C++ Application Monitoring: Tools and Libraries

Monitoring C++ Applications: Why and How to Do It

Monitoring is an integral part of developing and operating any serious C++ application. Without it, you remain blind: you don't know how much memory the program consumes, which functions are slowing down, or whether the server is crashing under load. In this article, we will review the key libraries and tools for monitoring C++ applications — from metric collection to profiling and logging.

1. System Metric Collection: Prometheus Client for C++

If your application runs as a microservice or part of a distributed system, you need a metric collection infrastructure. Prometheus is the de facto standard. For C++, there is an official client prometheus-cpp.

The library allows you to create counters, histograms, summaries, and expose them via an HTTP endpoint that the Prometheus server will poll.

#include <prometheus/counter.h>#include <prometheus/exposer.h>#include <prometheus/registry.h>

int main() {    using namespace prometheus;

    // Create an HTTP server on port 8080    Exposer exposer{"0.0.0.0:8080"};

    // Register metrics    auto registry = std::make_shared<Registry>();    auto& counter_family = BuildCounter()        .Name("http_requests_total")        .Help("Total number of HTTP requests")        .Register(*registry);

    auto& counter = counter_family.Add({{"method", "GET"}, {"endpoint", "/api"}});

    // Expose metrics    exposer.RegisterCollectable(registry);

    // Simulate requests    for (int i = 0; i < 100; ++i) {        counter.Increment();        std::this_thread::sleep_for(std::chrono::milliseconds(100));    }

    return 0;}

Pros:

• Native integration with the Prometheus/Grafana ecosystem
• Support for histograms and quantiles
• Asynchronous export without blocking the main thread

Cons:

• Requires setting up a Prometheus server and Grafana for visualization
• Not suitable for embedded systems with limited memory

2. Performance Profiling: Google Benchmark

For precise measurement of execution time for individual code sections (benchmarks), use Google Benchmark. It is not real-time monitoring, but an indispensable tool for regression performance testing.

#include <benchmark/benchmark.h>

static void BM_StringCreation(benchmark::State& state) {    for (auto _ : state) {        std::string empty_string;    }}BENCHMARK(BM_StringCreation);

static void BM_StringCopy(benchmark::State& state) {    std::string x = "hello";    for (auto _ : state) {        std::string copy(x);    }}BENCHMARK(BM_StringCopy);

BENCHMARK_MAIN();

Features:

• Automatic calculation of mean, median, standard deviation
• Support for multi-threaded benchmarks
• Report generation in JSON, CSV, console formats
• Integration with CI/CD (can compare results with baselines)

3. Logging: spdlog

Logs are the foundation of error and anomaly monitoring. spdlog is the fastest logging library for C++ (header-only, asynchronous mode).

#include <spdlog/spdlog.h>#include <spdlog/sinks/basic_file_sink.h>

int main() {    // Create a logger with file output    auto logger = spdlog::basic_logger_mt("file_logger", "logs/app.log");

    // Set the logging level    spdlog::set_level(spdlog::level::info);

    // Logs with different levels    logger->info("Application started, version {}", 1.0);    logger->warn("Memory near limit: {} MB", 512);    logger->