Elasticsearch in Action Second Edition Version 11

Elasticsearch in Action, Second Edition MEAP V11
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
Chapter 10: Full-text search
	10.1 Overview
		10.1.1 Precision
		10.1.2 Recall
	10.2 Sample data
	10.3 Match all (match_all) queries
		10.3.1 Building the match_all query
		10.3.2 Short form of a match_all query
	10.4 Match none (match_none) queries
	10.5 Match queries
		10.5.1 Format of a match query
		10.5.2 Searching using a match query
		10.5.3 Match query analysis
		10.5.4 Searching multiple words
		10.5.5 Matching at least a few words
		10.5.6 Fixing typos using the keyword fuzziness
	10.6 Match phrase (match_phrase) queries
		10.6.1 Match phrase with the keyword slop
	10.7 Match phrase prefix (match_phrase_prefix) queries
		10.7.1 Match phrase prefix using slop
	10.8 Multi-match (multi_match) queries
		10.8.1 Best fields
		10.8.2 Disjunction max (dis_max) queries
		10.8.3 Tie breakers
		10.8.4 Individual field boosting
	10.9 Query strings
	10.10 Query string (query_string) queries
		10.10.1 Fields in a query string query
		10.10.2 Default operator
	10.11 Query string with a phrase
	10.12 Fuzzy queries
	10.13 Simple string queries
	10.14 Simple_query_string queries
	10.15 Summary
Chapter 11: Compound queries
	11.1 Compound queries
	11.2 Sample products data
		11.2.1 Products schema
		11.2.2 Indexing products
	11.3 The Boolean (bool) query
		11.3.1 Bool query structure
		11.3.2 The must clause
		11.3.3 Enhancing the must clause
		11.3.4 The must_not clause
		11.3.5 Enhancing the must_not clause
		11.3.6 The should clause
		11.3.7 The filter clause
		11.3.8 All clauses combined
		11.3.9 Named queries
	11.4 Constant scores
	11.5 The boosting query
		11.5.1 Boosting query combined with bool queries
	11.6 Disjunction max (dis_max) query
	11.7 The function score queries
		11.7.1 Random_score function
		11.7.2 Script_score function
		11.7.3 Field_value_factor function
		11.7.4 Combining function scores
	11.8 Summary
Chapter 12: Advanced search
	12.1 Introducing location search
	12.2 Geospatial data types
		12.2.1 The geo_point data type
		12.2.2 The geo_shape data type
	12.3 Geospatial queries
	12.4 The geo_bounding box query
		12.4.1 Working with geoshape data
		12.4.2 Multiple formats of longitude and latitude
	12.5 The geo_distance query
	12.6 The geo_shape query
	12.7 The shape query
	12.8 The span queries
		12.8.1 Sample data
		12.8.2 Looking at the span_first query
		12.8.3 Looking at the span_near query
		12.8.4 Looking at the span_within query
		12.8.5 Looking at the span_or query
	12.9 Specialized queries
		12.9.1 Looking at the distance_feature query
		12.9.2 Looking at the pinned query
		12.9.3 Looking at the more_like_this query
		12.9.4 Looking at the percolate queries
	12.10 Summary
Chapter 13: Aggregations
	13.1 Overview
		13.1.1 The endpoint and the syntax
		13.1.2 Combining searches and aggregations
		13.1.3 Multiple and nested aggregations
		13.1.4 Ignoring the results
	13.2 Metric aggregations
		13.2.1 Sample data
		13.2.2 The value count metric
		13.2.3 The average metric
		13.2.4 The sum metric
		13.2.5 The minimum (min) and maximum (max) metrics
		13.2.6 The common stats metric
		13.2.7 The extended stats metric
		13.2.8 The cardinality metric
	13.3 Bucket aggregations
		13.3.1 Histograms
		13.3.2 Child-level aggregates
		13.3.3 Custom range aggregation
		13.3.4 The terms aggregation
		13.3.5 Multi-terms aggregation
	13.4 Parent and sibling aggregations
		13.4.1 Parent aggregations
		13.4.2 Sibling aggregations
	13.5 Pipeline aggregations
		13.5.1 Pipeline aggregation types
		13.5.2 ample data
		13.5.3 Syntax for the pipeline aggregations
		13.5.4 List of pipeline aggregations
		13.5.5 Cumulative sum parent aggregation
		13.5.6 Max and min sibling pipeline aggregations
	13.6 Summary