1331 lines
49 KiB
C++
1331 lines
49 KiB
C++
// PBI-040: Service layer performance benchmarks
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// These benchmarks test the app/services layer to catch performance regressions
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// in user-facing operations like get-by-name, cat, grep, search.
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#include <algorithm>
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#include <cctype>
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#include <chrono>
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#include <cstdlib>
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#include <filesystem>
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#include <fstream>
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#include <iostream>
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#include <memory>
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#include <numeric>
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#include <optional>
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#include <thread>
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#include <vector>
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#include "../common/env_compat.h"
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#include <benchmark/benchmark.h>
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#ifdef TRACY_ENABLE
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#include <tracy/Tracy.hpp>
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#endif
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#include "../common/test_helpers_catch2.h"
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#include "../integration/daemon/test_async_helpers.h"
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#include "../integration/daemon/test_daemon_harness.h"
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#include <yams/app/services/document_ingestion_service.h>
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#include <yams/app/services/retrieval_service.h>
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#include <yams/daemon/client/daemon_client.h>
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using namespace yams;
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using namespace yams::test;
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namespace {
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struct BenchConfig {
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bool embeddingsEnabled{false};
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// empty => leave daemon/backend defaults in place
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std::string embeddingBackend;
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std::string tuningProfile; // empty => use default
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bool operator==(const BenchConfig& other) const {
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return embeddingsEnabled == other.embeddingsEnabled &&
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embeddingBackend == other.embeddingBackend && tuningProfile == other.tuningProfile;
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}
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};
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// Global daemon harness for benchmark suite (setup once, reused across benchmarks)
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std::unique_ptr<DaemonHarness> g_harness;
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std::unique_ptr<daemon::DaemonClient> g_client;
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std::vector<std::string> g_test_docs; // Hashes of test documents
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std::vector<std::string> g_test_doc_names; // Names used for by-name retrieval
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BenchConfig g_activeConfig;
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std::optional<std::filesystem::path> g_activeConfigPath;
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std::vector<yams::test::ScopedEnvVar> g_environment;
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std::string g_observedEmbeddingBackend;
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std::string g_observedEmbeddingModel;
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uint32_t g_observedEmbeddingDim{0};
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uint64_t g_observedVectorCount{0};
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uint64_t g_embeddingRepairSucceeded{0};
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std::string lowerCopy(std::string value) {
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for (auto& c : value) {
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c = static_cast<char>(std::tolower(static_cast<unsigned char>(c)));
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}
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return value;
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}
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std::string embeddingBackendForArg(int64_t arg) {
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switch (arg) {
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case 1:
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return "simeon";
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case 2:
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return "onnxruntime";
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default:
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return {};
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}
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}
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std::string preferredModelForBackend(const std::string& backend) {
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if (backend == "simeon") {
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return "simeon-default";
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}
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if (backend == "onnxruntime") {
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return "all-MiniLM-L6-v2";
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}
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return {};
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}
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std::filesystem::path resolveBenchmarkPluginDir() {
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namespace fs = std::filesystem;
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if (const char* envPluginDir = std::getenv("YAMS_PLUGIN_DIR")) {
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return fs::path(envPluginDir);
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}
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const auto cwd = fs::current_path();
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const std::vector<fs::path> candidates = {
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cwd / "build" / "debug" / "plugins",
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cwd / "builddir" / "plugins",
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cwd / "build" / "plugins",
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};
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std::error_code ec;
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for (const auto& candidate : candidates) {
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if (fs::exists(candidate, ec) && fs::is_directory(candidate, ec)) {
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return candidate;
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}
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}
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return cwd / "builddir" / "plugins";
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}
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std::filesystem::path writeEmbeddingBackendConfig(const BenchConfig& config) {
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namespace fs = std::filesystem;
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const auto stamp = std::chrono::steady_clock::now().time_since_epoch().count();
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auto configDir =
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fs::temp_directory_path() / ("yams_retrieval_service_bench_" + std::to_string(stamp));
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fs::create_directories(configDir);
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auto configPath = configDir / "config.toml";
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std::ofstream out(configPath);
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out << "[embeddings]\n";
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out << "backend = \"" << config.embeddingBackend << "\"\n";
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const auto preferred = preferredModelForBackend(config.embeddingBackend);
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if (!preferred.empty()) {
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out << "preferred_model = \"" << preferred << "\"\n";
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}
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out.close();
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return configPath;
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}
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void updateObservedEmbeddingStatus(const daemon::StatusResponse& status) {
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g_observedEmbeddingBackend = status.embeddingBackend;
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g_observedEmbeddingModel = status.embeddingModel;
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g_observedEmbeddingDim = status.embeddingDim;
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if (auto it = status.requestCounts.find("vector_count"); it != status.requestCounts.end()) {
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g_observedVectorCount = it->second;
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} else {
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g_observedVectorCount = 0;
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}
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}
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void runBenchmarkEmbeddingRepair(const BenchConfig& config) {
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g_embeddingRepairSucceeded = 0;
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daemon::RepairRequest repairReq;
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repairReq.repairEmbeddings = true;
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repairReq.foreground = true;
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repairReq.force = true;
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repairReq.maxRetries = 1;
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repairReq.embeddingModel = preferredModelForBackend(config.embeddingBackend);
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auto timeout = std::chrono::milliseconds(180000);
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if (const auto configured = yams::config::read_env_milliseconds("YAMS_BENCH_REPAIR_WAIT_MS");
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configured.value) {
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timeout = *configured.value;
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}
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std::cout << "Running foreground embedding repair";
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if (!repairReq.embeddingModel.empty()) {
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std::cout << " (model=" << repairReq.embeddingModel << ")";
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}
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std::cout << "...\n";
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auto repairResult = cli::run_sync(g_client->callRepair(repairReq), timeout);
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if (!repairResult) {
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std::cerr << "WARNING: Embedding repair failed: " << repairResult.error().message << "\n";
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return;
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}
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const auto& resp = repairResult.value();
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std::cout << "Embedding repair result: success=" << (resp.success ? 1 : 0)
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<< " operations=" << resp.totalOperations << " succeeded=" << resp.totalSucceeded
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<< " failed=" << resp.totalFailed << " skipped=" << resp.totalSkipped << "\n";
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for (const auto& op : resp.operationResults) {
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if (op.operation == "embeddings") {
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g_embeddingRepairSucceeded = op.succeeded;
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std::cout << "Embedding repair operation: processed=" << op.processed
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<< " succeeded=" << op.succeeded << " failed=" << op.failed
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<< " skipped=" << op.skipped;
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if (!op.message.empty()) {
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std::cout << " message=" << op.message;
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}
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std::cout << "\n";
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}
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}
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}
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bool benchmarkFilterTargetsBackendAB(int argc, char** argv) {
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for (int i = 1; i < argc; ++i) {
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const std::string arg = argv[i] ? argv[i] : "";
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if (arg.rfind("--benchmark_filter=", 0) == 0 &&
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arg.find("BackendAB") != std::string::npos) {
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return true;
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}
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}
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return false;
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}
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std::size_t benchDocCount() {
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if (const auto configured = yams::config::read_env_size("YAMS_BENCH_DOC_COUNT");
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configured.value && *configured.value > 0) {
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return *configured.value;
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}
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return 500; // Default larger dataset for benchmarks
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}
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bool waitForSearchEngineReady(std::chrono::milliseconds timeout) {
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auto deadline = std::chrono::steady_clock::now() + timeout;
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while (std::chrono::steady_clock::now() < deadline) {
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auto status = cli::run_sync(g_client->status(), std::chrono::seconds(5));
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if (status) {
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auto it = status.value().readinessStates.find("search_engine");
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if (it != status.value().readinessStates.end() && it->second) {
