When using Couchbase to store documents containing text data, you would like the ability to search within those documents with natural language capabilities. This is precisely what the Couchbase Full Text Service has to offer. In this course, Implement Full-text Search in Couchbase, you will delve into how full-text indexes work in Couchbase and how these indexes can be created, used and configured. First, you will begin by exploring how full-text searches in general rank documents for each query which is sent to them. This includes concepts such as term frequency and inverse document frequency. Next, you will get hands-on and build full-text indexes in a Couchbase cluster and submit a variety of queries to them. Then, you will move on to how full-text searches are likely to be performed from an application – by submitting search requests using N1QL queries and the Couchbase REST API. Finally, you will explore the use of analyzers and filters to only include specific words and terms within a full-text index. When you are finished with this course, you will be well-versed in the options available to build, use, and configure full-text indexes in Couchbase. This will give you the skills needed to speed up text-based searches against the data in your Couchbase cluster, and deliver better search results to your end users.
Author Name: Kishan Iyer
Author Description:
I have a Masters in Computer Science from Columbia University and have worked previously as a developer and DevOps engineer. I now work at Loonycorn which is a studio for high-quality video content. My interests lie in the broad categories of Big Data, ML and Cloud.