Motivation
JMH 1.22 was released recently, we might as well use the latest when
running benchmarks.
Summary of changes:
https://mail.openjdk.java.net/pipermail/jmh-dev/2019-November/002879.html
Modifications
Update jmh dependencies in microbench module from version 1.21 to 1.22.
Result
Benchmarks run using latest JMH
Motivation:
Netty is very widely used which can lead to a lot of pain when we break API / ABI. We should make use japicmp-maven-plugin during the build to verify we do not introduce breakage by mistake.
Modifications:
- Add japicmp-maven-plugin to the build process
- Fix a method signature change in HttpProxyHandler that was flagged as a possible problem.
Result:
Ensure no API/ABI breakage accour between releases.
Motivation:
Netty executors doesn't have yet any means to compare with each others
nor to compare with the j.u.c. executors
Modifications:
A new benchmark measuring execute burst cost is being added
Result:
It's now possible to compare some of Netty executors with each others
and with the j.u.c. executors
Motivation:
We should use the latest jmh version which also supports -prof dtraceasm on MacOS.
Modifications:
Update to latest jmh version.
Result:
Better benchmark / profiling support on MacOS.
Motivation:
It is sometimes useful to be able to run benchmarks easily from the commandline and passs different arguments / options here. We should support this.
Modifications:
Add the benchmark-jar profile which allows to generate such an "uber-jar" that can be used directly to run benchmarks as documented at http://openjdk.java.net/projects/code-tools/jmh/.
Result:
More flexible way to run benchmarks.
Motivation:
Optimizing the Epoll channel needs an objective measure of how fast
it is.
Modification:
Add a simple, closed loop, ping-pong benchmark.
Result:
Benchmark can be used to measure #7816
Initial numbers:
```
Result "io.netty.microbench.channel.epoll.EpollSocketChannelBenchmark.pingPong":
22614.403 ±(99.9%) 797.263 ops/s [Average]
(min, avg, max) = (21093.160, 22614.403, 24977.387), stdev = 918.130
CI (99.9%): [21817.140, 23411.666] (assumes normal distribution)
Benchmark Mode Cnt Score Error Units
EpollSocketChannelBenchmark.pingPong thrpt 20 22614.403 ± 797.263 ops/s
```
Motivation:
DefaultHttp2FrameWriter#writeData allocates a DataFrameHeader for each write operation. DataFrameHeader maintains internal state and allocates multiple slices of a buffer which is a maximum of 30 bytes. This 30 byte buffer may not always be necessary and the additional slice operations can utilize retainedSlice to take advantage of pooled objects. We can also save computation and object allocations if there is no padding which is a common case in practice.
Modifications:
- Remove DataFrameHeader
- Add a fast path for padding == 0
Result:
Less object allocation in DefaultHttp2FrameWriter
Motivation:
IPv4/6 validation methods use allocations, which can be avoided.
IPv4 parse method use StringTokenizer.
Modifications:
Rewriting IPv4/6 validation methods to avoid allocations.
Rewriting IPv4 parse method without use StringTokenizer.
Result:
IPv4/6 validation and IPv4 parsing faster up to 2-10x.
