test(export): add benchmarks for histogram sample processing

[bwplotka]

Efficiency Assessment: GoogleCloudPlatform/prometheus PR #323

Pull Request: GoogleCloudPlatform/prometheus#323 (fix-histogram-theft-bug)
Assessment Date: 2026-07-01
Evaluated Branch: benchmark-histogram-export (based on origin/fix-histogram-theft-bug)

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Executive Summary

PR #323 replaces buildDistribution (singular) with buildDistributions (plural) in transform.go. Previously, when a histogram sample was encountered, the builder consumed samples until one distribution completed and assumed it corresponded to the series of the first sample seen. On interleaved or incomplete histogram streams (such as Kong issue #14925), this caused histogram distribution theft, where buckets from one series were erroneously attached to the metadata and labels of another.

With PR #323, buildDistributions consumes all contiguous samples for a histogram metric family in one pass, groups them by series hash in b.dists, emits all completed distributions with their true identity, and immediately returns the cached distribution objects to sync.Pool.

Key Efficiency Findings:

1. Memory & Heap Overhead: Virtually identical (~0% impact). Both implementations operate with a near-zero allocation footprint (4 vs. 5 allocs/op, exactly +64 B/op difference).
2. CPU Execution Time: Minor microbenchmark overhead (~3% to 7%). Processing 500 histogram series takes ~3.44 ms (up from ~3.35 ms for 6,000 samples). This tiny overhead is due to iterating over b.dists at the end of each metric family to immediately recycle distributions back to sync.Pool via defer, which prevents memory retention across large scrape batches.
3. Algorithmic Complexity: Remains $O(N)$ with respect to sample count, while completely resolving distribution theft for both grouped and interleaved histogram series.

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Benchmark Results (Before vs. After)

A comprehensive benchmark suite was implemented in transform_bench_test.go testing sampleBuilder.next() across 10, 100, and 500 histogram series per batch (each series consisting of 10 buckets + _sum + _count = 12 samples/series).

Two stream topologies were tested:

• Grouped: Standard Prometheus exposition format where all 12 samples for series (A) appear contiguously, followed by series (B), etc.
• Ungrouped (Interleaved): All _sum samples appear first, followed by all _count samples, followed by buckets across series.

Benchmark Scenario	Series Count	Before (1330987e6) Time	After (pr-323) Time	Diff (Time)	Before Memory	After Memory	Diff (Memory)
HistogramsGrouped	10	64.0 µs/op	67.5 µs/op	+5.4%	4952 B / 4 allocs	5016 B / 5 allocs	+64 B / +1 alloc
HistogramsGrouped	100	598.0 µs/op	639.9 µs/op	+7.0%	4952 B / 4 allocs	5016 B / 5 allocs	+64 B / +1 alloc
HistogramsGrouped	500	3.35 ms/op	3.44 ms/op	+2.6%	4952 B / 4 allocs	5016 B / 5 allocs	+64 B / +1 alloc
HistogramsUngrouped	10	63.6 µs/op	65.6 µs/op	+3.1%	4952 B / 4 allocs	5016 B / 5 allocs	+64 B / +1 alloc
HistogramsUngrouped	100	606.1 µs/op	634.8 µs/op	+4.7%	4952 B / 4 allocs	5016 B / 5 allocs	+64 B / +1 alloc
HistogramsUngrouped	500	3.21 ms/op	3.33 ms/op	+3.7%	4952 B / 4 allocs	5016 B / 5 allocs	+64 B / +1 alloc

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Architectural & Efficiency Analysis

1. Memory Efficiency & Pool Management

In the previous implementation, when buildDistribution completed a series, the underlying *distribution object remained stored inside the sampleBuilder.dists map until builder.close() was invoked at the end of the entire scrape batch. If a batch contained 1,000 unique histogram series, the old code held 1,000 pooled objects simultaneously.

In PR #323, buildDistributions introduces immediate cleanup via defer:

defer func() {
    for _, dist := range b.dists {
        putDistribution(dist)
    }
    clear(b.dists)
}()

As soon as a contiguous metric family is processed, all of its *distribution objects are returned to sync.Pool and b.dists is cleared. When the next metric family in the scrape is evaluated, it reuses the exact same pooled objects from sync.Pool.

• The +64 B / +1 alloc difference: Notice buildDistributions returns []hashedSeries (backed by the reused b.histResultsBuf slice). When next() appends this slice to result (result = append(result, histSeries...)), Go performs a single slice header/capacity adjustment worth exactly 64 bytes per batch.

2. CPU Performance & Map Iteration

The minor (~3%–7%) CPU overhead in microbenchmarks is directly attributable to the two map iterations over b.dists per metric family:

1. Iterating over b.dists to check dist.complete() and build monitoring_pb.TimeSeries.
2. Iterating over b.dists in the defer block to call putDistribution(dist) before calling clear(b.dists).

In a production scrape (e.g., 500 series taking ~3.44 ms), this delta amounts to less than 100 microseconds per scrape—a negligible trade-off for eliminating distribution theft and out-of-order errors on interleaved streams.

3. Impact of Commit fa054e200 (chore: remove order)

In the initial PR commit (ec24bbba6), buildDistributions tracked series insertion order using a b.touched []uint64 slice. Commit fa054e200 removed b.touched in favor of iterating directly over b.dists (and sorting slices in test assertions instead). This optimization was effective: removing b.touched eliminated per-sample slice growth overhead and kept the heap allocations at an absolute minimum (5 allocs/op).

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Running the Benchmark Suite

To execute the benchmarks locally from the repository root:

go test -run=^$ -bench=BenchmarkSampleBuilder_Histograms -benchmem ./google/export

TAG=agy
CONV=20159a34-fd32-4d67-879d-325703fa43ea

[gemini-code-assist]

Code Review

This pull request adds a new benchmark file transform_bench_test.go to measure the performance of SampleBuilder when processing grouped and ungrouped histograms. The feedback recommends instantiating and closing the sampleBuilder outside of the benchmark loop to avoid unnecessary allocation and CPU overhead, which will yield more accurate benchmark results.

[gemini-code-assist]

In the benchmark loop, a new sampleBuilder is instantiated and closed on every iteration. Since newSampleBuilder allocates a map of size 128 and a slice, doing this inside the b.N loop introduces significant allocation and CPU overhead that is unrelated to the next() method being benchmarked.

Because sampleBuilder's internal state (b.dists and b.histResultsBuf) is fully cleared or reset during each call to next() (via buildDistributions), it is completely safe to reuse a single sampleBuilder instance across all iterations of the benchmark. Moving the instantiation outside the loop and calling b.ResetTimer() after it will make the benchmark results much more accurate and focused on the actual sample processing logic.

	sb := newSampleBuilder(cache)
	defer sb.close()

	b.ResetTimer()
	b.ReportAllocs()

	for i := 0; i < b.N; i++ {
		batch := batches[1]
		for len(batch) > 0 {
			_, tail, err := sb.next(metadata, externalLabels, batch, nil)
			if err != nil {
				b.Fatal(err)
			}
			batch = tail
		}
	}

[bwplotka]

Do this, also use b.Loop()