The invisible cost of memory escape in go and how to eliminate It

Introduction

Performance problems are rarely solved by blind optimization. In high-traffic microservices environments, every memory allocation matters. ABEMA’s ad delivery system experienced periodic CPU spikes. The goal was not only to stabilize the system but to prepare the architecture to support new features without degradation. The key was to observe accurately before changing anything.

Background

The system exhibited:

• Periodic CPU spikes.
• High time spent in Garbage Collection (~30%).
• Extremely high memory allocation: 25.8 GiB per minute.
• Massive creation of short-lived heap objects.

Profiling revealed a single method concentrated both the largest CPU usage and the highest memory allocation. The problem was not the algorithm itself, but how it was implemented.

Development

First optimization: direct mutability

• We tried updating the original slice instead of creating a new one.
• Benchmark results:
• 16% speed improvement.
• 96% reduction in memory.

However, this solution introduced implicit mutability, which could create unwanted side effects for consumers of the code. It was efficient, but architecturally risky.

Final solution: iterators in Go 1.23

This approach made it possible to:

• Eliminate intermediate slices.
• Process data as a stream.
• Reduce heap allocations.
• Preserve immutability from the caller’s point of view.

Instead of returning a filtered slice, the method returns an iterator that applies chained filters without creating temporary structures.

Recommendations

• Instrument before optimizing.
• Analyze memory escape in critical code.
• Minimize intermediate structures in chained loops.
• Evaluate iterators when processing large data volumes.
• Measure real impact in production, not only in synthetic benchmarks.

Conclusions

In high-volume systems, small implementation choices scale exponentially. The problem wasn’t the algorithm it was the hidden memory cost. Reducing memory escape not only improves technical metrics; it increases stability, scalability, and the capacity for future innovation,this case shows that proper observability combined with thoughtful architectural decisions can completely transform a system’s performance.

Glossary

• Memory Escape: When a variable is allocated on the heap because the compiler cannot guarantee its lifetime on the stack.
• Heap Allocation: Memory allocated that is managed by the Garbage Collector.
• Garbage Collection (GC): The process that frees memory from unused objects.
• Iterator: A pattern that allows traversing data without creating intermediate structures.
• Range over func: A new Go 1.23 feature that allows iterating over functions acting as generators.