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return true;
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}
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}
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std::this_thread::sleep_for(std::chrono::milliseconds(200));
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}
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return false;
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}
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uint64_t getCountOrZero(const daemon::StatusResponse& st, const std::string& key) {
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if (auto it = st.requestCounts.find(key); it != st.requestCounts.end()) {
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return it->second;
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}
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return 0;
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}
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bool isVectorDbReady(const daemon::StatusResponse& st) {
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if (auto it = st.readinessStates.find("vector_db"); it != st.readinessStates.end()) {
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return it->second;
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}
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return st.vectorDbReady;
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}
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bool waitForCorpusIndexed(std::size_t expectedDocs, std::chrono::milliseconds timeout) {
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auto deadline = std::chrono::steady_clock::now() + timeout;
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int stableCount = 0;
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constexpr int stableRequired = 10;
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daemon::StatusResponse lastStatus;
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bool haveLastStatus = false;
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while (std::chrono::steady_clock::now() < deadline) {
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auto status = cli::run_sync(g_client->status(), std::chrono::seconds(5));
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if (!status) {
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std::this_thread::sleep_for(std::chrono::milliseconds(500));
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continue;
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}
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lastStatus = status.value();
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haveLastStatus = true;
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const auto& st = lastStatus;
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const uint64_t docsTotal = getCountOrZero(st, "documents_total");
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const uint64_t docsIndexed = getCountOrZero(st, "documents_indexed");
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const uint64_t postQueued = getCountOrZero(st, "post_ingest_queued");
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const uint64_t postInflight = getCountOrZero(st, "post_ingest_inflight");
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// `documents_indexed` tracks search/FTS visibility and can lag the canonical document
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// count in overlay-backed search builds. The service benchmarks need a stable, queryable
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// corpus, not necessarily a fully backfilled FTS counter, so accept the stored document
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// count once post-ingest is drained.
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const bool countsMet =
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(expectedDocs == 0) || (docsTotal >= static_cast<uint64_t>(expectedDocs) &&
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(docsIndexed >= static_cast<uint64_t>(expectedDocs) ||
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(postQueued == 0 && postInflight == 0)));
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const bool postDrained = (postQueued == 0 && postInflight == 0);
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if (countsMet && postDrained) {
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++stableCount;
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} else {
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stableCount = 0;
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}
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if (stableCount >= stableRequired) {
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return true;
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}
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std::this_thread::sleep_for(std::chrono::milliseconds(500));
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}
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if (haveLastStatus) {
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const auto& st = lastStatus;
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const uint64_t docsTotal = getCountOrZero(st, "documents_total");
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const uint64_t docsIndexed = getCountOrZero(st, "documents_indexed");
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const uint64_t postQueued = getCountOrZero(st, "post_ingest_queued");
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const uint64_t postInflight = getCountOrZero(st, "post_ingest_inflight");
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const uint64_t embedQueued = getCountOrZero(st, "embed_svc_queued");
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const uint64_t embedInflight = getCountOrZero(st, "embed_in_flight");
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const uint64_t vectorCount = getCountOrZero(st, "vector_count");
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bool searchReady = false;
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if (auto it = st.readinessStates.find("search_engine"); it != st.readinessStates.end()) {
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searchReady = it->second;
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}
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std::cerr << "WARNING: Corpus readiness timeout after " << timeout.count() << "ms"
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<< " (expectedDocs=" << expectedDocs << " docsTotal=" << docsTotal
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<< " docsIndexed=" << docsIndexed << " postQueued=" << postQueued
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<< " postInflight=" << postInflight << " embedQueued=" << embedQueued
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<< " embedInflight=" << embedInflight << " vectorCount=" << vectorCount
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<< " vectorDbReady=" << (isVectorDbReady(st) ? 1 : 0)
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<< " searchReady=" << (searchReady ? 1 : 0) << ")\n";
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}
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return false;
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}
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struct DrainMetrics {
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uint64_t maxEmbedQueued{0};
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uint64_t maxEmbedInflight{0};
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uint64_t maxPostQueued{0};
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uint64_t maxPostInflight{0};
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};
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bool waitForEmbeddingDrainWithMetrics(std::size_t minDocCount, std::chrono::milliseconds timeout,
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DrainMetrics& metrics) {
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auto deadline = std::chrono::steady_clock::now() + timeout;
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uint64_t lastVectorCount = 0;
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int stableCount = 0;
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constexpr int stableRequired = 10;
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while (std::chrono::steady_clock::now() < deadline) {
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auto status = cli::run_sync(g_client->status(), std::chrono::seconds(5));
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if (!status) {
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std::this_thread::sleep_for(std::chrono::milliseconds(500));
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continue;
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}
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const auto& st = status.value();
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const uint64_t vectorCount = getCountOrZero(st, "vector_count");
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const uint64_t embedQueued = getCountOrZero(st, "embed_svc_queued");
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const uint64_t embedInFlight = getCountOrZero(st, "embed_in_flight");
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const uint64_t postQueued = getCountOrZero(st, "post_ingest_queued");
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const uint64_t postInFlight = getCountOrZero(st, "post_ingest_inflight");
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metrics.maxEmbedQueued = std::max(metrics.maxEmbedQueued, embedQueued);
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metrics.maxEmbedInflight = std::max(metrics.maxEmbedInflight, embedInFlight);
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metrics.maxPostQueued = std::max(metrics.maxPostQueued, postQueued);
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metrics.maxPostInflight = std::max(metrics.maxPostInflight, postInFlight);
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const bool embedDrained = (embedQueued == 0 && embedInFlight == 0);
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if (vectorCount != lastVectorCount || !embedDrained) {
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stableCount = 0;
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lastVectorCount = vectorCount;
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} else {
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++stableCount;
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}
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const bool vectorReady = isVectorDbReady(st) || minDocCount == 0;
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if (vectorReady && embedDrained && stableCount >= stableRequired) {
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if (minDocCount == 0)
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return true;
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return vectorCount >= static_cast<uint64_t>(minDocCount);
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}
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std::this_thread::sleep_for(std::chrono::milliseconds(500));
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}
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return false;
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}
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bool waitForPostIngestDrainWithMetrics(std::chrono::milliseconds timeout, DrainMetrics& metrics) {
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auto deadline = std::chrono::steady_clock::now() + timeout;
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int stableCount = 0;
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constexpr int stableRequired = 10;
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while (std::chrono::steady_clock::now() < deadline) {
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auto status = cli::run_sync(g_client->status(), std::chrono::seconds(5));
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if (!status) {
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std::this_thread::sleep_for(std::chrono::milliseconds(500));
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continue;
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}
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const auto& st = status.value();
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const uint64_t postQueued = getCountOrZero(st, "post_ingest_queued");
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const uint64_t postInflight = getCountOrZero(st, "post_ingest_inflight");
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const uint64_t embedQueued = getCountOrZero(st, "embed_svc_queued");
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const uint64_t embedInflight = getCountOrZero(st, "embed_in_flight");
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metrics.maxEmbedQueued = std::max(metrics.maxEmbedQueued, embedQueued);
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metrics.maxEmbedInflight = std::max(metrics.maxEmbedInflight, embedInflight);
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metrics.maxPostQueued = std::max(metrics.maxPostQueued, postQueued);
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metrics.maxPostInflight = std::max(metrics.maxPostInflight, postInflight);
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const bool postDrained = (postQueued == 0 && postInflight == 0);
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if (postDrained) {
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++stableCount;
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} else {
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stableCount = 0;
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}
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if (stableCount >= stableRequired) {
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return true;
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}
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std::this_thread::sleep_for(std::chrono::milliseconds(500));
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}
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return false;
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}
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// Setup: Start daemon and add test documents
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void SetupBenchmarkSuite(const BenchConfig& config = {}) {
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if (g_harness && g_activeConfig == config)
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return; // Already initialized with same config
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g_client.reset();
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g_harness.reset();
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g_environment.clear();
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g_test_docs.clear();
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g_test_doc_names.clear();
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g_activeConfig = config;
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g_activeConfigPath.reset();
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g_observedEmbeddingBackend.clear();
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g_observedEmbeddingModel.clear();
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g_observedEmbeddingDim = 0;
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g_observedVectorCount = 0;
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g_embeddingRepairSucceeded = 0;
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std::cout << "\n=== Setting up benchmark environment ===\n";
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// Apply compatibility knobs through guards so each benchmark arm restores its exact host
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// environment before the next configuration.
