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how to configure Prometheus to use a custom promql query？

In today's fast-paced digital world, monitoring and analyzing metrics in real-time is crucial for maintaining the health and performance of your applications and infrastructure. Prometheus, an open-source monitoring and alerting toolkit, has gained significant popularity for its flexibility and ease of use. One of its powerful features is the ability to execute custom PromQL (Prometheus Query Language) queries. In this article, we will delve into how to configure Prometheus to use a custom PromQL query, enabling you to gain deeper insights into your metrics.

Understanding Prometheus and PromQL

Before we dive into the configuration process, let's quickly review what Prometheus and PromQL are.

Prometheus is a powerful open-source monitoring and alerting toolkit designed to be highly efficient and easy to use. It allows you to collect and store time-series data, visualize it, and set up alerts based on complex conditions.

PromQL is the query language used by Prometheus to retrieve and manipulate time-series data. It is a powerful language that supports a wide range of operations, including filtering, aggregating, and computing metrics.

Why Use Custom PromQL Queries?

Custom PromQL queries provide several benefits:

Granular Analysis: You can slice and dice your metrics to analyze specific aspects of your system in detail.
Ad-Hoc Queries: They allow you to execute one-off queries to answer specific questions about your metrics.
Alerting: You can use them to create custom alerts based on complex conditions.

Configuring Prometheus to Use a Custom PromQL Query

Now that we understand the importance of custom PromQL queries, let's explore how to configure Prometheus to use them.

Install Prometheus: Ensure that Prometheus is installed and running on your system. You can download it from the official Prometheus website.
Configure Prometheus: Open the Prometheus configuration file (prometheus.yml) using a text editor. This file is typically located at /etc/prometheus/prometheus.yml on Linux systems.
Add a New Query: To add a custom PromQL query, you need to define a new job in the scrape_configs section. Here's an example of how to add a custom query for a hypothetical user_count metric:

scrape_configs:

  - job_name: 'custom_query'

    static_configs:

      - targets:

          - 'localhost:9090'

        metrics_path: '/metrics'

        params:

          query: 'user_count{job="my_app"}'

In this example, the custom_query job scrapes metrics from the local Prometheus server on port 9090 and executes the user_count{job="my_app"} query.

Start Prometheus: After saving the configuration file, restart Prometheus to apply the changes.
Query the Custom Metrics: To query the custom metrics, use the Prometheus Query Interface or any other Prometheus client. For example, to retrieve the current value of user_count for the my_app job, you can use the following query:

user_count{job="my_app"}

Advanced Custom PromQL Queries

Once you have mastered the basics of custom PromQL queries, you can start exploring more advanced features. Here are a few examples:

Filtering: Use the filter() function to filter out specific time-series data based on a condition.
Aggregating: Use the sum(), avg(), min(), and max() functions to aggregate metrics over time or across different labels.
Computing: Use the rate(), irate(), and delta() functions to compute rates and changes over time.

Case Study: Monitoring Docker Containers with Custom PromQL Queries

Let's consider a scenario where you want to monitor the CPU and memory usage of your Docker containers using Prometheus and custom PromQL queries.

Deploy Prometheus: Deploy Prometheus in your Docker environment and configure it to scrape metrics from the Docker stats endpoint.
Custom Query for CPU Usage: Create a custom query to monitor the CPU usage of a specific container:

query: 'container_cpu_usage_seconds_total{job="docker", container="my_container"}'

Custom Query for Memory Usage: Similarly, create a custom query to monitor the memory usage of the same container:

query: 'container_memory_usage_bytes{job="docker", container="my_container"}'

Alerting: Set up alerts based on these custom queries to notify you when the CPU or memory usage exceeds a certain threshold.

By following these steps, you can effectively configure Prometheus to use custom PromQL queries, enabling you to gain deeper insights into your metrics and improve the performance and reliability of your applications and infrastructure.