In Prometheus, cardinality is the number of times series and it depends on the combination of metric names and labels. High cardinality may adversely affect Prometheus in terms of efficiency by increasing memory, storage requirements and slower query processing. This article provides a deeper insight into the cardinality concept; its significance, impacts it has on Prometheus metrics, and its management. Here you will come across fundamental terminologies, various types of metrics, and real-life examples, and the guidelines for their use will be discussed.

Cardinality in Prometheus

It defines the amount of numerically distinctive time series, which are characterized by the combination of metrics and labels in Prometheus. It is important to manage cardinality because high cardinality can result to increased consumption of resources hence the monitoring system may become slow and expensive.

In other words, if you have many unique labels or many label combinations for your metrics, your Prometheus server will take more memory and storage resources which might influence its performance.

Key Terminologies

1. Metrics

Metrics are the fundamental data units in Prometheus. They are used to symbolize a particular activity and which has to be overseen, for instance, requests per second, CPU usage, or memory usage.

2. Time Series

A time series is defined as a sequence of values that are obtained at certain time points and all of them refer to the same metric. However, it is named by Metric Name and a collection of key-value pairs called Labels.

3. Labels

Metrics are always associated with labels which are key-value strings that provides additional dimensions. Metric names contain different parts, such as http_requests_total, which could have labels like method=”GET” or status=200 to distinguishing different time series within the same metric.

4. High Cardinality

High cardinality is where the number of labels is high, thus, there would many possible combinations of the labels. For example, metrics like user_id or request_id can be a cause of a high cardinality pretty fast.

Why Cardinality Matters

Cardinality influences how your Prometheus server operates and how many resources it will use. Here’s why it matters:

• Memory Usage: The concept of memory is needed because every time series is unique and needs its place in the memory. High cardinality of the map increases memory consumption by a great extend.
• Query Performance: The aspect of high cardinality to queries suffer from reduced efficiency since Prometheus has to read more data before returning the results.
• Storage: More unique time series results in more data to store making the storage costs higher.

Understanding Metric Types and Cardinality

There are different kinds of metrics recognized by Prometheus, and cardinality reacts differently to different kinds. The following can help in the management of cardinality:

1. Counter

• Definition: A counter is a cumulative metric that increases monotonically. Some of the examples are total HTTP requests and the total errors that might have been meet during the process.
• Cardinality Impact: Counters normally have small cardinality unless, of course, there are associated labels which can have many different values.

Example:

http_requests_total{method="GET", status="200"}

2. Gauge

• Definition: A gauge represents a metric which can be raised or lowered. Examples include current memory usage or the number of active sessions.
• Cardinality Impact: They are also similar to counters such that gauges also have low cardinality.

Example:

memory_usage_bytes{host="server1"}

3. Histogram

• Definition: A histogram selects one or more observations (for instance, the time taken in requests) and then categorizes those observations in bins that are set beforehand.
• Cardinality Impact: Concerning the cardinality, histograms can lead to moderate cardinality particularly if many buckets have been configured.

Example:

http_request_duration_seconds_bucket{le="0.1"}

4. Summary

• Definition: A summary is the same as a histogram but we analyze quantile ranks or percentiles not buckets.
• Cardinality Impact: Summaries can also lead to moderate to high cardinality, the number of quantiles determines the level of cardinality possible.

Example:

http_request_duration_seconds_sum{quantile="0.99"}

Best Practices for Managing Cardinality

Efficient management of cardinality can become one of the key factors in maintaining the efficiency of Prometheus. Here are some best practices:

• Avoid High-Cardinality Labels: Reduce the possibility of using the labels that can have many different values like, user_id, session_id or request_id.
• Use Aggregation: Aggregate metrics whenever possible to limit the number of time series which will have to be created.
• Limit Label Combinations: It is advised not to have too many labels on a single metric because it can exponentially increase the number of time series.
• Employ Relabeling: Prometheus has utilization of relabeling configurations to remove or alter the label before storing it.
• Monitor Cardinality: This will help in the regular monitor of the cardinality of your metrics so as to detect and solve high cardinality problems in the early stages.

Practical Examples

Example 1: High cardinality issue

Problem: The metric measures the user actions using user_id as the identification label.

user_actions_total{user_id="12345", action="login"}

Impact: Due to increasing cardinality, the fact that every user_id generates a new time series.

Solution: Exclude or aggregate the label user_id.

Example 2: Using Aggregation

Problem: Tracking HTTP request metrics with multiple labels like method, status, and endpoint.

http_requests_total{method="GET", status="200", endpoint="/api/v1/resource"}

Impact: Many labels generate numerous unique time series.

Solution: One can aggregate the metrics by removing less valuable labels.

Conclusion

In conclusion, cardinality management is critical when it comes to maintaining high performance and efficiency in the monitoring system of Prometheus. Basically, high cardinality can cause lots of resource consumption challenges such as memory requirements, storage requirement, and query times. The major high cardinality challenges can thus be overcome by above best practices. Proactive management enables you to keep the monitoring structure lean and agile, while avoiding waste and possible performance issues. If these strategies are put into effect, they help to keep your Prometheus deployment strong and elastic.

Cardinality in Prometheus- FAQs

What is cardinality in Prometheus?

Cardinality in Prometheus is the number of different time series identified by a set of metric and label.

Why is high cardinality a problem?

The disadvantage of high cardinality is that it raises the amount of memory and storage required and can cause query performance to decrease.

How can I manage high cardinality?

It is necessary to reduce the high cardinality, apply aggregation, minimize the number of used labels at once, and use the relabeling technique.

What metric types are most affected by cardinality?

Based on the configuration of histograms and summaries, they are capable of causing moderate to high cardinality.

Can I monitor cardinality in Prometheus?

Yes, you can monitor cardinality by using Prometheus by tracking metrics that help you analyze time series counts memory usage.