Manning Early Access Program Elasticsearch in Action Second Edition Version 7

Elasticsearch in Action, Second Edition MEAP V07
Copyright
Welcome
Brief Contents
Chapter 1: Overview
	1.1 What makes a good search engine?
	1.2 Search is the new normal
		1.2.1 Structured vs unstructured (full-text) search
		1.2.2 Search supported by a database
		1.2.3 Full-text search engines
	1.3 Modern search engines
		1.3.1 Functional features
		1.3.2 Technical features
		1.3.3 Search is for everyone
		1.3.4 Analytics
		1.3.5 Integrated machine learning
	1.4 Introducing Elasticsearch
		1.4.1 Core areas
		1.4.2 Elastic Stack
		1.4.3 Popular adoption
		1.4.4 Elasticsearch use cases
	1.5 Prominent features of Elasticsearch
		1.5.1 Multifaceted text searching
		1.5.2 Analytics and aggregations
		1.5.3 RESTful over HTTP
		1.5.4 Schema-free engine
	1.6 Summary
Chapter 2: Getting started
	2.1 Priming with data
		2.1.1 An online bookstore
		2.1.2 Indexing documents
		2.1.3 Document APIs
		2.1.4 Using cURL
		2.1.5 Indexing our first document
		2.1.6 Constituents of the request
		2.1.7 Indexing more documents
	2.2 Retrieving Data
		2.2.1 Counting all documents
		2.2.2 Retrieving documents
		2.2.3 Full text queries
		2.2.4 Indexing more documents using the _bulk API
		2.2.5 Searching across multiple fields
		2.2.6 Search on a phrase
		2.2.7 Fuzzy queries
		2.2.8 Term-level queries
	2.3 Compound queries
		2.3.1 Boolean (bool) query
		2.3.2 The must (must) clause
		2.3.3 The must not (must_not) clause
		2.3.4 The should (should) clause
		2.3.5 The filter (filter) clause
	2.4 Aggregations
		2.4.1 Metrics
		2.4.2 Bucketing
	2.5 Summary
Chapter 3: Architecture
	3.1 A 10,000 foot overview
		3.1.1 Data in
		3.1.2 Processing the data
		3.1.3 Data out
	3.2 The building blocks
		3.2.1 Document
		3.2.2 Removal of types
		3.2.3 Index
		3.2.4 Data streams
		3.2.5 Shards and replicas
		3.2.6 Nodes and clusters
	3.3 Inverted indexes
		3.3.1 Example
	3.4 Relevancy
		3.4.1 Relevancy algorithms
		3.4.2 Relevancy (similarity) algorithms
	3.5 Routing Algorithm
	3.6 Scaling
		3.6.1 Scaling up (vertical scaling)
		3.6.2 Scaling out (horizontal scaling)
	3.7 Summary
Chapter 4: Mapping
	4.1 Overview of mapping
		4.1.1 Mapping definition
		4.1.2 Indexing a document for the first time
	4.2 Dynamic mapping
		4.2.1 The deducing types mechanism
		4.2.2 Limitations of dynamic mapping
	4.3 Explicit mapping
		4.3.1 Mapping using the indexing API
		4.3.2 Updating schema using the mapping API
		4.3.3 Modifying the existing fields is not allowed
		4.3.4 Type coercion
	4.4 Data types
		4.4.1 Data type classifications
		4.4.2 Developing mapping schemas
	4.5 Core Data Types
		4.5.1 The text data type
		4.5.2 The keywords data types
		4.5.3 The date data type
		4.5.4 Numeric data types
		4.5.5 The boolean data type
		4.5.6 The range data type
		4.5.7 The IP (ip) address data type
	4.6 Advanced data types
		4.6.1 The Geopoint (geo_point) data type
		4.6.2 The object data type
		4.6.3 The nested data type
		4.6.4 Flattened (flattened) data type
		4.6.5 The Join (join) data type
		4.6.6 Search as you type data type
	4.7 Multiple data types
	4.8 Summary
Chapter 5: Working with documents
	5.1 Indexing documents
		5.1.1 Document APIs
		Document identifiers
		Indexing a document with an identifier (PUT)
		Indexing a document without an identifier (POST)
		Using _create to avoid overriding a document
		The_create API in action
		5.1.2 Mechanics of indexing
		5.1.3 Customizing the refresh
	5.2 Retrieving documents
		5.2.1 Using the single document API
		5.2.2 Retrieving multiple documents
	5.3 Manipulating responses
		5.3.1 Removing metadata from the response
		5.3.2 Suppressing the source document
		5.3.3 Including and excluding fields
		Include fields (_source_includes)
		Exclude fields (_source_excludes)
		Include and exclude fields
	5.4 Updating documents
		5.4.1 Document update mechanics
		5.4.2 The _update API
		Adding new fields
		Modifying the existing fields
		5.4.3 Scripted updates
		Update actors using a script
		Removing an actor
		Adding a new field
		Removing a field
