This document discusses how the Azul Platform Prime JVM can improve Kafka performance without any code changes. It summarizes that the Azul JVM replaces HotSpot with the C4 garbage collector and Falcon JIT compiler to eliminate stop-the-world garbage collection pauses and improve adaptive compilation. This results in up to 20% better performance for Kafka workloads and allowed one customer to reduce their cloud hardware costs by 15% while maintaining throughput.

1. Better Kafka Performance
Without Changing Any Code
Presented by Simon Ritter, Deputy CTO | Azul Systems Inc.

2. 2
Kafka Architecture
Producer 1
Producer 2
Producer n
.
.
. Consumer 1
Consumer 2
Consumer n
.
.
.
Broker 1
Broker 2
Broker n
.
.
.
ZooKeeper
Kafka
JVM

3. 3
JVM Performance Challenges
• Latency
o Biggest issue is Garbage Collection
o Stop-the-world pauses for almost all collectors
o Pauses are typically proportional to heap size, not live data
• Throughput
o Adaptive JIT compilation: Interpreted, C1 compiled, C2 compiled
o Deoptimisations
o Level of optimisation is key
o Significant impact on number of brokers required

4. 4
Azul Platform Prime: A Better JVM
• Based on OpenJDK source code
• Passes all Java SE TCK/JCK tests
o Drop-in replacement for other JVMs
- No code changes, no recompilation
• Hotspot collectors replaced with C4
• Falcon JIT compiler
o C2 replacement
• ReadyNow! warm up elimination technology
• Only supported on Linux
o OS memory management interface

5. Azul Continuous Concurrent Compacting
Collector (C4)

6. 6
C4 Basics
• Generational (young and old)
o Uses the same GC collector for both
o For efficiency rather than pause containment
• All phases are parallel
• No STW compacting fallback
• Algorithm is mark, relocate, remap

7. 7
Loaded Value Barrier
• Read barrier
o Tests all object references as they are loaded
• Enforces two invariants
o Reference is marked through
o Reference points to correct object position
• Minimal performance overhead
o Test and jump (2 instructions)
o x86 architecture reduces this to one micro-op

8. 8
Concurrent Mark Phase
Root Set
GC Threads
App Threads
X
X
X
X
X

9. 9
Relocation Phase
Compaction
A B C D E
A’ B’ C’ D’ E’
A -> A’ B -> B’ C -> C’ D -> D’ E -> E’

10. 10
Remapping Phase
App Threads
GC Threads
A -> A’ B -> B’ C -> C’ D -> D’ E -> E’
X
X
X

11. 11
Measuring Platform Performance
• jHiccup
• Spends most of its time asleep
o Minimal effect on perfomance
o Wakes every 1 ms
o Records delta of time it expects to wake up
o Measured effect is what would be experienced by your application
• Generates histogram log files
o These can be graphed for easy evaluation

12. 12
Eliminating Kafka Latency
HotSpot Zing

13. 13
Azul Falcon JIT Compiler

14. 14
Advancing Adaptive Compilation
• Replacement for C2 compiler
• Azul Falcon JIT compiler
o Based on latest compiler research
o LLVM project
• Better performance
o Better intrinsics
o More inlining
o Fewer compiler excludes

15. 15
More Complex Code Example
• Conditional array cell addition loop
o Hard for compiler to identify for vector instruction use

16. 16
Traditional JVM JIT
Per element jumps
2 elements per iteration

17. 17
Falcon JIT
Using AVX2 vector instructions
32 elements per iteration
Broadwell E5-2690-v4

18. 18
Recent Kafka Customer Story
• Leading cloud-based IT security company
o Cloud security, compliance and other services
• Big Kafka user
o 2.5 billion messages across Kafka clusters daily
o Initially approached us about their Cassandra clusters and eliminating latency
• Kafka improvements
o 20% performance gain, out-of-the-box, with no tuning
o Falcon improved code generation
o Resulted in a 15% saving in cloud hardware costs
o Platform Core was effectively cheaper than free!

19. Summary

20. 20
The Azul Platform Prime JVM
• Kafka starts faster
• Kafka goes faster
• Kafka stays fast
• Eliminate GC latency effects and reduce cluster and node sizes
o Reduced cloud deployment costs
• Simple replacement for other JVMs
o No recoding necessary
Try Azul Platform Prime free for 30 days: azul.com/PrimeTrial

21. Questions?

22. Thank You.