10 Prometheus Labels Best Practices
Labels are an important part of Prometheus and can be very helpful when used correctly. Here are 10 best practices to follow.
Prometheus is an open-source monitoring system that is widely used for monitoring and alerting. It is used to collect metrics from various sources and store them in a time-series database. Labels are an important part of Prometheus and are used to identify and group metrics.
In this article, we will discuss 10 best practices for using labels in Prometheus. We will look at how to use labels to organize and query metrics, as well as how to use labels to create alerts. We will also discuss how to use labels to create dashboards and visualize data.
1. Use labels to add semantic information
Labels are used to add context and meaning to metrics, which makes it easier for users to query and analyze data.
For example, if you have a metric that measures the number of requests per second, adding labels such as “environment” (e.g., production or staging) and “service” (e.g., web server or database) can help you quickly identify where the requests are coming from and what services they are targeting.
Labels should also be kept consistent across all metrics in your system. This will make it easier to query and compare data points between different metrics. Finally, try to keep label names short but descriptive so that they are easy to read and understand.
2. Avoid using labels for dimensions with high cardinality
Labels are used to identify and group metrics, but when labels have high cardinality (i.e., many different values), it can lead to a large number of time series being created in the Prometheus database. This can cause performance issues as well as making it difficult to query for specific data points.
To avoid this issue, try to use labels only for dimensions with low cardinality. For example, if you’re monitoring web servers, you could label them by environment (production, staging, etc.), rather than labeling each server individually.
3. Prefer fewer, more specific metrics over many generic ones
When you have fewer, more specific metrics, it’s easier to identify and isolate problems. For example, if you have a metric for “total requests” but no way to differentiate between successful and failed requests, then it can be difficult to pinpoint the source of an issue. On the other hand, if you have separate metrics for each type of request (e.g., “successful requests”, “failed requests”), then it becomes much easier to diagnose any issues that arise.
Additionally, having fewer, more specific metrics makes it easier to create meaningful dashboards and alerts. With generic metrics, it can be hard to determine what is normal behavior and what isn’t. But with more specific metrics, it’s easier to set thresholds and alerting rules that are tailored to your system.
4. Keep your metric and label names short but descriptive
Prometheus labels are used to identify and group metrics, so it’s important that they be easy to read and understand. If your metric or label names are too long, they can become difficult to parse and interpret. Additionally, longer names take up more space in the Prometheus database, which can lead to slower query performance.
To ensure your metric and label names are short but descriptive, use abbreviations when possible and avoid using overly complex words. This will help make sure your labels are both readable and concise.
5. Use consistent naming conventions across all metrics
When you use consistent naming conventions, it makes it easier to query and analyze your metrics. For example, if all of your labels have the same prefix or suffix, then you can easily filter out specific metrics with a single query. This also helps when creating dashboards since you don’t need to manually enter each label name.
Additionally, using consistent naming conventions across all metrics ensures that everyone in your organization is on the same page when it comes to understanding what each metric means. This reduces confusion and allows for more efficient collaboration between teams.
6. Don’t use underscores in metric or label names
Underscores can cause confusion when trying to read the labels, as they are often used in different ways. For example, a label with an underscore might be interpreted as two separate words or phrases, which could lead to misinterpretation of the data. Additionally, underscores can make it difficult for Prometheus to parse and query metrics correctly.
To avoid these issues, use hyphens instead of underscores when creating metric and label names. This will help ensure that your labels are easy to read and interpret, and that Prometheus is able to accurately query them.
7. Only use allowed characters in metric and label names
Prometheus labels are used to identify and group metrics, so it’s important that they are consistent and easy to read. Allowed characters include alphanumeric characters (A-Z, 0-9), underscores (_) and colons (:). Unallowed characters include spaces, hyphens (-), periods (.), and other special characters.
Using only allowed characters in metric and label names helps ensure consistency across your Prometheus environment. It also makes it easier for users to quickly scan and understand the data being presented.
8. Be careful when adding new labels to existing metrics
When adding new labels to existing metrics, it can cause the cardinality of those metrics to increase significantly. This can lead to a decrease in query performance and an increase in storage costs. To avoid this, you should always consider the impact that adding new labels will have on your system before doing so.
It’s also important to ensure that all labels are properly documented and that their values are consistent across all metrics. This will help make sure that queries are accurate and efficient. Finally, be sure to use descriptive label names that clearly indicate what they represent.
9. Add the appropriate number of labels to a metric
Labels are used to identify and group metrics, so having too few labels can make it difficult to differentiate between different types of metrics. On the other hand, having too many labels can lead to a lot of duplicate data points that will clutter up your system.
The best practice is to add just enough labels to accurately describe the metric without adding unnecessary complexity. For example, if you’re tracking CPU usage for multiple servers, you might want to include labels such as server name, hostname, and environment type. This way, you’ll be able to easily distinguish between different servers and environments.
10. Label values should be strings
When Prometheus stores data, it uses a key-value store. This means that the labels are used as keys to identify and retrieve the corresponding values. If label values are not strings, then they will be converted into strings when stored in the database. This can lead to unexpected results if you’re expecting certain types of data (e.g., numbers) to remain intact.
By using strings for all label values, you ensure that your data is stored correctly and retrieved accurately. This also makes it easier to query and analyze your data since you don’t have to worry about type conversions.