Book Image

Mastering Elasticsearch 5.x - Third Edition

By : Bharvi Dixit
Book Image

Mastering Elasticsearch 5.x - Third Edition

By: Bharvi Dixit

Overview of this book

Elasticsearch is a modern, fast, distributed, scalable, fault tolerant, and open source search and analytics engine. Elasticsearch leverages the capabilities of Apache Lucene, and provides a new level of control over how you can index and search even huge sets of data. This book will give you a brief recap of the basics and also introduce you to the new features of Elasticsearch 5. We will guide you through the intermediate and advanced functionalities of Elasticsearch, such as querying, indexing, searching, and modifying data. We’ll also explore advanced concepts, including aggregation, index control, sharding, replication, and clustering. We’ll show you the modules of monitoring and administration available in Elasticsearch, and will also cover backup and recovery. You will get an understanding of how you can scale your Elasticsearch cluster to contextualize it and improve its performance. We’ll also show you how you can create your own analysis plugin in Elasticsearch. By the end of the book, you will have all the knowledge necessary to master Elasticsearch and put it to efficient use.
Table of Contents (20 chapters)
Mastering Elasticsearch 5.x - Third Edition
About the Author
About the Reviewer
Customer Feedback

Similarity model configuration

As we now know how to set the desired similarity for each field in our index, it's time to see how to configure them if we need them, which is actually pretty easy. What we need to do is use the index settings section to provide an additional similarity section. An example is as follows (this example is stored in the posts_custom_similarity.json file):

 "settings" : { 
  "index" : { 
   "similarity" : { 
    "mastering_similarity" : { 
     "type" : "classic", 
     "discount_overlaps" : false 
 "mappings" : { 
  "post" : { 
   "properties" : { 
    "id" : { "type" : "long", "store" : "yes" }, 
    "name" : { "type" : "text", "store" : "yes", "index" :  
"analyzed", "similarity" : "mastering_similarity" }, 
    "contents" : { "type" : "text", "store" : "no", "index" :  
"analyzed" } 

You can, of course...