ElasticSearch Cookbook

ElasticSearch Cookbook

By : Alberto Paro

Buy this Book

ElasticSearch Cookbook

By: Alberto Paro

Buy this Book

Overview of this book

ElasticSearch is one of the most promising NoSQL technologies available and is built to provide a scalable search solution with built-in support for near real-time search and multi-tenancy. This practical guide is a complete reference for using ElasticSearch and covers 360 degrees of the ElasticSearch ecosystem. We will get started by showing you how to choose the correct transport layer, communicate with the server, and create custom internal actions for boosting tailored needs. Starting with the basics of the ElasticSearch architecture and how to efficiently index, search, and execute analytics on it, you will learn how to extend ElasticSearch by scripting and monitoring its behaviour. Step-by-step, this book will help you to improve your ability to manage data in indexing with more tailored mappings, along with searching and executing analytics with facets. The topics explored in the book also cover how to integrate ElasticSearch with Python and Java applications. This comprehensive guide will allow you to master storing, searching, and analyzing data with ElasticSearch.

ElasticSearch Cookbook

Credits

About the Author

About the Reviewers

www.PacktPub.com

Preface

Free Chapter

Getting Started

Introduction

Understanding node and cluster

Understanding node services

Managing your data

Understanding cluster, replication, and sharding

Communicating with ElasticSearch

Using the HTTP protocol

Using the Native protocol

Using the Thrift protocol

Downloading and Setting Up ElasticSearch

Introduction

Downloading and installing ElasticSearch

Networking setup

Setting up a node

Setting up ElasticSearch for Linux systems (advanced)

Setting up different node types (advanced)

Installing a plugin

Installing a plugin manually

Removing a plugin

Changing logging settings (advanced)

Managing Mapping

Introduction

Using explicit mapping creation

Using dynamic templates in document mapping

Managing nested objects

Managing a child document

Mapping a multifield

Mapping a GeoPoint field

Mapping a GeoShape field

Mapping an IP field

Mapping an attachment field

Adding generic data to mapping

Mapping different analyzers

Standard Operations

Introduction

Creating an index

Deleting an index

Opening/closing an index

Putting a mapping in an index

Checking if an index or type exists

Managing index settings

Speeding up atomic operations (bulk)

Speeding up GET

Search, Queries, and Filters

Executing a scan query

Suggesting a correct query

Counting

Deleting by query

Matching all the documents

Querying/filtering for term

Querying/filtering for terms

Using a prefix query/filter

Using a Boolean query/filter

Using a range query/filter

Using span queries

Using the match query

Using the IDS query/filter

Using the has_child query/filter

Using the top_children query

Using the has_parent query/filter

Using a regexp query/filter

Using exists and missing filters

Using and/or/not filters

Using the geo_bounding_box filter

Using the geo_polygon filter

Using the geo_distance filter

Facets

Introduction

Executing facets

Executing terms facets

Executing range facets

Executing histogram facets

Executing date histogram facets

Executing filter/query facets

Executing statistical facets

Executing term statistical facets

Executing geo distance facets

Scripting

Introduction

Installing additional script plugins

Sorting using script

Computing return fields with scripting

Filtering a search via scripting

Updating with scripting

Rivers

Introduction

Managing a river

Using the CouchDB river

Using the MongoDB river

Using the RabbitMQ river

Using the JDBC river

Using the Twitter river

Cluster and Nodes Monitoring

Introduction

Controlling cluster health via API

Controlling cluster state via API

Getting nodes information via API

Getting node statistic via API

Installing and using BigDesk

Installing and using ElasticSerach-head

Installing and using SemaText SPM

Java Integration

Introduction

Creating an HTTP client

Creating a native client

Managing indices with the native client

Executing a standard search

Executing a facet search

Executing a scroll/scan search

Python Integration

Executing a standard search

Executing a facet search

Plugin Development

Introduction

Creating a site plugin

Creating a simple plugin

Creating a REST plugin

Creating a cluster action

Creating an analyzer plugin

Creating a river plugin

Index

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Using the top_children query

In the previous recipe, the has_child query consumes a large amount of memory because it requires to fetch IDs of all the children. To bypass this limitation in huge data contexts the top_children query allows fetching only the top children results.

Getting ready

You need a working ElasticSearch cluster and the data populated with the populate script.

How to do it...

For executing the top_children query, we need to perform the following steps:

We need to search the test-type parents of which the test-type2 top children have a term in the field value as value1. We can create this kind of query as follows:

curl -XPOST 'http://127.0.0.1:9200/test-index/test-type/_search' -d '{
    "query": {
            "top_children" : {
                "type" : "test-type2",
                "query" : {
                    "term" : {
                        "value" : "value1"
                    }
                },
                "score" : "max",
                "factor" : 5,
    ...

ElasticSearch Cookbook

By : Alberto Paro

ElasticSearch Cookbook

By: Alberto Paro

Overview of this book

Related Content you might be interested in

Current Title:

ElasticSearch Cookbook

Using the top_children query

Getting ready

How to do it...