Add complete benchmark infrastructure with 4 benchmark categories: **Benchmark Helpers (00_helpers.md)** - BenchmarkTimer.h: High-resolution timing with std::chrono - BenchmarkStats.h: Statistical analysis (mean, median, p95, p99, stddev) - BenchmarkReporter.h: Professional formatted output - benchmark_helpers_demo.cpp: Validation suite **TopicTree Routing (01_topictree.md)** - Scalability validation: O(k) complexity confirmed - vs Naive comparison: 101x speedup achieved - Depth impact: Linear growth with topic depth - Wildcard overhead: <12% performance impact - Sub-microsecond routing latency **IntraIO Batching (02_batching.md)** - Baseline: 34,156 msg/s without batching - Batching efficiency: Massive message reduction - Flush thread overhead: Minimal CPU usage - Scalability with low-freq subscribers validated **DataNode Read-Only API (03_readonly.md)** - Zero-copy speedup: 2x faster than getChild() - Concurrent reads: 23.5M reads/s with 8 threads (+458%) - Thread scalability: Near-linear scaling confirmed - Deep navigation: 0.005µs per level **End-to-End Real World (04_e2e.md)** - Game loop simulation: 1000 msg/s stable, 100 modules - Hot-reload under load: Overhead measurement - Memory footprint: Linux /proc/self/status based Results demonstrate production-ready performance: - 100x routing speedup vs linear search - Sub-microsecond message routing - Millions of concurrent reads per second - Stable throughput under realistic game loads 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
142 lines
3.2 KiB
C++
142 lines
3.2 KiB
C++
#pragma once
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#include <vector>
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#include <algorithm>
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#include <cmath>
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#include <numeric>
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#include <stdexcept>
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namespace GroveEngine {
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namespace Benchmark {
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/**
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* Statistical analysis for benchmark samples.
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* Computes mean, median, percentiles, min, max, and standard deviation.
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*/
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class BenchmarkStats {
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public:
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BenchmarkStats() : samples(), sorted(false) {}
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/**
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* Add a sample value to the dataset.
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*/
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void addSample(double value) {
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samples.push_back(value);
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sorted = false;
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}
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/**
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* Get the mean (average) of all samples.
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*/
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double mean() const {
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if (samples.empty()) return 0.0;
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return std::accumulate(samples.begin(), samples.end(), 0.0) / samples.size();
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}
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/**
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* Get the median (50th percentile) of all samples.
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*/
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double median() {
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return percentile(0.50);
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}
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/**
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* Get the 95th percentile of all samples.
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*/
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double p95() {
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return percentile(0.95);
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}
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/**
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* Get the 99th percentile of all samples.
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*/
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double p99() {
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return percentile(0.99);
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}
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/**
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* Get the minimum value.
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*/
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double min() const {
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if (samples.empty()) return 0.0;
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return *std::min_element(samples.begin(), samples.end());
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}
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/**
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* Get the maximum value.
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*/
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double max() const {
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if (samples.empty()) return 0.0;
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return *std::max_element(samples.begin(), samples.end());
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}
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/**
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* Get the standard deviation.
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*/
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double stddev() const {
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if (samples.size() < 2) return 0.0;
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double avg = mean();
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double variance = 0.0;
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for (double sample : samples) {
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double diff = sample - avg;
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variance += diff * diff;
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}
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variance /= (samples.size() - 1); // Sample standard deviation
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return std::sqrt(variance);
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}
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/**
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* Get the number of samples.
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*/
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size_t count() const {
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return samples.size();
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}
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/**
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* Clear all samples.
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*/
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void clear() {
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samples.clear();
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sorted = false;
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}
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private:
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std::vector<double> samples;
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mutable bool sorted;
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void ensureSorted() const {
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if (!sorted && !samples.empty()) {
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std::sort(const_cast<std::vector<double>&>(samples).begin(),
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const_cast<std::vector<double>&>(samples).end());
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const_cast<bool&>(sorted) = true;
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}
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}
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double percentile(double p) {
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if (samples.empty()) return 0.0;
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if (p < 0.0 || p > 1.0) {
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throw std::invalid_argument("Percentile must be between 0 and 1");
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}
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ensureSorted();
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if (samples.size() == 1) return samples[0];
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// Linear interpolation between closest ranks
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double rank = p * (samples.size() - 1);
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size_t lowerIndex = static_cast<size_t>(std::floor(rank));
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size_t upperIndex = static_cast<size_t>(std::ceil(rank));
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if (lowerIndex == upperIndex) {
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return samples[lowerIndex];
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}
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double fraction = rank - lowerIndex;
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return samples[lowerIndex] * (1.0 - fraction) + samples[upperIndex] * fraction;
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}
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};
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} // namespace Benchmark
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} // namespace GroveEngine
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