Elasticsearch 8.x Cookbook - Fifth Edition

By : Alberto Paro

Elasticsearch 8.x Cookbook - Fifth Edition

By: Alberto Paro

Overview of this book

Elasticsearch is a Lucene-based distributed search engine at the heart of the Elastic Stack that allows you to index and search unstructured content with petabytes of data. With this updated fifth edition, you'll cover comprehensive recipes relating to what's new in Elasticsearch 8.x and see how to create and run complex queries and analytics. The recipes will guide you through performing index mapping, aggregation, working with queries, and scripting using Elasticsearch. You'll focus on numerous solutions and quick techniques for performing both common and uncommon tasks such as deploying Elasticsearch nodes, using the ingest module, working with X-Pack, and creating different visualizations. As you advance, you'll learn how to manage various clusters, restore data, and install Kibana to monitor a cluster and extend it using a variety of plugins. Furthermore, you'll understand how to integrate your Java, Scala, Python, and big data applications such as Apache Spark and Pig with Elasticsearch and create efficient data applications powered by enhanced functionalities and custom plugins. By the end of this Elasticsearch cookbook, you'll have gained in-depth knowledge of implementing the Elasticsearch architecture and be able to manage, search, and store data efficiently and effectively using Elasticsearch.

Preface

Who this book is for

What this book covers

To get the most out of this book

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Chapter 1: Getting Started

Technical requirements

Downloading and installing Elasticsearch

Setting up networking

Setting up a node

Setting up Linux systems

Setting up different node roles

Setting up a coordinating-only node

Setting up an ingestion node

Installing plugins in Elasticsearch

Removing a plugin

Changing logging settings

Setting up a node via Docker

Deploying on Elastic Cloud Enterprise

Free Chapter

Chapter 2: Managing Mappings

Technical requirements

Using explicit mapping creation

Using dynamic templates in document mapping

Managing nested objects

Managing a child document with a join field

Adding a field with multiple mappings

Mapping a GeoPoint field

Mapping a GeoShape field

Mapping an IP field

Mapping an Alias field

Mapping a Percolator field

Mapping the Rank Feature and Feature Vector fields

Mapping the Search as you type field

Using the Range Fields type

Using the Flattened field type

Using the Point and Shape field types

Using the Dense Vector field type

Using the Histogram field type

Adding metadata to a mapping

Specifying different analyzers

Using index components and templates

Chapter 3: Basic Operations

Technical requirements

Creating an index

Deleting an index

Opening or closing an index

Putting a mapping in an index

Using ForceMerge on an index

Shrinking an index

Checking whether an index exists

Managing index settings

Using index aliases

Managing dangling indices

Resolving index names

Rolling over an index

Speeding up atomic operations (bulk operations)

Speeding up GET operations (multi-GET)

Chapter 4: Exploring Search Capabilities

Technical requirements

Executing a search

Sorting results

Highlighting results

Executing a scrolling query

Using the search_after functionality

Returning inner hits in results

Suggesting a correct query

Counting matched results

Matching all of the documents

Using a Boolean query

Using the search template

Chapter 5: Text and Numeric Queries

Technical requirements

Using a term query

Using a terms query

Using a terms set query

Using a prefix query

Using a wildcard query

Using a regexp query

Using span queries

Using a match query

Using a query string query

Using a simple query string query

Using the range query

Using an IDs query

Using the function score query

Using the exists query

Using a pinned query (XPACK)

