MongoDB, the NoSQL document database that stores its documents in a JSON-like format with schema, has been updated with improvements including gap filling for time series data and support for clustered indexes.
Commenting on the new features, Jane Fine, Director of Developer Experience at MongoDB, said that time series is the fastest-growing data-intensive workload for MongoDB customers, but that it’s common for time series data to have gaps, such as when an IoT sensor goes offline.
This causes problems because time series data needs to be continuous for analysis, and when creating histograms or correlating data sets.
To meet this need gap filling has been added to the MongoDB 5.3 Rapid Release. The two new aggregation stages are used to deal with missing data across time series. The $densify stage creates new documents to eliminate the gaps, and $fill sets values for the fields when a value is null or missing. Filling missing values can be done with a constant or using linear interpolation, carrying over the last observation or carrying backward the next observation.
The new release also adds the ability to create a collection with a clustered index. Collections created with a clustered index are called clustered collections, and the advantage they offer is that they enable faster queries without needing a secondary index, such as queries with range scans and equality comparisons on the clustered index key. They also have use less storage, which improves performance for queries and bulk inserts. Clustered collections can also eliminate the need for a secondary TTL (Time To Live) index.
Against these improvements, clustered collections do have additional performance improvements for inserts, updates, deletes, and queries.
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