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g_environment.emplace_back("YAMS_TUNING_PROFILE",
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config.tuningProfile.empty()
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? std::nullopt
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: std::optional<std::string>{config.tuningProfile});
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if (!config.tuningProfile.empty()) {
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std::cout << "Using tuning profile: " << config.tuningProfile << "\n";
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}
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g_environment.emplace_back("YAMS_BENCH_ENABLE_EMBEDDINGS",
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config.embeddingsEnabled ? std::string{"1"} : std::string{"0"});
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// Start daemon
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DaemonHarness::Options harnessOptions;
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if (config.embeddingsEnabled) {
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// Keep everything enabled: let PluginManager decide availability.
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harnessOptions.useMockModelProvider = false;
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harnessOptions.autoLoadPlugins = true;
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harnessOptions.configureModelPool = true;
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harnessOptions.modelPoolLazyLoading = false;
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harnessOptions.pluginDir = resolveBenchmarkPluginDir();
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if (!config.embeddingBackend.empty()) {
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// Backend A/B is driven through typed config so the benchmark exercises the same
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// ConfigResolver path as production. Clear the legacy env overlay while the
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// benchmark-owned config is active; guards restore it on teardown.
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g_environment.emplace_back("YAMS_EMBED_BACKEND", std::nullopt);
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if (config.embeddingBackend == "onnxruntime" &&
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!yams::config::getenv_nonempty("YAMS_ONNX_FORCE_CPU")) {
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// Keep release-validation numbers comparable and avoid platform EP failures
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// (e.g. CoreML partition bugs) from masking ONNX Runtime backend selection.
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g_environment.emplace_back("YAMS_ONNX_FORCE_CPU", std::string{"1"});
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}
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auto configPath = writeEmbeddingBackendConfig(config);
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harnessOptions.configPath = configPath;
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g_activeConfigPath = configPath;
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std::cout << "Embedding backend config: " << config.embeddingBackend
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<< " (config=" << configPath << ")\n";
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}
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std::cout << "Plugin dir: " << harnessOptions.pluginDir->string() << "\n";
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}
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g_harness = std::make_unique<DaemonHarness>(harnessOptions);
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if (!g_harness->start(std::chrono::seconds(5))) {
|
|
std::cerr << "ERROR: Failed to start daemon\n";
|
|
std::exit(1);
|
|
}
|
|
|
|
// Create client
|
|
daemon::ClientConfig cc;
|
|
cc.socketPath = g_harness->socketPath();
|
|
cc.autoStart = false;
|
|
g_client = std::make_unique<daemon::DaemonClient>(cc);
|
|
|
|
const bool embeddingsEnabled = []() {
|
|
if (const char* env = std::getenv("YAMS_BENCH_ENABLE_EMBEDDINGS")) {
|
|
return std::string(env) == "1";
|
|
}
|
|
return false;
|
|
}();
|
|
|
|
// Add test documents (small set for benchmarking)
|
|
const auto docCount = static_cast<int>(benchDocCount());
|
|
std::cout << "Adding test documents (" << docCount << ")...\n";
|
|
for (int i = 0; i < docCount; ++i) {
|
|
const std::string docName = "test_doc_" + std::to_string(i) + ".txt";
|
|
auto path = g_harness->dataDir() / docName;
|
|
std::ofstream ofs(path);
|
|
ofs << "Test document " << i << "\n";
|
|
ofs << "This is sample content for performance benchmarking.\n";
|
|
ofs << "Document ID: " << i << "\n";
|
|
// Add some variability to increase corpus size
|
|
ofs << "Lorem ipsum dolor sit amet, consectetur adipiscing elit. Sed do eiusmod tempor "
|
|
"incididunt ut labore et dolore magna aliqua.\n";
|
|
ofs << "Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip "
|
|
"ex ea commodo consequat.\n";
|
|
ofs << "Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu "
|
|
"fugiat nulla pariatur.\n";
|
|
ofs.close();
|
|
|
|
// Add via daemon
|
|
app::services::DocumentIngestionService docSvc;
|
|
app::services::AddOptions opts;
|
|
opts.socketPath = g_harness->socketPath().string();
|
|
opts.explicitDataDir = g_harness->dataDir().string();
|
|
opts.path = path.string();
|
|
opts.noEmbeddings = !embeddingsEnabled;
|
|
|
|
auto result = docSvc.addViaDaemon(opts);
|
|
if (result) {
|
|
// AddViaDaemon returns AddDocumentResponse, check hash field
|
|
if (!result.value().hash.empty()) {
|
|
g_test_docs.push_back(result.value().hash);
|
|
g_test_doc_names.push_back(docName);
|
|
}
|
|
}
|
|
}
|
|
|
|
std::cout << "Waiting for search engine readiness...\n";
|
|
if (!waitForSearchEngineReady(std::chrono::seconds(60))) {
|
|
std::cerr << "WARNING: Search engine not ready after 60s.\n";
|
|
}
|
|
|
|
// Benchmarks assume a queryable corpus; wait for post-ingest + FTS5/doc indexing to catch up.
|
|
// With faster ingestion this can otherwise race and lead to false stalls/timeouts.
