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PromQL

383 words, 2 min read
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Xinyang YU

Abstract

Important

We can select, filter, and use regular expressions with PromQL.

Using PromQL to monitor the 4 golden signals

4 Golden Monitoring Signals

PromQL Data Types

  • Special operations you can apply to your Prometheus data to transform and analyse it. They’re like built-in tools that help you make sense of your metric
  • Scalar: A single numerical value. For example, 10 is a scalar. While some PromQL functions can return a scalar, it’s important to note that not all of them do
  • Instant Vector: Data point at a single moment in time. Like a snapshot of metrics right now
  • Range Vector: Data point over a time period. Like a metric’s history over the last 5 minutes

Instant Vector VS Range Vector

Instant vectors show the current state, while range vectors show how things change over time.

PromQL Functions

  • Aggregation: sum(), avg(), max(), min(), count(), quantile()
  • Mathematical: abs(), ceil(), floor(), round(), exp(), log()
  • Time series: rate(), irate(), increase(), changes(), delta(), deriv()
  • Filtering: absent(), present()
  • Predictive: predict_linear(), holt_winters()
  • Refer to official docs for more details

rate() vs irate() vs increase()

The rate() function calculates the average per-second increase over the specified time range in the range vector.

The irate() function calculates the instantaneous per-second rate of increase based on the last two data points in the range vector.

The increase() function calculates the net increase in the value of the time series in a range vector.

What is sum(rate())?

You have two web servers (server1 and server2). Each counts HTTP requests (http_requests_total).

  • server1: 100 to 200 requests in 5 seconds
  • server2: 50 to 150 requests in 5 seconds.

sum(rate(http_requests_total[5m])) calculates:

  1. Individual rates: Each server has a rate of 20 requests/second (100 increase / 5 seconds).
  2. Total rate: 20 + 20 = 40 requests/second across both servers.

Therefore, sum(rate(...)) returns 40, the combined rate of all servers.

Label Matching

We use sum by (service) (rate(http_requests_total[5m])) to calculate combined rate for a particular service.

Maybe we have a frontend and a backend service, each powered by two servers. Each service handles some HTTP requests, and we want to check the status of each specific service.

References

Backlinks

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