Monitoring C++ Applications: Metrics, Alerts, and Best Practices

Monitoring C++ Applications: From Metric Collection to Alert Configuration

Modern C++ applications operate in high-load environments: from database servers to game engines and financial systems. Without monitoring, you risk missing a memory leak, performance drop, or critical failure. In this article, we will break down how to organize comprehensive monitoring for C++ applications: which metrics to collect, how to configure alerts, and which tools to use. You will get ready-made code examples and practical tips.

1. Why Monitor C++ Applications?

Monitoring is not just about "looking at logs." It is a system that allows you to:

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• Detect problems early — before they affect users.
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• Analyze performance — CPU, memory, disks, network.
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• Track business metrics — number of processed requests, response time.
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• Automate notifications — alerts when thresholds are exceeded.
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Without monitoring, you are working blindly. This is especially critical for C++ applications, where an error can lead to a segfault or undefined behavior.

2. Key Metrics for C++ Applications

Metrics are divided into three groups: system, application, and business metrics. Let's look at each.

2.1 System Metrics

These metrics show how the application uses OS resources:

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• CPU usage — processor load (user, system, idle).
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• Memory usage — RSS, VMS, memory leaks.
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• Disk I/O — read/write, latency.
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• Network I/O — incoming/outgoing traffic, errors.
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• File descriptors — number of open files.
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Example of collecting metrics via /proc/self/status (Linux):

#include <fstream>\n#include <string>\n#include <unordered_map>\n\nstd::unordered_map<std::string, std::string> parseProcStatus() {\n    std::ifstream file("/proc/self/status");\n    std::unordered_map<std::string, std::string> metrics;\n    std::string line;\n    while (std::getline(file, line)) {\n        auto pos = line.find(':');\n        if (pos != std::string::npos) {\n            std::string key = line.substr(0, pos);\n            std::string value = line.substr(pos + 2);\n            metrics[key] = value;\n        }\n    }\n    return metrics;\n}\n\n// Usage example:\nauto status = parseProcStatus();\nstd::cout << "VM size: " << status["VmSize"] << std::endl;\n

2.2 Application Metrics

These are specific to your application:

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• Function execution time — measurements using std::chrono.
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• Number of errors — exceptions, return codes.
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• Thread state — number of active/waiting threads.
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• Queue size — e.g., task queue.
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Example of time measurement:

#include <chrono>\n#include <iostream>\n\nclass Timer {\n    std::chrono::high_resolution_clock::time_point start;\npublic:\n    Timer() : start(std::chrono::high_resolution_clock::now()) {}\n    ~Timer() {\n        auto end = std::chrono::high_resolution_clock::now();\n        auto duration = std::chrono::duration_cast<std::chrono::microseconds>(end - start);\n        std::cout << "Execution time: " << duration.count() << " us\\n";\n    }\n};\n\nvoid heavyFunction() {\n    Timer t;\n    // ... your code\n}\n

2.3 Business Metrics

These reflect user value:

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• Requests per second (RPS).
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• Response time (p50, p95, p99).
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