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Mastering Elasticsearch 5.x - Third Edition电子书

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9.8

作者：Bharvi Dixit

出版社：Packt Publishing

出版时间：2017-02-01

字数：739.1万

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Master the intricacies of Elasticsearch 5 and use it to create flexible and scalable search solutions About This Book Master the searching, indexing, and aggregation features in ElasticSearch Improve users’ search experience with Elasticsearch’s functionalities and develop your own Elasticsearch plugins A comprehensive, step-by-step guide to master the intricacies of ElasticSearch with ease Who This Book Is For If you have some prior working experience with Elasticsearch and want to take your knowledge to the next level, this book will be the perfect resource for you.If you are a developer who wants to implement scalable search solutions with Elasticsearch, this book will also help you. Some basic knowledge of the query DSL and data indexing is required to make the best use of this book. What You Will Learn Understand Apache Lucene and Elasticsearch 5’s design and architecture Use and configure the new and improved default text scoring mechanism in Apache Lucene 6 Know how to overcome the pitfalls while handling relational data in Elasticsearch Learn about choosing the right queries according to the use cases and master the *ing module including new default *ing language, painlessly Explore the right way of scaling production clusters to improve the performance of Elasticsearch Master the searching, indexing, and aggregation features in Elasticsearch Develop your own Elasticsearch plugins to extend the functionalities of Elasticsearch In Detail Elasticsearch is a modern, fast, distributed, scalable, fault tolerant, and open source search and analytics engine. Elasticsearch leverages the capabilities of Apache Lucene, and provides a new level of control over how you can index and search even huge sets of data. This book will give you a brief recap of the basics and also introduce you to the new features of Elasticsearch 5. We will guide you through the intermediate and advanced functionalities of Elasticsearch, such as querying, indexing, searching, and modifying data. We’ll also explore advanced concepts, including aggregation, index control, sharding, replication, and clustering. We’ll show you the modules of monitoring and administration available in Elasticsearch, and will also cover backup and recovery. You will get an understanding of how you can scale your Elasticsearch cluster to contextualize it and improve its performance. We’ll also show you how you can create your own analysis plugin in Elasticsearch. By the end of the book, you will have all the knowledge necessary to master Elasticsearch and put it to efficient use. Style and approach This comprehensive guide covers intermediate and advanced concepts in Elasticsearch as well as their implementation. An easy-to-follow approach means you’ll be able to master even advanced querying, searching, and administration tasks with ease.

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Mastering Elasticsearch 5.x - Third Edition

Credits

About the Author

Acknowledgements

About the Reviewer

www.PacktPub.com

Why subscribe?

Customer Feedback

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Downloading the example code

Downloading the color images of this book

Errata

Piracy

Questions

1. Revisiting Elasticsearch and the Changes

An overview of Lucene

Getting deeper into the Lucene index

Inverted index

Segments

Norms

Term vectors

Posting formats

Doc values

Document analysis

Basics of the Lucene query language

Querying fields

Term modifiers

Handling special characters

An overview of Elasticsearch

The key concepts

Working of Elasticsearch

Introducing Elasticsearch 5.x

Introducing new features in Elasticsearch

New features in Elasticsearch 5.x

New features in Elasticsearch 2.x

The changes in Elasticsearch

Changes between 1.x to 2.x

Mapping changes

Query and filter changes

Security, reliability, and networking changes

Monitoring parameter changes

Changes between 2.x to 5.x

Mapping changes

No more string fields

Floats are default

Changes in numeric fields

Changes in geo_point fields

Some more changes

Summary

2. The Improved Query DSL

The changed default text scoring in Lucene - BM25

Precision versus recall

Recalling TF-IDF

Introducing BM25 scoring

BM25 scoring formula

Example - tuning BM25 with custom similarity

How BM25 differs from TF-IDF

Saturation point

Average document length

Re-factored Query DSL

Choosing the right query for the job

Query categorization

Basic queries

Compound queries

Understanding bool queries

Non-analyzed queries

Full text search queries

Pattern queries

Similarity supporting queries

Score altering queries

Position aware queries

Structure aware queries

The use cases

Example data

Basic queries use cases

Searching for values in range

Compound queries use cases

A Boolean query for multiple terms

Boosting some of the matched documents

Ignoring lower scoring partial queries

Not analyzed queries use cases

Limiting results to given tags

Full text search queries use cases

Using Lucene query syntax in queries

Handling user queries without errors

Pattern queries use cases

Autocomplete using prefixes

Pattern matching

Similarity supporting queries use cases

Finding terms similar to a given one

Score altering query use cases

Decreasing importance of books with a certain value

Pattern query use cases

Matching phrases

Spans, spans everywhere

Some more important changes in Query DSL

Query rewrite explained

Prefix query as an example

Getting back to Apache Lucene

Query rewrite properties

An example

Query templates

Introducing search templates

The Mustache template engine

Conditional expressions

Loops

Default values

Storing templates in files

Storing templates in a cluster

Summary

3. Beyond Full Text Search

Controlling multimatching

Multimatch types

Best fields matching

Cross fields matching

Most fields matching

Phrase matching

Phrase with prefixes matching

Controlling scores using the function score query

Built-in functions under the function score query

The weight function

The field value factor function

The script score function

Decay functions - linear, exp, and gauss

Query rescoring

What is query rescoring?

