MongoDB 5.2 database improves time series capabilities
The vendor is improving on capabilities introduced in MongoDB 5.0 with an update that continues the company's push to support operational and analytics workloads.
The new MongoDB 5.2 update, released on Jan. 19, is part of a quarterly release cycle that the vendor refers to as a rapid release, with the goal of bringing features to users faster than waiting for a major milestone version update.
MongoDB has been pivoting in the direction of support for more and different kinds of analytics for a while now.
Carl OlofsonAnalyst, IDC
One of the innovations first introduced in MongoDB 5.0 is time series data support, which is improved in the update. MongoDB 5.2 also introduces a series of more efficient data queries for both time series data and operational analytics.
"MongoDB has been pivoting in the direction of support for more and different kinds of analytics for a while now," IDC analyst Carl Olofson said.
Better time series data capabilities
Enterprises' interest in using time series technology for analytics is growing, according to Olofson. The latest MongoDB release is meant to take advantage of built-in cross-document optimizations to deliver high-performance time series query results, he said.
Among the new data queries in MongoDB 5.2 are the $top and $bottomoperators that enable users to more easily query data at the top or bottom of a given data set. Olofson said the modifications to MongoDB's query language, such as adding these mathematical functions, make such queries easier to pose.
While MongoDB does not support SQL as a query language, "these enhancements add some capabilities that SQL users expect," Olofson said. "This is all part of making MongoDB a suitable platform for at least certain types of analytics queries, and of course they are adding an emphasis on time series."
Improved compressions
Time series data inside MongoDB is also getting a feature known as columnar compression, which compresses data within a column to reduce the amount of storage space required.
Jane Fine, director of developer experience at MongoDB, explained that columnar compression builds on MongoDB's optimized schema introduced in 5.0. The optimized schema automatically stores time series data in a highly compressed format, while retaining the flexibility to model and evolve the metadata schema based on application.
In MongoDB 5.2, time series data collections now have a smaller storage footprint than before, which could lead to lower costs as users pay for the amount of storage they consume. She added that columnar compression enables the database to fit larger working data sets in memory, which will reduce overall storage I/O demand and improve performance.
"It creates more longevity in the data lifecycle as developers can efficiently store data for longer at a lower cost and reduce their timeline for archiving or deleting data," Fine said.
Long-running snapshot queries land in MongoDB 5.2
Beyond improvement to time series data, MongoDB 5.2 also includes what the vendor refers to as long-running snapshot queries. With this feature, users can run queries that take up to five minutes by default against a live transactional database while maintaining consistent snapshot isolation.
Fine explained that the long-running snapshot queries approach eliminates the need to create a static copy of data and move it out into a separate silo in order to power complex algorithms while preventing interference with a live operational workload.
"It's building on existing capabilities we have around workload isolation where you can have dedicated analytics nodes powering your analytics workloads with no impact on your transactional applications," she said.
