copy from https://blog.codecentric.de/en/2011/10/measure-java-performance-sampling-or-instrumentation/
In recent discussions, I noticed a confusion about the differences between measuring with Sampling andInstrumentation.
I hear about which one should be better than others, but I think it is important to understand how they work. Some tools even ask to choose between those two directly at startup, like JProfiler 7 in the picture on the right.
But how can you choose whichever fits best the given situation without understanding those concepts?
So lets step back for a moment and think about how to measure the performance of a computer program:
a) We add measurement code into the real code. The measurement code tells us how long the real code took to execute.
b) We add an external observer, which examines the running real code and tells us what code executed at any given time.
Both approaches will work and give results, however they are fundamentally different! So I am going to explain how they work.
The examples I will use in this post are based on Java and the JVM, but are applicable to other languages, especially .Net with its CLR as well.
So how can we in Java add measurement code?
It turns out there are actually a few established methods for measuring by adding measurement code:
- Manually add some System.out.println code for important methods. Execution time is printed to the log.
- Create some kind of javax.management beans to record time. They can recorded manually and queried with tools later on.
- Use AOP Libraries to build Aspects which record code execution time.
- Build an JVMTI Agent, which uses APIs to add code and record execution time.
Those methods overlap in one way or the other. In the end, all will have code on top of the real application code for calculating the time used to execute. The former approaches usually involve some kind of basic file logging or JMX (JSR-3, JSR-250). JMX was designed to gather metrics for management and to be able to change settings. The latter methods are more dynamic and do not require hardcoding during development. Adding code like this is called “instrumentation” and usually involves bytecode modification.
How can we observe externally in Java?
Ideally we would like to observe from outside the runtime (the JVM). JMX was made accessible externally inJSR-160, but JMX prevents us from seeing many details, as it is just high level. While there are other APIs that allow us reading the state of the JVM, none really tells us how fast code executes. To do better pseudo-external observation, we create a thread, which has this observing duty, just inside the monitored JVM. That thread will look from time to time into the other threads and record their activity. The interval between those inspections should be small enough to capture many details. This external observation is called “(time-)sampling”. With timesampling, the monitoring is not continous, but it does cover all requests or threads.
In this post, I am going to compare both approaches using an easy to understand example. Because it is designed to be easily understandable, it uses naive code and does not contain optimizations.
Example code
So first off, here is the code:
Attached as ZIP, or SamplingVsInstrumentation on my private GitHub.
We have a class Demo, which running all our fake production code. It has a few methods called like this: method100ms()
The name includes the average execution time to allow easier readings of the results. Unfortunately in real code no method name will have this information 
There is a method0ms(), which does some minor code execution, so it is not zero milliseconds, but much faster than one millisecond.
DemoRunner has two methods for executing Demo class business methods;
a) Mixed Demo will run Demo with [100, 1, 100, 500, 1, 100, 1, 50, 50] methods
b) Mass Demo will run the 0ms method a hundred million times.
So we can run it like this:
public static void main(final String[] args) {
mixedDemo();
massDemo();
}
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And it will complete. But we do not know anything about it. We can use an external tool to get some kind of result: A stopwatch.
On my machine (Dell E6420, Intel 2520 2,5Ghz 4 Core CPU, 64bit Win, Java 1.6.0_27) it takes about a second to run the mixed demo and almost three seconds for the plenty 0ms method invocations.
So let us add some outside measurement code to get more precise numbers:
public static void main(final String[] args) {
long start = System.currentTimeMillis();
mixedDemo();
long end = System.currentTimeMillis();
System.out.printf("%s Demo completed in %dms%n", DemoType.MIXED, end - start);
start = System.currentTimeMillis();
massDemo();
end = System.currentTimeMillis();
System.out.printf("%s Demo completed in %dms%n", DemoType.MASS, end - start);
}
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Which gives us:
Running Demo with [100, 1, 100, 500, 1, 100, 1, 50, 50] methods MIXED Demo completed in 967ms Running Demo with 100000000 0ms methods MASS Demo completed in 2781ms |
Lets talk about Overhead
When measuring, you will distort the results. Generally speaking, the measured value will differ from the true value by so called systematic error and a random error. Systematic errors are introduced by the measurement instruments and measured or estimated to a certain extend, while random errors cannot be predicted.
When the CPU executes measuring code instead of real code we usually speak of “overhead”, which results in systematic errors in measurements. It also consumes CPU cycles which could have been used by other production code and can as such influence also unmeasured code behaviour. Additionally, the real important overhead is the delay othe regular transactions through the system. Additional system ressource usage can usally be tolerated.
