MongoDB Time Series features

Columnar compression and automatic bucketing dramatically reduce your database storage footprint, lowering costs and improving performance.

Block-based processing delivers significantly faster query performance for large-scale time series aggregations and analytics.

Automatically expire old data with time-to-live policies, simplifying data retention and reducing storage costs over time.

Analyze trends, calculate moving averages, and perform complex time-based calculations with native window functions.

Handle missing data points using specialized gap-filling and densification aggregation stages for complete analytical accuracy.

Support the entire time series data lifecycle from ingest, storage, analysis, and visualization to archiving.

Update and delete time series data freely, giving you flexibility and control over your data.

Horizontally distribute large data sets to reduce latency and comply with data sovereignty regulations.

Advanced support for compound indexes on all fields, along with geo and clustered indexes, optimized for efficient querying.