|
|
{
|
|
auto timeout = std::chrono::milliseconds(180000);
|
|
if (const char* env = std::getenv("YAMS_BENCH_INDEX_WAIT_MS")) {
|
|
timeout = std::chrono::milliseconds(
|
|
static_cast<std::chrono::milliseconds::rep>(std::stoll(env)));
|
|
}
|
|
std::cout << "Waiting for corpus to be indexed (FTS5/doc counters)...\n";
|
|
if (!waitForCorpusIndexed(g_test_docs.size(), timeout)) {
|
|
std::cerr << "WARNING: Corpus indexing not observed as ready; proceeding anyway.\n";
|
|
}
|
|
}
|
|
|
|
if (embeddingsEnabled) {
|
|
if (!config.embeddingBackend.empty()) {
|
|
runBenchmarkEmbeddingRepair(config);
|
|
} else {
|
|
std::cout << "Waiting for embedding queue to drain...\n";
|
|
auto timeout = std::chrono::milliseconds(600000);
|
|
if (const char* env = std::getenv("YAMS_BENCH_EMBED_WAIT_MS")) {
|
|
timeout = std::chrono::milliseconds(
|
|
static_cast<std::chrono::milliseconds::rep>(std::stoll(env)));
|
|
}
|
|
DrainMetrics metrics;
|
|
if (!waitForEmbeddingDrainWithMetrics(g_test_docs.size(), timeout, metrics)) {
|
|
std::cerr << "WARNING: Embedding drain timeout; proceeding anyway.\n";
|
|
}
|
|
std::cout << "Embedding drain observed peaks: embedQueued=" << metrics.maxEmbedQueued
|
|
<< " embedInflight=" << metrics.maxEmbedInflight
|
|
<< " postQueued=" << metrics.maxPostQueued
|
|
<< " postInflight=" << metrics.maxPostInflight << "\n";
|
|
}
|
|
|
|
if (auto status = cli::run_sync(g_client->status(), std::chrono::seconds(5))) {
|
|
updateObservedEmbeddingStatus(status.value());
|
|
std::cout << "Embedding runtime: available="
|
|
<< (status.value().embeddingAvailable ? 1 : 0)
|
|
<< " backend=" << g_observedEmbeddingBackend
|
|
<< " model=" << g_observedEmbeddingModel << " dim=" << g_observedEmbeddingDim
|
|
<< " vectorCount=" << g_observedVectorCount << "\n";
|
|
}
|
|
}
|
|
|
|
std::cout << "Setup complete: " << g_test_docs.size() << " documents added\n\n";
|
|
}
|
|
|
|
void TeardownBenchmarkSuite() {
|
|
g_client.reset();
|
|
g_harness.reset();
|
|
g_environment.clear();
|
|
g_test_docs.clear();
|
|
g_test_doc_names.clear();
|
|
g_activeConfig = {};
|
|
g_activeConfigPath.reset();
|
|
g_observedEmbeddingBackend.clear();
|
|
g_observedEmbeddingModel.clear();
|
|
g_observedEmbeddingDim = 0;
|
|
g_observedVectorCount = 0;
|
|
g_embeddingRepairSucceeded = 0;
|
|
g_environment.clear();
|
|
}
|
|
|
|
} // anonymous namespace
|
|
|
|
// Benchmark: Get by name with ready FTS5 index (nominal case)
|
|
static void BM_RetrievalService_GetByName_FTS5Ready(benchmark::State& state) {
|
|
#ifdef TRACY_ENABLE
|
|
ZoneScopedN("BM_GetByName_FTS5Ready");
|
|
#endif
|
|
|
|
if (g_test_docs.empty()) {
|
|
state.SkipWithError("No test documents available");
|
|
return;
|
|
}
|
|
|
|
size_t success_count = 0;
|
|
std::vector<int64_t> latencies_us;
|
|
|
|
for (auto _ : state) {
|
|
auto start = std::chrono::high_resolution_clock::now();
|
|
|
|
#ifdef TRACY_ENABLE
|
|
{
|
|
ZoneScopedN("getByHash");
|
|
#endif
|
|
// Use hash-based retrieval (fastest path)
|
|
daemon::GetRequest req;
|
|
req.hash = g_test_docs[success_count % g_test_docs.size()];
|
|
|
|
auto result = cli::run_sync(g_client->get(req), std::chrono::milliseconds(2000));
|
|
|
|
if (result) {
|
|
success_count++;
|
|
}
|
|
#ifdef TRACY_ENABLE
|
|
}
|
|
#endif
|
|
|
|
auto end = std::chrono::high_resolution_clock::now();
|
|
auto latency_us =
|
|
std::chrono::duration_cast<std::chrono::microseconds>(end - start).count();
|
|
latencies_us.push_back(latency_us);
|
|
}
|
|
|
|
// Calculate statistics
|
|
std::sort(latencies_us.begin(), latencies_us.end());
|
|
auto p50 = latencies_us[latencies_us.size() / 2];
|
|
auto p95 = latencies_us[static_cast<size_t>(latencies_us.size() * 0.95)];
|
|
auto max_us = latencies_us.back();
|
|
|
|
state.counters["success_count"] = static_cast<double>(success_count);
|
|
state.counters["p50_us"] = static_cast<double>(p50);
|
|
state.counters["p95_us"] = static_cast<double>(p95);
|
|
state.counters["max_us"] = static_cast<double>(max_us);
|
|
|
|
// Check performance target: < 500ms (500,000 us)
|
|
if (p95 > 500000) {
|
|
std::cerr << "⚠️ WARNING: P95 latency " << (p95 / 1000.0) << "ms exceeds 500ms target\n";
|
|
}
|
|
}
|
|
|
|
// Benchmark: Cat by name (CLI-equivalent retrieval path)
|
|
static void BM_RetrievalService_CatByName(benchmark::State& state) {
|
|
#ifdef TRACY_ENABLE
|
|
ZoneScopedN("BM_CatByName");
|
|
#endif
|
|
|
|
if (g_test_doc_names.empty()) {
|
|
state.SkipWithError("No document names available for cat benchmark");
|
|
return;
|
|
}
|
|
|
|
app::services::RetrievalService rsvc;
|
|
app::services::RetrievalOptions ropts;
|
|
ropts.socketPath = g_harness->socketPath();
|
|
ropts.explicitDataDir = g_harness->dataDir();
|
|
ropts.requestTimeoutMs = 5000;
|
|
ropts.enableStreaming = false;
|
|
|
|
size_t success_count = 0;
|
|
std::vector<int64_t> latencies_us;
|
|
|
|
for (auto _ : state) {
|
|
auto start = std::chrono::high_resolution_clock::now();
|
|
|
|
#ifdef TRACY_ENABLE
|
|
{
|
|
ZoneScopedN("catByName");
|
|
#endif
|
|
app::services::GetOptions getOpts;
|
|
getOpts.name = g_test_doc_names[success_count % g_test_doc_names.size()];
|
|
getOpts.byName = true;
|
|
getOpts.raw = true;
|
|
|
|
auto result = rsvc.get(getOpts, ropts);
|
|
|
|
if (result && result.value().hasContent) {
|
|
success_count++;
|
|
}
|
|
#ifdef TRACY_ENABLE
|
|
}
|
|
#endif
|
|
|
|
auto end = std::chrono::high_resolution_clock::now();
|
|
auto latency_us =
|
|
std::chrono::duration_cast<std::chrono::microseconds>(end - start).count();
|
|
latencies_us.push_back(latency_us);
|
|
}
|
|
|
|
if (latencies_us.empty()) {
|
|
state.SkipWithError("No samples collected for cat benchmark");
