Downsampling in Prometheus: Practical Strategies to Manage Cardinality and Query Performance

Prometheus is a widespread open-source monitoring and alerting toolkit widely used for collecting and querying metrics from various systems and applications. However, as the number of metrics collected by Prometheus grows, so does the challenge of managing the increasing cardinality of time series data. Downsampling is a critical technique to address this challenge and improve query performance. In this article, we will explore the concept of downsampling in Prometheus and discuss practical strategies to manage cardinality effectively.

Understanding the Cardinality Challenge

Prometheus stores time series data in a highly efficient and scalable manner. Each unique combination of metric name, labels, and timestamp represents a separate time series. While this design is excellent for providing detailed and granular insights into system performance, it can lead to a high cardinality problem as the number of time series increases.

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Read more on the difference between samples vs. metrics vs. cardinality.

High cardinality can impact Prometheus in several ways:

Increased Storage Requirements: Storing many time series can consume significant storage space.
Slower Queries: As the number of time series grows, querying and retrieving data becomes slower, impacting query performance.
Resource Consumption: High cardinality can lead to increased CPU and memory usage on Prometheus servers, potentially causing performance issues.

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Check out Levitate - our managed time series data warehouse built to solve the High Cardinality challenge.

To address these challenges, downsampling is employed to reduce the number of time series while retaining essential information for analysis.

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Read more on how to manage Prometheus Cardinality.

What Is Downsampling?

Downsampling aggregates or reduces the granularity of time series data to create a smaller set of representative time series. By aggregating data over time intervals, downsampling minimizes the number of data points, helping to mitigate the cardinality problem and improve query performance. Downsampling can be particularly beneficial for long-term data retention and historical analysis.

Practical Downsampling Strategies

Here are some practical strategies for downsampling in Prometheus:

1. Aggregation

Aggregation involves reducing the granularity of time series data by computing summary statistics over fixed time intervals. Standard aggregation functions include sum, average, maximum, and minimum. Prometheus supports aggregation through processes like sum_over_time() and avg_over_time().

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Try out Levitate's streaming aggregation pipelines to handle high cardinality metrics.

2. Retention Policies

Prometheus supports using retention policies, allowing you to specify different data retention periods for various time series. By defining appropriate retention policies, you can keep high-resolution data for recent periods and downsample older data to reduce cardinality.

3. Recording Rules

Recording rules in Prometheus allow you to precompute and store aggregated time series data. You can create recording rules to downsample high-cardinality metrics and store the downsampled data, making it readily available for queries without impacting query performance.

Learn more on difference between Streaming Aggregation vs. Recording Rules.

4. Remote Storage

Consider using remote storage managed Prometheus solutions, such as Levitate, to offload historical data and store metrics for long-term storage without downsampling. These solutions provide efficient ways to manage high-cardinality data while still making it accessible for querying.

5. Custom Scripts

Custom scripts or external tools may sometimes be necessary to perform downsampling based on specific requirements. These scripts can aggregate data, apply custom logic, and generate downsampled time series data that suits your needs.

For more in-depth information on downsampling and aggregating metrics in Prometheus, you can refer to the article on Last9's blog. The article provides practical strategies and insights into effectively managing cardinality and query performance in Prometheus, making it a valuable resource for those looking to optimize their Prometheus setup.

In conclusion, downsampling is a crucial technique in Prometheus for managing the cardinality of time series data and improving query performance. By using streaming aggregation, retention policies, recording rules, remote storage, or custom scripts, you can tailor your downsampling approach to meet your specific monitoring and analysis needs. With the right downsampling strategies, you can maintain efficient and effective Prometheus monitoring over the long term.