GC easy – Universal Java GC Log Analyser

Imagine this. A small script is fetching product data from an API, adjusting the format, sending it forward. Nothing fancy. Part of your usual flow. Then, without any warning, the JVM’s garbage collector stutters. Everything freezes in an instant while you’re focused elsewhere. The impact hits late. Imagine your app’s memory divided into two areas. When new data arrives, it sits briefly in one zone – vanishing quickly, sometimes after just moments. Meanwhile, another section keeps hold of information far more tightly. Unused bits eventually get removed by an automatic cleaner called the garbage collector. Sounds clear? Well, reality tends to twist that clarity.

Even so, when scanning memory completely, every app thread freezes – what people call “stop-the-world.” In running systems, these garbage collector pauses can freeze operations for several seconds straight. Risks that lie beneath often escape checks, surfacing only under live conditions. An unnoticed flaw in how JVM manages memory remains out of sight until deployment brings it forward.

Finding it during tests means fixes happen early, before issues spread. Problems get handled without fuss, just time on your side. If you’d like a foundational refresher before diving deeper, this overview of what Java Garbage Collection is and how it works explains the object lifecycle, heap structure, and collection process in detail.

Simulating Java Garbage Collection Under Production Load

One catalog download hits up to two megabytes when promotions run. A trial setup mimics constant big data chunks piling up in memory using G1GC, capped at four gigabytes maximum heap space:

List<byte[]> storage = new ArrayList<>();
while (true) {
for (int i = 0; i < 40; i++) storage.add(new byte[6_000_000]);
Thread.sleep(40);
if (storage.size() > 600) storage.subList(0, 300).clear();
}

Finding patterns feels impossible once numbers start running together. Fast filling screens make it worse – one line after another disappears too quickly. Watching those GC logs move past slowly drains attention. Then came a moment when a teammate brought up some tools capable of breaking down GC logs. A light idea hit – perhaps those logs could be turned into something easier to follow. With curiosity kicking in, the files landed on GCeasy, processing began, seconds passed. Our eyes stayed fixed on the display. We realized what was happening from the generated report: 

• Over 3,000 humongous allocations
• Heap utilization nearing 94%

The problem was no longer theoretical. It was also measurable.

Java Garbage Collection Analysis with GCeasy

Let me go ahead and break down the three key warnings that the GCeasy report highlighted: 

Warning #1: Heap Usage Near Capacity

Almost out of memory space. Running near maximum – between 3.5 and 3.75 gigabytes used out of a total four. Pressure built up quickly, leaving little room to spare.

                                 Fig : Heap usage Between 3.5-3.75 GB capacity

Warning #2: Throughput vs Pause Time Reality

Looks solid at first glance, yet hides what matters. Throughput 95.014%, average pause 1.19ms, max pause 23.1ms—all looked acceptable. But 6,756 GC events accumulated 8+ seconds stop-the-world time, revealing constant fragmentation pressure.

                                Fig: Key Performance Indicators highlighted in GCeasy Report

Warning #3: Humongous Allocation Triggers

The smoking gun. Every GC was triggered by humongous allocation—the pattern that causes production disasters.

[2026-02-07T13:33:24.956+0300][gc] GC(27816)
Pause Young (Concurrent Start) (G1 Humongous Allocation)
2400M->2400M(4096M) 1.213ms
[safepoint] Application threads stopped: 1.213ms

Humongous Objects in Java Garbage Collection

A single piece of data might take up about two megabytes when the total storage hits four gigabytes under G1GC’s layout. When something passes one megabyte in size, it earns the tag “huge” – such items skip early stages, landing straight in the older section that needs unbroken blocks. Loading a 1.2-megabyte JSON through ObjectMapper.readValue() often pulls on these large chunks again and again. Over time, gaps form between used areas like scattered patches in soil. These voids grow harder to reuse as they become isolated pockets across the space. Finding one big chunk grows tough over time. As it stretches on, fitting pieces in turns fussy. Everything halts once G1GC passes – movement stops dead. Quiet settles, like a held breath, until restart begins, sluggish at first. Waiting hangs thick, then progress creeps ahead.

GC N Pause Full G1 Large Allocation
3841M-> 1022M(4096M) 11,417 ms
Frozen app processes let go after 11.4 seconds one the system paused safely

Hold on. Out of nowhere, that massive G1 chunk clamps the memory tight – heap jumps to 94%, sudden and sharp. Results confirm it happened again. Back comes the pulse, known yet biting. Once more.