Instrumentation
To better measure what the Demo code is doing, I will build an instrumentation agent based on AOP withaspectj loadtime weaving. This will add some extra code invocation to some methods I specify with a so called “pointcut expression”.
aspectj will enhance the bytecode of classes when they are loaded. The pointcut expression describes the signature of the method which shall be instrumented.
In this case an @Around advice is used, which is passed the so called JoinPoint, which is actually a pointer to the real code that was about to be executed. Aspectj uses an JVMTI Agent and does the hard work for me. I just have to write a so called Aspect to do my measurements.
The interesting part of the code is this:
@Around(" call(void de.codecentric.performance.Demo.method* (..)) ")
public void aroundDemoMethodCall(final ProceedingJoinPoint thisJoinPoint) throws Throwable {
long start = System.currentTimeMillis();
thisJoinPoint.proceed();
long end = System.currentTimeMillis();
String currentMethod = thisJoinPoint.getSignature().toString();
if (executionPath.size() < MAX_EXECUTION_PATH) {
executionPath.add(currentMethod);
}
MethodStatistics statistics = methodStatistics.get(currentMethod);
if (statistics == null) {
statistics = new MoreMethodStatistics(currentMethod);
methodStatistics.put(currentMethod, statistics);
}
statistics.addTime(end - start);
overhead += System.currentTimeMillis() - end;
}
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As you can see, I give explicit method names to intercept: call(void de.codecentric.performance.Demo.method* (..)).
I record start time at the beginning, and endtime after executing the method. Additionally I store the current method name in “execution path” (unless it reached its maximum), and I record method statistics for the current method. I also record how much time I spent recording this data in a field called “overhead”.
Running this Instrumentation gives me this:
Running Demo with [100, 1, 100, 500, 1, 100, 1, 50, 50] methods MIXED Demo completed in 950ms Trace Aspect recorded following results: void de.codecentric.performance.Demo.method500ms() 501ms (min: 501ms, max: 501ms) - 1 invocations void de.codecentric.performance.Demo.method100ms() 303ms (min: 101ms, max: 101ms) - 3 invocations void de.codecentric.performance.Demo.method50ms() 102ms (min: 51ms, max: 51ms) - 2 invocations void de.codecentric.performance.Demo.method1ms() 6ms (min: 2ms, max: 2ms) - 3 invocations Code Execution Path: void de.codecentric.performance.Demo.method100ms() void de.codecentric.performance.Demo.method1ms() void de.codecentric.performance.Demo.method100ms() void de.codecentric.performance.Demo.method500ms() void de.codecentric.performance.Demo.method1ms() void de.codecentric.performance.Demo.method100ms() void de.codecentric.performance.Demo.method1ms() void de.codecentric.performance.Demo.method50ms() void de.codecentric.performance.Demo.method50ms() Agent internal Overhead 2ms Agent Overhead 91ms Running Demo with 100000000 0ms methods MASS Demo completed in 7261ms Trace Aspect recorded following results: void de.codecentric.performance.Demo.method0ms() 2892ms (min: 0ms, max: 2ms) - 100000000 invocations Code Execution Path: void de.codecentric.performance.Demo.method0ms() void de.codecentric.performance.Demo.method0ms() [...] void de.codecentric.performance.Demo.method0ms() void de.codecentric.performance.Demo.method0ms() Execution Path incomplete! Agent internal Overhead 2836ms Agent Overhead 4ms |
We can clearly see, the instrumentation caught all the different 8 method invocations in the first example and did quite accurately record the time spent. It can tell us also in which order these methods executed. But it has a problem, as the second output shows us. The execution path was very long. One hundred million executions the aspect would need to keep in memory. Thats why I put in a limit there.
So what about Overhead?
There are two kinds of overhead measured by my demo code. Both are not really accurate, but give good indication where time is spent by the agent.
The internal one counts the time spent the agent doing the statistics. It is internal because it cannot be differentiated externally and looks like time the actual business method takes to execute. And there is the overhead which can be seen externally. This is the time required to set up the instrumentation and to print the results.
