Advanced C++ Application Monitoring: Metrics and Alerts

Advanced C++ Application Monitoring: Metrics and Alerts

In modern development of high-load systems in C++, it is not enough to simply write efficient code. To ensure stability and performance in production, it is necessary to implement a full-fledged monitoring system. At an advanced level, this means not only collecting basic metrics (CPU, memory) but also creating custom business metrics, configuring smart alerts, and integrating with observability systems. In this article, we will look at how to build production-ready monitoring for C++ applications using Prometheus, Grafana, and custom metrics.

1. Fundamentals of Advanced C++ Monitoring

Why is basic monitoring insufficient? Traditional tools (htop, perf) show overall load but do not provide insight into the internal state of your C++ application. Advanced monitoring includes:

• Business metrics — number of processed requests, response time, queue size.
• Performance metrics — allocation latency, mutex lock time, number of cache misses.
• Resource metrics — memory leaks (via active object counting), number of open file descriptors.
• Threshold-based alerts — not just "CPU > 90%", but "p99 latency > 500ms for 5 minutes".

To implement this, we will need the prometheus-cpp library — a client for exporting metrics in Prometheus format. It allows you to create Counters, Histograms, and Gauges.

2. Integrating the Prometheus Client into a C++ Application

Let's look at an example of connecting and configuring metrics. Install the library via vcpkg or Conan:

# Example installation via vcpkgvcpkg install prometheus-cpp

Now let's write a basic class for exporting metrics via an HTTP endpoint (usually /metrics):

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

class MetricsExporter {public:    MetricsExporter(const std::string& address = "0.0.0.0:8080")        : exposer_(address) {        registry_ = std::make_shared<prometheus::Registry>();        exposer_.RegisterCollectable(registry_);        initMetrics();    }

private:    void initMetrics() {        // Request counter        auto& counter_family = prometheus::BuildCounter()            .Name("http_requests_total")            .Help("Total number of HTTP requests")            .Register(*registry_);        requests_counter_ = &counter_family.Add({{"endpoint", "/api/v1"}});

        // Request duration histogram        auto& histogram_family = prometheus::BuildHistogram()            .Name("http_request_duration_seconds")            .Help("Request duration in seconds")            .Register(*registry_);        request_duration_ = &histogram_family.Add(            {{"endpoint", "/api/v1"}},            prometheus::Histogram::BucketBoundaries{0.01, 0.05, 0.1, 0.5, 1.0, 2.0}        );    }

    prometheus::Exposer exposer_;    std::shared_ptr<prometheus::Registry> registry_;    prometheus::Counter* requests_counter_;    prometheus::Histogram* request_duration_;};

In production code, you will call requests_counter_->Increment() on each request and update the histogram via request_duration_->Observe(latency_seconds). This will give you real data about the load.

3. Collecting Advanced Metrics: From Leaks to Performance

Now let's add custom metrics specific to C++. For example, tracking the number of active objects in a memory pool:

#include <atomic>#include &l