		Adding multiple fields
		Adding a conditional update script
		Anatomy of a script
		Passing data to the scripts
		5.4.4 Replacing documents
		5.4.5 Upsert
		Updates as upsert
		5.4.6 Updating by a query
	5.5 Deleting documents
		5.5.1 Deleting with an ID
		5.5.2 Deleting by query (_delete_by_query)
		5.5.3 Delete by range query
		5.5.4 Deleting all documents
	5.6 Working with documents in bulk
		5.6.1 Format of the _bulk API
		5.6.2 Bulk indexing documents
		5.6.3 Independent entities and multiple actions
		Create action
		Update action
		Delete action
		5.6.4 Bulk requests using cURL
	5.7 Reindexing documents
	5.8 Summary
Chapter 6: Indexing operations
	6.1 Indexing operations
	6.2 Creating indices
		6.2.1 Creating indices implicity (automatic creation)
		6.2.2 Creating indices explicitly
		6.2.3 Index with custom settings
		6.2.4 Index with mappings
		6.2.5 Index with aliases
	6.3 Reading indices
		6.3.1 Reading public indices
		6.3.2 Reading hidden indices
	6.4 Deleting indices
	6.5 Closing and opening indices
		6.5.1 Closing indices
		6.5.2 Opening indices
	6.6 Index templates
		6.6.1 Creating index templates
	6.7 Monitoring and managing indices
		6.7.1 Index statistics
		6.7.2 Multiple indices and statistics
	6.8 Advanced operations
		6.8.1 Splitting an index
		6.8.2 Shrinking an index
		6.8.3 Rolling over an index alias
	6.9 Index life-cycle management (ILM)
		6.9.1 About the index life cycle
		6.9.2 Managing life cycle manually
		6.9.3 Life cycle with rollover
	6.10 Summary
Chapter 7: Text analysis
	7.1 Overview
		7.1.1 Querying unstructured data
		7.1.2 Analyzers to the rescue
	7.2 Analyzer module
		7.2.1 Tokenization
		7.2.2 Normalization
		7.2.3 Anatomy of an analyzer
		7.2.4 Testing analyzers
	7.3 Built-in analyzers
		7.3.1 Standard analyzer
		7.3.2 Simple analyzer
		7.3.3 Whitespace analyzer
		7.3.4 Keyword analyzer
		7.3.5 Fingerprint analyzer
		7.3.6 Pattern analyzer
		7.3.7 Language analyzers
	7.4 Custom analyzers
		7.4.1 Advanced customization
	7.5 Specifying analyzers
		7.5.1 Analyzers for indexing
		7.5.2 Analyzers for searching
	7.6 Character filters
		7.6.1 Types of character filters
	7.7 Tokenizers
		7.7.1 Standard tokenizer
		7.7.2 N-gram and edge_ngram tokenizers
		7.7.3 Other tokenizers
	7.8 Token filters
		7.8.1 Stemmer filter
		7.8.2 Shingle filter
		7.8.3 Synonym filter
	7.9 Summary
Chapter 8: Introducing search
	8.1 Overview
	8.2 How does search work?
	8.3 Search fundamentals
		8.3.1 Search endpoint
		8.3.2 Query vs filter context
	8.4 Movie sample data
	8.5 Anatomy of a request and response
		8.5.1 Search request
		8.5.2 Search response
	8.6 URI request search
		8.6.1 Search movies by title
		8.6.2 Search a specific movie
		8.6.3 Additional parameters
		8.6.4 Query DSL supports URI requests
	8.7 Query DSL
		8.7.1 Sample query
		8.7.2 Query DSL for cURL
		8.7.3 Query DSL for aggregations
		8.7.4 Leaf and compound queries
	8.8 Search features
		8.8.1 Pagination
		8.8.2 Highlighting
		8.8.3 Explanation
		8.8.4 Sorting
		8.8.5 Manipulating the results
		8.8.6 Searching across indices and data streams
	8.9 Summary
Chapter 9: Term-level search
	9.1 Overview of term-level search
		9.1.1 Term-level queries are not analyzed
	9.2 Term queries
		9.2.1 Term queries on text fields
		9.2.2 Example: Applying a term query on a movie’s title
		9.2.3 Shortened term-level queries
	9.3 Terms queries
		9.3.1 Terms lookup
	9.4 IDs queries
	9.5 Exists queries
		9.5.1 Non existent field check
	9.6 Range queries
		9.6.1 Range queries with data math
	9.7 Wildcard queries
		9.7.1 Expensive queries
	9.8 Prefix queries
		9.8.1 Speeding up prefix queries
	9.9 Fuzzy queries
	9.10 Summary
Appendix A: Installation
	A.1 Installing Elasticsearch
		A.1.1 Download the Elasticsearch Binary
		A.1.2 Starting up on Windows OS
		A.1.3 Checking the server’s health
		A.1.4 Starting up on Mac OS
		A.1.5 Installing via Docker
		A.1.6 Testing the Server
	A.2 Working with Kibana
		A.2.1 Installing the Binary for Windows
		A.2.2 Kibana on Windows OS
		A.2.3 Installing on MacOS
		A.2.4 Installing via Docker