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Elasticsearch 7 Quick Start Guide电子书

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67人正在读 | 0人评论 6.7

作       者:Anurag Srivastava

出  版  社:Packt Publishing

出版时间:2019-10-24

字       数:19.0万

所属分类: 进口书 > 外文原版书 > 电脑/网络

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Get the most out of Elasticsearch 7’s new features to build, deploy, and manage efficient applications Key Features * Discover the new features introduced in Elasticsearch 7 * Explore techniques for distributed search, indexing, and clustering * Gain hands-on knowledge of implementing Elasticsearch for your enterprise Book Description Elasticsearch is one of the most popular tools for distributed search and analytics. This Elasticsearch book highlights the latest features of Elasticsearch 7 and helps you understand how you can use them to build your own search applications with ease. Starting with an introduction to the Elastic Stack, this book will help you quickly get up to speed with using Elasticsearch. You'll learn how to install, configure, manage, secure, and deploy Elasticsearch clusters, as well as how to use your deployment to develop powerful search and analytics solutions. As you progress, you'll also understand how to troubleshoot any issues that you may encounter along the way. Finally, the book will help you explore the inner workings of Elasticsearch and gain insights into queries, analyzers, mappings, and aggregations as you learn to work with search results. By the end of this book, you'll have a basic understanding of how to build and deploy effective search and analytics solutions using Elasticsearch. What you will learn * Install Elasticsearch and use it to safely store data and retrieve it when needed * Work with a variety of analyzers and filters * Discover techniques to improve search results in Elasticsearch * Understand how to perform metric and bucket aggregations * Implement best practices for moving clusters and applications to production * Explore various techniques to secure your Elasticsearch clusters Who this book is for This book is for software developers, engineers, data architects, system administrators, and anyone who wants to get up and running with Elasticsearch 7. No prior experience with Elasticsearch is required.
目录展开

About Packt

Why subscribe?

Contributors

About the authors

About the reviewer

Packt is searching for authors like you

Preface

Who this book is for

What this book covers

To get the most out of this book

Download the example code files

Download the color images

Conventions used

Get in touch

Reviews

Introduction to Elastic Stack

Brief history and background

Why use Elasticsearch?

What is log analysis?

Elastic Stack architecture

Elasticsearch

Kibana

Logstash

Beats

Filebeat

Metricbeat

Packetbeat

Auditbeat

Winlogbeat

Heartbeat

Use cases of the Elastic Stack

System monitoring

Log management

Application performance monitoring

Data visualization

Summary

Installing Elasticsearch

Installation of Elasticsearch

Installing Elasticsearch on Linux

Installing Elasticsearch using the Debian package

Installing Elasticsearch using the rpm package

Installing rpm manually

SysV

systemd

Installing Elasticsearch using MSI Windows Installer

Elasticsearch upgrade on Windows

Uninstall Elasticsearch on Windows

Installing Elasticsearch on macOS

Checking whether Elasticsearch is running

Summary

Many as One – the Distributed Model

API conventions

Handling multiple indices

Common options for the API response

Cluster state and statistics

Cluster health status

Cluster state

Cluster stats

Cluster administration

Node state and statistics

Operating system information

Process information

Plugin information

Index APIs

Document APIs

Single-document APIs

Creating a document

Viewing a document

Deleting a document

Delete by query

Updating a document

Multi-document APIs

Summary

Prepping Your Data – Text Analysis and Mapping

What is an analyzer?

Anatomy of an analyzer

How to use an analyzer

The custom analyzer

The standard analyzer

The simple analyzer

The whitespace analyzer

The stop analyzer

The keyword analyzer

The pattern analyzer

The language analyzer

The fingerprint analyzer

Normalizers

Tokenizers

The standard tokenizer

The letter tokenizer

The lowercase tokenizer

The whitespace tokenizer

The keyword tokenizer

The pattern tokenizer

The simple pattern tokenizer

Token filters

Character filters

The HTML strip character filter

The mapping character filter

The pattern replace character filter

Mapping

Datatypes

The simple datatype

The complex datatype

The specialized datatype

Multi-field mapping

Dynamic mapping

Explicit mapping

Summary

Let's Do a Search!

Introduction to data search

Search API

URI search

Request body search

Query

From/size

Sort

Source filtering

Fields

Script fields

Doc value fields

Post filter

Highlighting

Rescoring

Search type

Scroll

Preference

Explanation

Version

min_score

Named queries

Inner hits

Field collapsing

Search template

Multi search template

Search shards API

Suggesters

Multi search API

Count API

Validate API

Explain API

Profile API

Profiling queries

Profiling aggregations

Profiling considerations

Field capabilities API

Summary

Performance Tuning

Data sparsity

Solutions to common problems

Mixing exact search with stemming

Inconsistent scoring

How to tune for indexing speed

Bulk requests

Smart use of the Elasticsearch cluster

Increasing the refresh interval

Disabling refresh and replicas

Allocating memory to the filesystem cache

Using auto generated IDs

Using faster hardware

Indexing buffer size

How to tune for search speed

Allocating memory to the filesystem cache

Using faster hardware

Document modeling

Searching as few fields as possible

Pre-index data

Mapping identifiers as keywords

Avoiding scripts

Searching with rounded dates

Force-merging read-only indices

Prepping global ordinals

Prepping the filesystem cache

Using index sorting for conjunctions

Using preferences to optimize cache utilization

Balancing replicas

How to tune search queries with the Profile API

Faster phrase queries

Faster prefix queries

How to tune for disk usage

Disabling unused features

Do not use default dynamic string mappings

Monitoring shard size

Disabling source

Using compression

Force merge

Shrink indices

Using the smallest numeric type needed

Putting fields in order

Summary

Aggregating Datasets

What is an aggregation framework?

Advantages of aggregations

Structure of aggregations

Metrics aggregations

Avg aggregation

Weighted avg aggregation

Cardinality aggregation

Extended stats aggregation

Max aggregation

Min aggregation

Percentiles aggregation

Scripted metric aggregation

Stats aggregation

Sum aggregation

Bucket aggregations

Adjacency matrix aggregation

Auto-interval date histogram aggregation

Intervals

Composite aggregation

Date histogram aggregation

Date range aggregation

Filter/filters aggregation

Geo distance aggregation

Geohash grid aggregation

Geotile grid aggregation

Histogram aggregation

Significant terms aggregation

Significant text aggregation

Terms aggregation

Pipeline aggregations

Avg bucket aggregation

Derivative aggregation

Max bucket aggregation

Min bucket aggregation

Sum bucket aggregation

Stats bucket aggregation

Extended stats bucket aggregation

Percentiles bucket aggregation

Moving function aggregation

Cumulative sum aggregation

Bucket script aggregation

Bucket selector aggregation

Bucket sort aggregation

Matrix aggregations

Matrix stats

Summary

Best Practices

Failure to obtain the required data

Incorrectly processed text

Gazillion shards problem

Elasticsearch as a generic key-value store

Scripting and halting problem

The best cluster configuration approaches

Cloud configuration

On-site configuration

Data-ingestion patterns

Index aliases to simplify workflow

Why use aliases?

Using index templates to save time

Using _msearch for e-commerce applications

Using the Scroll API to read large datasets

Data backup and snapshots

Monitoring snapshot status

Managing snapshots

Deleting a snapshot

Restoring a snapshot

Renaming indices

Restoring to another cluster

Data Analytics using Elasticsearch

Summary

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