Instrumenting code is a powerful technique for monitoring the performance of Java applications. By using Java's Traceable and Instrumented classes, developers can gather detailed information about how their code is running and identify potential bottlenecks.
Instrumentation can be used to monitor many aspects of an application's performance, including memory usage, garbage collection activity, and thread execution. In this article, we'll focus on using instrumentation to monitor method execution times.
Instrumentation allows developers to gather performance data without having to modify their code. This is especially useful for monitoring production systems, where code changes can be difficult to deploy.
Instrumentation can also be used to monitor code that is not under the developer's control, such as third-party libraries. In this case, instrumentation can be used to gather data about how the library is being used and identify potential performance issues.
Java's Traceable and Instrumented classes provide a simple way to instrument code and collect performance data. These classes are part of the java.lang.instrument package and are available in Java 5 and later.
To use the Traceable and Instrumented classes, developers need to create a subclass of java.lang.instrument.Instrumentation and override the premain() method. This method is called before the application's main() method is executed.
In the premain() method, the developer needs to create an instance of their Traceable or Instrumented subclass and register it with the Java Virtual Machine (JVM). The JVM will then call the methods in the Traceable or Instrumented subclass when certain events occur, such as a method being invoked.
Here's a simple example of a Traceable subclass that tracks method invocation times:
public class MyTraceable extends Traceable {
private Map<String, Long> methodTimes = new HashMap<String, Long>();
@Override
public void traceMethodInvocation(MethodInfo methodInfo) {
String methodName = methodInfo.getMethodName();
if (methodTimes.containsKey(methodName)) {
long totalTime = methodTimes.get(methodName);
totalTime += methodInfo.getElapsedTime();
methodTimes.put(methodName, totalTime);
} else {
methodTimes.put(methodName, methodInfo.getElapsedTime());
}
}
public Map<String, Long> getMethodTimes() {
return methodTimes;
}
}
In the example above, the traceMethodInvocation() method is called whenever a method is invoked. The methodInfo parameter contains information about the method, including the method's name and the amount of time that it took to execute.
The traceMethodInvocation() method stores the method invocation times in a map. The getMethodTimes() method can be used to retrieve the map.
To register the MyTraceable class with the JVM, the premain() method can be used:
public static void premain(String args, Instrumentation instrumentation) {
instrumentation.addTransformer(new MyTraceable());
}
In the premain() method, the MyTraceable class is registered with the instrumentation object. The instrumentation object is used to add class transformers to the JVM. A class transformer is a piece of code that modifies the bytecode of a class before it is loaded by the JVM.
The MyTraceable class is registered as a class transformer so that its traceMethodInvocation() method will be called whenever a method is invoked.
Once the MyTraceable class is registered, the application can be run as usual. The traceMethodInvocation() method will be called every time a method is invoked, and the method invocation times will be tracked in the MyTraceable object.
The data collected by the MyTraceable class can be used to identify potential bottlenecks in the application. To do this, the data can be sorted by the amount of time spent in each method.
The methods that take the longest amount of time to execute are potential bottlenecks. These methods can be further analyzed to see if they can be optimized.
Instrumentation is a powerful technique for monitoring the performance of Java applications. By using Java's Traceable and Instrumented classes, developers can gather detailed information about how their code is running and identify potential bottlenecks.