تكامل المقاييس والمراقبة

Welcome to Metrics & Monitoring Integration! In modern systems, understanding what's happening inside your application is crucial. This is called observability.

Observability relies on three pillars:

• Logs: Records of discrete events.
• Traces: End-to-end request flows across services.
• Metrics: Aggregated numerical data points over time.

This lesson focuses on metrics, which give you a high-level view of system health and performance trends.

Metrics are numerical measurements collected at regular intervals. They help you answer questions like:

• How many requests per second is my service handling?
• What's the average response time?
• How much memory is my Erlang VM using?
• Are there any errors occurring?

By tracking these over time, you can spot trends, identify bottlenecks, and react to issues proactively.

There are several common types of metrics:

• Counters: A single value that only ever goes up (e.g., total requests, errors).
• Gauges: A single value that can go up or down (e.g., current active users, CPU usage).
• Histograms: Sample observations (like request durations) and count them in configurable buckets, often providing sum and count.
• Summaries: Similar to histograms but calculate configurable quantiles (e.g., 99th percentile latency) on the client side.

Erlang provides some basic system statistics out of the box through the erlang:statistics/1 function. These are useful for fundamental VM health checks.

You can get data like:

• scheduler_wall_time: CPU time spent by schedulers.
• io: Total I/O operations.
• memory: Memory usage details.

While useful, these often aren't enough for application-specific insights. We need custom metrics.

For application-specific metrics, you'll often create your own. A common pattern is to use a gen_server process to manage and update metric values, ensuring atomic updates.

Try running this example of a simple counter managed by a gen_server:

For more advanced and standardized metric collection in Erlang/Elixir, the Telemetry library is widely used. It provides a common way to emit events from your application.

Instead of manually updating a counter, you can:

• Define telemetry events (e.g., [:my_app, :request, :start], [:my_app, :request, :stop]).
• Attach handlers to these events to process and aggregate metrics.

This decouples event emission from metric collection, making your system more flexible.

Once you collect metrics, you need to make them available to external monitoring systems. This usually involves an exporter.

An exporter is a component (often an HTTP server) that:

• Gathers current metric values from your application.
• Formats them into a standard format (e.g., Prometheus text format).
• Serves them via an HTTP endpoint (e.g., /metrics).

Monitoring systems then 'scrape' this endpoint to collect the data.

Prometheus is a popular open-source monitoring system. It works on a pull model: instead of your application pushing metrics, Prometheus regularly 'scrapes' (pulls) metrics from configured targets (your application's exporter endpoints).

It then stores these metrics as time-series data, allowing you to query and analyze trends over time. Prometheus is excellent for operational monitoring and alerting.

Erlang applications integrate with Prometheus by exposing an HTTP endpoint that serves metrics in the Prometheus text exposition format. This is a simple, human-readable format.

Here's an example of what Prometheus expects to scrape:

While Prometheus stores and queries metrics, Grafana is the go-to tool for visualizing them. Grafana connects to various data sources (like Prometheus) and allows you to build interactive dashboards.

With Grafana, you can:

• Create graphs, charts, and tables.
• Combine data from multiple metrics.
• Set up alerts based on visual thresholds.
• Share dashboards with your team.

You've learned about collecting and exposing metrics, and how Prometheus and Grafana work together. Let's test your understanding!

Great job! You've covered the essentials of metrics and monitoring integration:

• The importance of observability and the role of metrics.
• How to collect custom metrics in Erlang, including using gen_server processes.
• The concept of exporters and the Prometheus text format.
• How Prometheus acts as a pull-based time-series database.
• How Grafana provides powerful visualization for your metrics.

With these tools, you can gain deep insights into your Erlang applications!