"We’ve all tried adjusting the number partitions, replica fetch size, batch size, socket buffer size, number of I/O threads … you could spend an hour enumerating the tuning options for Kafka to squeeze a few percent of extra performance. Finding the right combination of parameters is closer to alchemy than engineering work, time consuming and in bigger deployments sometimes even impossible! What if there's a better way? What if you could ""just"" make Kafka more efficient and gain better performance without months of engineering effort? Since Kafka is written in Java and Scala, in this talk, we'll discuss the effect of Java Virtual Machine (JVM) on a Kafka cluster. We'll demonstrate that running Kafka on Azul Prime JVM can reduce the latency by up to 40% while handling the same load. We’ll go beyond shiny marketing-y charts, dig into flamegraphs, and look at assembly code to fully understand where the speed up is coming from. After the session, you'll understand the importance of the underlaying JVM and how you can leverage this knowledge to boost the performance of the cluster to achieve better SLAs or reduce the infrastructure costs."

1. Boosting Kafka Performance
in a Day
Jiří Holuša
Sr. Product Manager @ Azul
Current 2023
San Jose

2. 2
About me

3. 3
About me
Jiří Holuša @ Azul

4. 4
Pain points with Kafka
Speed

5. 5
Pain points with Kafka
Speed Long response times

6. 6
Pain points with Kafka
Speed Long response times Infrastructure cost

7. 7
Pain points with Kafka
Speed Long response times Infrastructure cost
Improved performance addresses these pain points

8. 8
Running Kafka
Server
Event flow

9. 9
Running Kafka
Java Virtual Machine (JVM)
Server
Event flow

10. 10
Running Kafka
Java Virtual Machine (JVM)
Server
Event flow

11. 11
Running Kafka
Java Virtual Machine (JVM)
Server
Zulu
Event flow

12. 12
Running Kafka
Java Virtual Machine (JVM)
Server
Zulu
= JAVA
Event flow

13. 13
Running Kafka
Java Virtual Machine (JVM)
Server
Zulu
Event flow

14. 14
Running Kafka
Java Virtual Machine (JVM)
Server
Prime
Event flow

15. 15
Running Kafka
Server
Prime
Event flow
Up to +40%
Java Virtual Machine (JVM)

16. 16
Running Kafka
Server
Prime
Event flow
Up to +40%
?
Java Virtual Machine (JVM)

17. 17
Just in Time (JIT) compiler
Compiler optimizations

18. 18
Execution profile

19. 19
Reading a flamegraph

20. 20
Clear optimization opportunity

21. 21
Execution profile

22. 22
Method inlining
void foo() {
for (int i = 0; i < 10; i++) {
print(„Inline me!“);
}
}
void bar() {
if (importantCondidition)
i += 20
foo()
}
void bar() {
if (importantCondidition)
i += 20
for (int i = 0; i < 10; i++) {
print(„Inline me!“);
}
}

23. 23
Method inlining
void foo() {
for (int i = 0; i < 10; i++) {
print(„Inline me!“);
}
}
void bar() {
if (importantCondidition)
i += 20
foo()
}
void bar() {
if (importantCondidition)
i += 20
for (int i = 0; i < 10; i++) {
print(„Inline me!“);
}
}

24. 24
Method inlining (Azul Zulu)

25. 25
Method inlining (Azul Zulu)

26. 26
Method inlining (Azul Prime)

27. 27
Method inlining (Azul Prime)

28. 28
Optimizations
Azul Zulu

29. 29
Optimizations
Azul Zulu
Per element jumps
2 elements per iteration

30. 30
Optimizations
Azul Zulu
Azul Prime

31. 31
Optimizations
Azul Zulu
Azul Prime
Using AVX2 vector instructions
32 elements per iteration

32. 32
• The choice of JVM can drastically affect Kafka‘s performance
Takeaways

33. 33
• The choice of JVM can drastically affect Kafka‘s performance
• By improving Kafka performance, you can address the pain points – speed, response times and cost
Takeaways

34. 34
• The choice of JVM can drastically affect Kafka‘s performance
• By improving Kafka performance, you can address the pain points – speed, response times and cost
• Azul Platform Prime is the fastest JVM out there
Takeaways

35. 35
• The choice of JVM can drastically affect Kafka‘s performance
• By improving Kafka performance, you can address the pain points – speed, response times and cost
• Azul Platform Prime is the fastest JVM out there
• It‘s not just for Kafka – it works any for Java application!
Takeaways

36. 36
• The choice of JVM can drastically affect Kafka‘s performance
• By improving Kafka performance, you can address the pain points – speed, response times and cost
• Azul Platform Prime is the fastest JVM out there
• It‘s not just for Kafka – it works any for Java application!
• These are not optimizations you can do manually as a developer
Takeaways

37. Questions?