Monitoring C++ Applications: Libraries and Tools

Monitoring C++ Applications: Tools and Libraries for Metrics Collection and Logging

Developing in C++ often involves creating high-load systems where performance and stability are critical. Without quality monitoring, it is impossible to quickly identify bottlenecks, memory leaks, or anomalous behavior. This article reviews the main libraries and approaches for organizing monitoring of C++ applications — from metrics collection to integration with popular observability systems.

Why is monitoring C++ applications necessary?

Key monitoring tasks:

Tracking CPU, memory, disk, and network usage.
Measuring request processing latency.
Collecting error and exception counts.
Profiling hot paths in the code.
Automatic alerting when threshold values are reached.

Main Libraries for C++ Monitoring

1. Prometheus-CPP (prometheus-cpp)

One of the most popular libraries for collecting and exporting metrics in Prometheus format. It allows creating counters, histograms, and gauges.

Usage example:

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

// HTTP endpoint for metrics collectionprometheus::Exposer exposer{"0.0.0.0:8080"};auto registry = std::make_shared<prometheus::Registry>();

// Create a counterauto& counter_family = prometheus::BuildCounter()    .Name("http_requests_total")    .Help("Total HTTP requests")    .Register(*registry);

auto& http_requests = counter_family.Add({{"method", "GET"}});

// Expose metricsexposer.RegisterCollectable(registry);

// Increment the counter on each requesthttp_requests.Increment();

2. OpenTelemetry C++

A modern standard for collecting traces, metrics, and logs. OpenTelemetry for C++ provides an SDK for instrumenting code and exporting data to any backend (Jaeger, Zipkin, Prometheus).

Example of trace setup:

#include <opentelemetry/trace/provider.h>#include <opentelemetry/exporters/otlp/otlp_http_exporter.h>#include <opentelemetry/sdk/trace/tracer_provider.h>

// Initialize exporterauto exporter = std::unique_ptr<opentelemetry::sdk::trace::SpanExporter>(    new opentelemetry::exporter::otlp::OtlpHttpExporter());

auto processor = std::unique_ptr<opentelemetry::sdk::trace::SpanProcessor>(    new opentelemetry::sdk::trace::SimpleSpanProcessor(std::move(exporter)));

auto provider = std::shared_ptr<opentelemetry::trace::TracerProvider>(    new opentelemetry::sdk::trace::TracerProvider(std::move(processor)));

opentelemetry::trace::Provider::SetTracerProvider(provider);

// Create a spanauto tracer = provider->GetTracer("my_app");auto span = tracer->StartSpan("process_request");span->SetAttribute("user_id", 12345);span->End();

3. spdlog + metrics

Although spdlog is a logging library, it is often combined with custom metrics. For example, you can log function execution times and parse the logs with third-party tools (Filebeat + Elasticsearch).

Example of timing with spdlog:

#include <spdlog/spdlog.h>#include <chrono>

void process_data() {    auto start = std::chrono::high_resolution_clock::now();    // ... business logic ...    auto end = std::chrono::high_resolution_clock::now();    auto duration = std::chrono::duration_cast<std::chrono::milliseconds>(end - start);    spdlog::info("process_data took {} ms", duration.count());}