flatten ActivityTreeNodeStats to reduce heap allocations#46469
flatten ActivityTreeNodeStats to reduce heap allocations#46469
Conversation
… 5-min window The pprof delta-heap profile showed NewActivityTreeNodeStats as the #1 allocation hotspot: 95.5MB / 1.1M objects (15.95% of total) in a 5-minute window. The child function atomic.NewUint64 alone accounted for 18MB / 1.18M objects (18.72%). Root cause: each Stats object allocated ~630 individual *atomic.Uint64 pointers via maps — one per event type (MaxKernelEventType ≈ 65) times 10 counters each (1 processedCount + 5 addedCount + 4 droppedCount). The outer map[model.EventType]*statsPerEventType added further overhead. This commit replaces all heap-allocated pointer-based maps with inline fixed-size arrays of value-type atomics: - Stats.counts: map[model.EventType]*statsPerEventType → [model.MaxKernelEventType]statsPerEventType (flat array) - statsPerEventType.processedCount: *atomic.Uint64 → atomic.Uint64 (inline value) - statsPerEventType.addedCount: map[NodeGenerationType]*atomic.Uint64 → [MaxNodeGenerationType + 1]atomic.Uint64 (5-element array) - statsPerEventType.droppedCount: map[NodeDroppedReason]*atomic.Uint64 → [nodeDroppedReasonCount]atomic.Uint64 (4-element array) The entire stats structure is now a single contiguous ~5KB allocation (inline in Stats) instead of 630+ individual heap objects per instance. NewActivityTreeNodeStats() becomes trivial zero-value initialization. Additional improvements in SendStats: - Switched from map range to index-based iteration with pointer access to avoid copying non-copyable atomic.Uint64 values. - Replaced fmt.Sprintf("event_type:%s", evtType) with string concat ("event_type:" + evtType.String()), eliminating ~87k fmt.Sprintf allocations per SendStats cycle. - Replaced fmt.Sprintf("reason:%s", reason) with reason.Tag() which returns pre-built cached strings. Migrated from go.uber.org/atomic to sync/atomic (stdlib): - .Inc() → .Add(1) - .Swap(0) unchanged Estimated savings: ~113MB alloc_space, ~2.28M object allocations per 5-minute profiling window. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Static quality checks✅ Please find below the results from static quality gates Error
Gate failure full details
Note: Some gates exceeded limits but are non-blocking because the size hasn't increased from the ancestor commit. Successful checksInfo
16 successful checks with minimal change (< 2 KiB)
On-wire sizes (compressed)
|
Regression DetectorRegression Detector ResultsMetrics dashboard Baseline: 562d57d Optimization Goals: ✅ No significant changes detected
|
| perf | experiment | goal | Δ mean % | Δ mean % CI | trials | links |
|---|---|---|---|---|---|---|
| ➖ | docker_containers_cpu | % cpu utilization | +0.10 | [-3.04, +3.23] | 1 | Logs |
Fine details of change detection per experiment
| perf | experiment | goal | Δ mean % | Δ mean % CI | trials | links |
|---|---|---|---|---|---|---|
| ➖ | ddot_metrics_sum_delta | memory utilization | +0.39 | [+0.19, +0.59] | 1 | Logs |
| ➖ | uds_dogstatsd_20mb_12k_contexts_20_senders | memory utilization | +0.32 | [+0.26, +0.37] | 1 | Logs |
| ➖ | otlp_ingest_logs | memory utilization | +0.21 | [+0.12, +0.30] | 1 | Logs |
| ➖ | ddot_metrics_sum_cumulativetodelta_exporter | memory utilization | +0.16 | [-0.07, +0.39] | 1 | Logs |
| ➖ | docker_containers_memory | memory utilization | +0.11 | [+0.03, +0.18] | 1 | Logs |
| ➖ | docker_containers_cpu | % cpu utilization | +0.10 | [-3.04, +3.23] | 1 | Logs |
| ➖ | file_to_blackhole_0ms_latency | egress throughput | +0.07 | [-0.39, +0.53] | 1 | Logs |
| ➖ | file_to_blackhole_100ms_latency | egress throughput | +0.00 | [-0.04, +0.05] | 1 | Logs |
| ➖ | uds_dogstatsd_to_api | ingress throughput | -0.00 | [-0.13, +0.12] | 1 | Logs |