|
|
return;
|
|
}
|
|
|
|
std::sort(latencies_us.begin(), latencies_us.end());
|
|
auto p50 = latencies_us[latencies_us.size() / 2];
|
|
auto p95 = latencies_us[static_cast<size_t>(latencies_us.size() * 0.95)];
|
|
auto max_us = latencies_us.back();
|
|
|
|
state.counters["success_count"] = static_cast<double>(success_count);
|
|
state.counters["p50_us"] = static_cast<double>(p50);
|
|
state.counters["p95_us"] = static_cast<double>(p95);
|
|
state.counters["max_us"] = static_cast<double>(max_us);
|
|
|
|
if (p95 > 500000) {
|
|
std::cerr << "⚠️ WARNING: Cat P95 latency " << (p95 / 1000.0)
|
|
<< "ms exceeds 500ms target\n";
|
|
}
|
|
}
|
|
|
|
// Benchmark: Search service query performance (keyword only)
|
|
static void BM_RetrievalService_Search_Keyword(benchmark::State& state) {
|
|
#ifdef TRACY_ENABLE
|
|
ZoneScopedN("BM_Search_Keyword");
|
|
#endif
|
|
|
|
std::vector<int64_t> latencies_us;
|
|
size_t total_results = 0;
|
|
|
|
for (auto _ : state) {
|
|
auto start = std::chrono::high_resolution_clock::now();
|
|
|
|
#ifdef TRACY_ENABLE
|
|
{
|
|
ZoneScopedN("searchQuery");
|
|
#endif
|
|
daemon::SearchRequest req;
|
|
req.query = "document";
|
|
req.limit = 20;
|
|
req.pathsOnly = false;
|
|
req.searchType = "keyword";
|
|
req.fuzzy = false;
|
|
|
|
auto result = cli::run_sync(g_client->search(req), std::chrono::milliseconds(2000));
|
|
|
|
if (result) {
|
|
total_results += result.value().results.size();
|
|
}
|
|
#ifdef TRACY_ENABLE
|
|
}
|
|
#endif
|
|
|
|
auto end = std::chrono::high_resolution_clock::now();
|
|
auto latency_us =
|
|
std::chrono::duration_cast<std::chrono::microseconds>(end - start).count();
|
|
latencies_us.push_back(latency_us);
|
|
}
|
|
|
|
if (latencies_us.empty()) {
|
|
state.SkipWithError("No samples collected for search benchmark");
|
|
return;
|
|
}
|
|
|
|
std::sort(latencies_us.begin(), latencies_us.end());
|
|
auto p95 = latencies_us[static_cast<size_t>(latencies_us.size() * 0.95)];
|
|
auto max_us = latencies_us.back();
|
|
|
|
state.counters["total_results"] = static_cast<double>(total_results);
|
|
state.counters["p95_us"] = static_cast<double>(p95);
|
|
state.counters["max_us"] = static_cast<double>(max_us);
|
|
|
|
if (p95 > 500000) {
|
|
std::cerr << "⚠️ WARNING: Search P95 latency " << (p95 / 1000.0)
|
|
<< "ms exceeds 500ms target\n";
|
|
}
|
|
}
|
|
|
|
// Benchmark: Exercise post-ingest queue under configured concurrency limits
|
|
// This benchmark ingests a batch of documents while tracking peak queue/inflight
|
|
// counts reported by the daemon. It surfaces whether tuning caps throttle
|
|
// progress (e.g., when profiles change or caps are too low).
|
|
static void BM_PostIngest_Throughput(benchmark::State& state) {
|
|
#ifdef TRACY_ENABLE
|
|
ZoneScopedN("BM_PostIngest_Throughput");
|
|
#endif
|
|
|
|
// Configure harness per-iteration (profile & embeddings)
|
|
BenchConfig cfg;
|
|
cfg.embeddingsEnabled = state.range(1) != 0;
|
|
switch (state.range(2)) {
|
|
case 1:
|
|
cfg.tuningProfile = "Efficient";
|
|
break;
|
|
case 2:
|
|
cfg.tuningProfile = "Balanced";
|
|
break;
|
|
case 3:
|
|
cfg.tuningProfile = "Aggressive";
|
|
break;
|
|
default:
|
|
cfg.tuningProfile.clear();
|
|
break; // default profile
|
|
}
|
|
SetupBenchmarkSuite(cfg);
|
|
|
|
// Small batch per iteration to keep runtime reasonable
|
|
const int batchSize = static_cast<int>(state.range(0));
|
|
|
|
for (auto _ : state) {
|
|
state.PauseTiming();
|
|
// Create fresh files for this iteration
|
|
std::vector<std::filesystem::path> paths;
|
|
paths.reserve(batchSize);
|
|
for (int i = 0; i < batchSize; ++i) {
|
|
const std::string docName =
|
|
"bench_pi_" + std::to_string(i) + "_" + std::to_string(state.iterations()) + ".txt";
|
|
auto path = g_harness->dataDir() / docName;
|
|
std::ofstream ofs(path);
|
|
ofs << "Post-ingest bench doc " << i << " iteration " << state.iterations() << "\n";
|
|
ofs << std::string(1024, 'x') << "\n";
|
|
ofs.close();
|
|
paths.push_back(path);
|
|
}
|
|
|
|
app::services::DocumentIngestionService docSvc;
|
|
app::services::AddOptions opts;
|
|
opts.socketPath = g_harness->socketPath().string();
|
|
opts.explicitDataDir = g_harness->dataDir().string();
|
|
opts.noEmbeddings = true; // Focus on post-ingest stages
|
|
|
|
DrainMetrics metrics;
|
|
state.ResumeTiming();
|
|
// Fire the batch
|
|
for (auto& path : paths) {
|
|
opts.path = path.string();
|
|
auto result = docSvc.addViaDaemon(opts);
|
|
benchmark::DoNotOptimize(result);
|
|
}
|
|
|
|
// Wait for post-ingest queue to drain to capture peak queue sizes
|
|
state.PauseTiming();
|
|
auto timeout = std::chrono::milliseconds(120000);
|
|
waitForPostIngestDrainWithMetrics(timeout, metrics);
|
|
|
|
state.counters["max_embed_queued"] = static_cast<double>(metrics.maxEmbedQueued);
|
|
state.counters["max_embed_inflight"] = static_cast<double>(metrics.maxEmbedInflight);
|
|
state.counters["max_post_queued"] = static_cast<double>(metrics.maxPostQueued);
|
|
state.counters["max_post_inflight"] = static_cast<double>(metrics.maxPostInflight);
|
|
}
|
|
}
|
|
// Args: batchSize, embeddings(0/1), profile(0=default,1=Efficient,2=Balanced,3=Aggressive)
|
|
BENCHMARK(BM_PostIngest_Throughput)
|
|
->Args({10, 0, 0})
|
|
->Args({10, 0, 1})
|
|
->Args({10, 0, 2})
|
|
->Args({10, 0, 3})
|
|
->Args({25, 0, 0})
|
|
->Args({25, 0, 2})
|
|
->Args({25, 0, 3})
|
|
->Args({50, 0, 0})
|
|
->Args({50, 0, 2})
|
|
->Args({50, 1, 2})
|
|
->Args({25, 1, 2})
|
|
->Args({25, 1, 3});
|
|
|
|
// Benchmark: Search with fuzzy enabled (tests SymSpell expansion + FTS5)
|
|
static void BM_RetrievalService_Search_Fuzzy(benchmark::State& state) {
