10 Prometheus Metrics Best Practices

Prometheus is an open-source monitoring system that is widely used for collecting metrics from applications and services. It is a powerful tool for collecting and analyzing metrics, but it can be difficult to get the most out of it. To make the most of Prometheus, it is important to follow best practices when setting up and managing your metrics.

In this article, we will discuss 10 best practices for using Prometheus metrics. We will cover topics such as setting up metrics, monitoring performance, and alerting. By following these best practices, you can ensure that you are getting the most out of your Prometheus metrics.

1. Use the same metric name for different resources

When you use the same metric name for different resources, it makes it easier to compare and analyze data across multiple sources. This is especially useful when you’re trying to identify trends or correlations between different metrics. For example, if you have two web servers that are running the same application, but one of them is performing better than the other, using the same metric name will make it easier to compare their performance.

Using the same metric name also helps with scalability. If you need to add more resources in the future, you won’t have to create new metrics; instead, you can just reuse existing ones. This saves time and effort, as well as reducing complexity.

2. Don’t use labels to differentiate metrics

Labels are meant to be used for filtering and aggregation, not as a way to differentiate metrics.

Using labels to differentiate metrics can lead to an explosion of metrics in your system, which can make it difficult to manage and query them. Instead, use different metric names to differentiate between metrics. This will help keep your Prometheus setup organized and efficient.

3. Avoid using underscores in your metric names

Underscores can be difficult to read and interpret, especially when you have multiple metrics with similar names. This makes it harder for users to quickly identify the metric they are looking for. Additionally, underscores can cause confusion when using Prometheus query language (PromQL) as some operators use underscores in their syntax.

To avoid these issues, use hyphens instead of underscores in your metric names. This will make them easier to read and understand, while also avoiding any potential conflicts with PromQL.

4. Expose only one type of metric per endpoint

When you expose multiple types of metrics in a single endpoint, it can be difficult to parse and interpret the data. It also makes it harder for Prometheus to scrape the data accurately. By exposing only one type of metric per endpoint, you make it easier for Prometheus to collect the data and for users to understand what they are looking at.

Additionally, when you have multiple types of metrics exposed in a single endpoint, it can lead to confusion about which metrics should be used for alerting or graphing. This can lead to incorrect alerts being triggered or inaccurate graphs being generated. To avoid this, always expose only one type of metric per endpoint.

5. Add a prefix to your metric names

Adding a prefix to your metric names helps you organize and differentiate between metrics from different services. This makes it easier for you to find the metrics you need when troubleshooting or monitoring performance. It also allows you to quickly identify which service is responsible for a particular metric, making it easier to track down issues.

Prefixing your metric names can also help you avoid naming collisions with other services that may be using similar metrics. By adding a unique identifier to each of your metrics, you can ensure that they won’t conflict with any existing metrics in your system.

6. Always add a help string to your metrics

A help string is a short description of what the metric measures. It’s important to include this information because it helps users understand what the metric means and how they can use it. Without a help string, users may not be able to interpret the data correctly or make informed decisions based on the metrics.

Adding a help string also makes your metrics easier to search for in Prometheus’ query language. This allows users to quickly find the metrics they need without having to manually scroll through all of them.

7. Keep your metric names short and simple

Prometheus metrics are stored in a time-series database, which means that the more complex your metric names are, the more data will be stored. This can lead to slower query times and increased storage costs. Additionally, longer metric names make it harder for users to understand what they’re looking at when viewing graphs or dashboards.

To keep your metric names short and simple, use only lowercase letters, numbers, underscores, and hyphens. Avoid using special characters like periods, commas, and brackets. Also, try to limit yourself to two words per metric name.

8. Prefer numeric values over strings

Numeric values are easier to process and compare, which makes them more suitable for alerting. Strings can be difficult to parse and may require additional processing before they can be used in an alert. Additionally, numeric values allow you to use mathematical operations like addition or subtraction when creating alerts.

Finally, it’s important to remember that Prometheus metrics should be as concise as possible. Numeric values take up less space than strings, so using them will help keep your metrics lean and efficient.

9. Try to avoid exposing absolute numbers

Absolute numbers are hard to interpret and can be misleading. For example, if you expose the number of requests your application has processed in a given time period, it’s difficult to tell whether that number is good or bad without context. It could mean that your application is performing well, but it could also mean that something is wrong.

Instead, try to use relative metrics such as request rate (requests per second) or latency (time taken for each request). These metrics provide more meaningful information about how your application is performing.

10. Be consistent with your units

When you’re monitoring your system, it’s important to be able to compare metrics across different sources. If the units of measurement are inconsistent, then it becomes difficult to make meaningful comparisons. For example, if one metric is measured in bytes and another is measured in megabytes, it will be hard to tell which one is larger without doing a conversion.

By being consistent with your units, you can easily compare metrics from different sources and get an accurate picture of what’s going on in your system. This makes it easier to identify trends and anomalies, as well as spot potential problems before they become serious issues.