This article examines why Java is often labeled slow, dispels three major misconceptions about interpretation, garbage‑collection pauses, and startup time, presents concrete benchmark data against other languages, and explains how modern JVM features and framework choices actually shape performance.
A recent study shows that deliberately shortening or misspelling Java variable names can reduce latency and increase throughput by up to 49%, revealing hidden performance costs in the JVM's string handling and prompting a data‑driven, selective naming strategy for high‑throughput systems.
This article introduces JMH, explains why it outperforms simple loops or other tools, and provides step‑by‑step Maven setup, benchmark creation, execution, and advanced annotations such as @Warmup, @Fork, @Setup, Blackhole usage, and SpringBoot integration for accurate Java micro‑benchmarking.
This article introduces JMH, explains its key features, and presents microbenchmark results comparing thread‑exclusive, thread‑shared, Snowflake, UUID, and Snowflake‑algorithm GUID generation methods under various thread counts, accompanied by the full Java test code.
This article explains why measuring Java code performance is essential, introduces the JMH microbenchmark framework, details its key annotations and configuration options, and provides step‑by‑step instructions and code examples for building, writing, and running benchmarks to obtain reliable results.
This article explains why simple timing is unreliable in Java due to JIT compilation, introduces the official JMH tool for microbenchmarking, outlines best‑practice tips, demonstrates how to set up a Maven project, write benchmark code, run tests, interpret results, and details each JMH annotation.
This article explains what benchmarking is, introduces the JMH framework for Java, shows how to add JMH dependencies, walks through a simple "Hello JMH" example with full source and output, demonstrates using JMH with Spring Boot, details the most important options and annotations, and highlights common pitfalls to avoid when writing reliable micro‑benchmarks.
This article explains how to quickly benchmark Java code performance, introduces the JMH microbenchmarking tool, provides sample JMH benchmark code comparing System.currentTimeMillis() and System.nanoTime(), and presents the results showing nanoTime's superior throughput.
This article introduces Java Microbenchmark Harness (JMH), explains why warm‑up is necessary, details common annotations, shows how to set up a Maven project, provides a complete benchmark example comparing LinkedList iteration methods, and demonstrates how to run and interpret the results.
This article introduces JMH, the official Java microbenchmarking tool, explains why warm‑up is needed, shows how to build a Maven project, provides a complete LinkedList iteration benchmark example, demonstrates common JMH annotations, and outlines how to run and interpret benchmark results.
This article details the performance tuning of Vivo's Java-based push recommendation service, covering background challenges, measurement metrics, hotspot code and JVM GC optimizations, custom split utilities, map key improvements, off‑heap caching, and the resulting throughput and latency gains.
The Vivo Push recommendation service was dramatically accelerated by replacing regex‑based String.split with a custom utility, converting massive String‑key maps to long keys, and moving large caches off‑heap, which together cut latency by about 32%, boosted throughput roughly 45% and nearly doubled overall performance.
This article introduces Java Microbenchmark Harness (JMH), explains why warm‑up is needed, walks through project setup with Maven, demonstrates benchmark code for LinkedList iteration, details common JMH annotations, shows how to run and interpret results, and concludes with practical usage tips, while also mentioning a promotional book giveaway.
This article introduces Java Microbenchmark Harness (JMH), explains why warm‑up is needed, shows how to build a benchmark project with Maven, provides a complete LinkedList iteration benchmark example with all relevant JMH annotations, demonstrates execution commands, and interprets the resulting performance reports.
This article explains how to use JMH for precise Java micro‑benchmarks, covering JVM warm‑up, project setup with Maven, writing benchmark methods, configuring annotations, running tests, interpreting results, and provides practical code examples and tips for reliable performance measurement.
This article examines the performance of four Java integer-to-string conversion methods—Integer.toString, String.valueOf, a+"", and ""+a—by conducting raw loop tests and detailed JMH microbenchmarks, analyzing JVM warm-up, JIT, and OSR effects to reveal reliable measurement practices.
This article introduces JMH, the Java Microbenchmark Harness, explains why traditional main‑method benchmarks are unreliable, and provides step‑by‑step instructions, code examples, and best‑practice annotations for accurately measuring method‑level performance in Java applications.
JMeter’s reported throughput can be misleading because it includes local processing time, especially when response validation such as regex extraction adds overhead, leading to significant underestimation of actual server load; the article demonstrates this with experiments and suggests micro‑benchmark corrections to obtain accurate results.
The article explains how to isolate and measure the time spent on request signature generation in an API performance test using micro‑benchmarks, provides Java and Groovy code examples, and discusses subtracting non‑request overhead to obtain calibrated throughput.
This article introduces JMH, explains how to add dependencies, write and run microbenchmarks for string concatenation, describes key annotations, highlights common pitfalls, and shows how to package, visualize, and integrate JMH benchmarks within Java projects.
JMH is a Java harness that enables precise, reproducible microbenchmarking through annotations, state management, threading, and profiling, and this guide walks through its core features—benchmark modes, state scopes, setup/teardown, fork control, blackholes, and advanced options—illustrated with sample code and results.
This article describes how Tubi’s engineering team built and optimized a real‑time recommendation backend, using ScalaMeter microbenchmarks and wrk2 load testing to measure latency, throughput and error rates, and demonstrates scaling the service across multiple machines with custom scripts.
This article introduces Java Microbenchmark Harness (JMH), explains why precise benchmarking matters, and walks through twelve typical testing pitfalls—such as dead‑code elimination, constant folding, loop misuse, fork isolation, method inlining, false sharing, branch prediction, and multithreading—showing how JMH helps developers obtain reliable performance measurements.
This article shares Vipshop’s extensive Java performance‑optimization experience, introduces the open‑source VJTools project for profiling and troubleshooting, and presents practical insights on common pitfalls, reliable techniques, JIT fundamentals, micro‑benchmarking, useful tools, and real‑world case studies from Vipshop’s architecture.
The author compares Rust and Go using a series of micro‑benchmarks across common algorithms, presenting CPU time, elapsed time, memory usage and code size, and concludes that while Rust is roughly 30 % faster, Go produces about 30 % less code, making it a better choice for enterprise applications.
