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Editorial Team
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Asked: 2026-05-22T01:09:29+00:00

For the profiler which I implement using JVMTI I would like to start measuring

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For the profiler which I implement using JVMTI I would like to start measuring the execution time of all Java methods. The JVMTI offers the events:

  • MethodEntry
  • MethodExit

So this would be quite easy to implement, however I came across this note in the API:

Enabling method entry or exit events will significantly degrade performance on many platforms and is thus not advised for performance critical usage (such as profiling). Bytecode instrumentation should be used in these cases.

But my profiling agent works headless, which means the collected data is serialized and sent via socket to a server application displaying the results. How should I realize this using byte code instrumentation. I am kind of confused how to go on from here. Could someone explain to me, if I have to switch the strategy or how can I approach this problem?

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1 Answer

  1. Editorial Team

    I don’t know about the Sun JVM but the IBM JVM goes into what we call FullSpeedDebug mode when you request the MethodEntry/Exit events…. FSD slows down execution quite a bit.

    As you say you can use BCI as my profiler does but unless you are selective about which methods you instrument you will also see a slow down. For example my profiler inserts a if(profiling) callProfilerHook() on every entry and all of the possible exits in a method all object creates and some other areas as well…. These additional checks can slow down execution by over 50%…

    As for how to BCI… well I wrote my own C library to do it… it’s technically not hard (hint just delete the StackMapTable) but I may take you a while.. Alternatively you can use ASM et. al.

    Finally… you callBackHook will add overhead and on small methods render the reported CPU/Clock time meaningless unless you perform some sophisticated overhead calculation… even if you do this your callback code affects the shape of the processor L1 caches and the Java code becomes less efficient because it has less room..

    My profiler basically ignores the reported times as I visualize the execution in an interesting way… I’m looking to understand the flow of all of the code, in fact in most cases what code is running (most Java projects have no idea of the millions on lines of third-party code running in their app)

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