Advanced Elasticsearch 7.0: A practical guide to designing, indexing, and querying advanced distributed search engines

Cover
Title Page
Copyright and Credits
Dedication
About Packt
Contributors
Table of Contents
Preface
Section 1: Fundamentals and Core APIs
	Chapter 1: Overview of Elasticsearch 7
		Preparing your environment
		Running Elasticsearch
			Basic Elasticsearch configuration
			Important system configuration
		Talking to Elasticsearch
			Using Postman to work with the Elasticsearch REST API
		Elasticsearch architectural overview
			Elastic Stack architecture
			Elasticsearch architecture
				Between the Elasticsearch index and the Lucene index
		Key concepts
			Mapping concepts across SQL and Elasticsearch
			Mapping
			Analyzer
				Standard analyzer
		API conventions
		New features
			New features to be discussed
			New features with description and issue number
		Breaking changes
			Aggregations changes
			Analysis changes
			API changes
			Cluster changes
			Discovery changes
			High-level REST client changes
			Low-level REST client changes 
			Indices changes
			Java API changes
			Mapping changes
			ML changes
			Packaging changes
			Search changes
			Query DSL changes
			Settings changes
			Scripting changes
		Migration between versions
		Summary
	Chapter 2: Index APIs
		Index management APIs
			Basic CRUD APIs
		Index settings
			Index templates
		Index aliases
			Reindexing with zero downtime
			Grouping multiple indices
			Views on a subset of documents
			Miscellaneous
		Monitoring indices
			Indices stats
			Indices segments, recovery, and share stores
		Index persistence
		Advanced index management APIs
			Split index 
			Shrink index 
			Rollover index 
		Summary
	Chapter 3: Document APIs
		The Elasticsearch document life cycle
			What is a document?
			The document life cycle
		Single document management APIs
			Sample documents
			Indexing a document
			Retrieving a document by identifier
			Updating a document
			Removing a document by identifier
		Multi-document management APIs
			Retrieving multiple documents
			Bulk API
			Update by query API
			Delete by query API
			Reindex API
				Copying documents
		Migration from a multiple mapping types index
		Summary
	Chapter 4: Mapping APIs
		Dynamic mapping
			Mapping rules
			Dynamic templates
		Meta fields in mapping
		Field datatypes
			Static mapping for the sample document 
		Mapping parameters
		Refreshing mapping changes for static mapping
		Typeless APIs working with old custom index types
		Summary
	Chapter 5: Anatomy of an Analyzer
		An analyzer\'s components
		Character filters
			The html_strip filter
			The mapping filter
			The pattern_replace filter
		Tokenizers
		Token filters
		Built-in analyzers
		Custom analyzers
		Normalizers
		Summary
	Chapter 6: Search APIs
		Indexing sample documents
		Search APIs
			URI search
			Request body search
				The sort parameter
				The scroll parameter
				The search_after parameter
				The rescore parameter
				The _name parameter
				The collapse parameter
				The highlighting parameter
				Other search parameters
		Query DSL
			Full text queries
				The match keyword
				The query string keyword
				The intervals keyword
			Term-level queries
			Compound queries
			The script query
		The multi-search API
		Other search-related APIs
			The _explain API
			The _validate API
			The _count API
			The field capabilities API
			Profiler
			Suggesters
		Summary
Section 2: Data Modeling, Aggregations Framework, Pipeline, and Data Analytics
	Chapter 7: Modeling Your Data in the Real World
		The Investor Exchange Cloud
		Modeling data and the approaches
			Data denormalization
			Using an array of objects datatype
			Nested object mapping datatypes
			Join datatypes
				Parent ID query
				has_child query
				has_parent query
		Practical considerations
		Summary
	Chapter 8: Aggregation Frameworks
		ETF historical data preparation
		Aggregation query syntax
		Matrix aggregations
			Matrix stats
		Metrics aggregations
			avg
			weighted_avg
			cardinality
			value_count
			sum
			min
			max
			stats
			extended_stats
			top_hit
			percentiles
			percentile_ranks
			median_absolute_deviation
			geo_bound
			geo_centroid
			scripted_metric
		Bucket aggregations
			histogram
			date_histogram
			auto_date_histogram
			ranges
			date_range
			ip_range
			filter
			filters
			term
			significant_terms
			significant_text
			sampler
			diversified_sampler
			nested
			reverse_nested
			global
			missing
			composite
			adjacency_matrix
			parent
			children
			geo_distance
			geohash_grid
			geotile_grid
		Pipeline aggregations
