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MongoDB 5.3 enhances time series data capabilities

MongoDB is continuing to grow its multimodel database capabilities, adding the ability to help users with gap filling for missing data sets, in the new 5.3 release.

The MongoDB 5.3 release is out on Wednesday, providing users of the database platform with new features designed to help improve time series data support.

MongoDB is a JSON-based NoSQL document database. In recent years, MongoDB has increasingly supported additional data models, becoming what is known as a multimodel database. With the MongoDB 5.0 database that was launched back in July 2021, the database vendor introduced time series data support. In the months since the initial MongoDB 5.0 release, the company has issued multiple updates as part of its quarterly release cycle. With MongoDB 5.2, which was released Jan. 21, new data query capabilities that can help with time series data were added.

Time series data is once again the primary focus for updates with the introduction of MongoDB 5.3, with a new set of capabilities around a feature known as gap filling. In time series data, there can sometimes be gaps from devices or sensors transmitting data. The gaps can then pose challenges for data analytics and operational use cases that rely on having continuous data entries in the database. Gap filling provides a set of database approaches to literally fill the gaps.

"Gap filling is fundamental if a comprehensive time series API is desired," explained Carl Olofson, an analyst at IDC.

Why gap filling for time series data matters in MongoDB 5.3

According to Olofson, users will often not only port the data to specialized time series data sets, but also use a combination of tools to check the data, detect the gaps, fill them and do the analysis. He said that in his view, the promise of MongoDB is that users will have an easier path to making effective use of time series data.

The process of gap filling can be tricky. Olofson noted that there are multiple techniques that can be used, depending on factors such as whether the sequence of values follows a detectable pattern or seems random, or is dependent on some other factor.

"I think MongoDB's inclusion of gap filling is necessary to create a reliable data platform where everything is ready and available to do reliable time series analysis," Olofson said.

Gap filling functions in MongoDB 5.3
MongoDB 5.3 provides users with a series of gap filling functions,including $densify, which adds new entries for missing data, and $fill,which provides the appropriate value.

Data support and MongoDB 5.3

MongoDB is using two specific techniques to enable gap filling, with the $densify and $fill commands.

Gap filling is fundamental if a comprehensive time series API is desired.
Carl OlofsonResearch vice president, IDC

With $densify, the database creates new documents for missing entries that come from a time series data source, such as a sensor. The $fill command then provides the actual values or data for the new entries. Jane Fine, director of developer experience at MongoDB, explained that gaps can be filled using three methods: constant values, linear interpolation or carrying the last event forward.

"Gap filling is a very important tool to use when charting or analyzing time series data," Fine said.

The next release of MongoDB is expected to be a major update, with the launch of MongoDB 6.0 in June. Among the capabilities slated for the next update are a series of enhanced security features, including enhanced support for Key Management Interoperability Protocol for encryption key management.

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