PromQL Cheat Sheet: A Quick Guide to Prometheus Query Language
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is an open-source monitoring and alerting toolkit that has gained significant popularity in DevOps and systems monitoring. At the core of Prometheus lies , a powerful and flexible query language used to extract valuable insights from the collected metrics. In this guide, we will explore the basics of PromQL and provide query examples for an example use case.
You have a web app that you maintain. You'd like to have some observability into the traffic of your application. Your environment consists of 3 production web servers and 1 staging web server. Below is a table of instance vectors for your servers.
web_prod_1
production
100
110
web_prod_2
production
200
220
web_prod_3
production
300
330
web_stg_1
staging
10
20
allows you to query , which consists of and their corresponding . The basic syntax for querying time series is as follows:
Example:
To query the total HTTP requests metric for your fleet of servers, you would use:
The above example would return an instance vector for each server in your fleet.
The above example would return an instance vector for each production server in your fleet.
=: Select labels that are exactly equal to the provided string.
!=: Select labels that are not equal to the provided string.
=~: Select labels that regex-match the provided string.
!~: Select labels that do not regex-match the provided string.
So, to select all of our staging servers, we could use the following query:
PromQL provides various aggregation functions to summarize and aggregate time series data. Here are a few commonly used functions:
sum: Calculates the sum of all matching time series.
avg: Computes the average value of matching time series.
min: Returns the minimum value among all matching time series.
max: Returns the maximum value among all matching time series.
Example:
To calculate the average HTTP requests across all production instances, you can use:
and The above would first return the instance vectors and then generate the average:
rate: Calculates the "per-second rate of increase" of a time series over a specified time range.
irate: Similar to rate, but calculates the "instantaneous per-second rate of increase" of a time series over a specified time range by only considering the last 2 points.
increase: Computes the "absolute increase" in a time series value over a specified time range.
Example:
To calculate the number of HTTP requests you are getting for your entire production fleet.
The above would first return the instance vectors, then calculate the difference between the vector values t1-t0, then sum them.
By keeping this cheat sheet handy, you'll be able to navigate PromQL queries efficiently and unlock the full potential of Prometheus for monitoring and alerting in your systems.
selectors allow you to filter and focus on specific labels to extract relevant metrics. To filter the time series, append a comma-separated list of label matchers in curly braces {}
Additionally, PromQL provides the following :
Regex matches are fully anchored. A match of env=~"foo" is treated as env=~"^foo$". You can using the Golang flavor.
PromQL allows you to work with , representing time series data over a specified time range. This is particularly useful for analyzing trends and patterns. Here are a few important range functions:
Enjoying this content? Check out our full article on Counter Rates and Increases here:
PromQL is a versatile and powerful query language that empowers users to extract valuable insights from metrics. By mastering the basics covered in this cheat sheet, you'll be well-equipped to explore and analyze your monitoring data effectively. Remember, this blog post only scratches the surface. Experiment with different and to make the most of PromQL's capabilities.
At PagerTree we monitor our systems extensively; here are some of the common queries we use. The metrics (and metric names) we use are provided by the or our own metric label name.
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