We can see that the overhead of instrumentation is low in the first case, but outputting the data to standard out took some time. In the second demo, the output was faster, because there was less data, but the internal overhead was huge. However there is a problem with the overall overhead. The internal overhead differs from the total time minus method time. 7261ms – 2892ms = 4369 ms of the time was not spent in running real code. But the agent only claims to account for 2836ms. The delta is accounted on inaccuracy both of the external and internal time measures.And of course there is some code execution inside the instrumentation which is not added to the overhead time (like the method invocation cost ofaroundDemoMethodCall(JoinPoint thisJoinPoint))
Sampling
My sampling code will create a daemon thread, which will look into the main thread every 10ms and track the activity. The interesting code of that agent is this:
@Override
public void run() {
lastSample = System.currentTimeMillis();
while (true) {
try {
Thread.sleep(interval);
} catch (InterruptedException e) {
Thread.currentThread().interrupt();
}
String currentMethod = getCurrentMethod();
long currentSample = System.currentTimeMillis();
addMeasurementsIfStillInMethod(currentMethod, currentSample);
lastMethod = currentMethod;
lastSample = currentSample;
overhead += System.currentTimeMillis() - currentSample;
}
}
private void addMeasurementsIfStillInMethod(final String currentMethod, final long currentSample) {
if (currentMethod.equals(lastMethod)) {
MethodStatistics statistics = methodStatistics.get(currentMethod);
if (statistics == null) {
statistics = new MethodStatistics(currentMethod);
methodStatistics.put(currentMethod, statistics);
}
statistics.addTime(currentSample - lastSample);
} else {
if (executionPath.size() < MAX_EXECUTION_PATH) {
executionPath.add(getParentMethod() + " > " + currentMethod);
}
}
}
private String getCurrentMethod() {
StackTraceElement topOfStack = monitoredThread.getStackTrace()[0];
return formatStackElement(topOfStack);
}
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So the agent will sleep its given interval, wake up and find out what method is executing on the monitored thread using monitoredThread.getStackTrace()[0]. Then it records the current time to find out how long it has been sleeping since the last sample (this will be likely around 10ms but might differ!). Then it will find out if the code is still in the same method as the last time. This is quite important, because the agent can only record the execution time when the same method is seen back-to-back. If the method is seen the first time, we add it to the execution path (also respecting the same limit here). Then we update internal states and calculate overhead for the statistics part.
Agent monitoring thread main with sampling interval of 10ms Running Demo with [100, 1, 100, 500, 1, 100, 1, 50, 50] methods MIXED Demo completed in 916ms Agent stopped - Results: void de.codecentric.performance.Demo.method500ms() 488ms void de.codecentric.performance.Demo.method100ms() 285ms void java.lang.Thread.sleep() 101ms Code Execution Path: void de.codecentric.performance.Demo.runCode() > void de.codecentric.performance.Demo.method100ms() void de.codecentric.performance.Demo.runCode() > void de.codecentric.performance.Demo.method500ms() void de.codecentric.performance.Demo.runCode() > void de.codecentric.performance.Demo.method100ms() void de.codecentric.performance.Demo.method50ms() > void java.lang.Thread.sleep() Agent internal Overhead 4ms Agent Overhead 36ms Agent monitoring thread main with sampling interval of 10ms Running Demo with 100000000 0ms methods MASS Demo completed in 2959ms Agent stopped - Results: void de.codecentric.performance.Demo.method0ms() 2736ms Code Execution Path: void de.codecentric.performance.DemoRunner.massDemo() > void de.codecentric.performance.DemoRunner.massDemo() void de.codecentric.performance.Demo.runCode() > void de.codecentric.performance.Demo.method0ms() Agent internal Overhead 0ms Agent Overhead 0ms |
So we can clearly see that sampling had problems capturing the 1 millisecond methods. But we see aThread.sleep(), which we have not seen with instrumentation. Because the sampling has much easier access to previous executing method using monitoredThread.getStackTrace()[1], we discover that it is method50ms which is invoking thread sleep. But the execution path is missing a few short invocations. The invocations of 100ms, 1ms and 100ms are seen as one about 200ms long invocation of the method called 100ms. It kind of automatically filters the performance wise irrelevant 1ms execution, so this chain is presented as 200ms execution of method100ms. This is mainly due to the fact that the agent will not see code which returns faster than the sampling interval. When doing sampling, there are other aspects to consider in respect to the sampling interval. A good paper on that topic is: “Evaluating the Accuracy of Java Profilers”
As we can see, sampling gives the expected results on the second demo code without problems with the executionpath length.
Overhead again
So we can see that in the first example, the overhead is quite similar to instrumentation. But in the second example the internal overhead is drastically lower. We only miss 223ms (2959ms – 2736ms) but this seems not to be caused by our internal measures. Also this agent runs in a different thread, which makes some overhead not resulting in longer execution time of the real code. And this would utilize multiple cores easily.
Conclusion
Both, instrumentation and sampling, have different characteristics with their own advantages and disadvantages.
They are caused by the fundamental difference and can be mitigated to some extend by clever construction of the agents but never removed.
Instrumentation
- Has access to invocation count, precise actual/max/min/avg execution times and precise invocation order.
- Needs to have configuration on what methods to instrument. Instrumentation needs to be balanced to exclude mass invocations or invocations where the measuring code outweighs the measured code.
- Generally has much more data to process.
Sampling
- Stable overhead, mainly determined by sampling interval, not by measured code.
- Execution Hot Spots are shown instead of fine granular execution path and time.
- Can discover unknown code.
- Runs easily on separate core.