28,14 €
Mehr erfahren.
- Herausgeber: Packt Publishing
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
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.
Das E-Book können Sie in Legimi-Apps oder einer beliebigen App lesen, die das folgende Format unterstützen:
Seitenzahl: 171
Veröffentlichungsjahr: 2019
Ähnliche
Elasticsearch 7 Quick Start Guide
Copyright © 2019 Packt Publishing
All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.
Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author(s), nor Packt Publishing or its dealers and distributors, will be held liable for any damages caused or alleged to have been caused directly or indirectly by this book.
Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.
Commissioning Editor:Amey VarangaonkarAcquisition Editor:Reshma RamanContent Development Editor:Roshan KumarSenior Editor: Jack CummingsTechnical Editor: Manikandan KurupCopy Editor: Safis EditingProject Coordinator:Kirti PisatProofreader: Safis EditingIndexer:Tejal Daruwale SoniProduction Designer:Shraddha Falebhai
First published: October 2019
Production reference: 1231019
Published by Packt Publishing Ltd. Livery Place 35 Livery Street Birmingham B3 2PB, UK.
ISBN 978-1-78980-332-7
www.packt.com
Packt.com
Subscribe to our online digital library for full access to over 7,000 books and videos, as well as industry leading tools to help you plan your personal development and advance your career. For more information, please visit our website.
Why subscribe?
Spend less time learning and more time coding with practical eBooks and Videos from over 4,000 industry professionals
Improve your learning with Skill Plans built especially for you
Get a free eBook or video every month
Fully searchable for easy access to vital information
Copy and paste, print, and bookmark content
Did you know that Packt offers eBook versions of every book published, with PDF and ePub files available? You can upgrade to the eBook version at www.packt.com and as a print book customer, you are entitled to a discount on the eBook copy. Get in touch with us at [email protected] for more details.
At www.packt.com, you can also read a collection of free technical articles, sign up for a range of free newsletters, and receive exclusive discounts and offers on Packt books and eBooks.
Contributors
About the authors
Anurag Srivastavais a senior technical lead in a multinational software company. He has more than 12 years' experience in web-based application development. He is proficient in designing architecture for scalable and highly available applications. He has handled development teams and multiple clients from all over the globe over the past 10 years of his professional career. He has significant experience with the Elastic Stack (Elasticsearch, Logstash, and Kibana) for creating dashboards using system metrics data, log data, application data, and relational databases. He has authored three other books—Mastering Kibana 6.x, and Kibana 7 Quick Start Guide, and Learning Kibana 7 - Second Edition, all published by Packt.
Douglas Miller is an expert in helping fast-growing companies to improve performance and stability, and in building search platforms using Elasticsearch. Clients (including Walgreens, Nike, Boeing, and Dish Networks) have seen sales increase, fast performance times, and lower overall costs in terms of the total costs of ownership for their Elasticsearch clusters.
About the reviewer
Craig Brown is an independent consultant, offering services for Elasticsearch and other big data software. He is a core Java developer with 25+ years' experience and more than 10 years of Elasticsearch experience. He has also practiced with machine learning, Hadoop, and Apache Spark, is a co-founder of the Big Mountain Data user group in Utah, and is a speaker on Elasticsearch and other big data topics.
Craig has founded NosqlRevolution LLC, focusing on Elasticsearch and big data services, and PicoCluster LLC, a desktop data center designed for learning and prototyping cluster computing and big data frameworks.
Packt is searching for authors like you
If you're interested in becoming an author for Packt, please visit authors.packtpub.com and apply today. We have worked with thousands of developers and tech professionals, just like you, to help them share their insight with the global tech community. You can make a general application, apply for a specific hot topic that we are recruiting an author for, or submit your own idea.
Table of Contents
Title Page
Copyright and Credits
Elasticsearch 7 Quick Start Guide
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
Other Books You May Enjoy
Leave a review - let other readers know what you think
Preface
Elasticsearch is one of the most popular tools for distributed open source search and analytics. This book will help you in understanding everything about the new features of Elasticsearch, and how to use them efficiently for searching, aggregating, and indexing data with speed and accuracy, while also helping you understand how you can use them to build your own search applications with ease. You will also acquire a basic understanding of how to build and deploy effective search and analytics solutions using Elasticsearch.
Starting with an introduction to the Elastic Stack, this book will help you quickly get up to speed with using Elasticsearch. Next, you'll learn how to deploy and manage Elasticsearch clusters, as well as how to use your deployment to develop powerful search and analytics solutions. As you progress, you'll also discover how to install, configure, manage, and secure Elasticsearch clusters, in addition to understanding how to troubleshoot any issues you may encounter along the way. Finally, the book helps you explore the inner workings of Elasticsearch and gain insights into queries, analyzers, mappings, and aggregations as you learn to work with search results.
Who this book is for
This book is for software developers, engineers, data architects, system administrators, or anyone who wants to get up and running with Elasticsearch 7.
What this book covers
Chapter 1, Introduction to Elastic Stack, will give you a brief history and background on Elasticsearch. We will also get introduced to log analysis and will cover some of the core components of the Elastic Stack architecture.
Chapter 2, Installing Elasticsearch, will cover the installation process of Elasticsearch in different environments. We will also look into installation using the Debian and rpm packages, followed by installation on Windows using the MSI installer of Elasticsearch.