|
|
#ifdef TRACY_ENABLE
|
|
ZoneScopedN("BM_Search_Fuzzy");
|
|
#endif
|
|
|
|
std::vector<int64_t> latencies_us;
|
|
size_t total_results = 0;
|
|
|
|
for (auto _ : state) {
|
|
auto start = std::chrono::high_resolution_clock::now();
|
|
|
|
daemon::SearchRequest req;
|
|
req.query = "documnt"; // Intentional typo to test fuzzy
|
|
req.limit = 20;
|
|
req.pathsOnly = false;
|
|
req.fuzzy = true;
|
|
req.similarity = 0.6;
|
|
|
|
auto result = cli::run_sync(g_client->search(req), std::chrono::milliseconds(2000));
|
|
|
|
if (result) {
|
|
total_results += result.value().results.size();
|
|
}
|
|
|
|
auto end = std::chrono::high_resolution_clock::now();
|
|
auto latency_us =
|
|
std::chrono::duration_cast<std::chrono::microseconds>(end - start).count();
|
|
latencies_us.push_back(latency_us);
|
|
}
|
|
|
|
if (latencies_us.empty()) {
|
|
state.SkipWithError("No samples collected for fuzzy search benchmark");
|
|
return;
|
|
}
|
|
|
|
std::sort(latencies_us.begin(), latencies_us.end());
|
|
auto p50 = latencies_us[latencies_us.size() / 2];
|
|
auto p95 = latencies_us[static_cast<size_t>(latencies_us.size() * 0.95)];
|
|
auto max_us = latencies_us.back();
|
|
|
|
state.counters["total_results"] = static_cast<double>(total_results);
|
|
state.counters["p50_us"] = static_cast<double>(p50);
|
|
state.counters["p95_us"] = static_cast<double>(p95);
|
|
state.counters["max_us"] = static_cast<double>(max_us);
|
|
|
|
if (p95 > 500000) {
|
|
std::cerr << "⚠️ WARNING: Fuzzy Search P95 latency " << (p95 / 1000.0)
|
|
<< "ms exceeds 500ms target\n";
|
|
}
|
|
}
|
|
|
|
// Benchmark: Hybrid search (keyword + semantic + vector)
|
|
static void BM_RetrievalService_Search_Hybrid(benchmark::State& state) {
|
|
#ifdef TRACY_ENABLE
|
|
ZoneScopedN("BM_Search_Hybrid");
|
|
#endif
|
|
|
|
std::vector<int64_t> latencies_us;
|
|
size_t total_results = 0;
|
|
|
|
for (auto _ : state) {
|
|
auto start = std::chrono::high_resolution_clock::now();
|
|
|
|
daemon::SearchRequest req;
|
|
req.query = "test document performance";
|
|
req.limit = 20;
|
|
req.pathsOnly = false;
|
|
req.searchType = "hybrid";
|
|
req.fuzzy = false;
|
|
|
|
auto result = cli::run_sync(g_client->search(req), std::chrono::milliseconds(3000));
|
|
|
|
if (result) {
|
|
total_results += result.value().results.size();
|
|
}
|
|
|
|
auto end = std::chrono::high_resolution_clock::now();
|
|
auto latency_us =
|
|
std::chrono::duration_cast<std::chrono::microseconds>(end - start).count();
|
|
latencies_us.push_back(latency_us);
|
|
}
|
|
|
|
if (latencies_us.empty()) {
|
|
state.SkipWithError("No samples collected for hybrid search benchmark");
|
|
return;
|
|
}
|
|
|
|
std::sort(latencies_us.begin(), latencies_us.end());
|
|
auto p50 = latencies_us[latencies_us.size() / 2];
|
|
auto p95 = latencies_us[static_cast<size_t>(latencies_us.size() * 0.95)];
|
|
auto max_us = latencies_us.back();
|
|
|
|
state.counters["total_results"] = static_cast<double>(total_results);
|
|
state.counters["p50_us"] = static_cast<double>(p50);
|
|
state.counters["p95_us"] = static_cast<double>(p95);
|
|
state.counters["max_us"] = static_cast<double>(max_us);
|
|
|
|
if (p95 > 1000000) {
|
|
std::cerr << "⚠️ WARNING: Hybrid Search P95 latency " << (p95 / 1000.0)
|
|
<< "ms exceeds 1000ms target\n";
|
|
}
|
|
}
|
|
|
|
// Benchmark: direct daemon embedding generation, sweeping the selected provider backend.
|
|
// This provides a stable A/B signal for provider selection and embedding throughput without
|
|
// coupling the result to vector-index rebuild policy.
|
|
static void BM_EmbeddingBackendAB_Generate(benchmark::State& state) {
|
|
#ifdef TRACY_ENABLE
|
|
ZoneScopedN("BM_EmbeddingBackendAB_Generate");
|
|
#endif
|
|
|
|
const auto backend = embeddingBackendForArg(state.range(0));
|
|
if (backend.empty()) {
|
|
state.SkipWithError("Unknown backend arg");
|
|
return;
|
|
}
|
|
|
|
BenchConfig cfg;
|
|
cfg.embeddingsEnabled = true;
|
|
cfg.embeddingBackend = backend;
|
|
SetupBenchmarkSuite(cfg);
|
|
|
|
auto statusResult = cli::run_sync(g_client->status(), std::chrono::seconds(5));
|
|
if (!statusResult) {
|
|
state.SkipWithError("Unable to read daemon status for embedding backend validation");
|
|
return;
|
|
}
|
|
const auto status = statusResult.value();
|
|
updateObservedEmbeddingStatus(status);
|
|
const auto observed = lowerCopy(status.embeddingBackend + " " + status.embeddingModel + " " +
|
|
status.embeddingModelPath + " " + status.version);
|
|
if (!status.embeddingAvailable) {
|
|
state.SkipWithError("Embedding backend is not available");
|
|
return;
|
|
}
|
|
if (backend == "simeon" && observed.find("simeon") == std::string::npos) {
|
|
state.SkipWithError("Requested simeon but daemon did not report a Simeon provider");
|
|
return;
|
|
}
|
|
if (backend == "onnxruntime" && observed.find("onnx") == std::string::npos &&
|
|
observed.find("abimodelprovider") == std::string::npos) {
|
|
state.SkipWithError("Requested onnxruntime but daemon did not report an ONNX/ABI provider");
|
|
return;
|
|
}
|
|
|
|
const auto docCount = static_cast<std::size_t>(std::max<int64_t>(1, state.range(1)));
|
|
std::vector<std::string> texts;
|
|
texts.reserve(docCount);
|
|
for (std::size_t i = 0; i < docCount; ++i) {
|
|
texts.push_back("YAMS embedding throughput document " + std::to_string(i) +
|
|
" for backend " + backend +
|
|
". This sample text exercises daemon batch embedding generation.");
|
|
}
|
|
const auto model = preferredModelForBackend(backend);
|
|
state.SetLabel("requested=" + backend + " observed=" + status.embeddingBackend +
|
|
" model=" + status.embeddingModel);
|
|
|
|
std::vector<int64_t> latencies_us;
