Elasticsearch Server 3rd edition by Rafal Kuc Marek Rogozinski ISBN 1785888811 978-1785888816

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Product details:

ISBN 10: 1785888811
ISBN 13:  978-1785888816
Author:  Rafal Kuc  Marek Rogozinski 

 

About This Book

• Boost the searching capabilities of your system through synonyms, multilingual data handling, nested objects and parent-child documents
• Deep dive into the world of data aggregation and data analysis with ElasticSearch
• Explore a wide range of ElasticSearch modules that define the behavior of a cluster

Who This Book Is For

If you are a competent developer and want to learn about the great and exciting world of ElasticSearch, then this book is for you. No prior knowledge of Java or Apache Lucene is needed.

What You Will Learn

• Configure, create, and retrieve data from your indices
• Use an ElasticSearch query DSL to create a wide range of queries
• Discover the highlighting and geographical search features offered by ElasticSearch
• Find out how to index data that is not flat or data that has a relationship
• Exploit a prospective search to search for queries not documents
• Use the aggregations framework to get more from your data and improve your client’s search experience
• Monitor your cluster state and health using the ElasticSearch API as well as third-party monitoring solutions
• Discover how to properly set up ElasticSearch for various use cases

In Detail

ElasticSearch is a very fast and scalable open source search engine, designed with distribution and cloud in mind, complete with all the goodies that Apache Lucene has to offer. ElasticSearch’s schema-free architecture allows developers to index and search unstructured content, making it perfectly suited for both small projects and large big data warehouses, even those with petabytes of unstructured data.

This book will guide you through the world of the most commonly used ElasticSearch server functionalities. You’ll start off by getting an understanding of the basics of ElasticSearch and its data indexing functionality. Next, you will see the querying capabilities of ElasticSearch, followed by a through explanation of scoring and search relevance. After this, you will explore the aggregation and data analysis capabilities of ElasticSearch and will learn how cluster administration and scaling can be used to boost your application performance. You’ll find out how to use the friendly REST APIs and how to tune ElasticSearch to make the most of it. By the end of this book, you will have be able to create amazing search solutions as per your project’s specifications.

Style and approach

This step-by-step guide is full of screenshots and real-world examples to take you on a journey through the wonderful world of full text search provided by ElasticSearch.

Elasticsearch Server 3rd Table of contents:

