MongoDB strengthens its search functions for developers

MongoDB’s cloud -managed Atlas database is enriched with a range of analytics and search capabilities that expand the possibilities for developers for new application use cases. The publisher also presents using Queryable Encryption what it displays as the first encrypted diagram of the search function.

MongoDB demonstrates its ambition to provide developers with a data platform that expands the various use cases supported by its document -focused database technology. Two years ago, Mark Porter oversaw the American publisher’s engineering teams and when we met with the CTO, he clearly explained MongoDB’s desire to integrate analytical functions into the platform’s capabilities. , search and all synchronization technologies on mobiles. It’s a question here of re-inventing the 3rd level of three-level architectures, he explained to us on a visit to Paris (his lecture will soon be published in LMI). At the annual MongoDB World conference, held this week in New York, a series of announcements were made in this direction by the provider of open source solutions.

Among the new features is the Column Store Indexes feature, which will be delivered later this year. For developers, this will facilitate the integration of “in-app” analytical functions with the possibility to create and maintain an index specifically developed for this purpose. The latter will significantly speed up many common queries without having to change the document structure or have to move the data elsewhere. MongoDB explained that in addition, the analytical nodes will be able to scale separately, allowing teams to adjust the performance of their queries separately without the risk of exceeding or under-dimensional their processing capacities. . Improvements are made to time series collections in MongoDB version 6.0 with support for secondary indexes for measurements and optimization to sort time data faster. With search functions, Atlas Search is enriched with Search Facets so that developers can quickly create a richer search experience for users.

Virtual database capabilities

In managing the development lifecycle, MongoDB announced the capabilities to modify and move data faster in Atlas, the cloud -managed version of its database. Atlas Data Lake will provide fully managed object storage capabilities that will optimize the high performance of analytical queries. This will reformat the data and create index partitions as it retrieves data from Atlas databases.

In addition, the Data Federation will allow teams to create virtual databases to work with data located in different sources (MongoDB clusters and storage buckets). For SQL enthusiasts, the Atlas SQL Interface provides tools for interacting with Atlas read-only data. “It simplifies native mode queries and data visualization in Atlas using SQL tools while maintaining the flexibility of the document model”, the editor explains in a press release.

In the preview, Queryable Encryption

On the deployment capabilities side, MongoDB announced the overall availability of Atlas Serverless to deploy serverless functions in three major public clouds. The publisher also presents Queryable Encryption in the preview, which it presents as the first encrypted schema of the search function. This makes it possible to perform searches on data that remains encrypted at all times in the database.

The encryption keys do not leave the application and the database server cannot access them, MongoDB explains. Queryable Encryption is based on standard NIST cryptographic primitives. This functionality provides protection including threats coming from within the company.

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