| ➖ | tcp_dd_logs_filter_exclude | ingress throughput | -0.00 | [-0.09, +0.09] | 1 | Logs |
| ➖ | uds_dogstatsd_to_api_v3 | ingress throughput | -0.01 | [-0.14, +0.12] | 1 | Logs |
| ➖ | file_to_blackhole_1000ms_latency | egress throughput | -0.01 | [-0.44, +0.42] | 1 | Logs |
| ➖ | file_to_blackhole_500ms_latency | egress throughput | -0.03 | [-0.41, +0.35] | 1 | Logs |
| ➖ | ddot_metrics_sum_cumulative | memory utilization | -0.04 | [-0.20, +0.12] | 1 | Logs |
| ➖ | quality_gate_idle | memory utilization | -0.06 | [-0.11, -0.02] | 1 | Logs bounds checks dashboard |
| ➖ | quality_gate_idle_all_features | memory utilization | -0.31 | [-0.34, -0.27] | 1 | Logs bounds checks dashboard |
| ➖ | file_tree | memory utilization | -0.51 | [-0.56, -0.46] | 1 | Logs |
| ➖ | quality_gate_metrics_logs | memory utilization | -0.53 | [-0.73, -0.33] | 1 | Logs bounds checks dashboard |
| ➖ | otlp_ingest_metrics | memory utilization | -0.61 | [-0.76, -0.45] | 1 | Logs |
| ➖ | ddot_metrics | memory utilization | -0.76 | [-0.97, -0.54] | 1 | Logs |
| ➖ | ddot_logs | memory utilization | -0.79 | [-0.86, -0.72] | 1 | Logs |
| ➖ | quality_gate_logs | % cpu utilization | -1.31 | [-2.82, +0.19] | 1 | Logs bounds checks dashboard |
| ➖ | tcp_syslog_to_blackhole | ingress throughput | -2.02 | [-2.11, -1.94] | 1 | Logs |
Bounds Checks: ✅ Passed
| perf | experiment | bounds_check_name | replicates_passed | links |
|---|---|---|---|---|
| ✅ | docker_containers_cpu | simple_check_run | 10/10 | |
| ✅ | docker_containers_memory | memory_usage | 10/10 | |
| ✅ | docker_containers_memory | simple_check_run | 10/10 | |
| ✅ | file_to_blackhole_0ms_latency | lost_bytes | 10/10 | |
| ✅ | file_to_blackhole_0ms_latency | memory_usage | 10/10 | |
| ✅ | file_to_blackhole_1000ms_latency | lost_bytes | 10/10 | |
| ✅ | file_to_blackhole_1000ms_latency | memory_usage | 10/10 | |
| ✅ | file_to_blackhole_100ms_latency | lost_bytes | 10/10 | |
| ✅ | file_to_blackhole_100ms_latency | memory_usage | 10/10 | |
| ✅ | file_to_blackhole_500ms_latency | lost_bytes | 10/10 | |
| ✅ | file_to_blackhole_500ms_latency | memory_usage | 10/10 | |
| ✅ | quality_gate_idle | intake_connections | 10/10 | bounds checks dashboard |
| ✅ | quality_gate_idle | memory_usage | 10/10 | bounds checks dashboard |
| ✅ | quality_gate_idle_all_features | intake_connections | 10/10 | bounds checks dashboard |
| ✅ | quality_gate_idle_all_features | memory_usage | 10/10 | bounds checks dashboard |
| ✅ | quality_gate_logs | intake_connections | 10/10 | bounds checks dashboard |
| ✅ | quality_gate_logs | lost_bytes | 10/10 | bounds checks dashboard |
| ✅ | quality_gate_logs | memory_usage | 10/10 | bounds checks dashboard |
| ✅ | quality_gate_metrics_logs | cpu_usage | 10/10 | bounds checks dashboard |
| ✅ | quality_gate_metrics_logs | intake_connections | 10/10 | bounds checks dashboard |
| ✅ | quality_gate_metrics_logs | lost_bytes | 10/10 | bounds checks dashboard |
| ✅ | quality_gate_metrics_logs | memory_usage | 10/10 | bounds checks dashboard |
Explanation
Confidence level: 90.00%
Effect size tolerance: |Δ mean %| ≥ 5.00%
Performance changes are noted in the perf column of each table:
- ✅ = significantly better comparison variant performance
- ❌ = significantly worse comparison variant performance
- ➖ = no significant change in performance
A regression test is an A/B test of target performance in a repeatable rig, where "performance" is measured as "comparison variant minus baseline variant" for an optimization goal (e.g., ingress throughput). Due to intrinsic variability in measuring that goal, we can only estimate its mean value for each experiment; we report uncertainty in that value as a 90.00% confidence interval denoted "Δ mean % CI".
