Book Image

Elasticsearch Essentials

Book Image

Elasticsearch Essentials

Overview of this book

With constantly evolving and growing datasets, organizations have the need to find actionable insights for their business. ElasticSearch, which is the world's most advanced search and analytics engine, brings the ability to make massive amounts of data usable in a matter of milliseconds. It not only gives you the power to build blazing fast search solutions over a massive amount of data, but can also serve as a NoSQL data store. This guide will take you on a tour to become a competent developer quickly with a solid knowledge level and understanding of the ElasticSearch core concepts. Starting from the beginning, this book will cover these core concepts, setting up ElasticSearch and various plugins, working with analyzers, and creating mappings. This book provides complete coverage of working with ElasticSearch using Python and performing CRUD operations and aggregation-based analytics, handling document relationships in the NoSQL world, working with geospatial data, and taking data backups. Finally, we’ll show you how to set up and scale ElasticSearch clusters in production environments as well as providing some best practices.

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Elasticsearch Essentials
Jan, 2016 | 240 pages
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Table of Contents (18 chapters)
Elasticsearch Essentials
Elasticsearch Essentials
Credits
Credits
About the Author
About the Author
Acknowledgments
Acknowledgments
About the Reviewer
About the Reviewer
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
Getting Started with Elasticsearch
Getting Started with Elasticsearch
Introducing Elasticsearch
Installing and configuring Elasticsearch
Basic operations with Elasticsearch
Summary
2
Understanding Document Analysis and Creating Mappings
Understanding Document Analysis and Creating Mappings
Text search
Document analysis
Elasticsearch mapping
Summary
3
Putting Elasticsearch into Action
Putting Elasticsearch into Action
CRUD operations using elasticsearch-py
CRUD operations using Java
Creating a search database
Elasticsearch Query-DSL
Understanding Query-DSL parameters
Search requests using Python
Search requests using Java
Sorting your data
Document routing
Summary
4
Aggregations for Analytics
Aggregations for Analytics
Introducing the aggregation framework
Metric aggregations
Bucket aggregations
Combining search, buckets, and metrics
Memory pressure and implications
Summary
5
Data Looks Better on Maps: Master Geo-Spatiality
Data Looks Better on Maps: Master Geo-Spatiality
Introducing geo-spatial data
Working with geo-point data
Geo-aggregations
Geo-shapes
Summary
6
Document Relationships in NoSQL World
Document Relationships in NoSQL World
Relational data in the document-oriented NoSQL world
Working with nested objects
Parent-child relationships
Considerations for using document relationships
Summary
7
Different Methods of Search and Bulk Operations
Different Methods of Search and Bulk Operations
Introducing search types in Elasticsearch
Cheaper bulk operations
Multi get and multi search APIs
Data pagination
Practical considerations for bulk processing
Summary
8
Controlling Relevancy
Controlling Relevancy
Introducing relevant searches
The Elasticsearch out-of-the-box tools
Controlling relevancy with custom scoring
Summary
9
Cluster Scaling in Production Deployments
Cluster Scaling in Production Deployments
Node types in Elasticsearch
Introducing Zen-Discovery
Node upgrades without downtime
Upgrading Elasticsearch version
Best Elasticsearch practices in production
Creating a cluster
Scaling your clusters
Summary
10
Backups and Security
Backups and Security
Introducing backup and restore mechanisms
Securing Elasticsearch
Summary
Index
Index

About the Reviewer

Alberto Paro is an engineer, project manager, and software developer. He currently works as a CTO at Big Data Technologies and as a freelance international consultant on software engineering for big data and NoSQL solutions. He loves to study emerging solutions and applications mainly related to Big Data processing, NoSQL, natural language processing, and neural networks. He began programming in BASIC on a Sinclair Spectrum when he was eight years old, and he has a lot of experience of using different operating systems, applications, and programming languages.

In 2000, he graduated in computer science engineering from Politecnico di Milano with a thesis on designing multiuser and multidevice web applications. He assisted the professors at the university for about a year. Then, he came in contact with The Net Planet Company and loved their innovative ideas; he started working on knowledge management solutions and advanced data mining products. In the summer of 2014, his company was acquired by Big Data technologies, where he currently works and uses mainly Scala and Python on state-of-the-art Big Data software (Spark, Akka, Cassandra, and YARN). In 2013, he started freelancing as a consultant for Big Data technologies, machine learning, and Elasticsearch.

In his spare time, when he is not playing with his children, he likes to work on open source projects. When he was in high school, he started contributing to projects related to the GNOME environment (gtkmm). One of his preferred programming languages is Python, and he wrote one of the first NoSQL backends on Django for MongoDB (Django-MongoDB-engine). He is also a fan of the Scala language and enjoys spreading his love of technology: he was a presenter of Big Data concepts at Scala Day Italy 2015 on Scala.JS and Big Data Tech Italian Conference in Florence.

In 2010, he began using Elasticsearch to provide search capabilities to some Django e-commerce sites and developed PyES (a Pythonic client for Elasticsearch), as well as the initial part of the Elasticsearch MongoDB driver. He is the author of ElasticSearch Cookbook and ElasticSearch Cookbook Second Edition as well as a technical reviewer of Elasticsearch Server, Second Edition, and the video course, Building a Search Server with ElasticSearch, all of which have been published by Packt Publishing.

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