Structure of the rescore query

Rescore parameters

To sum up

Elasticsearch scripting

The syntax

Scripting changes across different versions

Painless - the new default scripting language

Using Painless as your scripting language

Variable definition in scripts

Conditionals

Loops

An example

Sorting results based on scripts

Sorting based on multiple fields

Lucene expressions

The basics

An example

Summary

4. Data Modeling and Analytics

Data modeling techniques in Elasticsearch

Managing relational data in Elasticsearch

The object type

The nested documents

Parent - child relationship

Parent-child relationship in the cluster

Finding child documents with a parent ID query

A few words about alternatives

An example of data denormalization

Data analytics using aggregations

Instant aggregations in Elasticsearch 5.0

Revisiting aggregations

Metric aggregations

Bucket aggregations

Pipeline aggregations

Calculating average monthly sales using avg_bucket aggregation

Calculating the derivative for the sum of the monthly sale

The new aggregation category - Matrix aggregation

Understanding matrix stats

Dealing with missing values

Summary

5. Improving the User Search Experience

Correcting user spelling mistakes

Testing data

Getting into technical details

Suggesters

Using a suggester under the _search endpoint

Understanding the suggester response

Multiple suggestion types for the same suggestion text

The term suggester

Configuring the Elasticsearch term suggester

Common term suggester options

Additional term suggester options

The phrase suggester

Usage example

Configuring the phrase suggester

Basic configuration

Configuring smoothing models

Configuring candidate generators

The completion suggester

The logic behind the completion suggester

Using the completion suggester

Indexing data

Querying data

Custom weights

Using fuzziness with the completion suggester

Implementing your own auto-completion

Creating an index

Understanding the parameters

Configuring settings

Configuring mappings

Indexing documents

Querying documents for auto-completion

Working with synonyms

Preparing settings for synonym search

Formatting synonyms

Synonym expansion versus contraction

Summary

6. The Index Distribution Architecture

Configuring an example multi-node cluster

Choosing the right amount of shards and replicas

Sharding and overallocation

A positive example of overallocation

Multiple shards versus multiple indices

Replicas

Routing explained

Shards and data

Let's test routing

Indexing with routing

Routing in practice

Querying

Aliases

Multiple routing values

Shard allocation control

Allocation awareness

Forcing allocation awareness

Shard allocation filtering

What include, exclude, and require mean

Runtime allocation updating

Index level updates

Cluster level updates

Defining total shards allowed per node

Defining total shards allowed per physical server

Inclusion

Requirement

Exclusion

Disk-based allocation

Query execution preference

Introducing the preference parameter

An example of using query execution preference

Stripping data on multiple paths

Index versus type - a revised approach for creating indices

Summary

7. Low-Level Index Control

Altering Apache Lucene scoring

Available similarity models

Setting a per-field similarity

Similarity model configuration

Choosing the default similarity model

Configuring the chosen similarity model

Configuring the TF-IDF similarity

Configuring the BM25 similarity

Configuring the DFR similarity

Configuring the IB similarity

Configuring the LM Dirichlet similarity

Configuring the LM Jelinek Mercer similarity

Choosing the right directory implementation - the store module

The store type

The simple file system store - simplefs

The new I/O filesystem store - niofs

The mmap filesystem store - mmapfs

The default store type - fs

NRT, flush, refresh, and transaction log

Updating the index and committing changes

Changing the default refresh time

The transaction log

The transaction log configuration

Handling corrupted translogs

Near real-time GET

Segment merging under control

Merge policy changes in Elasticsearch

Configuring the tiered merge policy

Merge scheduling

The concurrent merge scheduler

Force merging

Understanding Elasticsearch caching

Node query cache

Configuring node query cache

Shard request cache

Enabling and disabling the shard request cache

Request cache settings

Cache invalidation

The field data cache

Field data or doc values

Using circuit breakers

The parent circuit breaker

The field data circuit breaker

The request circuit breaker

In flight requests circuit breaker

Script compilation circuit breaker

Summary

8. Elasticsearch Administration

Node types in Elasticsearch

Data node

Master node

Ingest node

Tribe node

Coordinating nodes/Client nodes

Discovery and recovery modules

Discovery configuration

Zen discovery

The unicast Zen discovery configuration

The master election configuration

Zen discovery fault detection and configuration

No Master Block

The Amazon EC2 discovery

The EC2 plugin installation

The EC2 plugin's generic configuration

Optional EC2 discovery configuration options

The EC2 nodes scanning configuration

Other discovery implementations

The gateway and recovery configuration

The gateway recovery process

Configuration properties

