You should now have example targets listening on http://localhost:8080/metrics , http://localhost:8081/metrics , and http://localhost:8082/metrics . Now we will configure Prometheus to scrape these new targets. Let's group all three endpoints into one job called node.

If we have two different metrics with the same dimensional labels, we can apply binary operators to them and elements on both sides with the same label set will get matched and propagated to the output. For example, this expression returns the unused memory in MiB for every instance (on a fictional cluster scheduler exposing these metrics about the instances it runs):

January 2, 2023 - Prometheus uses a special query language called PromQL that’s built to query metrics, for example — give me the total number of HTTP GET requests made in the past 5 minutes:

In order for these assets to be found, you will have to run Prometheus from the root of the cloned repository. Note also that this directory does not include the React UI unless it has been built explicitly using make assets or make build. An example of the above configuration file can be found here.

April 17, 2026 - Then open your Prometheus UI at http://localhost:9090 and check the Alerts page. You should see the LowDiskSpace alert, but it will be green (inactive) if your disk usage is not over 80%. To trigger an alert, you can consume additional disk space by using the dd command. This example creates a 10GB file (it may take a while).

June 10, 2026 - For metrics being collected by the Prometheus client, this can include areas such as a number of concurrent requests or how much of a CPU is being utilized over a period of time. Histogram: More advanced and complex than counters or gauges, a histogram takes a sample of observations and counts them in buckets that can be a configuration by the user. As an example, a user may want to understand memory usage percent segmented by pods across a Kubernetes cluster in given points in time.

First, deploy one instance of this example application, which listens and exposes metrics on port 8080 using the following Deployment manifest. Then, in order for Prometheus to be able to discover and scrape these metrics, in this repository we provide Pod Monitor custom resource as an example.

January 3, 2020 - The example shows 3 distinct metrics (http_server_requests_total, http_server_request_duration_seconds and http_server_exceptions_total) each with its own type, and responsible for monitoring a specific aspect of the application. Prometheus has very limited metric types to keep things simple.

The Prometheus monitoring system and time series database. - prometheus/prometheus

For example, enter the following expression to graph the per-second HTTP request rate returning status code 200 happening in the self-scraped Prometheus:

For example, applying the rate function to gauge floats will most likely produce a nonsensical result, but the query will be processed without complains. However, if applied to gauge histograms, the result of the query will be annotated with a warning. In Prometheus's expression language, an ...

March 25, 2019 - In simplest terms, a “job_name” begins the configuration for how to scrape your targets. In the example you can see that we will be scraping the prometheus server at port 9090 and then the docker engine at port 9323 every 15 seconds for metrics.

For example, a time series with the metric name api_http_requests_total and the labels method="POST" and handler="/messages" could be written like this:

Let's consider a web server as an example application and we want to extract a certain metric like the number of API calls processed by the web server. So we add certain instrumentation code using the Prometheus client library and expose the metrics information.

For example, when the number of requests is high, the application may become slow. If you have the request count metric, you can determine the cause and increase the number of servers to handle the load. The Prometheus ecosystem consists of multiple components, many of which are optional:

September 2, 2023 - Counters give the absolute value of something, such as prometheus_http_requests_total or prometheus_sd_consul_rpc_duration_seconds_count. While counters only count upward, gauges will count upward and downward. Gauges might provide an average or range of values.

Prometheus supports templating in the annotations and labels of alerts, as well as in served console pages. Templates have the ability to run queries against the local database, iterate over data, use conditionals, format data, etc.

June 15, 2026 - This query returns all time series with the metric name http_requests_total. It’s as straightforward as it gets—simply naming the metric you want to see. You’ll get back every label combination Prometheus has stored for this metric.

August 1, 2022 - Understanding how the Prometheus time-series engine stores data gives you an idea of intended query results before, while, and as your expressions execute. For example, if the data you're looking for has different timestamps/or is just unknown in complexity, and you wish to aggregate (sum or average) occurrences, it might be best for you to create subqueries that end with tabular views.

November 6, 2019 - When getting started with Prometheus (or with time-series monitoring more generally), these are the easiest types to understand because it's easy to connect them to values you can imagine monitoring, like how much system resources your application is using or how many events it has processed. "A counter is a cumulative metric that represents a single monotonically increasing counter whose value can only increase or be reset to zero on restart. For example, you can use a counter to represent the number of requests served, tasks completed, or errors."