👀 Views: 24 💬 Answers: 1 📅 Created: 2025-06-22

java concurrency performance concurrenthashmap java17 Java

I've searched everywhere and can't find a clear answer. I'm stuck on something that should probably be simple. I'm facing significant performance degradation when using `ConcurrentHashMap` in a high-load multi-threaded environment in my Java 17 application. Specifically, during peak load times, operations like `putIfAbsent` and `remove` are noticeably slower, with response times increasing to over 500 milliseconds. I've implemented a caching mechanism that relies heavily on this data structure, and I'm worried that the current performance will lead to a bottleneck as traffic increases. Here's a simplified version of my code: ```java import java.util.concurrent.ConcurrentHashMap; import java.util.concurrent.ExecutorService; import java.util.concurrent.Executors; public class CacheExample { private final ConcurrentHashMap<String, String> cache = new ConcurrentHashMap<>(); public void putValue(String key, String value) { cache.putIfAbsent(key, value); } public String getValue(String key) { return cache.get(key); } public void removeValue(String key) { cache.remove(key); } public void simulateLoad(int numberOfThreads) { ExecutorService executor = Executors.newFixedThreadPool(numberOfThreads); for (int i = 0; i < numberOfThreads; i++) { final String key = "key" + i; executor.submit(() -> { putValue(key, "value" + i); getValue(key); removeValue(key); }); } executor.shutdown(); } } ``` In my testing, even with a modest number of threads (around 100), I find that the average performance drops significantly. I've tried tuning the size of the thread pool and the initial capacity of the `ConcurrentHashMap`, but it doesn't seem to help. The GC logs also indicate regular full GCs, which might be contributing to the latency. I'm using OpenJDK 17 on a 16-core machine with 32GB RAM and the default garbage collector. Is there a more efficient way to handle high concurrency with `ConcurrentHashMap`? Should I be considering a different data structure or perhaps using a custom implementation? Any insights into performance optimization or alternative approaches would be greatly appreciated! This is part of a larger REST API I'm building. Cheers for any assistance!