing,” Proceedings of the VLDB
Endowment, volume 8, number 12, pages 1792–1803, August 2015. http://www.vldb.org/pvldb/vol8/p1792-Akidau.pdf
2. Shirshanka Das, Chavdar Botev, Kapil Surlaker, et al.: “All Aboard the Databus!,” at ACM Symposium on Cloud
Computing (SoCC), October 2012. http://www.socc2012.org/s18-das.pdf
3. Pat Helland: “Immutability Changes Everything,” at 7th Biennial Conference on Innovative Data Systems Research (CIDR),
January 2015. http://www.cidrdb.org/cidr2015/Papers/CIDR15_Paper16.pdf
4. Nathan Marz and James Warren: “Big Data: Principles and best practices of scalable realtime data systems.” Manning,
April 2015, ISBN 9781617290343. http://manning.com/marz/
5. Martin Kleppmann: “Designing data-intensive applications.” O’Reilly Media, to appear. http://dataintensive.net
6. Martin Kleppmann and Jay Kreps: “Kafka, Samza and the Unix philosophy of distributed data.” IEEE Data Engineering
Bulletin, December 2015. http://martin.kleppmann.com/papers/kafka-debull15.pdf
7. Jay Kreps: “Why local state is a fundamental primitive in stream processing.” 31 July 2014. http://radar.oreilly.com/
2014/07/why-local-state-is-a-fundamental-primitive-in-stream-processing.html
8. Jay Kreps: “Questioning the Lambda Architecture.” July 2014. http://radar.oreilly.com/2014/07/questioning-the-lambda-
architecture.html
9. Jay Kreps: “I ♥︎ Logs.” O'Reilly Media, September 2014. http://shop.oreilly.com/product/0636920034339.do
10. Praveen Neppalli Naga: “Real-time Analytics at Massive Scale with Pinot.” 29 Sept 2014. http://
engineering.linkedin.com/analytics/real-time-analytics-massive-scale-pinot
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