			Sibling family
				avg_bucket 
				max_bucket
				min_bucket
				sum_bucket
				stats_bucket
				extended_stats_bucket
				percentiles_bucket
			Parent family
				cumulative_sum
				derivative
				bucket_script
				bucket_selector
				bucket_sort
				serial_diff
				Moving average aggregation
					simple
					linear
					ewma
					holt
					holt_winters
				Moving function aggregation
					max
					min
					sum
					stdDev
					unweightedAvg
					linearWeightedAvg
					ewma
					holt
					holtWinters
		Post filter on aggregations
		Summary
	Chapter 9: Preprocessing Documents in Ingest Pipelines
		Ingest APIs
		Accessing data in pipelines
		Processors
		Conditional execution in pipelines
		Handling failures in pipelines
		Summary
	Chapter 10: Using Elasticsearch for Exploratory Data Analysis
		Business analytics
		Operational data analytics
		Sentiment analysis
		Summary
Section 3: Programming with the Elasticsearch Client
	Chapter 11: Elasticsearch from Java Programming
		Overview of Elasticsearch Java REST client
		The Java low-level REST client
			The Java low-level REST client workflow
				REST client initialization
				Performing requests using a REST client 
				Handing responses
			Testing with Swagger UI
			New features
		The Java high-level REST client
			The Java high-level REST client workflow
				REST client initialization
				Performing requests using the REST client
				Handling responses
			Testing with Swagger UI
			New features
		Spring Data Elasticsearch
		Summary
	Chapter 12: Elasticsearch from Python Programming
		Overview of the Elasticsearch Python client
		The Python low-level Elasticsearch client
			Workflow for the Python low-level Elasticsearch client
				Client initialization
				Performing requests
				Handling responses
		The Python high-level Elasticsearch library
			Illustrating the programming concept
				Initializing a connection
				Performing requests 
				Handling responses
			The query class 
			The aggregations class
		Summary
Section 4: Elastic Stack
	Chapter 13: Using Kibana, Logstash, and Beats
		Overview of the Elastic Stack
			Running the Elastic Stack with Docker
		Running Elasticsearch in a Docker container
		Running Kibana in a Docker container
		Running Logstash in a Docker container
		Running Beats in a Docker container
		Summary
	Chapter 14: Working with Elasticsearch SQL
		 Overview
		Getting started
		Elasticsearch SQL language
			Reserved keywords
			Data type
			Operators
			Functions
				Aggregate
				Grouping
				Date-time
				Full-text search 
				Mathematics
				String
				Type conversion
				Conditional
				System
			Elasticsearch SQL query syntax
			New features
		Elasticsearch SQL REST API
		Elasticsearch SQL JDBC
			Upgrading Elasticsearch from a basic to a trial license
			Workflow of Elasticsearch SQL JDBC 
			Testing with Swagger UI
		Summary
	Chapter 15: Working with Elasticsearch Analysis Plugins
		What are Elasticsearch plugins?
			Plugin management
		Working with the ICU Analysis plugin
			Examples
		Working with the Smart Chinese Analysis plugin
			Examples
		Working with the IK Analysis plugin
			Examples
			Configuring a custom dictionary in the IK Analysis plugin
		Summary
Section 5: Advanced Features
	Chapter 16: Machine Learning with Elasticsearch
		Machine learning with Elastic Stack
			Machine learning APIs
			Machine learning jobs
				Sample data
				Running a single-metric job
					Creating index patterns
					Creating a new machine learning job
					Examining the result
		Machine learning using Elasticsearch and scikit-learn
		Summary
	Chapter 17: Spark and Elasticsearch for Real-Time Analytics
		Overview of ES-Hadoop
		Apache Spark support
		Real-time analytics using Elasticsearch and Apache Spark
			Building a virtual environment to run the sample ES-Hadoop project
			Running the sample ES-Hadoop project
			Running the sample ES-Hadoop project using a prepared Docker image
			Source code
		Summary
	Chapter 18: Building Analytics RESTful Services
		Building a RESTful web service with Spring Boot
			Project program structure
			Running the program and examining the APIs
			Main workflow anatomy
				Building the analytic model
				Performing daily update data
				Getting the registered symbols
				Building the scheduler
		Integration with the Bollinger Band
		Building a Java Spark ML module for k-means anomaly detection
			Source code
		Testing Analytics RESTful services
			Testing the build-analytics-model API
			Testing the get-register-symbols API
		Working with Kibana to visualize the analytics results
		Summary
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