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Memory Lock Behavior: Heap Usage at Target Bounds

Context

During our GC tuning campaign, we observed an unexpected but highly desirable behavior: memory usage didn't just stabilize—it "locked" firmly at the preset target boundaries.

Production Observation

During Campaign (Steady State)

Memory During Campaign

Observations: 1. Memory stability: Heap usage stabilized at expected level (not growing unbounded) 2. Limit enforcement: No services hit GOMEMLIMIT OOM 3. Predictable behavior: Memory "locked" at target bounds 4. Expected behavior: Vast majority of container memory usage met expectations, remained stable

Key finding: During the campaign, memory was firmly locked at preset boundaries, demonstrating high stability of the overall solution.

What Causes This Behavior?

The "lock" effect is the result of several mechanisms working together:

  1. GC Pacer target accuracy: The Pacer calculates the trigger point precisely based on GOGC and live heap size
  2. Scavenger buffer retention: Without GOMEMLIMIT, the runtime retains a 10% buffer above target
  3. Steady-state allocation: In production services with stable traffic, allocation rates are predictable
  4. Feedback loop: The allocation-assist mechanism provides natural backpressure when heap approaches limit

Why This Matters

Before observation: Concerns that memory would fluctuate wildly or grow unbounded

After observation: Confidence that: - Memory usage is predictable and bounded - The GC Pacer + GOMEMLIMIT combination provides tight control - Operations planning can rely on stable memory profiles

Quantitative Evidence

From our campaign across 67 services:

  • Zero OOM incidents: No services hit GOMEMLIMIT out-of-memory
  • Clear ceilings: Memory usage graphs show visible "ceilings" at expected levels
  • Stable baselines: 10-minute rolling windows showed minimal variance (<5% jitter)

Theoretical Validation

This behavior confirms our understanding from multiple GC articles:

  1. Pacer Theory: The trigger point calculation (trigger = live_heap + (target_heap - live_heap) / assist_ratio) provides accurate heap control
  2. Scavenging Theory: The runtime manages idle memory efficiently, returning only what's truly unused
  3. Allocation-Assist Loop: The feedback mechanism prevents runaway heap growth

Practical Implications

For Capacity Planning

  • Memory usage can be accurately predicted based on target heap settings
  • No need for large "safety margins" beyond the GOMEMLIMIT buffer

For Alerting

  • Alert thresholds can be set close to the target (e.g., 95% of GOMEMLIMIT)
  • Minimal false positives from normal fluctuation

For Tuning

  • Higher confidence in pushing GOGC higher
  • Memory behavior is deterministic, not probabilistic