September 26, 2025 - These data points are stored as time series, and each unique combination of metric name and labels becomes a separate time series. When you run a count query, Prometheus looks at all the relevant time series and performs calculations on them. This architecture is why Prometheus is so fast at handling large amounts of time-series data, but it also explains why counting unique values requires special techniques. count(count by (status_code) (http_requests_total))

Similar to a histogram, a summary samples observations (usually things like request durations and response sizes). While it also provides a total count of observations and a sum of all observed values, it calculates configurable quantiles over a sliding time window. A summary with a base metric name of <basename> exposes multiple time series during a scrape:

December 17, 2020 - If your UI does not already show you this list in one way or another (it really should!), you can query for it by first selecting all series, then grouping by the special metric name label __name__: ... Note: A more efficient, non-PromQL way to get the same information is to use Prometheus's label values metadata endpoint to get all values for the __name__ label, e.g.: https://demo.promlabs.com/api/v1/label/name/values.

For starters, let’s pull the total number of series. That is the count of all unique label combinations (including the __name__ label). ... This query relies on one simple rule: Prometheus Query Language (PromQL) requires to provide either a metric name or at least one label matcher.

February 21, 2025 - Metric name suffix: The suffix usually indicates either the metric type or the base unit of the measurement (typically in plural form). Metric type is often reflected in the suffix, such as _total and _count for counters. It can be combined ...

Return the per-second rate for all time series with the http_requests_total metric name, as measured over the last 5 minutes: ... Assuming that the http_requests_total time series all have the labels job (fanout by job name) and instance (fanout by instance of the job), we might want to sum over the rate of all instances, so we get fewer output time series, but still preserve the job dimension:

Similar to a histogram, a summary samples observations (usually things like request durations and response sizes). While it also provides a total count of observations and a sum of all observed values, it calculates configurable quantiles over a sliding time window. A summary with a base metric name of <basename> exposes multiple time series during a scrape:

Also note that this applies to units in the narrow sense (like the units in the table below), but not to countable things in general. For example, connections or notifications are not considered units for this rule and do not have to be at the end of the metric name.

July 21, 2025 - The Histogram is the most powerful way to measure latency and other distributed values in Prometheus. Instead of storing every single measurement, it counts how many fall into pre-configured buckets.

Metrics are typically exposed by applications on HTTP endpoints (typically /metrics), and scraped periodically by Prometheus. This pull-based model ensures that Prometheus has control over data collection frequency and consistency. Prometheus offers four core metric types to capture diverse system behaviors: Counters for tracking ever-increasing values, like the total number of exceptions thrown.

February 28, 2022 - This article combines the theory ... of Prometheus’ counter metric. We will see how the PromQL functions rate, increase, irate, and resets work, and to top it off, we will look at some graphs generated by counter metrics on production data.

June 17, 2026 - Prometheus metric names should use snake_case, start with a single-word application or domain prefix (for example http_ or node_), and end with the unit of measurement (for example _seconds or _bytes).

April 9, 2026 - This is for compatibility between OpenMetrics and the Prometheus text format, as OpenMetrics requires the _total suffix. Counter(name, documentation, labelnames=(), namespace='', subsystem='', unit='', registry=REGISTRY) namespace, subsystem, and name are joined with underscores to form the full metric name:

December 17, 2025 - However, understanding how many unique values exist for a given label is crucial for cardinality management and query optimization. This guide shows you how to count unique label values using PromQL. Use count(group by (label_name) (metric_name)) to ...

December 16, 2015 - To know which metrics are using the most resources it'd be good to count how many time series each has, and then display the top 10. This can be done with an expression selecting all metrics, aggregating a count based on the metric name and returning the top 10 in the expression browser (make ...

For the default OpenMetrics 1.0 and Prometheus text formats, counters are exposed with the _total suffix. You can name a counter either service_time_seconds or service_time_seconds_total; the exposed name will be service_time_seconds_total in both cases. The experimental OpenMetrics 2.0 writer ...

July 30, 2021 - Again, the same memory usage method is used here, but with different metric names. node_filesystem_avail_bytes/node_filesystem_size_bytes*100 · Prometheus has its own native query language, PromQL, which you use to select, aggregate, and compute over metrics.

to Prometheus Users · So far, we have a promql query that shows the total amount of time series per metric (count by (__name__)({__name__=~".+"})) and also per target(scrape_samples_scraped). We also can take the total amount of chunk samples ( prometheus_tsdb_compaction_chunk_samples_count).