Monitoring C++ Applications: Advanced Metrics and Alerts

Monitoring C++ Applications: Advanced Metrics and Alert Configuration

In high-load systems, monitoring C++ applications is no longer an option—it is a necessity. Basic CPU and memory collection no longer provides the full picture. At an advanced level, it is necessary to track internal queues, execution time of critical sections, the number of resource leaks, and even the state of thread pools. In this article, we will look at how to organize metric collection, configure alerts, and integrate a C++ application with modern monitoring systems such as Prometheus and Grafana.

1. Application-Level Metric Collection

The first step towards advanced monitoring is implementing instrumentation directly into the code. Instead of external agents, we add counters, histograms, and gauges directly into the business logic.

Consider an example using the Prometheus-CPP library. Suppose we have an HTTP request handler and we want to track:

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• The number of processed requests (counter).
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• Request processing time (histogram).
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• The number of errors (counter with labels).
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#include <prometheus/counter.h>\n#include <prometheus/histogram.h>\n#include <prometheus/registry.h>\n\nusing namespace prometheus;\n\n// Global metric registry\nstd::shared_ptr<Registry> registry = std::make_shared<Registry>();\n\n// Create a request counter\nauto& request_counter = BuildCounter()\n    .Name("http_requests_total")\n    .Help("Total number of HTTP requests")\n    .Register(*registry);\n\n// Create a histogram for response time\nauto& request_duration = BuildHistogram()\n    .Name("http_request_duration_seconds")\n    .Help("Request duration in seconds")\n    .Register(*registry);\n\n// Request handling function\nvoid handle_request(const std::string& endpoint) {\n    auto start = std::chrono::high_resolution_clock::now();\n\n    // Simulate processing\n    std::this_thread::sleep_for(std::chrono::milliseconds(rand() % 100));\n\n    auto end = std::chrono::high_resolution_clock::now();\n    std::chrono::duration<double> elapsed = end - start;\n\n    // Update metrics\n    request_counter.Increment();\n    request_duration.Observe(elapsed.count());\n}\n

This approach allows you to obtain precise performance data for each endpoint, rather than averaged system indicators.

2. Advanced Metrics: Queues, Thread Pools, and Leaks

For high-load C++ applications, it is critical to track the state of internal resources. Let's consider three key types of metrics:

2.1. Message Queue Depth

If the application uses task queues (e.g., based on std::queue with mutexes), add a gauge for the current queue size:

#include <prometheus/gauge.h>\n\nauto& queue_size = BuildGauge()\n    .Name("task_queue_size")\n    .Help("Current number of tasks in queue")\n    .Register(*registry);\n\n// In producer/consumer code\nvoid push_task(Task t) {\n    std::lock_guard<std::mutex> lock(mutex);\n    queue.push(t);\n    queue_size.Set(queue.size()); // update metric\n}\n

2.2. Thread Pool State

Track the number of active, waiting, and blocked threads. For this, you can use std::thread::hardware_concurrency() and custom counters:

auto& threads_active = BuildGauge()\n    .Name("thread_pool_active_threads")\n    .Help("Number of currently active threads")\n    .Register(*registry);\n\n// At task start/completion\nthreads_active.Set(active_thread_count);\n

2.3. Resource Leaks (Handles, Memory)

By intercepting new/delete or using custom allocators, you can track the number of live objects of a specific type:

auto& live_connection