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Observability of the Java Virtual Machine

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The JVM is one of the most observable runtimes. It provides us lots of tools for troubleshooting a JVM application in production. 1. Thread observability Threads are how the JVM actually does work. When something is wrong in production, the symptom is almost always a thread: stopped, blocked, leaking etc. Thread dumps work on any JVM with no  instrumentation, no agents, no restarts. <Example project link with /threaddump endpoint>         // (1) Deadlock — two threads grab the same pair of locks in opposite order.         new Thread(() -> grab(LOCK_A, LOCK_B), "deadlock-A-then-B").start();         new Thread(() -> grab(LOCK_B, LOCK_A), "deadlock-B-then-A").start(); http://localhost:8080/actuator/threaddump To list the JVMS, we can use the command below. PS C:\observe-jvm> jps -lv 25296 jdk.jcmd/sun.tools.jps.Jps -Dapplication.home=C:\Program Files\Microsoft\jdk-21.0.3.9-hotspot -Xms8m -Djdk.module.main=...
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Revisiting the Axon Framework

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Axon is a CQRS Framework for scalable and high-performance applications. Lets look at the evolution of the API from Axon 1 to Axon 3: With Axon 3, Aggregate   interface is introduced and we pass the method to the execute() method: Axon 1: @CommandHandler public void handle( final ChangeClientNameCommand command, UnitOfWork unitOfWork) { Client client = clientRepository .load( new StringAggregateIdentifier(command.getClientIdentifier())); client.changeNameAs(command.getNewName()); } Axon 3: @CommandHandler public void handle( final ChangeClientNameCommand command) { Aggregate client = clientRepository .load(command.getClientIdentifier().toString()); client.execute(aggregateRoot -> aggregateRoot.changeNameAs( command .getNewName())); } In the past Client entity was derived from a class called "AbstractAnnotatedAggregateRoot". Now in Axon 3, it is just a POJO and the Repository does not return a Client entity but an "aggregate...
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The WeakReference class, monitoring memory leak and garbage collection in a Java application

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 Below is a Stack implementation that uses an internal resizeable array structure.  public class MyStack< T > implements Stack< T > { private static final int CAPACITY = 100 ; private Object[] array ; private int pos = 0 ; public MyStack () { this . array = new Object[ CAPACITY ] ; } @Override public void push ( T item) { if ( pos >= array . length / 2 ) { Object[] newArray = new Object[ pos * 2 ] ; System. arraycopy ( array , 0 , newArray , 0 , array . length ) ; array = newArray ; } array [ pos ++] = item ; } @Override public T pop () { if (isEmpty()) { throw new RuntimeException( "empty stack" ) ; } @SuppressWarnings ( "unchecked" ) T item = ( T ) array [ pos - 1 ] ; pos -= 1 ; return item ; } @Override @SuppressWarnings ( "unchecked" ) public T peek...
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Simplescalar Simulator - Part 2: sim-outorder.c

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sim-outorder implements a very detailed out-of-order issue superscalar processor with a two-level memory system and speculative execution support. This simulator is a performance simulator, tracking the latency of all pipeline operations. Let's have a look at SimpleScalar simulator's code related to iTLB. (Instruction Translation Lookaside Buffer) First, there are simulator options related to iTLB: /* register simulator-specific options */ void sim_reg_options(struct opt_odb_t *odb) { /* TLB options */ opt_reg_string(odb, "-tlb:itlb", "instruction TLB config, i.e., { |none}", &itlb_opt, "itlb:16:4096:4:l", /* print */TRUE, NULL); opt_reg_int(odb, "-tlb:lat", "inst/data TLB miss latency (in cycles)", &tlb_miss_lat, /* default */30, /* print */TRUE, /* format */NULL); } /* check simulator-specific option values */ void  sim_check_options(... ) { /* sim-outorder.c  line: 1098*/   /* u...
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Notes on Java Performance

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Performance monitoring, performance profiling and performance tuning are different activities. For example, monitoring is not an intrusive action but profiling is. Because it may be changing the responsiveness of the running application. User CPU utilization vs Kernel CPU utilization: The ideal situation is when Kernel CPU utilization is 0%. So ideally, all the CPU cycles must be spent on our application code. JVM just in time compiler performs dynamic optimizations. So it makes decisions while the program is running and generates native code with better performance. Java 1.0 was completely interpreted but beginning with Java 1.1.8, there was a just-in-time compiler. It was 8 times faster. After that, paralellizing the garbage collector was a huge improvement. There are hundreds of Java tuning flags. https://github.com/ScottOaks/JavaPerformanceTuning Boolean flags use this syntax: -XX:+FlagName enables the flag, and -XX:-FlagName disables the flag. 32 BIT VS 64 BIT Fr...
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