A Go service can burn CPU collecting garbage without having a memory leak.

That is the counterintuitive part of Zomato's Go garbage collection story.

The reported symptoms were striking: garbage collection consuming 25–60% of CPU in some services, alongside containers getting OOM-killed without an obvious memory leak.

These look like separate problems. But they can share a common cause: the garbage collector's pacing decisions don't automatically account for the container's actual memory budget.

The problem with doubling the live heap

Go's garbage collector uses GOGC to control when the next collection should happen.

With the default GOGC=100, the GC target is roughly based on the live heap growing by 100% relative to the previous cycle, with additional accounting for GC roots and other implementation details.

In simplified terms:

Next GC target ≈ live heap × (1 + GOGC/100)

At GOGC=100:

Live heap:       500 MiB
Approx. target: 1,000 MiB

That target is reasonable as a starting point when the process has enough memory available.

But containers introduce another constraint: the process has a memory budget imposed from outside the Go runtime.

The GC's heap-growth target and the container's memory ceiling are two different things.

When they aren't coordinated, two failure modes become possible.

Failure mode 1: OOM kills without a leak

Suppose a container has a memory limit of 1 GiB.

The Go process has a live heap of 600 MiB. A simplified GOGC=100 calculation puts the next target around 1,200 MiB.

But the container cannot safely accommodate that much memory. The process also needs room for goroutine stacks, runtime metadata, native allocations, and other memory outside the live heap.

The container can hit its limit before the collector's normal heap target becomes appropriate.

The result can be an OOM kill even when reachable memory isn't growing uncontrollably.

That doesn't mean every OOM without an obvious leak is a GC pacing issue. Transient allocation spikes, native memory, and other causes still need investigation.

The important point is that a heap-growth target alone isn't a container memory budget.

Failure mode 2: Excessive GC CPU

Now consider the opposite situation.

A service has a relatively small live heap and plenty of memory available in its container.

The GC keeps using a target derived from that live heap. Depending on the allocation rate and workload, it may collect frequently even though the container has significant unused headroom.

Every collection consumes CPU.

At fleet scale, unnecessary GC work can become a meaningful compute cost.

This is why the following combination deserves attention:

• High GC CPU.
• A relatively flat live-heap graph.
• No convincing evidence of sustained heap growth.

It is a diagnostic clue, not proof. A service can allocate and discard large volumes of temporary objects while maintaining a flat live heap. Allocation rate, GC frequency, and allocation profiles matter too.

But before treating every high-GC-CPU incident as a memory leak, inspect the collector's pacing and the container's actual memory constraints.

The fix: Give Go a realistic memory limit

Go provides GOMEMLIMIT to let applications communicate a soft memory limit to the runtime.

Unlike GOGC, which controls GC aggressiveness in relation to heap growth, GOMEMLIMIT helps the runtime account for its overall memory footprint when deciding how aggressively to collect.

Conceptually, the runtime must balance two competing costs:

• Collect too late, and the process risks exhausting its container memory budget.
• Collect too aggressively, and the application wastes CPU reclaiming memory it could have used safely.

A memory limit gives the runtime another constraint to consider.

In the Zomato case, the reported approach used container metadata to derive a per-container limit, refreshed it periodically, and combined memory tuning with CPU scheduling adjustments.

A limit around 90% of the container ceiling can be a starting point for investigation, not a universal safe value. The correct margin depends on the application's non-heap memory, traffic spikes, allocation behavior, and operational requirements.

The goal is to give the runtime a realistic budget while preserving enough headroom for the rest of the process.

Why GOMEMLIMIT isn't a hard wall

This distinction matters in production.

GOMEMLIMIT is a soft limit, not a strict process-memory cap. The runtime can exceed it when necessary, and it does not guarantee that a container will avoid OOM kills.

The runtime also has to balance memory pressure against GC CPU overhead.

If the limit is too high, it may offer little protection against container memory pressure. If it is too low, the runtime can spend excessive CPU collecting garbage in an attempt to stay within the target.

That is why blindly setting GOMEMLIMIT equal to the container limit is not a complete solution.

A production rollout should account for:

1. The container's actual memory limit.
2. Heap and non-heap memory usage.
3. Peak allocation rates and traffic bursts.
4. GC CPU and pause behavior.
5. OOM events and memory headroom after deployment.

Start with representative workloads, observe the results, and tune based on measurements rather than assuming a single multiplier works everywhere.

I hope you find this resource useful and that it helps you better understand things when working on new projects!

TL; DR - A bone dry technical article about continuous integration, of little interest to non-programmers.

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