ElasticSearch Cookbook

Book Image

ElasticSearch Cookbook

By : Alberto Paro

Book Image

ElasticSearch Cookbook

By: Alberto Paro

Overview of this book

ElasticSearch Cookbook Second Edition

ElasticSearch Cookbook Second Edition

Credits

About the Author

About the Author

Acknowledgments

Acknowledgments

About the Reviewers

About the Reviewers

www.PacktPub.com

www.PacktPub.com

Preface

Free Chapter

Getting Started

Getting Started

Understanding nodes and clusters

Understanding node services

Managing your data

Understanding clusters, replication, and sharding

Communicating with ElasticSearch

Using the HTTP protocol

Using the native protocol

Using the Thrift protocol

Downloading and Setting Up

Downloading and Setting Up

Downloading and installing ElasticSearch

Setting up networking

Setting up a node

Setting up for Linux systems

Setting up different node types

Installing plugins in ElasticSearch

Installing a plugin manually

Removing a plugin

Changing logging settings

Managing Mapping

Managing Mapping

Using explicit mapping creation

Mapping base types

Mapping an object

Mapping a document

Using dynamic templates in document mapping

Managing nested objects

Managing a child document

Adding a field with multiple mappings

Mapping a geo point field

Mapping a geo shape field

Mapping an IP field

Mapping an attachment field

Adding metadata to a mapping

Specifying a different analyzer

Mapping a completion suggester

Basic Operations

Basic Operations

Creating an index

Deleting an index

Opening/closing an index

Putting a mapping in an index

Getting a mapping

Deleting a mapping

Refreshing an index

Flushing an index

Optimizing an index

Checking if an index or type exists

Managing index settings

Using index aliases

Indexing a document

Getting a document

Deleting a document

Updating a document

Speeding up atomic operations (bulk operations)

Speeding up GET operations (multi GET)

Search, Queries, and Filters

Search, Queries, and Filters

Executing a search

Sorting results

Highlighting results

Executing a scan query

Suggesting a correct query

Counting matched results

Deleting by query

Matching all the documents

Querying/filtering for a single term

Querying/filtering for multiple terms

Using a prefix query/filter

Using a Boolean query/filter

Using a range query/filter

Using span queries

Using a match query

Using an ID query/filter

Using a has_child query/filter

Using a top_children query

Using a has_parent query/filter

Using a regexp query/filter

Using a function score query

Using exists and missing filters

Using and/or/not filters

Using a geo bounding box filter

Using a geo polygon filter

Using geo distance filter

Using a QueryString query

Using a template query

Aggregations

Executing an aggregation

Executing the stats aggregation

Executing the terms aggregation

Executing the range aggregation

Executing the histogram aggregation

Executing the date histogram aggregation

Executing the filter aggregation

Executing the global aggregation

Executing the geo distance aggregation

Executing nested aggregation

Executing the top hit aggregation

Scripting

Installing additional script plugins

Managing scripts

Sorting data using script

Computing return fields with scripting

Filtering a search via scripting

Updating a document using scripts

Rivers

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 Node Monitoring

Cluster and Node Monitoring

Controlling cluster health via the API

Controlling cluster state via the API

Getting cluster node information via the API

Getting node statistics via the API

Managing repositories

Executing a snapshot

Restoring a snapshot

Installing and using BigDesk

Installing and using ElasticSearch Head

Installing and using SemaText SPM

Installing and using Marvel

Java Integration

Java Integration

Creating an HTTP client

Creating a native client

Managing indices with the native client

Managing mappings

Managing documents

Managing bulk actions

Building a query

Executing a standard search

Executing a search with aggregations

Executing a scroll/scan search

Python Integration

Python Integration

Creating a client

Managing indices

Managing mappings

Managing documents

Executing a standard search

Executing a search with aggregations

Plugin Development

Plugin Development

Creating a site plugin

Creating a native plugin

Creating a REST plugin

Creating a cluster action

Creating an analyzer plugin

Creating a river plugin

Index

Customer Reviews

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Using the MongoDB river

MongoDB is a very common NoSQL data storage system used all over the world. One of the main things that it lacks is that it was not designed for text searching.

Although the latest MongoDB version provides full-text search, its completeness and functionality are, by far, more limited than the current ElasticSearch version. So, it's quite common to use MongoDB as the data store and ElasticSearch to search. The MongoDB river, initially developed by me and now maintained by Richard Louapre, helps to create a bridge between these two applications.

Getting ready

You will need a working ElasticSearch cluster and a working MongoDB instance installed on the same machine as ElasticSearch, with replica sets enabled (http://docs.mongodb.org/manual/tutorial/deploy-replica-set/ and http://docs.mongodb.org/manual/tutorial/convert-standalone-to-replica-set/). You need to restore the sample data available in mongodb/data with the following command:

mongorestore –d escookbook escookbook...