1. Getting Started with Elasticsearch Cluster

Full text searching

The Lucene glossary and architecture

Input data analysis

Indexing and querying

Scoring and query relevance

The basics of Elasticsearch

Key concepts of Elasticsearch

Index

Document

Document type

Mapping

Key concepts of the Elasticsearch infrastructure

Nodes and clusters

Shards

Replicas

Gateway

Indexing and searching

Installing and configuring your cluster

Installing Java

Installing Elasticsearch

Running Elasticsearch

Shutting down Elasticsearch

The directory layout

Configuring Elasticsearch

The system-specific installation and configuration

Installing Elasticsearch on Linux

Installing Elasticsearch using RPM packages

Installing Elasticsearch using the DEB package

Elasticsearch configuration file localization

Configuring Elasticsearch as a system service on Linux

Elasticsearch as a system service on Windows

Manipulating data with the REST API

Understanding the REST API

Storing data in Elasticsearch

Creating a new document

Automatic identifier creation

Retrieving documents

Updating documents

Dealing with non-existing documents

Adding partial documents

Deleting documents

Versioning

Usage example

Versioning from external systems

Searching with the URI request query

Sample data

URI search

Elasticsearch query response

Query analysis

URI query string parameters

The query

The default search field

Analyzer

The default operator property

Query explanation

The fields returned

Sorting the results

The search timeout

The results window

Limiting per-shard results

Ignoring unavailable indices

The search type

Lowercasing term expansion

Wildcard and prefix analysis

Lucene query syntax

Summary

2. Indexing Your Data

Elasticsearch indexing

Shards and replicas

Write consistency

Creating indices

Altering automatic index creation

Settings for a newly created index

Index deletion

Mappings configuration

Type determining mechanism

Disabling the type determining mechanism

Tuning the type determining mechanism for numeric types

Tuning the type determining mechanism for dates

Index structure mapping

Type and types definition

Fields

Core types

Common attributes

String

Number

Boolean

Binary

Date

Multi fields

The IP address type

Token count type

Using analyzers

Out-of-the-box analyzers

Defining your own analyzers

Default analyzers

Different similarity models

Setting per-field similarity

Available similarity models

Configuring default similarity

Configuring BM25 similarity

Configuring DFR similarity

Configuring IB similarity

Batch indexing to speed up your indexing process

Preparing data for bulk indexing

Indexing the data

The _all field

The _source field

Additional internal fields

Introduction to segment merging

Segment merging

The need for segment merging

The merge policy

The merge scheduler

Throttling

Introduction to routing

Default indexing

Default searching

Routing

The routing parameters

Routing fields

Summary

3. Searching Your Data

Querying Elasticsearch

The example data

A simple query

Paging and result size

Returning the version value

Limiting the score

Choosing the fields that we want to return

Source filtering

Using the script fields

Passing parameters to the script fields

Understanding the querying process

Query logic

Search type

Search execution preference

Search shards API

Basic queries

The term query

The terms query

The match all query

The type query

The exists query

The missing query

The common terms query

The match query

The Boolean match query

The phrase match query

The match phrase prefix query

The multi match query

The query string query

Running the query string query against multiple fields

The simple query string query

The identifiers query

The prefix query

The fuzzy query

The wildcard query

The range query

Regular expression query

The more like this query

Compound queries

The bool query

The dis_max query

The boosting query

The constant_score query

The indices query

Using span queries

A span

Span term query

Span first query

Span near query

Span or query

Span not query

Span within query

Span containing query

Span multi query

Performance considerations

Choosing the right query

The use cases

Limiting results to given tags

Searching for values in a range

Boosting some of the matched documents

Ignoring lower scoring partial queries

Using Lucene query syntax in queries

Handling user queries without errors

Autocomplete using prefixes

Finding terms similar to a given one

Matching phrases

Spans, spans everywhere

Summary

4. Extending Your Querying Knowledge

Filtering your results

The context is the key

Explicit filtering with bool query

Highlighting

Getting started with highlighting

Field configuration

Under the hood

Forcing highlighter type

Configuring HTML tags

Controlling highlighted fragments

Global and local settings

Require matching

Custom highlighting query

The Postings highlighter

Validating your queries

Using the Validate API

Sorting data

Default sorting

Selecting fields used for sorting

Sorting mode

Specifying behavior for missing fields

Dynamic criteria

Calculate scoring when sorting

Query rewrite

Prefix query as an example

Getting back to Apache Lucene

Query rewrite properties

Summary

5. Extending Your Index Structure

Indexing tree-like structures

Data structure

Analysis

Indexing data that is not flat

Data

Objects

Arrays

Mappings

Final mappings

Sending the mappings to Elasticsearch

To be or not to be dynamic

Disabling object indexing

Using nested objects

Scoring and nested queries

Using the parent-child relationship

Index structure and data indexing

Child mappings

Parent mappings

The parent document

Child documents

Querying

Querying data in the child documents

Querying data in the parent documents

Performance considerations

Modifying your index structure with the update API

The mappings

Adding a new field to the existing index

Modifying fields of an existing index

Summary

6. Make Your Search Better

Introduction to Apache Lucene scoring

When a document is matched

Default scoring formula

Relevancy matters

Scripting capabilities of Elasticsearch

Objects available during script execution

Script types

In file scripts

Inline scripts

Indexed scripts

Querying with scripts

Scripting with parameters

Script languages

Using other than embedded languages

Using native code

The factory implementation

Implementing the native script

The plugin definition

Installing the plugin

Running the script

Searching content in different languages

Handling languages differently

Handling multiple languages

Detecting the language of the document

Sample document

The mappings

Querying

Queries with an identified language

Queries with an unknown language

Combining queries

Influencing scores with query boosts

The boost

Adding the boost to queries

Modifying the score

Constant score query

Boosting query

The function score query

Structure of the function query

The weight factor function

Field value factor function

The script score function

The random score function

Decay functions

When does index-time boosting make sense?

