Just over two months after completing the much-touted initial rollout of SQL support for its popular document store, Couchbase Inc. is releasing yet another major update aimed at tying up the loose ends left over from the launch. As of today, the homegrown N1QL structured query language is fully functional, complete with the ability to create, read, update and delete data as well as a couple of new optimization options go along meant to help developers take full advantage of the improvements.

The first of the two additions is a logical construct known as a covering index that Couchbase borrowed from the world of legacy relational systems to allow for queries that only target certain subsets of the data in a deployment to be run with the appropriate level of precision. If, for instance, an analyst wants to update the telephone numbers of customers from a city that recently changed its area code, then they can use the feature to instantly apply the changes to the relevant records without having to wait on a full database scan to be performed first in order to find them.

Removing the need for a preliminary search can enable queries to complete noticeably faster than with standard indexes, a speed gain that users will be able to further increase with the help of the other performance-boosting capability introduced in Couchbase Server 4.1. Prepared statements are another carry-over from relational systems that essentially make it possible to run an operation against multiple different datasets without having to separately plan, parse and launch the query for each one. Instead, the computations performed for the initial run are reused in the subsequent repetitions to save overhead.

The improvements make N1QL a much more viable alternative to the native query format of Couchbase Server and thereby extends its appeal to a broader audience. In particular, the release targets large organizations that have historically relied mainly on SQL-based systems and don’t wish to go through the hassle of training hundreds of engineers in learning a new language before being able to switch to a more modern platform. Removing that knowledge gap not only reduces training expenses but also holds the potential to shave months off database migration projects.

Image via Geralt