Integración de métricas y monitorización
Implemente la recopilación de métricas e intégrela con sistemas externos de monitorización, como Prometheus y Grafana, para mejorar la observabilidad del sistema.
Integración de métricas y monitorización es una lección gratuita de Erlang OTP: Distributed & Fault-Tolerant Systems Programming en CoddyKit. Esta es la lección 3 de 4. Puedes leer la lección completa abajo gratuitamente — luego la practicas en el navegador con un editor de código integrado y un tutor de IA 24/7. Forma parte de la ruta de aprendizaje de Erlang OTP: Distributed & Fault-Tolerant Systems Programming, y tu progreso se sincroniza en la web y la app de CoddyKit. El curso de Erlang OTP: Distributed & Fault-Tolerant Systems Programming incluye 4 lecciones en total.
Partes de esta lección aún no han sido traducidas y se muestran en inglés.
Observability with Metrics
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.
Why Metrics Matter
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.
Types of Metrics
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's Built-in Statistics
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.
Custom Metric Collection
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:
-module(main).
-export([main/0]).
% --- simple_counter.erl (simplified for single file demo) ---
% A simple GenServer to maintain a counter
-behaviour(gen_server).
-export([init/1, handle_call/3, handle_cast/2, handle_info/2, terminate/2, code_change/3]).
% GenServer callbacks
init([]) -> {ok, 0}.
handle_call(get_value, _From, State) -> {reply, State, State}.
handle_cast(increment, State) -> {noreply, State + 1}.
handle_cast(stop, _State) -> {stop, normal, ok}.
handle_info(_Info, State) -> {noreply, State}.
terminate(_Reason, _State) -> ok.
code_change(_OldVsn, State, _Extra) -> {ok, State}.
% Client API for our counter
start_counter() -> gen_server:start({local, my_counter}, ?MODULE, [], []).
increment_counter() -> gen_server:cast(my_counter, increment).
get_counter_value() -> gen_server:call(my_counter, get_value).
stop_counter() -> gen_server:cast(my_counter, stop).
% --- End simple_counter.erl ---
main() ->
io:format("Starting a simple counter process...~n"),
{ok, _Pid} = start_counter(),
io:format("Initial counter value: ~p~n", [get_counter_value()]),
increment_counter(),
io:format("After 1 increment: ~p~n", [get_counter_value()]),
increment_counter(),
increment_counter(),
io:format("After 3 increments: ~p~n", [get_counter_value()]),
stop_counter(),
io:format("Counter process stopped.~n").The Telemetry Library
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.
Exposing Metrics
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: A Pull-Based System
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.
Prometheus Text Format
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:
# HELP my_app_requests_total Total number of requests processed.
# TYPE my_app_requests_total counter
my_app_requests_total 1234
# HELP my_app_active_users Current number of active users.
# TYPE my_app_active_users gauge
my_app_active_users 50
# HELP my_app_request_duration_seconds Request duration in seconds.
# TYPE my_app_request_duration_seconds histogram
my_app_request_duration_seconds_bucket{le="0.1"} 100
my_app_request_duration_seconds_bucket{le="0.5"} 250
my_app_request_duration_seconds_bucket{le="1.0"} 350
my_app_request_duration_seconds_bucket{le="+Inf"} 400
my_app_request_duration_seconds_sum 150.0
my_app_request_duration_seconds_count 400Grafana for Visualization
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.
Monitoring Integration Check
You've learned about collecting and exposing metrics, and how Prometheus and Grafana work together. Let's test your understanding!
Recap & Next Steps
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_serverprocesses. - 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!
Preguntas frecuentes
¿La lección «Integración de métricas y monitorización» es gratis?
Sí — el texto completo de «Integración de métricas y monitorización» es gratis para leer aquí en la web. Para practicarla de forma interactiva (editor de código integrado y tutor de IA 24/7) y desbloquear el resto del curso de Erlang OTP: Distributed & Fault-Tolerant Systems Programming, actualiza a CoddyKit PRO. El curso de Erlang OTP: Distributed & Fault-Tolerant Systems Programming incluye 4 lecciones en total.
¿Qué aprenderé en «Integración de métricas y monitorización»?
Implemente la recopilación de métricas e intégrela con sistemas externos de monitorización, como Prometheus y Grafana, para mejorar la observabilidad del sistema. Practicas Erlang OTP: Distributed & Fault-Tolerant Systems Programming con código real que ejecutas directamente en el navegador, y un tutor de IA 24/7 responde tus preguntas mientras trabajas en la lección.
¿Necesito experiencia previa para empezar Erlang OTP: Distributed & Fault-Tolerant Systems Programming?
No se requiere experiencia previa. Erlang OTP: Distributed & Fault-Tolerant Systems Programming en CoddyKit está estructurado para principiantes hasta estudiantes avanzados, así que puedes empezar aquí o desde el inicio y avanzar a tu ritmo. Esta es la lección 3 de 4.
¿Cuánto tiempo toma la lección «Integración de métricas y monitorización»?
La mayoría de las lecciones de CoddyKit toman alrededor de 5–10 minutos. Cada una es compacta e interactiva, así que avanzas constantemente y retomas exactamente por donde dejaste en la web y la app.
¿Puedo escribir y ejecutar código en esta lección de Erlang OTP: Distributed & Fault-Tolerant Systems Programming?
Sí. Cada lección de Erlang OTP: Distributed & Fault-Tolerant Systems Programming incluye un editor de código integrado, así que escribes y ejecutas código real directamente en tu navegador y obtienes retroalimentación instantánea de IA — sin configuración local necesaria.
Todas las lecciones de este curso
- Técnicas de perfilado en Erlang
- Trazado y depuración de sistemas distribuidos
- Integración de métricas y monitorización
- Análisis de memoria y ajuste de la recolección de basura