how to pass multiple query parameters in prometheus query

Lussy04

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23-01-2025

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Prometheus, a powerful monitoring and alerting system, allows flexible querying of time-series data. Often, you need to filter this data using multiple criteria, requiring the use of multiple query parameters. This article explains how to effectively pass multiple query parameters in your Prometheus queries. Understanding this is crucial for accurately analyzing your monitored systems.

Understanding Prometheus Query Language

Before diving into multiple parameters, let's quickly review the basics. Prometheus uses a query language based on PromQL (Prometheus Query Language). Simple queries often involve selecting a metric and applying label filters. For example: http_requests_total{method="GET", code="200"} selects the http_requests_total metric where the method label is "GET" and the code label is "200".

Combining Multiple Query Parameters

The power of PromQL lies in its ability to combine multiple criteria. We achieve this primarily through the use of the and operator, which allows the conjunction of multiple label selectors.

Using the and Operator

The simplest and most common way to specify multiple parameters is to chain label selectors using and. Each selector is evaluated independently, and only time series matching all conditions are returned.

Example:

Let's say you want to find the total number of HTTP requests that were GET requests and resulted in a 200 status code, but only for requests originating from a specific server, say server="server01". The query would look like this:

http_requests_total{method="GET", code="200", server="server01"}

This query combines three label selectors using implicit and operations. Each selector must match for a time series to be included in the result. Adding more selectors follows the same pattern.

Combining and with other operators

You can combine the and operator with other PromQL operators for more complex filtering. For example, you could use the or operator to include results matching either of two sets of criteria:

(http_requests_total{method="GET", code="200"} or http_requests_total{method="POST", code="201"}) and server="server01"

This query would return results for GET requests with 200 status codes or POST requests with 201 status codes, but only from server01. The parentheses are crucial to ensure the correct order of operations.

Handling Regular Expressions

For more flexible matching, especially when dealing with a large number of potential label values, you can use regular expressions within your label selectors:

http_requests_total{method=~"GET|POST", code="200", server=~"server.*"}

This query uses regular expressions to match either "GET" or "POST" for the method and any server name starting with "server" for the server label.

Advanced Techniques for Multiple Parameters

For extremely complex scenarios with many parameters, consider these techniques:

• Grouping and Aggregation: Use aggregation functions like sum, avg, count to combine results from multiple subsets of your data after filtering with multiple parameters.
• Subqueries: Utilize subqueries for layered filtering. A subquery can perform an initial filtering step, and the outer query can then further refine the results.
• Recording Rules: For frequently used complex queries, consider creating recording rules. These predefined queries store their results as new metrics, simplifying future analysis.

Example: Monitoring Application Performance

Let's imagine you're monitoring an application with metrics like application_response_time_seconds with labels instance, environment, and status. To analyze average response times for production instances with successful (200) responses, you'd use:

avg(application_response_time_seconds{environment="production", status="200"})

This efficiently uses multiple parameters to drill down into specific aspects of your application's performance.

Conclusion

Passing multiple query parameters in Prometheus is straightforward using the and operator to combine label selectors. Mastering this technique, along with other PromQL features like regular expressions and aggregation functions, empowers you to analyze your monitoring data with precision and extract valuable insights. Remember to leverage parentheses for complex queries to ensure the correct order of operations and consider advanced techniques like subqueries and recording rules for improved efficiency as your monitoring needs evolve. This enables more effective monitoring and faster identification of performance bottlenecks and potential issues.