Garbage Collection Myths: What Developers Still Get Wrong About Memory Management

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Few topics in software engineering spark as much quiet debate as garbage collection (GC). For decades, it’s been treated as both a blessing and a bottleneck — the mysterious background process that “just works” until it doesn’t.

Developers love to joke about it:

“Java’s slow because of GC.”
“Go pauses too much.”
“.NET leaks memory if you look at it wrong.”
“Python doesn’t really free anything.”

But here’s the truth — most of what we believe about garbage collection is based on outdated assumptions from the early 2000s.

Today’s GC systems are no longer clunky stop-the-world beasts. They’re highly tuned, often concurrent, and optimized for real-world workloads.
Still, myths persist. Let’s dissect a few.

Myth 1: Garbage Collection Always Causes Long Pauses

This is the granddaddy of GC myths.

Early JVMs did have “stop-the-world” pauses that froze everything until memory was swept clean. But modern collectors like G1, ZGC, and Shenandoah in Java have redefined what’s possible — they perform concurrent marking and compaction, keeping pause times under 10ms, even on heaps of hundreds of gigabytes.

Go’s GC also evolved from millisecond-level pauses to sub-millisecond latency, with each release improving concurrent sweeping.

.NET’s generational GC follows a similar path — it collects short-lived objects quickly, rarely touching the rest of the heap.

Even Python, despite using reference counting and a cyclic collector, doesn’t arbitrarily freeze your program unless you’re allocating and discarding massive object graphs in tight loops.

Reality: GC pauses still exist, but they’re rarely the monster they once were.

Myth 2: Manual Memory Management Is Always Faster

“Real programmers use malloc and free.”
It’s a nice slogan — until you leak memory or double-free a pointer.

Manual memory management gives you control, yes, but it comes with cognitive overhead and a high risk of errors. GC systems, by contrast, are optimized to exploit allocation patterns humans can’t easily track.

For example:

• Java and .NET use generational heuristics — since most objects die young, the collector focuses on reclaiming those first.

• Go’s allocator is designed around goroutine lifetimes and stack growth.

• Even in Python, the GC and refcounting combo handle ephemeral allocations far more efficiently than naive manual memory reuse.

So, unless you’re writing a real-time trading engine in C or managing GPU memory directly, modern GC systems are fast enough and safer by design.

Myth 3: GC Is the Same Across All Languages

“Garbage collection” sounds like one concept — but it’s really a spectrum of trade-offs.

Java’s collectors are highly tunable — you can set heap ratios, pause targets, and parallel threads.
Go’s GC focuses on simplicity and predictability, avoiding tuning knobs in favor of auto-optimization.
.NET gives developers transparency through diagnostic APIs and profiling tools.
Python’s approach trades off raw speed for flexibility, especially in extension-heavy workloads.

Reality: GC isn’t a single algorithm. It’s an evolving ecosystem of strategies tuned to each runtime’s philosophy.

Myth 4: GC Makes Performance Unpredictable

This one has a kernel of truth — unpredictability can happen, especially under memory pressure.
But most modern GCs are adaptive systems that continuously monitor allocation rates and CPU utilization to optimize themselves in real time.

For example:

• Java’s ZGC scales threads dynamically.

• Go’s GC adjusts its pacing based on live heap size versus target utilization.

• .NET Core exposes GC.TryStartNoGCRegion() for critical low-latency sections.

If performance still fluctuates, the issue usually isn’t GC itself — it’s excessive object churn, memory fragmentation, or misconfigured resource pooling.

In short: GC doesn’t make your performance unpredictable; your allocation habits do.

Myth 5: Disabling GC Improves Performance

Many developers — especially in games or embedded systems — try to turn GC off during critical paths.

But this is like cutting the brakes to go faster. You might gain a few milliseconds, but eventually you crash.

Uncollected objects lead to heap bloat, page faults, and unpredictable slowdowns when the GC finally kicks in.

Instead, tuning GC (via generation sizes, pacing parameters, or allocation batching) often yields better long-term performance than disabling it.

So, What Should Developers Do Instead?

1. Understand your language’s GC model.
   Don’t treat “garbage collection” as a black box — read the docs for your runtime’s specific implementation.

2. Profile allocations, not just CPU.
   Tools like VisualVM, Go’s pprof, .NET dotMemory, or Python’s tracemalloc show where memory churn happens.

3. Reduce short-lived allocations.
   Reuse buffers, cache frequently used objects, and prefer structs or value types where applicable.

4. Test under realistic workloads.
   GC behaves differently under load — what works in a dev environment may not scale.

5. Keep up with runtime updates.
   GC improvements are constant. A minor version bump can yield measurable latency gains.

Final Thought: The Invisible Hand of Performance

Garbage collection used to be a bottleneck.
Now, it’s a finely tuned performance partner — invisible when managed well, disastrous only when misunderstood.

The myth that “GC is slow” belongs to another era.
In 2025, the smarter developer isn’t the one who avoids GC — it’s the one who understands how to work with it.

Because as our programs grow more concurrent, connected, and complex, the best performance wins will come not from fighting automation — but from trusting the systems that quietly keep our software alive.

Until next newsletter,

— Nullpointer Club