time-series-database

To configure retention policies for a time-series database, you define how long raw data is stored, such as keeping raw metrics for 7 days, optimizing storage costs while retaining essential historical data.

Downsampling in time-series data management is the process of aggregating data over time, such as creating hourly averages from minute data, to reduce storage costs and improve query performance for historical analytics.

You downsample time-series data to hourly aggregates by setting downsampling strategies that aggregate minute data into hourly averages, allowing you to retain long-term historical data efficiently for monitoring systems.

You need downsampling for observability metrics when managing high data volumes in monitoring systems, allowing you to keep raw data briefly while storing long-term historical aggregates to balance query performance and storage costs.

The best way to reduce storage costs for time-series data is combining retention policies with downsampling strategies, keeping raw data for short periods while retaining long-term aggregated data for historical analytics.

Yes, configuring time-series retention requires understanding data volume, query patterns, and cost constraints to select appropriate aggregation levels and retention periods that match your monitoring and analytics needs.