For each experiment, we decide whether a change in performance is a "regression" -- a change worth investigating further -- if all of the following criteria are true:
-
Its estimated |Δ mean %| ≥ 5.00%, indicating the change is big enough to merit a closer look.
-
Its 90.00% confidence interval "Δ mean % CI" does not contain zero, indicating that if our statistical model is accurate, there is at least a 90.00% chance there is a difference in performance between baseline and comparison variants.
-
Its configuration does not mark it "erratic".
CI Pass/Fail Decision
✅ Passed. All Quality Gates passed.
- quality_gate_idle_all_features, bounds check memory_usage: 10/10 replicas passed. Gate passed.
- quality_gate_idle_all_features, bounds check intake_connections: 10/10 replicas passed. Gate passed.
- quality_gate_idle, bounds check intake_connections: 10/10 replicas passed. Gate passed.
- quality_gate_idle, bounds check memory_usage: 10/10 replicas passed. Gate passed.
- quality_gate_logs, bounds check memory_usage: 10/10 replicas passed. Gate passed.
- quality_gate_logs, bounds check lost_bytes: 10/10 replicas passed. Gate passed.
- quality_gate_logs, bounds check intake_connections: 10/10 replicas passed. Gate passed.
- quality_gate_metrics_logs, bounds check memory_usage: 10/10 replicas passed. Gate passed.
- quality_gate_metrics_logs, bounds check intake_connections: 10/10 replicas passed. Gate passed.
- quality_gate_metrics_logs, bounds check cpu_usage: 10/10 replicas passed. Gate passed.
- quality_gate_metrics_logs, bounds check lost_bytes: 10/10 replicas passed. Gate passed.
|
This pull request has been automatically marked as stale because it has not had activity in the past 15 days. It will be closed in 30 days if no further activity occurs. If this pull request is still relevant, adding a comment or pushing new commits will keep it open. Also, you can always reopen the pull request if you missed the window. Thank you for your contributions! |
|
This pull request was automatically closed because it has been stale for 15 days with no activity. If this pull request is still relevant, please reopen it or create a new pull request with updated information. Thanks! |
The pprof delta-heap profile showed NewActivityTreeNodeStats as the #1 allocation hotspot: 95.5MB / 1.1M objects (15.95% of total) in a 5-minute window. The child function atomic.NewUint64 alone accounted for 18MB / 1.18M objects (18.72%).
Root cause: each Stats object allocated ~630 individual *atomic.Uint64 pointers via maps — one per event type (MaxKernelEventType ≈ 65) times 10 counters each (1 processedCount + 5 addedCount + 4 droppedCount). The outer map[model.EventType]*statsPerEventType added further overhead.
This commit replaces all heap-allocated pointer-based maps with inline fixed-size arrays of value-type atomics:
Stats.counts: map[model.EventType]*statsPerEventType → [model.MaxKernelEventType]statsPerEventType (flat array)
statsPerEventType.processedCount: *atomic.Uint64 → atomic.Uint64 (inline value)
statsPerEventType.addedCount: map[NodeGenerationType]*atomic.Uint64 → [MaxNodeGenerationType + 1]atomic.Uint64 (5-element array)
statsPerEventType.droppedCount: map[NodeDroppedReason]*atomic.Uint64 → [nodeDroppedReasonCount]atomic.Uint64 (4-element array)
The entire stats structure is now a single contiguous ~5KB allocation (inline in Stats) instead of 630+ individual heap objects per instance. NewActivityTreeNodeStats() becomes trivial zero-value initialization.
Additional improvements in SendStats:
Migrated from go.uber.org/atomic to sync/atomic (stdlib):
Estimated savings: ~113MB alloc_space, ~2.28M object allocations per 5-minute profiling window.
What does this PR do?
Motivation
Describe how you validated your changes
Additional Notes