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  • Learning Elasticsearch
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Learning Elasticsearch

Learning Elasticsearch

By : Andhavarapu
4.3 (4)
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Learning Elasticsearch

Learning Elasticsearch

4.3 (4)
By: Andhavarapu

Overview of this book

Elasticsearch is a modern, fast, distributed, scalable, fault tolerant, and open source search and analytics engine. You can use Elasticsearch for small or large applications with billions of documents. It is built to scale horizontally and can handle both structured and unstructured data. Packed with easy-to- follow examples, this book will ensure you will have a firm understanding of the basics of Elasticsearch and know how to utilize its capabilities efficiently. You will install and set up Elasticsearch and Kibana, and handle documents using the Distributed Document Store. You will see how to query, search, and index your data, and perform aggregation-based analytics with ease. You will see how to use Kibana to explore and visualize your data. Further on, you will learn to handle document relationships, work with geospatial data, and much more, with this easy-to-follow guide. Finally, you will see how you can set up and scale your Elasticsearch clusters in production environments.
Table of Contents (11 chapters)
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Preface
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Free Chapter
1
Introduction to Elasticsearch
Introduction to Elasticsearch
Basic concepts of Elasticsearch
Interacting with Elasticsearch
How does search work?
Scalability and availability
Summary
2
Setting Up Elasticsearch and Kibana
Setting Up Elasticsearch and Kibana
Installing Elasticsearch
Installing Kibana
Query format used in this book (Kibana Console)
Using cURL or Postman
Health of the cluster
Summary
3
Modeling Your Data and Document Relations
Modeling Your Data and Document Relations
Mapping
Difference between full-text search and exact match
Core data types
Complex data types
Geo data type
Specialized data type
Mapping the same field with different mappings
Handling relations between different document types
Routing
Summary
4
Indexing and Updating Your Data
Indexing and Updating Your Data
Indexing your data
Updating your data
Using Kibana to discover
Using Elasticsearch in your application
Concurrency
Translog
Primary and Replica shards
Summary
5
Organizing Your Data and Bulk Data Ingestion
Organizing Your Data and Bulk Data Ingestion
Bulk operations
Reindex API
Ingest Node
Organizing your data
Shrink API
Summary
6
All About Search
All About Search
Different types of queries
Sample data
Querying Elasticsearch
Relevance
Searching for same value across multiple fields
Caching
Summary
7
More Than a Search Engine (Geofilters, Autocomplete, and More)
More Than a Search Engine (Geofilters, Autocomplete, and More)
Sample data
Correcting typos and spelling mistakes
Making suggestions based on the user input
Highlighting
Handling document relations using parent-child
Handling document relations using nested
Scripting
Post Filter
Reverse search using the percolate query
Geo and Spatial Filtering
Multi search
Search templates
Querying Elasticsearch from Java application
Summary
8
How to Slice and Dice Your Data Using Aggregations
How to Slice and Dice Your Data Using Aggregations
Aggregation basics
Types of aggregations
Using Kibana to visualize aggregations
Caching
Doc values
Field data
Summary
9
Production and Beyond
Production and Beyond
Configuring Elasticsearch
Multinode cluster
How nodes discover each other
X-Pack
Monitoring
Thread pools
Elasticsearch server logs
Summary
10
Exploring Elastic Stack (Elastic Cloud, Security, Graph, and Alerting)
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Exploring Elastic Stack (Elastic Cloud, Security, Graph, and Alerting)
Elastic Cloud
Security
Graph
Alerting
Summary

Graph

In this section, we will discuss Graph, which is part of the X-Pack Gold and Platinum subscription. Graph lets you discover and analyze relationships in your data. It works on your existing indexes and doesn't require any special configuration. The Graph has two components:

  • The functionality required for Elasticsearch to compute the Graph.
  • The UI in Kibana to visualize the graphical representation.

To better explain the functionality of Graph, let's build a recommendation system for an online store. We want to know the relations between items frequently bought together and use that information to make suggestions to the users. This information can be very valuable. For example, in a physical store, items frequently bought together can be placed adjacent to each other. We can also use this information to give the user a coupon or e-mail the user about various...

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