Defining boosting in the mappings

Words with the same meaning

Synonym filter

Synonyms in the mappings

Synonyms stored on the file system

Defining synonym rules

Using Apache Solr synonyms

Explicit synonyms

Equivalent synonyms

Expanding synonyms

Using WordNet synonyms

Query or index-time synonym expansion

Understanding the explain information

Understanding field analysis

Explaining the query

Summary

7. Aggregations for Data Analysis

Aggregations

General query structure

Inside the aggregations engine

Aggregation types

Metrics aggregations

Minimum, maximum, average, and sum

Missing values

Using scripts

Field value statistics and extended statistics

Value count

Field cardinality

Percentiles

Percentile ranks

Top hits aggregation

Additional parameters

Geo bounds aggregation

Scripted metrics aggregation

Buckets aggregations

Filter aggregation

Filters aggregation

Terms aggregation

Counts are approximate

Minimum document count

Range aggregation

Keyed buckets

Date range aggregation

IPv4 range aggregation

Missing aggregation

Histogram aggregation

Date histogram aggregation

Time zones

Geo distance aggregations

Geohash grid aggregation

Global aggregation

Significant terms aggregation

Choosing significant terms

Multiple value analysis

Sampler aggregation

Children aggregation

Nested aggregation

Reverse nested aggregation

Nesting aggregations and ordering buckets

Buckets ordering

Pipeline aggregations

Available types

Referencing other aggregations

Gaps in the data

Pipeline aggregation types

Min, max, sum, and average bucket aggregations

Cumulative sum aggregation

Bucket selector aggregation

Bucket script aggregation

Serial differencing aggregation

Derivative aggregation

Moving avg aggregation

Predicting future buckets

The models

Summary

8. Beyond Full-text Searching

Percolator

The index

Percolator preparation

Getting deeper

Controlling the size of returned results

Percolator and score calculation

Combining percolators with other functionalities

Getting the number of matching queries

Indexed document percolation

Elasticsearch spatial capabilities

Mapping preparation for spatial searches

Example data

Additional geo_field properties

Sample queries

Distance-based sorting

Bounding box filtering

Limiting the distance

Arbitrary geo shapes

Point

Envelope

Polygon

Multipolygon

An example usage

Storing shapes in the index

Using suggesters

Available suggester types

Including suggestions

Suggester response

Term suggester

Term suggester configuration options

Additional term suggester options

Phrase suggester

Configuration

Completion suggester

Indexing data

Querying indexed completion suggester data

Custom weights

Context suggester

Context types

Using context

Using the geo location context

The Scroll API

Problem definition

Scrolling to the rescue

Summary

9. Elasticsearch Cluster in Detail

Understanding node discovery

Discovery types

Node roles

Master node

Data node

Client node

Configuring node roles

Setting the cluster’s name

Zen discovery

Master election configuration

Configuring unicast

Fault detection ping settings

Cluster state updates control

Dealing with master unavailability

Adjusting HTTP transport settings

Disabling HTTP

HTTP port

HTTP host

The gateway and recovery modules

The gateway

Recovery control

Additional gateway recovery options

Indices recovery API

Delayed allocation

Index recovery prioritization

Templates and dynamic templates

Templates

An example of a template

Dynamic templates

The matching pattern

Field definitions

Elasticsearch plugins

The basics

Installing plugins

Removing plugins

Elasticsearch caches

Fielddata cache

Fielddata size

Circuit breakers

Fielddata and doc values

Shard request cache

Enabling and configuring the shard request cache

Per request shard request cache disabling

Shard request cache usage monitoring

Node query cache

Indexing buffers

When caches should be avoided

The update settings API

The cluster settings API

The indices settings API

Summary

10. Administrating Your Cluster

Elasticsearch time machine

Creating a snapshot repository

Creating snapshots

Additional parameters

Restoring a snapshot

Cleaning up – deleting old snapshots

Monitoring your cluster’s state and health

Cluster health API

Controlling information details

Additional parameters

Indices stats API

Docs

Store

Indexing, get, and search

Additional information

Nodes info API

Returned information

Nodes stats API

Cluster state API

Cluster stats API

Pending tasks API

Indices recovery API

Indices shard stores API

Indices segments API

Controlling the shard and replica allocation

Explicitly controlling allocation

Specifying node parameters

Configuration

Index creation

Excluding nodes from allocation

Requiring node attributes

Using the IP address for shard allocation

Disk-based shard allocation

Configuring disk based shard allocation

Disabling disk based shard allocation

The number of shards and replicas per node

Allocation throttling

Cluster-wide allocation

Allocation awareness

Forcing allocation awareness

Filtering

What do include, exclude, and require mean

Manually moving shards and replicas

Moving shards

Canceling shard allocation

Forcing shard allocation

Multiple commands per HTTP request

Allowing operations on primary shards

Handling rolling restarts

Controlling cluster rebalancing

Understanding rebalance

Cluster being ready

The cluster rebalance settings

Controlling when rebalancing will be allowed

Controlling the number of shards being moved between nodes concurrently

Controlling which shards may be rebalanced

The Cat API

The basics

Using Cat API

Common arguments

The examples

Getting information about the master node

Getting information about the nodes

Retrieving recovery information for an index

Warming up

Defining a new warming query

Retrieving the defined warming queries

Deleting a warming query

Disabling the warming up functionality

Choosing queries for warming

Index aliasing and using it to simplify your everyday work

An alias

Creating an alias

Modifying aliases

Combining commands

Retrieving aliases

Removing aliases

Filtering aliases

Aliases and routing

Zero downtime reindexing and aliases

Summary

11. Scaling by Example

Hardware

Physical servers or a cloud

CPU

RAM memory

Mass storage

The network

How many servers

Cost cutting

Preparing a single Elasticsearch node

The general preparations

Avoiding swapping

File descriptors

Virtual memory

The memory

Field data cache and breaking the circuit

Use doc values

RAM buffer for indexing

Index refresh rate

Thread pools

Horizontal expansion

Automatically creating the replicas

Redundancy and high availability

Cost and performance flexibility

Continuous upgrades

Multiple Elasticsearch instances on a single physical machine

Preventing a shard and its replicas from being on the same node

Designated node roles for larger clusters

Query aggregator nodes

Data nodes

Master eligible nodes

Preparing the cluster for high indexing and querying throughput

Indexing related advice

Index refresh rate

Thread pools tuning

Automatic store throttling

Handling time-based data

Multiple data paths

Data distribution

Bulk indexing

RAM buffer for indexing

Advice for high query rate scenarios

Shard request cache

Think about the queries

Parallelize your queries

Field data cache and breaking the circuit

Keep size and shard size under control

Monitoring

Elasticsearch HQ

Marvel

SPM for Elasticsearch

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Tags:

Rafal Kuc,Marek Rogozinski,Elasticsearch Server