async-profiler

This project is a proof-of-concept implementation of a Java profiler that features the HotSpot-specific AsyncGetCallTrace API for Java call stacks, combined with perf_events for native call stacks. The general approach is receiving call stacks generated by perf_events and matching them up with call stacks generated by AsyncGetCallTrace, in order to produce an accurate profile of both Java and native code, including kernel frames.

This approach has the following advantages compared to using perf_events directly with a Java agent that translates addresses to Java method names:

• Works on older Java versions because it doesn't require -XX:+PreserveFramePointer, which is only available in Java 1.8u60 and later.

• Does not introduce the performance overhead from -XX:+PreserveFramePointer, which can in rare cases be as high as 10%.

• Does not require generating a map file to map Java code addresses to method names.

• Works with interpreter frames.

• Does not require writing out a perf.data file for further processing in user space scripts.

Because of the requirement for perf_events, this tool works only on Linux platforms. Additionally, on some Linux versions, two runtime variables need to be tuned in order to allow non-root users to use perf_events.

Building

Build status:

Make sure the JAVA_HOME environment variable points to your JDK installation, and then run make. GCC is required. After building, the profiler agent binary will be in the build subdirectory. Additionally, a small Java application that can load the agent into the target process will also be compiled to the build subdirectory.

Basic Usage

As of Linux 4.6, capturing kernel call stacks using perf_events from a non- root process requires setting two runtime variables. You can set them using sysctl or as follows:

# echo 1 > /proc/sys/kernel/perf_event_paranoid
# echo 0 > /proc/sys/kernel/kptr_restrict

To run the agent and pass commands to it, the helper script profiler.sh is provided. A typical workflow would be to launch your Java application, attach the agent and start profiling, exercise your performance scenario, and then stop profiling. The agent's output, including the profiling results, will be displayed in the Java application's standard output.

Example:

$ jps
9234 Jps
8983 Computey
$ ./profiler.sh start 8983
$ ./profiler.sh stop 8983

Alternatively, you may specify -d (duration) argument to profile the application for a fixed period of time with a single command.

$ ./profiler.sh -d 30 8983

By default, the profiling frequency is 1000Hz (every 1ms of CPU time). Here is a sample of the output printed to the Java application's terminal:

--- Execution profile ---
Total:                   687
Unknown (native):        1 (0.15%)

Samples: 679 (98.84%)
    [ 0] Primes.isPrime
    [ 1] Primes.primesThread
    [ 2] Primes.access$000
    [ 3] Primes$1.run
    [ 4] java.lang.Thread.run

... a lot of output omitted for brevity ...

         679 (98.84%) Primes.isPrime
           4 (0.58%)  __do_softirq

... more output omitted ...

This indicates that the hottest method was Primes.isPrime, and the hottest call stack leading to it comes from Primes.primesThread.

Generating Flame Graphs

To generate flame graphs or other visualizations from the collected profiling information, you will need to dump the raw collected traces to a file and then post-process it. The following example uses Brendan Gregg's FlameGraph scripts, but a similar solution can be tailored to other visualization tools.

$ jps
9234 Jps
8983 Computey
$ ./profiler.sh -d 30 -o flamegraph -f /tmp/traces.txt 8983
$ FlameGraph/flamegraph.pl --colors=java /tmp/traces.txt > /tmp/flamegraph.svg

Profiler Options

The following is a complete list of the command-line options accepted by profiler.sh script.

• start - starts profiling in semi-automatic mode, i.e. profiler will run until stop command is explicitly called.

• stop - stops profiling and prints the report.

• status - prints profiling status: whether profiler is active and for how long.

• -d N - the profiling duration, in seconds. If no start, stop or status option is given, the profiler will run for the specified period of time and then automatically stop.
  Example: ./profiler.sh -d 30 8983

• -i N - sets the profiling interval, in nanoseconds. Only CPU active time is counted. No samples are collected while CPU is idle. The default is 1000000 (1ms).
  Example: ./profiler.sh -i 100000 8983

• -b N - sets the frame buffer size, in the number of Java method ids that should fit in the buffer. If you receive messages about an insufficient frame buffer size, increase this value from the default.
  Example: ./profiler.sh -b 5000000 8983

• -o fmt[,fmt...] - specifies what information to dump when profiling ends. This is a comma-separated list of the following options:

  • summary - dump basic profiling statistics;
  • traces[=N] - dump call traces (at most N samples);
  • methods[=N] - dump hot methods (at most N samples);
  • flamegraph - dump raw call traces in the format used by FlameGraph script. This is a collection of call stacks, where each line is a semicolon separated list of frames followed by the sample count. For example:

  java/lang/Thread.run;Primes$1.run;Primes.access$000;Primes.primesThread;Primes.isPrime 1056
  

  The default format is summary,traces=200,methods=200.

• -f FILENAME - the file name to dump the profile information to.
  Example: ./profiler.sh -o flamegraph -f /tmp/traces.txt 8983

Restrictions/Limitations

• On most Linux systems, perf_events captures call stacks with a maximum depth of 127 frames. On recent Linux kernels, this can be configured using sysctl kernel.perf_event_max_stack or by writing to the /proc/sys/kernel/perf_event_max_stack file.

• Profiler allocates 8kB perf_event buffer for each thread of the target process. Make sure /proc/sys/kernel/perf_event_mlock_kb value is large enough (more than 8 * threads) when running under unprivileged user. Otherwise the message "perf_event mmap failed: Operation not permitted" will be printed, and no native stack traces will be collected.

• There is no bullet-proof guarantee that the perf_events overflow signal is delivered to the Java thread in a way that guarantees no other code has run, which means that in some rare cases, the captured Java stack might not match the captured native (user+kernel) stack.

• You will not see the non-Java frames preceding the Java frames on the stack. For example, if start_thread called JavaMain and then your Java code started running, you will not see the first two frames in the resulting stack. On the other hand, you will see non-Java frames (user and kernel) invoked by your Java code.