Slide 1
Slide 1 text
No content
Slide 2
Slide 2 text
No content
Slide 3
Slide 3 text
No content
Slide 4
Slide 4 text
No content
Slide 5
Slide 5 text
No content
Slide 6
Slide 6 text
No content
Slide 7
Slide 7 text
No content
Slide 8
Slide 8 text
No content
Slide 9
Slide 9 text
No content
Slide 10
Slide 10 text
No content
Slide 11
Slide 11 text
No content
Slide 12
Slide 12 text
No content
Slide 13
Slide 13 text
No content
Slide 14
Slide 14 text
No content
Slide 15
Slide 15 text
No content
Slide 16
Slide 16 text
Kafka at LinkedIn
• 350+ commodity machines
• 8,000+ topics
• 140,000+ partitions
• 278 Billion messages/day
• 49 TB/day in
• 176 TB/day out
• Peak Load
– 4.4 Million messages per second
– 6 Gigabits/sec Inbound
– 21 Gigabits/sec Outbound
Slide 17
Slide 17 text
No content
Slide 18
Slide 18 text
No content
Slide 19
Slide 19 text
No content
Slide 20
Slide 20 text
No content
Slide 21
Slide 21 text
No content
Slide 22
Slide 22 text
No content
Slide 23
Slide 23 text
No content
Slide 24
Slide 24 text
No content
Slide 25
Slide 25 text
No content
Slide 26
Slide 26 text
No content
Slide 27
Slide 27 text
No content
Slide 28
Slide 28 text
No content
Slide 29
Slide 29 text
No content
Slide 30
Slide 30 text
No content
Slide 31
Slide 31 text
No content
Slide 32
Slide 32 text
No content
Slide 33
Slide 33 text
No content
Slide 34
Slide 34 text
No content
Slide 35
Slide 35 text
No content
Slide 36
Slide 36 text
No content
Slide 37
Slide 37 text
No content
Slide 38
Slide 38 text
No content
Slide 39
Slide 39 text
No content
Slide 40
Slide 40 text
No content
Slide 41
Slide 41 text
References
1. Jay Kreps: “I ♥︎ Logs.” O'Reilly Media, September 2014. http://shop.oreilly.com/product/0636920034339.do
2. 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
3. Martin Kleppmann: “Designing data-intensive applications.” O’Reilly Media, to appear in 2015. http://dataintensive.net
4. Martin Kleppmann: “Rethinking caching in web apps.” 1 October 2012. http://martin.kleppmann.com/2012/10/01/
rethinking-caching-in-web-apps.html
5. Martin Kleppmann: “Moving faster with data streams: The rise of Samza at LinkedIn.” 14 July 2014. http://
engineering.linkedin.com/stream-processing/moving-faster-data-streams-rise-samza-linkedin
6. Jakob Homan: “Real time insights into LinkedIn's performance using Apache Samza.” 18 Aug 2014. http://
engineering.linkedin.com/samza/real-time-insights-linkedins-performance-using-apache-samza
7. Nathan Marz and James Warren: “Big Data: Principles and best practices of scalable realtime data systems.” Manning
MEAP, to appear January 2015. http://manning.com/marz/
8. 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
9. Mahesh Balakrishnan, Dahlia Malkhi, Ted Wobber, et al.: “Tango: Distributed Data Structures over a Shared Log,” at
24th ACM Symposium on Operating Systems Principles (SOSP), pages 325–340, November 2013. http://
research.microsoft.com/pubs/199947/Tango.pdf
10. Peter Bailis, Alan Fekete, Ali Ghodsi, Joseph M Hellerstein, and Ion Stoica: “Scalable Atomic Visibility with RAMP
Transactions,” at ACM International Conference on Management of Data (SIGMOD), June 2014. http://
www.bailis.org/papers/ramp-sigmod2014.pdf
11. Roshan Sumbaly, Jay Kreps, and Sam Shah: “The ‘Big Data’ Ecosystem at LinkedIn,” at ACM International Conference
on Management of Data (SIGMOD), July 2013. http://www.slideshare.net/s_shah/the-big-data-ecosystem-at-
linkedin-23512853
12. Apache Samza documentation. http://samza.incubator.apache.org
Slide 42
Slide 42 text
No content