Grafana: Querying Multiple Metrics Efficiently

Oct 23, 2025 by Jhon Lennon 47 views

So, you're diving into Grafana and want to visualize multiple metrics using a single query? Awesome! You've come to the right place. Let's break down how you can achieve this, making your dashboards more efficient and insightful. This is a common task, especially when you're trying to correlate different aspects of your system's performance or behavior.

Understanding the Basics

Before we jump into the specifics, it's essential to understand how Grafana queries work. Grafana doesn't store data itself; instead, it queries data from various data sources like Prometheus, InfluxDB, Elasticsearch, and others. Each data source has its own query language and capabilities. The method for querying multiple metrics in one go will depend on the data source you're using.

The core idea is to leverage the features of your data source's query language to fetch and combine the metrics you need. This often involves using functions, operators, or specific syntax that allows you to select multiple time series or calculate new metrics based on existing ones, all within a single query. For example, in Prometheus, you might use the rate() function to calculate the per-second average rate of increase of a counter, and then combine it with another metric using arithmetic operators or functions like sum() or avg(). Understanding these underlying principles will empower you to craft efficient and effective queries, no matter the complexity of your monitoring needs. The right approach can significantly improve dashboard performance and reduce the load on your data source.

Querying Multiple Metrics in Prometheus

Prometheus is a popular choice for monitoring, so let's focus on how to query multiple metrics efficiently within it. The PromQL language is incredibly powerful, allowing for complex queries that can fetch and manipulate multiple time series.

Using Comma (`,`) to Fetch Multiple Metrics

The simplest way to fetch multiple metrics is by separating them with a comma in your query. For example, if you want to visualize cpu_usage_idle and cpu_usage_system, your query would look like this:

cpu_usage_idle, cpu_usage_system

This query will return two separate time series, one for each metric. Grafana will then plot these on the same graph, allowing you to compare them visually.

Combining Metrics with Operators

PromQL allows you to combine metrics using arithmetic operators like +, -, *, and /. For instance, if you want to calculate the total CPU usage by summing cpu_usage_user and cpu_usage_system, you can use the following query:

cpu_usage_user + cpu_usage_system

This will return a single time series representing the sum of the two metrics. You can also use comparison operators like >, <, ==, >=, and <= to filter metrics based on their values. This is useful for creating alerts or highlighting specific conditions in your dashboards. For instance, you might want to visualize only the CPU usage that exceeds a certain threshold, or to compare the CPU usage of different servers.

Using Functions for Aggregation

PromQL provides a rich set of functions for aggregating and manipulating time series. Functions like sum(), avg(), min(), max(), count(), and stddev() can be used to calculate aggregate values across multiple time series or within a single time series over time. For example, to calculate the average CPU usage across all instances, you can use the following query:

avg(cpu_usage_idle)

You can also use the by() clause to group the results by specific labels. For example, to calculate the average CPU usage per instance, you can use the following query:

avg by (instance) (cpu_usage_idle)

This will return a separate time series for each instance, showing its average CPU usage. Functions like rate() and irate() are essential for working with counters, allowing you to calculate the per-second increase or decrease of a metric. These functions are crucial for understanding the rate of change of events, such as the number of requests per second or the number of errors per minute. Properly using these functions ensures accurate and meaningful visualizations of your data.

Using Regular Expressions

Regular expressions are super handy when you want to grab metrics that follow a certain pattern. Let's say you have metrics like http_request_total_api_v1, http_request_total_api_v2, and http_request_total_api_v3. Instead of listing them all out, you can use a regular expression to select them all at once:

http_request_total_api_v[123]

This query fetches all metrics that match the pattern http_request_total_api_v[123]. Regular expressions are a powerful tool for simplifying your queries and making them more maintainable, especially when dealing with a large number of metrics that follow a consistent naming convention. They allow you to dynamically select metrics based on patterns, adapting to changes in your infrastructure without requiring manual updates to your queries. Mastering regular expressions in PromQL can significantly improve your ability to analyze and visualize your data effectively.

Querying Multiple Metrics in InfluxDB

InfluxDB uses its own query language called InfluxQL. Here’s how you can query multiple metrics in InfluxDB.

Basic Selection

To select multiple fields (metrics) from a measurement, you can specify them in the SELECT clause, separated by commas. For example, if you have a measurement called cpu with fields usage_idle and usage_system, you can query them like this:

SELECT usage_idle, usage_system FROM cpu

This will return two columns, one for usage_idle and one for usage_system, allowing you to visualize both metrics on the same graph in Grafana.

