Implement Full-text Search in Couchbase

Beyond indexes for keyword searches, Couchbase also offers full-text indexes to search within document text using natural language capabilities. This course gives you a conceptual and hands-on understanding of full-text searches in Couchbase.
Course info
Level
Advanced
Updated
Jul 7, 2020
Duration
2h 48m
Table of contents
Course Overview
Getting Started with Full-text Search
Searching with Full-text Indexes and Query Strings
Performing Full-text Search Using REST APIs and N1QL
Configuring Full-text Search Indexes
Using Custom Analyzers and Filters in Full-text Search Indexes
Description
Course info
Level
Advanced
Updated
Jul 7, 2020
Duration
2h 48m
Description

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.

About the author
About the author

An engineer at heart, I am drawn to any interesting technical topic. Big Data, ML and Cloud are presently my topics of interest.

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Section Introduction Transcripts
Section Introduction Transcripts

Course Overview
Hi, and welcome to this course, Implement Full‑text Search in Couchbase. My name is Kishan Iyer, and I will be your instructor for this course. A little about myself first. I have a masters degree in computer science from Columbia University and have previously worked in companies such as Deutsche Bank and WebMD in New York. I presently work for Loonycorn, a studio for high‑quality video content. When using cloud base to store documents, which contains text data, we would like the ability to search within those documents with natural language capabilities. And this is precisely what the Couchbase Full Text Service has to offer. In this course, we delve into how full‑text indexes work in Couchbase, and how these indexes can be created, used, and configured. We begin by exploring our full‑text searches in general, run documents for each query which is sent to them. This includes concepts such a term frequency and inverse document frequency. We then get hands on and build full‑text indexes in a Couchbase cluster and submit a variety of queries to them. We move on after that 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. While doing so, we make use of a query object and delve into how this can be configured to perform a variety of searches. The next topic we cover is the configuration of full‑text indexes. This includes the mapping of specific document types in a bucket to the index and only including specific fields from those documents. Finally, we explore the use of analyzers and filters to only include specific words and terms within a full‑text index. Once you have finished 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 you need to speed up text‑based searches against the data in your Couchbase cluster and deliver better search results to your end users.