Epsilon: A No-Op Garbage Collector

Epsilon is a no-op garbage collector in Java (introduced in JEP 318, Java 11) that allocates memory but does not reclaim it, leading to a JVM crash with an OutOfMemoryError when the heap is exhausted. It’s enabled with -XX:+UnlockExperimentalVMOptions -XX:+UseEpsilonGC. Below, I’ll explain Epsilon briefly and provide a sample use case with code to demonstrate its application.

Important Concepts

Purpose:

A minimal garbage collector that avoids memory reclamation to eliminate GC overhead.

Key Features:

Allocates memory but does not free it.
Minimal latency and no GC pauses.
Crashes the JVM when memory is exhausted.

Use Cases:

Performance testing to measure GC overhead.
Memory pressure testing to validate heap limits.
Short-lived jobs where GC is unnecessary.
Ultra-low latency applications with predictable memory usage.

Risks:

Unsuitable for production due to crash risk.
Requires precise memory management to avoid heap exhaustion.

Sample Use Case: Memory Pressure Testing

Scenario: A developer wants to test whether a Java application’s memory usage stays within a specific heap limit (e.g., 1.5 GB) during execution. Using Epsilon, the application will crash if it exceeds the allocated heap, providing a clear validation of memory constraints.

Why Epsilon?: Epsilon ensures no memory is reclaimed, so any allocation beyond the heap limit triggers an immediate crash, making it ideal for testing memory boundaries without interference from GC activity.

Sample Code: This program simulates a workload that allocates objects to test heap usage. It creates a list of byte arrays, each consuming a fixed amount of memory, until the heap is exhausted.

import java.util.ArrayList;
import java.util.List;

public class MemoryPressureTest {
    public static void main(String[] args) {
        List<byte[]> memoryHog = new ArrayList<>();
        int allocationSize = 10 * 1024 * 1024; // 10 MB per allocation
        int allocationCount = 0;

        try {
            while (true) {
                // Allocate a 10 MB byte array
                byte[] chunk = new byte[allocationSize];
                memoryHog.add(chunk);
                allocationCount++;
                System.out.println("Allocated " + (allocationCount * 10) + " MB");
            }
        } catch (OutOfMemoryError e) {
            System.out.println("Heap exhausted after allocating " + (allocationCount * 10) + " MB");
            System.out.println("Test complete: Application stayed within heap limit or crashed as expected.");
        }
    }
}Code language: JavaScript (javascript)

How to Run:

Compile the code: javac MemoryPressureTest.java
Run with Epsilon and a fixed heap size (e.g., 1.5 GB):

java -XX:+UnlockExperimentalVMOptions -XX:+UseEpsilonGC -Xmx1500m MemoryPressureTestCode language: CSS (css)

Observe the output. The program will allocate 10 MB chunks, print progress, and crash with an OutOfMemoryError when the 1.5 GB heap is exhausted.

Expected Output (example):

Allocated 10 MB
Allocated 20 MB
...
Allocated 1490 MB
Heap exhausted after allocating 1500 MB
Test complete: Application stayed within heap limit or crashed as expected.Code language: JavaScript (javascript)

Analysis:

The program crashes when it exceeds the 1.5 GB heap, confirming the application’s memory usage.
If the developer expects the application to use less than 1.5 GB, a crash indicates a memory issue (e.g., a leak or miscalculation).
By adjusting -Xmx (e.g., to 1 GB with -Xmx1000m), developers can test stricter limits.

Why This Use Case Fits Epsilon:

No GC Interference: Epsilon ensures the test reflects raw memory allocation without GC reclaiming memory, providing a precise measurement of heap usage.
Clear Failure Mode: The crash (via OutOfMemoryError) is a definitive signal that the heap limit was exceeded, simplifying validation.
Controlled Environment: The predictable allocation pattern (10 MB chunks) allows developers to estimate and verify memory needs.

Real-World Application: This approach is useful for testing microservices or batch jobs where memory budgets are strict. For example, a containerized application in Kubernetes might have a 1.5 GB memory limit, and Epsilon can validate that the application respects this limit during development or CI/CD pipelines.