Profiling Java Garbage Collection Memory Hotspots

What GCeasy revealed was clear – massive memory usage. We ran async-profiler,  next, mostly out of curiosity. Only then did the trail land on one exact line of code Run the script called profiler with an option for humongous object allocation in G1 heap, set duration to one minute.

The flame graph revealed: CatalogClient.fetchCatalog() → ObjectMapper.readValue() → Arrays.copyOf(byte[], 1.2MB). Fault lay in Jackson’s setup of byte arrays. Start by trying JDK Flight Recorder instead. Huge objects form – then JFR spots them through ‘Allocation Outside TLAB’ signals. A different trace emerges each time memory stretches beyond small blocks.These logs often carry full call paths revealing where oversized allocations happen. Watch those traces closely – they expose what code triggers the spikes.

Reducing Java Garbage Collection Pauses

Streaming deserialization reduces memory usage and GC pressure by processing data in chunks instead of loading it entirely into memory.

Application Fix: Streaming Deserialization

The table demonstrates the new approach that reduces memory usage and improves performance by reading data in chunks through streaming, instead of the old method that loads the entire data into memory.

JVM Fix: G1 Region Size Tuning (Temporary)

Begin running the Java application from app.jar by setting the heap space to 4 megabytes when using G1GC settings.

Today it sits at 2MB. Once upon a time, 1.2MB felt massive – but that didn’t last long. It still matters, though not nearly as much.

Wait – this solution won’t last. Larger sizes slow G1 down. Once JSON crosses 2.1MB, problems will return. That said, when systems face real load, speed rarely holds up for long.

If you’re exploring deeper heap and region tuning strategies, this guide on G1GC tuning best practices provides practical configuration recommendations across different workload patterns.

Modern Java Garbage Collection Alternatives

Modern Java Garbage Collection algorithms like Generational ZGC and Shenandoah minimize pause times while handling large allocations more efficiently than legacy collectors.

Generational ZGC Performance Characteristics

When big chunks of data move through G1GC, trouble follows – its structure just does not handle them well. Enter Java 25: fresh garbage collection tools appear, among them Generational ZGC, which slashes pauses beneath a single millisecond, regardless of size. Large memory requsts? They now blend in without fuss. What once stood out as massive becomes ordinary, swallowed by better flow.

java -XX:+UseZGC -Xmx4g -jar app.jar

Shenandoah GC Pause Behavior

A split second passes here, nearly ten elsewhere – small pauses between cycles in Shenandoah. Then compression begins, shrinking occupied memory by about 20 percent or higher. Space grows leaner, yet speed stays steady.

java -XX:+UseShenandoahGC -Xmx4g -jar app.jar

G1GC vs ZGC vs Shenandoah: Java Garbage Collection Comparison

This table provides a comparative summary of the performance characteristics and preferred usage scenarios of three different Garbage Collectors used in Java: G1GC, ZGC, and Shenandoah.

Choosing the right collector depends heavily on workload patterns, heap size, and latency goals. For a broader evaluation of modern and legacy collectors, this guide on Java Garbage Collection best GC algorithms provides a deeper comparative breakdown.

Preventing Java Garbage Collection Performance Issues

• Spotting issues early comes from keeping GC logs active in running systems – impact on speed is tiny.
• Every now then a tool such as GCeasy catches loops that repeat, skipping the need for someone to go through logs by hand.
• Frequently, tags marked “Humongous” suggest a Full GC could arrive shortly afterward.
• When lots of activity happens, notice the way memory shifts.
• Chunks of data work better when fed one at a time – this eases pressure on memory. Spreading tasks across intervals keeps systems from slowing down.

Conclusion: Rethinking Java Garbage Collection Strategy

Testing revealed:

• 3,378 minor GCs (~2.37 ms each)
• 3,379 Full GCs (~15.1 ms each)
• Nearly one minute of cumulative pause time

All while heap usage stayed near maximum capacity.

When traffic stays high, enormous object allocations scatter data unevenly across memory space, making cleanup take much longer; tools like GCeasy spot these patterns clearly. 

Fig: Minor and Full GC statistics in the GCeasy report

Instead of loading everything into big arrays before processing, reading piece by piece avoids those massive temporary blocks completely. Even if logic works perfectly, how it uses memory can still break stability on Java runtimes. Garbage Collection isn’t optional when systems go live – it shapes real performance daily.