We're very excited to announce that we've released new extensions to our search capabilities within the Cloudize API Framework today.

When MongoDB announced the release of MongoDB Atlas Search (even before it became generally available), we recognized that it represented a killer feature that customers would love, and we wanted to expose its capabilities as a standard filter within our API Framework as quickly as possible. The timing was opportune, as we were literally designing and building all of our other filtering capabilities within the Cloudize API Framework at the time. The challenge, of course, was that Atlas Search was radically different from MongoDB's standard MQL query capabilities, and it took some time to understand how to effectively and efficiently expose the capability within our technologies in such a way that a developer would be able to say "I want search, or auto-complete, on this resource", and 💥 the capability was instantly available.

We've never regretted going early on this technology, as it's an absolute favourite with our customers. However, one of the downsides of going early is that the capability was still quite immature, and we had to implement several strategies to achieve things that were not available at the time. Since then, the Atlas Search team at MongoDB have done an amazing job, and the capability has evolved substantially since its initial release. Many of the workarounds we had to implement in our framework are no longer required, and a substantial list of new capabilities are now available that weren't there when we did our initial integration, and that meant that they were not immediately surfaced through our API Framework technologies.

Today's release addresses that.

One of the wonderful features MongoDB released within Atlas Search after our initial integration was faceting.

Many customers and engineers struggle to understand faceting. Simply put, faceting helps you expose new filtering opportunities to your user when they perform a search.

Consider an example where a person is searching for widgets on your website. They provide a search term or phrase that describes the widgets they're looking for, and the Atlas Search engine provides a resultset.

When faceting is implemented, other filtering dimensions are exposed within the resultset. For example, let's say "colour" was a dimension that was faceted. Now, the user would receive the resultset for their search term and learn that there are 13 Blue widgets, 9 Red widgets, and 44 Yellow widgets related to that search term.

With some UI magic, your website entices users to click the colour they want, and 💥 we've narrowed their search, and you've helped them "zoom-in" on what they really want. Of course, you can expose multiple dimensions through facetting, offering your users multiple options to narrow your dataset without having to scroll through all the results to find the widget they are looking for — the result is better navigation efficiency and happier users — and when your website makes it easy for users to find what they want, that builds trust and translates to it being easy to transact with your business and orders increase.

Best of all, the Cloudize API Framework and our Tesseract Design Technologies do all the work for you. You nominate which filters should provide faceting and ask for those facets to be surfaced through the new facets query string parameter. The resultset will include facets on the specified dimensions, and you can present them to your user in your UI.

Looking ahead, we will continue to review the Atlas Search feature set as new capabilities become available. Where they support the goals and objectives of our API Framework technologies, we'll look to build integrations that enable our customers to leverage those capabilities without having to write custom queries or code.

In addition, we expect to support a new AI Search Filter in the next major release of the API Framework (planned for release in the first quarter of 2025). This capability will allow users to perform searches based on meaning rather than textual content, adding considerable value to customers with appropriate use cases.