|
|
size_t success_count = 0;
|
|
size_t failure_count = 0;
|
|
|
|
for (auto _ : state) {
|
|
auto start = std::chrono::high_resolution_clock::now();
|
|
|
|
daemon::BatchEmbeddingRequest req;
|
|
req.texts = texts;
|
|
req.modelName = model;
|
|
req.normalize = true;
|
|
req.batchSize = texts.size();
|
|
|
|
auto result =
|
|
cli::run_sync(g_client->generateBatchEmbeddings(req), std::chrono::milliseconds(30000));
|
|
if (result && result.value().successCount == texts.size()) {
|
|
success_count += result.value().successCount;
|
|
} else {
|
|
++failure_count;
|
|
}
|
|
|
|
auto end = std::chrono::high_resolution_clock::now();
|
|
latencies_us.push_back(
|
|
std::chrono::duration_cast<std::chrono::microseconds>(end - start).count());
|
|
}
|
|
|
|
if (latencies_us.empty()) {
|
|
state.SkipWithError("No samples collected for embedding backend A/B benchmark");
|
|
return;
|
|
}
|
|
std::sort(latencies_us.begin(), latencies_us.end());
|
|
auto p50 = latencies_us[latencies_us.size() / 2];
|
|
auto p95 = latencies_us[static_cast<size_t>(latencies_us.size() * 0.95)];
|
|
auto max_us = latencies_us.back();
|
|
const auto totalLatencyUs =
|
|
std::accumulate(latencies_us.begin(), latencies_us.end(), int64_t{0});
|
|
const auto avg_us = latencies_us.empty()
|
|
? int64_t{0}
|
|
: totalLatencyUs / static_cast<int64_t>(latencies_us.size());
|
|
|
|
state.counters["success_count"] = static_cast<double>(success_count);
|
|
state.counters["failure_count"] = static_cast<double>(failure_count);
|
|
state.counters["batch_docs"] = static_cast<double>(docCount);
|
|
state.counters["texts_per_iter"] = static_cast<double>(texts.size());
|
|
state.counters["avg_us"] = static_cast<double>(avg_us);
|
|
state.counters["p50_us"] = static_cast<double>(p50);
|
|
state.counters["p95_us"] = static_cast<double>(p95);
|
|
state.counters["max_us"] = static_cast<double>(max_us);
|
|
state.counters["avg_docs_per_s"] =
|
|
avg_us > 0 ? static_cast<double>(docCount) * 1'000'000.0 / static_cast<double>(avg_us)
|
|
: 0.0;
|
|
state.counters["p50_docs_per_s"] =
|
|
p50 > 0 ? static_cast<double>(docCount) * 1'000'000.0 / static_cast<double>(p50) : 0.0;
|
|
state.counters["p95_docs_per_s"] =
|
|
p95 > 0 ? static_cast<double>(docCount) * 1'000'000.0 / static_cast<double>(p95) : 0.0;
|
|
state.counters["embedding_dim"] = static_cast<double>(g_observedEmbeddingDim);
|
|
state.counters["repair_succeeded"] = static_cast<double>(g_embeddingRepairSucceeded);
|
|
|
|
if (failure_count > 0) {
|
|
state.SkipWithError("Embedding backend A/B generation saw daemon errors");
|
|
}
|
|
}
|
|
|
|
// Benchmark: Get by name when document doesn't exist (tests fast-path error handling)
|
|
static void BM_RetrievalService_GetByName_NotFound(benchmark::State& state) {
|
|
#ifdef TRACY_ENABLE
|
|
ZoneScopedN("BM_GetByName_NotFound");
|
|
#endif
|
|
|
|
std::vector<int64_t> latencies_us;
|
|
size_t fast_error_count = 0;
|
|
|
|
for (auto _ : state) {
|
|
auto start = std::chrono::high_resolution_clock::now();
|
|
|
|
#ifdef TRACY_ENABLE
|
|
{
|
|
ZoneScopedN("getByHash_nonexistent");
|
|
#endif
|
|
// Try to get nonexistent document
|
|
daemon::GetRequest req;
|
|
req.hash = "0000000000000000"; // Nonexistent hash
|
|
|
|
auto result = cli::run_sync(g_client->get(req), std::chrono::milliseconds(2000));
|
|
|
|
// Should fail quickly with NotFound
|
|
if (!result) {
|
|
fast_error_count++;
|
|
}
|
|
#ifdef TRACY_ENABLE
|
|
}
|
|
#endif
|
|
|
|
auto end = std::chrono::high_resolution_clock::now();
|
|
auto latency_us =
|
|
std::chrono::duration_cast<std::chrono::microseconds>(end - start).count();
|
|
latencies_us.push_back(latency_us);
|
|
}
|
|
|
|
std::sort(latencies_us.begin(), latencies_us.end());
|
|
auto p95 = latencies_us[static_cast<size_t>(latencies_us.size() * 0.95)];
|
|
auto max_us = latencies_us.back();
|
|
|
|
state.counters["fast_errors"] = static_cast<double>(fast_error_count);
|
|
state.counters["p95_us"] = static_cast<double>(p95);
|
|
state.counters["max_us"] = static_cast<double>(max_us);
|
|
|
|
// Check target: fast error < 1000ms (1,000,000 us)
|
|
// CRITICAL: This is the 15-minute hang bug we fixed in 040-1
|
|
if (p95 > 1000000) {
|
|
std::cerr << "🔴 CRITICAL: P95 error latency " << (p95 / 1000.0)
|
|
<< "ms exceeds 1000ms target (possible hang!)\n";
|
|
}
|
|
}
|
|
|
|
// Benchmark: List documents (tests metadata query performance)
|
|
static void BM_RetrievalService_List(benchmark::State& state) {
|
|
#ifdef TRACY_ENABLE
|
|
ZoneScopedN("BM_List");
|
|
#endif
|
|
|
|
std::vector<int64_t> latencies_us;
|
|
size_t total_docs = 0;
|
|
|
|
for (auto _ : state) {
|
|
auto start = std::chrono::high_resolution_clock::now();
|
|
|
|
#ifdef TRACY_ENABLE
|
|
{
|
|
ZoneScopedN("listDocuments");
|
|
#endif
|
|
daemon::ListRequest req;
|
|
req.limit = 20;
|
|
|
|
auto result = cli::run_sync(g_client->list(req), std::chrono::milliseconds(2000));
|
|
|
|
if (result) {
|
|
total_docs += result.value().items.size();
|
|
}
|
|
#ifdef TRACY_ENABLE
|
|
}
|
|
#endif
|
|
|
|
auto end = std::chrono::high_resolution_clock::now();
|
|
auto latency_us =
|
|
std::chrono::duration_cast<std::chrono::microseconds>(end - start).count();
|
|
latencies_us.push_back(latency_us);
|
|
}
|
|
|
|
std::sort(latencies_us.begin(), latencies_us.end());
|
|
auto p95 = latencies_us[static_cast<size_t>(latencies_us.size() * 0.95)];
|
|
auto max_us = latencies_us.back();
|
|
|
|
state.counters["total_docs"] = static_cast<double>(total_docs);
|
|
state.counters["p95_us"] = static_cast<double>(p95);
|
|
state.counters["max_us"] = static_cast<double>(max_us);
|
|
|
|
if (max_us > 1000000) {
|
|
