Designing Schema for Elasticsearch

Elasticsearch is a very popular search and analytics engine which helps you get up and running with search for your site or application in no time. This course covers how to improve search nuances by designing the right schema for your documents.
Course info
Rating
(30)
Level
Intermediate
Updated
Feb 23, 2018
Duration
2h 59m
Table of contents
Course Overview
Modeling Data in Elasticsearch
Managing Relational Content
Working with Geo-spatial Data
Designing for Scale
Description
Course info
Rating
(30)
Level
Intermediate
Updated
Feb 23, 2018
Duration
2h 59m
Description

You can get better search results beyond the basic out-of-the-box search experience with Elasticsearch. In this course, Designing Schema for Elasticsearch, you will learn how to configure indexes to get more nuanced and meaningful search results. First, you will use dynamic and explicit mapping which allows you to specify field types within your document, which in turn determines how they are indexed and searched. Next, you will learn how you can map relationships and hierarchies from the traditional RDBMS world to the flat world of Elasticsearch. Finally, you will see Elasticsearch's special features, working with geospatial data such as GPS, and time-based data such as log files, and also aliasing indices to share them across multiple users for a better search experience. At the end of this course, you will have hands-on experience designing your Elasticsearch indexes and mappings to work well with different kinds of data, such as hierarchical, geospatial, or time-based data.

About the author
About the author

A problem solver at heart, Janani has a Masters degree from Stanford and worked for 7+ years at Google. She was one of the original engineers on Google Docs and holds 4 patents for its real-time collaborative editing framework.

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Section Introduction Transcripts
Section Introduction Transcripts

Course Overview
Hi, my name is Janani Ravi, and welcome to this course on schema design in Elasticsearch. A little bit about myself. I have a Masters degree in Electrical Engineering from Stanford and have worked at companies such as Microsoft, Google, and Flipkart. At Google, I was one of the first engineers working on real-time collaborative editing in Google Docs, and I hold four patents for those underlying technologies. I currently work on my own startup, Loonycorn, a studio for high-quality video content. Elasticsearch is a very popular search and analytics engine, which can help you get up and running with search for your website or application in no time at all. Elasticsearch seems schemaless and works well right out of the box. But as your search corpus grows, it's more important to get search that is meaningful, nuanced, and correct. That requires a little more work. This is exactly where this course comes in. This course covers topics such as dynamic and explicit mapping, which allows you to specify field types within your document, which in turn determines how your documents are indexed and searched. You'll also learn how you can map relationships and hierarchies from the traditional RDBMS world to the flat world of Elasticsearch. Application-side joins, nested objects, parent-child relationships are some constructs that Elasticsearch offers to modeled relationships. Elasticsearch also has special features to work with geospatial data, such as GPS coordinates and time-based data such as log files. It's also possible to alias indices and share them across multiple users to get a better search experience. This course covers all this and more with a focus on real examples, queries, and set up.