In this article, I will explain the Metrics that Prometheus Uses.
Prometheus is an open-source monitoring and alerting system that collects and stores time-series data from various sources. It provides a flexible query language, PromQL, for querying and analyzing metrics, and also supports various metric types. The following list shows some of the metrics that Prometheus uses.
- Counter: A counter is a monotonically increasing metric that represents a cumulative count of events. It can be used to track the number of requests to a web server or the number of errors encountered by an application.
- Gauge: A gauge is a metric that represents a value that can increase or decrease over time. It can be used to track metrics such as CPU usage, memory usage, or the number of active connections to a database.
- Histogram: A histogram is a metric that samples observations and counts them in configurable buckets. It can be used to track metrics such as request latency or response size, and can be used to generate percentiles and other summary statistics.
- Summary: A summary is similar to a histogram, but it calculates the percentiles and other summary statistics on the client side, rather than the server side. This can be useful for tracking metrics such as request latency, where the exact distribution of values is important.
- Untyped: An untyped metric is a catch-all metric that can represent any numeric value. It is used when the metric type is unknown or when the metric type changes over time.
Prometheus also supports labels, which allow you to attach metadata to your metrics. This can be useful for filtering and aggregating metrics based on specific criteria, such as the endpoint of a web request or the source of a log entry.
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