Using Functions

InfluxQL also supports various functions for aggregating and transforming data. You can use functions like mean(), sum(), min(), max(), and count() to calculate aggregate values across multiple points in time. For example, to calculate the average CPU usage, you can use the following query:

SELECT mean(usage_idle) FROM cpu

You can also use the GROUP BY clause to group the results by time intervals or tags. For example, to calculate the average CPU usage per minute, you can use the following query:

SELECT mean(usage_idle) FROM cpu GROUP BY time(1m)

This will return the average CPU usage for each minute, allowing you to see how the CPU usage changes over time. Functions like derivative() and non_negative_derivative() are particularly useful for analyzing rates of change, similar to rate() in Prometheus. These functions help you understand the trends and patterns in your data, providing valuable insights into the behavior of your systems.

Using Regular Expressions in Field Keys

Similar to Prometheus, InfluxDB allows you to use regular expressions in your queries. However, instead of using them directly in the metric name, you use them in the WHERE clause or in tag filters. For example, if you have tags like host_01, host_02, and host_03, you can use a regular expression to select data from all hosts that match a certain pattern:

SELECT usage_idle FROM cpu WHERE host =~ /host_0[123]/

This query fetches the usage_idle metric from all hosts that match the regular expression host_0[123]. Regular expressions provide a flexible way to filter your data based on tag values, making it easier to analyze specific subsets of your data.

Querying Multiple Metrics in Elasticsearch

Elasticsearch uses its own query language, which is based on JSON. Here’s how you can query multiple metrics.

Using the `terms` Aggregation

One way to query multiple metrics is by using the terms aggregation. This allows you to group your data by a specific field and then calculate metrics for each group. For example, if you have documents with a field called metric_name that contains the names of different metrics, you can use the terms aggregation to group the documents by metric_name and then calculate the average value for each metric:

{
  "size": 0,
  "aggs": {
    "metrics": {
      "terms": {
        "field": "metric_name"
      },
      "aggs": {
        "avg_value": {
          "avg": {
            "field": "metric_value"
          }
        }
      }
    }
  }
}

This query will return a list of metric names and their corresponding average values. The size: 0 parameter tells Elasticsearch not to return the actual documents, only the aggregation results. The terms aggregation is a powerful tool for analyzing data across different categories or groups, providing insights into the distribution and characteristics of your data.

Using Multiple Queries in Grafana

Grafana allows you to define multiple queries for a single graph. You can add multiple queries to the same panel, each fetching different metrics. Grafana will then plot these metrics on the same graph, allowing you to compare them visually. This is a simple and effective way to visualize multiple metrics, especially when you don't need to combine them mathematically.

To add multiple queries, simply click the "Add query" button in the panel editor. You can then configure each query separately, specifying the data source, metric, and any necessary filters or transformations. Grafana provides a flexible and intuitive interface for managing multiple queries, allowing you to create complex and informative dashboards with ease.

Using the `bool` Query

The bool query allows you to combine multiple queries using boolean logic (e.g., must, should, must_not). You can use this to filter your data based on multiple criteria. For example, you might want to select documents where the metric_name is either cpu_usage or memory_usage:

{
  "query": {
    "bool": {
      "should": [
        {
          "term": {
            "metric_name": "cpu_usage"
          }
        },
        {
          "term": {
            "metric_name": "memory_usage"
          }
        }
      ]
    }
  }
}

This query will return all documents where the metric_name is either cpu_usage or memory_usage. The bool query is a versatile tool for combining multiple conditions, allowing you to create complex and precise queries that meet your specific needs. It supports various boolean operators, including must (all conditions must be met), should (at least one condition must be met), and must_not (none of the conditions must be met), providing a rich set of options for filtering your data.

General Tips for Efficient Querying

Use Indexes: Make sure your data source is properly indexed. Indexes speed up query performance significantly.
Limit the Time Range: Avoid querying unnecessarily large time ranges. Focus on the period you need to analyze.
Optimize Your Queries: Review your queries and look for ways to simplify them or use more efficient functions.
Cache Results: Configure caching in Grafana to reduce the load on your data source.

By following these tips, you can ensure that your Grafana dashboards are performant and responsive.

Conclusion

Querying multiple metrics in Grafana can be achieved in several ways, depending on your data source. By understanding the capabilities of your data source's query language and leveraging Grafana's features, you can create powerful and insightful dashboards. Whether you're using Prometheus, InfluxDB, or Elasticsearch, the key is to optimize your queries and use the appropriate functions and operators to fetch and combine the metrics you need. So go ahead, guys, give these techniques a try and take your Grafana dashboards to the next level!