Chapter 3, Many as One – the Distributed Model, will cover how to interact with Elasticsearch using REST calls to call different operations. We will also look at how we can handle multiple indices, followed by looking at some of the common options for the API response. We will also learn how to create, delete, and retrieve indices.
Chapter 4, Prepping Your Data – Text Analysis and Mapping, will walk through the details of how full text is analyzed and indexed in Elasticsearch, followed by looking into some of the various analyzers and filters and how they can be configured. We will also learn how Elasticsearch mappings are used for defining documents and fields and storing and indexing them, including how to define multi-fields and custom analyzers.
Chapter 5, Let's Do a Search!, will go into further detail regarding data searches, where we will cover URI search and body search. We will also cover some query examples using term, from/size, sort, and source filtering. Following that, we will also cover highlighting, rescoring, search type, and named queries.
Chapter 6, Performance Tuning, will cover data sparsity and how to improve the performance of Elasticsearch. We will also cover how to adjust the search speed by means of allocating memory to the filesystem cache, faster hardware, document modeling, pre-index data, avoiding replicas, and so on.
Chapter 7, Aggregating Datasets, will cover how to aggregate datasets and will explain the different types of aggregations that Elasticsearch supports.
Chapter 8, Best Practices, will cover the best practices we can follow in order to manage an Elasticsearch cluster.
To get the most out of this book
No prior experience with the Elastic Stack is required. The steps for installing and running Elasticsearch are covered in the book.
Download the example code files
You can download the example code files for this book from your account at www.packt.com. If you purchased this book elsewhere, you can visit www.packtpub.com/support and register to have the files emailed directly to you.
You can download the code files by following these steps:
Log in or register at
www.packt.com
.
Select the
Support
tab.
Click on
Code Downloads
.
Enter the name of the book in the
Search
box and follow the onscreen instructions.
Once the file is downloaded, please make sure that you unzip or extract the folder using the latest version of:
WinRAR/7-Zip for Windows
Zipeg/iZip/UnRarX for Mac
7-Zip/PeaZip for Linux
The code bundle for the book is also hosted on GitHub at https://github.com/PacktPublishing/Elasticsearch-7-Quick-Start-Guide. In case there's an update to the code, it will be updated on the existing GitHub repository.
We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!
Download the color images
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. You can download it here: http://www.packtpub.com/sites/default/files/downloads/9781789803327_ColorImages.pdf.
Conventions used
There are a number of text conventions used throughout this book.
CodeInText: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: "Let's take the example of kibana_sample_data_flight data to understand how we can prettify the results using the pretty keyword."
A block of code is set as follows:
PUT
index_name
{ "settings": { "number_of_shards": 1 }, "mappings": { "_doc": { "properties": { "field_number_1": { "type": "text" } } } }}
Any command-line input or output is written as follows:
curl -L -O https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.1.1-linux-x86_64.tar.gz
Bold: Indicates a new term, an important word, or words that you see on screen. For example, words in menus or dialog boxes appear in the text like this. Here is an example: "A manual uninstall must be performed through Add or remove programs."
Get in touch
Feedback from our readers is always welcome.
General feedback: If you have questions about any aspect of this book, mention the book title in the subject of your message and email us at [email protected].
Errata: Although we have taken every care to ensure the accuracy of our content, mistakes do happen. If you have found a mistake in this book, we would be grateful if you would report this to us. Please visit www.packtpub.com/support/errata, selecting your book, clicking on the Errata Submission Form link, and entering the details.
Piracy: If you come across any illegal copies of our works in any form on the internet, we would be grateful if you would provide us with the location address or website name. Please contact us at [email protected] with a link to the material.
If you are interested in becoming an author: If there is a topic that you have expertise in, and you are interested in either writing or contributing to a book, please visit authors.packtpub.com.
Reviews
Please leave a review. Once you have read and used this book, why not leave a review on the site that you purchased it from? Potential readers can then see and use your unbiased opinion to make purchase decisions, we at Packt can understand what you think about our products, and our authors can see your feedback on their book. Thank you!
For more information about Packt, please visit packt.com.
Introduction to Elastic Stack
The Elastic Stack consists of Elasticsearch, Logstash, and Kibana, which together form the ELK Stack. Elasticsearch is an open source search engine developed by Shay Banon, with an easy-to-use web interface that provides excellent flexibility through plugins that expand the functionality of a wide range of applications. Because it is open source, it is easily accessible to everyone, and user input provides great feedback for ongoing, constant improvement of the product. Elasticsearch can be used for everything from simple to complex searches. For example, a simple search for old maps could involve counting the number of cartographers, or studying cartographers' products, or analyzing map contents. Many criteria can be used for searches, for a wide range of purposes.
Elasticsearch supports multi-tenancy, meaning it can store multiple indices on a server, and information can be retrieved from multiple indices using a single query. It uses documents with JSON format; for requests, responses, and during transfer, they are automatically indexed. In this chapter, we are going to cover the following topics:
Brief history and background
Why use Elasticsearch?
What is log analysis?
Elastic Stack architecture
Use cases of the Elastic Stack
Brief history and background
Developed in 2012, Elastic is an open source company that develops a distributed open source search engine based on Lucene. The history of Elastic starts with its main founder, Shay Banon, who wanted to explore making searching easier. In 2004, he released his first open source search-based product called Compass. This first iteration of open source search tools served as an inspiration, and, from Compass onward, searching has improved.
Around Elasticsearch grew a small community that would later lead to important partnerships that grew the company's capabilities. Jordan Sissel was working on a plugin ingestion tool named Logstash