Chapter 6: Relationships and Geo Queries

Technical requirements

Using the has_child query

Using the has_parent query

Using the nested query

Using the geo_bounding_box query

Using the geo_shape query

Using the geo_distance query

Chapter 7: Aggregations

Executing an aggregation

Executing a stats aggregation

Executing a terms aggregation

Executing a significant terms aggregation

Executing a range aggregation

Executing a histogram aggregation

Executing a date histogram aggregation

Executing a filter aggregation

Executing a filters aggregation

Executing a global aggregation

Executing a geo distance aggregation

Executing a children aggregation

Executing a nested aggregation

Executing a top hit aggregation

Executing a matrix stats aggregation

Executing a geo bounds aggregation

Executing a geo centroid aggregation

Executing a geotile grid aggregation

Executing a sampler aggregation

Executing a pipeline aggregation

Chapter 8: Scripting in Elasticsearch

Painless scripting

Installing additional scripting languages

Managing scripts

Sorting data using scripts

Computing return fields with scripting

Filtering a search using scripting

Using scripting in aggregations

Updating a document using scripts

Reindexing with a script

Scripting in ingest processors

Chapter 9: Managing Clusters

Controlling the cluster health using the health API

Controlling the cluster state using the API

Getting cluster node information using the API

Getting node statistics using the API

Using the task management API

Using the hot threads API

Managing the shard allocation

Monitoring segments with the segment API

Cleaning the cache

Chapter 10: Backups and Restoring Data

Managing repositories

Executing a snapshot

Restoring a snapshot

Setting up an NFS share for backups

Reindexing from a remote cluster

Chapter 11: User Interfaces

Installing Kibana

Managing Kibana Discover

Visualizing data with Kibana

Using Kibana Dev Tools

Chapter 12: Using the Ingest Module

Pipeline definition

Inserting an ingest pipeline

Getting an ingest pipeline

Deleting an ingest pipeline

Simulating an ingest pipeline

Built-in processors

The grok processor

Using the ingest attachment plugin

Using the ingest GeoIP processor

Using the enrichment processor

Chapter 13: Java Integration

Creating a standard Java HTTP client

Creating a low-level Elasticsearch client

Using the Elasticsearch official Java client

Managing indices

Managing mappings

Managing documents

Managing bulk actions

Building a query

Executing a standard search

Executing a search with aggregations

Executing a scroll search

Integrating with DeepLearning4j

Chapter 14: Scala Integration

Creating a client in Scala

Managing indices

Managing mappings

Managing documents

Executing a standard search

Executing a search with aggregations

Integrating with DeepLearning.scala

Chapter 15: Python Integration

Executing a standard search

Executing a search with aggregations

Integrating with NumPy and scikit-learn

Using AsyncElasticsearch

Using Elasticsearch with FastAPI

Chapter 16: Plugin Development

Creating a plugin

Creating an analyzer plugin

Creating a REST plugin

Creating a cluster action

Creating an ingest plugin

Chapter 17: Big Data Integration

Installing Apache Spark

Indexing data using Apache Spark

Indexing data with meta using Apache Spark

Reading data with Apache Spark

Reading data using Spark SQL

Indexing data with Apache Pig

Using Elasticsearch with Alpakka

Using Elasticsearch with MongoDB

Chapter 18: X-Pack

ILM – managing the index life cycle

ILM – automating rollover

Using the SQL Rest API

Using SQL via JDBC

Using X-Pack Security

Using alerting to monitor data events

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What this book covers

Chapter 1, Getting Started, covers the basic steps to start using Elasticsearch, from the simple installation to the cloud. We also cover several setup cases.

Chapter 2, Managing Mappings, covers the correct definition of the data fields to improve both indexing and searching quality.

Chapter 3, Basic Operations, introduces the most common actions that are required to ingest data in Elasticsearch and manage it.

Chapter 4, Exploring Search Capabilities, talks about executing searches, sorting, and related API calls. The APIs discussed in this chapter are the essential ones.

Chapter 5, Text and Numeric Queries, talks about the search DSL part of text and numeric fields – the core of the search functionalities of Elasticsearch.

Chapter 6, Relationships and Geo Queries, talks about queries that work on related documents (child/parent and nested) and geo-located fields.

Chapter 7, Aggregations, covers another capability of Elasticsearch, the possibility to execute analytics on search results to improve both the user experience and to drill down on the information contained in Elasticsearch.

Chapter 8, Scripting in Elasticsearch, shows how to customize Elasticsearch with scripting and how to use the scripting capabilities in different parts of Elasticsearch (search, aggregation, and ingestion) using different languages. The chapter is mainly focused on Painless, the new scripting language developed by the Elastic team.

Chapter 9, Managing Clusters, shows how to analyze the behavior of a cluster/node to understand common pitfalls.

Chapter 10, Backups and Restoring Data, covers one of the most important components in managing data: backing up. It shows how to manage a distributed backup and the restoration of snapshots.

Chapter 11, User Interfaces, describes two of the most common user interfaces for Elasticsearch: Cerebro, mainly used for admin activities, and Kibana, with X-Pack as a common UI extension for Elasticsearch.

Chapter 12, Using the Ingest Module, talks about the ingest functionality for importing data into Elasticsearch via an ingestion pipeline.

Chapter 13, Java Integration, describes how to integrate Elasticsearch in a Java application using both REST and native protocols.

Chapter 14, Scala Integration, describes how to integrate Elasticsearch in Scala using elastic4s – an advanced type-safe and feature-rich Scala library based on the native Java API.

Chapter 15, Python Integration, covers the usage of the official Elasticsearch Python client.

Chapter 16, Plugin Development, describes how to create native plugins to extend Elasticsearch functionalities. Some examples show the plugin skeletons, the setup process, and the building of them.

Chapter 17, Big Data Integration, covers how to integrate Elasticsearch in common big data tools, such as Apache Spark and Apache Pig.

Chapter 18, X-Pack, covers the extra functionalities provided by XPack, including security, machine learning, SQL, and reporting.

Elasticsearch 8.x Cookbook - Fifth Edition

By : Alberto Paro

Elasticsearch 8.x Cookbook - Fifth Edition

By: Alberto Paro

Overview of this book

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