When working with Prometheus, it is essential to understand the concept of data types. Data types in Prometheus help define the structure and format of the data that can be stored and queried within the system.
What are Prometheus Data Types?
Prometheus supports several built-in data types that are used to represent different kinds of metrics. These data types include:
- Counter: A counter is a cumulative metric that represents a single monotonically increasing value. Counters cannot be decreased, and their primary use case is to track values like the total number of requests or events.
- Gauge: A gauge is a metric that represents a single numerical value that can arbitrarily go up or down.
Gauges are used to track instantaneous values such as CPU usage or memory usage.
- Summary: A summary samples observations over a sliding time window and provides metrics like the total sum, count, and quantiles (percentiles) of those observations.
- Histogram: A histogram samples observations and counts them into configurable buckets. It also provides metrics like the total sum, count, and quantiles (percentiles) of those observations.
Prometheus also allows users to define their own custom data types using its query language PromQL.
Understanding Each Data Type
A counter in Prometheus is represented by a numeric value that only goes up. Counters are typically used to measure cumulative values such as the number of requests served by an API server or the number of tasks completed by a worker.
The primary operation on counters is incrementing their value. However, keep in mind that counters cannot be decreased or reset to a specific value. They are always increasing.
A gauge in Prometheus represents a single numerical value that can increase or decrease arbitrarily. Gauges are used to measure instantaneous values that can go up or down, such as CPU usage, memory usage, or the number of active connections.
Gauges are very useful for tracking resource usage metrics where the value can fluctuate over time.
A summary in Prometheus samples observations over a sliding time window and provides metrics such as the total sum, count, and quantiles (percentiles) of those observations.
This data type is particularly useful when you need to track statistical information about a metric over time.
A histogram in Prometheus samples observations and counts them into configurable buckets. It provides metrics like the total sum, count, and quantiles (percentiles) of those observations.
Histograms are useful when you want to measure the distribution of values for a particular metric. For example, you may want to measure the response time distribution of an API endpoint.
Understanding Prometheus data types is crucial for effectively monitoring and analyzing your system’s metrics. By leveraging counters, gauges, summaries, and histograms, you can gain valuable insights into various aspects of your application’s performance and behavior.