franz1981

Scalability Test: Spring vs Quarkus — Local, 20 Apr 2026

Environment

• Machine: AMD Ryzen 9 7950X 16-Core (32 CPUs), 64 GB RAM
• OS: Fedora Linux 43, kernel 6.19.11-200.fc43.x86_64
• Java: 25.0.2-tem
• Quarkus: 3.34.3
• Spring Boot 4: 4.0.5
• CPU pinning: app=0-3, db=4-6, load-gen=7-9, monitor=10, otel=11-13
• Network: host networking (--use-container-host-network)

franz1981

import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

# 1. Set up parameter ranges
# Global injection rate from 10k to 90k TPS
Lambdas = np.linspace(10000, 90000, 20)
# Service times from 5 us to 150 us
S_FJ = np.linspace(0.000005, 0.000150, 20)
S_IO = np.linspace(0.000005, 0.000150, 20)

franz1981

	private static final ScopedValue<ExecutionContext> EXECUTION_CTX = ScopedValue.newInstance();

	private static final class ExecutionContext {
		private final ReentrantLock lock = new ReentrantLock(true);
		private final AtomicReference<CountDownLatch> suspended = new AtomicReference<>(null);
		/**
		 * This can be called by any thread!
		 */
		public void resume() {
			var toResume = suspended.getAndSet(null);

franz1981

/*
 * Copyright 2025 The Netty Project
 *
 * The Netty Project licenses this file to you under the Apache License,
 * version 2.0 (the "License"); you may not use this file except in compliance
 * with the License. You may obtain a copy of the License at:
 *
 *   https://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software

franz1981

Improving Service Time in Queuing Theory: A Disproportionate Impact on Response Time

In queuing theory, enhancing the service time—the duration it takes to serve a customer or process a request—can have a remarkably significant and often disproportionate impact on the overall response time. While it may seem intuitive that faster service leads to shorter waits, the mathematical principles of queuing theory reveal a non-linear relationship, meaning a small improvement in service speed can yield a much larger reduction in total time spent in the system, especially as the system becomes busier.

The response time is the total time a customer or request spends in a system, from arrival to departure. It is the sum of the waiting time (time spent in the queue) and the service time itself. The effectiveness of improving service time is most clearly understood through its effect on system utilization.

---

The Role of System Utilization

franz1981

#include <stdio.h>
#include <string.h>
#include <stdlib.h>
#include <sys/epoll.h>
#include <sys/eventfd.h>
#include <unistd.h>
#include <pthread.h>
#include <errno.h>

void* writer_thread(void* arg) {

franz1981

package redhat.app.services.benchmark;

import java.util.concurrent.TimeUnit;

import org.openjdk.jmh.annotations.Benchmark;
import org.openjdk.jmh.annotations.BenchmarkMode;
import org.openjdk.jmh.annotations.CompilerControl;
import org.openjdk.jmh.annotations.Fork;
import org.openjdk.jmh.annotations.Measurement;
import org.openjdk.jmh.annotations.Mode;

franz1981

package red.hat.puzzles.loom;

import java.lang.invoke.MethodHandle;
import java.lang.invoke.MethodHandles;
import java.lang.reflect.InvocationTargetException;
import java.lang.reflect.Method;
import java.util.concurrent.TimeUnit;

import org.openjdk.jmh.annotations.Benchmark;
import org.openjdk.jmh.annotations.BenchmarkMode;

franz1981

$ java -version
openjdk version "11.0.16.1" 2022-08-12
OpenJDK Runtime Environment Temurin-11.0.16.1+1 (build 11.0.16.1+1)
OpenJDK 64-Bit Server VM Temurin-11.0.16.1+1 (build 11.0.16.1+1, mixed mode)

Running application: https://github.com/topicusonderwijs/tribe-krd-quarkus

franz1981

import java.util.Objects;

/**
 * It's a growable ring buffer that allows to move tail/head sequences, clear, append, set/replace at specific positions.
 */
final class ArrayRingBuffer<T> {

    private static final Object[] EMPTY = new Object[0];
    // it points to the next slot after the last element
    private int tailSequence;