state.SkipWithError("List latency exceeded 1s — possible hang");
|
|
return;
|
|
}
|
|
}
|
|
|
|
// Benchmark: Grep search (tests FTS5 query + content retrieval)
|
|
static void BM_GrepService_Search(benchmark::State& state) {
|
|
#ifdef TRACY_ENABLE
|
|
ZoneScopedN("BM_Grep");
|
|
#endif
|
|
|
|
std::vector<int64_t> latencies_us;
|
|
size_t total_matches = 0;
|
|
size_t timeout_count = 0;
|
|
size_t error_count = 0;
|
|
|
|
for (auto _ : state) {
|
|
auto start = std::chrono::high_resolution_clock::now();
|
|
|
|
#ifdef TRACY_ENABLE
|
|
{
|
|
ZoneScopedN("grepSearch");
|
|
#endif
|
|
daemon::GrepRequest req;
|
|
req.pattern = "document";
|
|
req.pathsOnly = false;
|
|
|
|
auto result = cli::run_sync(g_client->grep(req), std::chrono::milliseconds(2000));
|
|
|
|
if (!result) {
|
|
const auto& err = result.error();
|
|
if (err.code == ErrorCode::Timeout) {
|
|
++timeout_count;
|
|
} else {
|
|
++error_count;
|
|
}
|
|
} else {
|
|
total_matches += result.value().matches.size();
|
|
}
|
|
#ifdef TRACY_ENABLE
|
|
}
|
|
#endif
|
|
|
|
auto end = std::chrono::high_resolution_clock::now();
|
|
auto latency_us =
|
|
std::chrono::duration_cast<std::chrono::microseconds>(end - start).count();
|
|
latencies_us.push_back(latency_us);
|
|
}
|
|
|
|
if (latencies_us.empty()) {
|
|
state.SkipWithError("No samples collected for grep benchmark");
|
|
return;
|
|
}
|
|
|
|
std::sort(latencies_us.begin(), latencies_us.end());
|
|
auto p95 = latencies_us[static_cast<size_t>(latencies_us.size() * 0.95)];
|
|
auto max_us = latencies_us.back();
|
|
|
|
state.counters["total_matches"] = static_cast<double>(total_matches);
|
|
state.counters["p95_us"] = static_cast<double>(p95);
|
|
state.counters["max_us"] = static_cast<double>(max_us);
|
|
state.counters["timeout_count"] = static_cast<double>(timeout_count);
|
|
state.counters["error_count"] = static_cast<double>(error_count);
|
|
|
|
if (max_us > 1000000) {
|
|
std::cerr << "🔴 CRITICAL: Grep latency " << (max_us / 1000.0)
|
|
<< "ms exceeded 1s; potential hang detected\n";
|
|
state.SkipWithError("Grep latency exceeded 1s");
|
|
return;
|
|
}
|
|
|
|
if (timeout_count > 0) {
|
|
std::cerr << "🔴 CRITICAL: Grep benchmark experienced " << timeout_count
|
|
<< " timeout(s); potential hang detected\n";
|
|
state.SkipWithError("Grep timeout detected");
|
|
return;
|
|
}
|
|
|
|
if (error_count > 0) {
|
|
std::cerr << "⚠️ WARNING: Grep benchmark observed " << error_count
|
|
<< " non-timeout error(s).\n";
|
|
}
|
|
|
|
// Target: < 500ms for grep with sync indexing (when 040-4 is complete)
|
|
if (p95 > 500000) {
|
|
std::cerr << "⚠️ INFO: Grep P95 " << (p95 / 1000.0)
|
|
<< "ms (target < 500ms with sync indexing in 040-4)\n";
|
|
}
|
|
}
|
|
|
|
// Register benchmarks with appropriate iteration counts
|
|
BENCHMARK(BM_RetrievalService_GetByName_FTS5Ready)->Unit(benchmark::kMicrosecond)->Iterations(100);
|
|
|
|
BENCHMARK(BM_RetrievalService_CatByName)->Unit(benchmark::kMicrosecond)->Iterations(60);
|
|
|
|
BENCHMARK(BM_RetrievalService_GetByName_NotFound)->Unit(benchmark::kMicrosecond)->Iterations(10);
|
|
|
|
BENCHMARK(BM_RetrievalService_List)->Unit(benchmark::kMicrosecond)->Iterations(50);
|
|
|
|
BENCHMARK(BM_GrepService_Search)->Unit(benchmark::kMicrosecond)->Iterations(30);
|
|
|
|
BENCHMARK(BM_RetrievalService_Search_Keyword)->Unit(benchmark::kMicrosecond)->Iterations(40);
|
|
|
|
BENCHMARK(BM_RetrievalService_Search_Fuzzy)->Unit(benchmark::kMicrosecond)->Iterations(40);
|
|
|
|
BENCHMARK(BM_RetrievalService_Search_Hybrid)->Unit(benchmark::kMicrosecond)->Iterations(20);
|
|
|
|
// Arg backend: 1=simeon, 2=onnxruntime.
|
|
BENCHMARK(BM_EmbeddingBackendAB_Generate)
|
|
->ArgNames({"backend", "docs"})
|
|
->Args({1, 100})
|
|
->Args({2, 100})
|
|
->Args({1, 1000})
|
|
->Args({2, 1000})
|
|
->Unit(benchmark::kMicrosecond)
|
|
->Iterations(3);
|
|
|
|
// Custom main to setup/teardown daemon
|
|
int main(int argc, char** argv) {
|
|
std::cout << "\n";
|
|
std::cout << "╔═══════════════════════════════════════════════════════════╗\n";
|
|
std::cout << "║ YAMS Service Layer Performance Benchmarks (PBI-040) ║\n";
|
|
std::cout << "╚═══════════════════════════════════════════════════════════╝\n";
|
|
std::cout << "\nPerformance Targets:\n";
|
|
std::cout << " • GetByName (FTS5 ready): P95 < 500ms\n";
|
|
std::cout << " • Cat (by name): P95 < 500ms\n";
|
|
std::cout << " • GetByName (not found): P95 < 1000ms (fast error)\n";
|
|
std::cout << " • Search (keyword): P95 < 500ms\n";
|
|
std::cout << " • Search (fuzzy/SymSpell): P95 < 500ms\n";
|
|
std::cout << " • Search (hybrid): P95 < 1000ms\n";
|
|
std::cout << " • List documents: Responsive metadata queries\n";
|
|
std::cout << " • Grep search: P95 < 500ms (with sync indexing)\n";
|
|
std::cout << "\n";
|
|
|
|
// Setup daemon and test data. The embedding backend A/B benchmark owns its setup per backend
|
|
// row, so skip this default harness when the filter targets only that validation pass.
|
|
const bool deferSetupToBackendAB = benchmarkFilterTargetsBackendAB(argc, argv);
|
|
if (!deferSetupToBackendAB) {
|
|
SetupBenchmarkSuite();
|
|
}
|
|
|
|
// Run benchmarks
|
|
::benchmark::Initialize(&argc, argv);
|
|
if (::benchmark::ReportUnrecognizedArguments(argc, argv)) {
|
|
TeardownBenchmarkSuite();
|
|
return 1;
|
|
}
|
|
::benchmark::RunSpecifiedBenchmarks();
|
|
::benchmark::Shutdown();
|
|
|
|
// Cleanup
|
|
TeardownBenchmarkSuite();
|
|
|
|
std::cout << "\n✅ Benchmark suite completed\n";
|
|
std::cout << "💡 Review P95 latencies above for regressions\n";
|
|
std::cout << "🔴 Critical warnings indicate potential hangs or severe slowdowns\n\n";
|
|
|
|
return 0;
|
|
}
|