The local gateway

Low-level recovery configuration

Cluster-level recovery configuration

The indices recovery API

The human-friendly status API - using the cat API

The basics of cat API

Using the cat API

Cat API common arguments

The examples of cat API

Getting information about the master node

Getting information about the nodes

Changes in cat API - Elasticsearch 5.0

Host field removed from the cat nodes API

Changes to cat recovery API

Changes to cat nodes API

Changes to cat field data API

Backing up

The snapshot API

Saving backups on a filesystem

Creating snapshot

Registering repository path

Registering shared file system repository in Elasticsearch

Creating snapshots

Getting snapshot information

Deleting snapshots

Saving backups in the cloud

The S3 repository

The HDFS repository

The Azure repository

The Google cloud storage repository

Restoring snapshots

Example - restoring a snapshot

Restoring multiple indices

Renaming indices

Partial restore

Changing index settings during restore

Restoring to different cluster

Summary

9. Data Transformation and Federated Search

Preprocessing data within Elasticsearch with ingest nodes

Working with ingest pipeline

The ingest APIs

Creating a pipeline

Getting pipeline details

Deleting a pipeline

Simulating pipelines for debugging purposes

Handling errors in pipelines

Tagging errors within the same document and index

Indexing error prone documents in a different index

Ignoring errors altogether

Working with ingest processors

Append processor

Convert processor

Grok processor

Federated search

The test clusters

Creating the tribe node

Reading data with the tribe node

Master-level read operations

Writing data with the tribe node

Master-level write operations

Handling indices conflicts

Blocking write operations

Summary

10. Improving Performance

Query validation and profiling

Validating expensive queries before execution

Query profiling for detailed query execution reports

Understanding the profile API response

Consideration for profiling usage

Very hot threads

Usage clarification for the hot threads API

The hot threads API response

Scaling Elasticsearch

Vertical scaling

Horizontal scaling

Automatically creating replicas

Redundancy and high availability

Cost and performance flexibility

Continuous upgrades

Multiple Elasticsearch instances on a single physical machine

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

Designated nodes' roles for larger clusters

Query aggregator nodes

Data nodes

Master eligible nodes

Using Elasticsearch for high load scenarios

General Elasticsearch-tuning advice

The index refresh rate

Thread pools tuning

Data distribution

Advice for high query rate scenarios

Node query cache and shard query cache

Think about the queries

Using routing

Parallelize your queries

Keeping size and shard_size under control

High indexing throughput scenarios and Elasticsearch

Bulk indexing

Keeping your document fields under control

The index architecture and replication

Tuning the write-ahead log

Thinking about storage

RAM buffer for indexing

Managing time-based indices efficiently using shrink and rollover APIs

The shrink API

Requirements for indices to be shrunk

Shrinking an index

Rollover API

Using the rollover API

Passing additional settings with a rollover request

Pattern for creating new index name

Summary

11. Developing Elasticsearch Plugins

Creating the Apache Maven project structure

Understanding the basics

The structure of the Maven Java project

The idea of POM

Running the build process

Introducing the assembly Maven plugin

Understanding the plugin descriptor file

Creating a custom REST action

The assumptions

Implementation details

Using the REST action class

The constructor

Handling requests

Writing responses

The plugin class

Informing Elasticsearch about our REST action

Time for testing

Building the REST action plugin

Installing the REST action plugin

Checking whether the REST action plugin works

Creating the custom analysis plugin

Implementation details

Implementing TokenFilter

Implementing the TokenFilter factory

Implementing the class custom analyzer

Implementing the analyzer provider

Implementing the analyzer plugin

Informing Elasticsearch about our custom analyzer

Testing our custom analysis plugin

Building our custom analysis plugin

Installing the custom analysis plugin

Checking whether our analysis plugin works

Summary

12. Introducing Elastic Stack 5.0

Overview of Elastic Stack 5.0

Introducing Logstash, Beats, and Kibana

Working with Logstash

Logstash architecture

Installing Logstash

Installing Logstash from binaries

Installing Logstash from APT repositories

Installing Logstash from YUM repositories

Configuring Logstash

Example - shipping system logs using Logstash

Starting Logstash

Introducing Beats as data shippers

Working with Metricbeat

Installing Metricbeat

Configuring Metricbeat

Running Metricbeat

Loading a sample Kibana dashboard into Elasticsearch

Working with Kibana

Installing Kibana

Kibana configuration

Starting Kibana

Exploring and visualizing data on Kibana

Understanding the Kibana Management screen

Discovering data on Kibana

Using the Dashboard screen to create/load dashboards

Using Sense

Summary

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