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.
What you'll learn
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.
Table of contents
- Version Check 0m
- Module Overview 2m
- Prerequisites and Course Overview 2m
- Elasticsearch Overview and Install 6m
- Shards, Replicas, and the TF/IDF Algorithm 5m
- Mappings 6m
- Demo: Default Mappings 6m
- Demo: Numeric and Date Detection 3m
- Demo: Explicit Mappings 5m
- Demo: Mapped vs. Unmapped Fields 5m
- Demo: Dynamic Templates for Custom Mapping 7m
- Analyzers 6m
- Demo: Character Filters and Token Filters 5m
- Demo: Built-in and Custom Analyzers 7m
- Module Overview 1m
- Relational Word vs. Flat World 6m
- Application-side Joins 3m
- Demo: Denormalizing Data 3m
- Nested Objects 3m
- Demo: Nested Objects 4m
- Modeling Hierarchy with Parent Child Relationships 5m
- Demo: Parent Child Documents Using Join 4m
- Demo: Querying Child Documents Using Parent ID 3m
- Demo: Has Child, Has Parent Queries 2m
- Demo: Children Aggregation 6m
- Demo: Multi-level Joins 4m
About the author
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. After spending years working in tech in the Bay Area, New York, and Singapore at companies such as Microsoft, Google, and Flipkart, Janani finally decided to combine her love for technology with her passion for teaching. She is now the co-founder of Loonycorn, a content studio focused on providing ... more