Learning Elasticsearch 7.x: Index, Analyze, Search and Aggregate Your Data Using Elasticsearch
- Length: 310 pages
- Edition: 1
- Language: English
- Publisher: BPB Publications
- Publication Date: 2020-12-03
- ISBN-10: 9389898307
- ISBN-13: 9789389898309
- Sales Rank: #3170421 (See Top 100 Books)
A step-by-step guide that will teach you how to use Elasticsearch in your application effectively
Key Features
- Get familiar with the core concepts of Elasticsearch.
- Understand how the search engine works and how Elasticsearch is different from other similar tools.
- Learn to install Elasticsearch on different operating systems..
- Get familiar with the components of Elastic Stack such as Kibana, Logstash, and Beats, etc.
- Learn how to import data from different sources such as RDBMS, and files, etc
Description
In the modern Information Technology age, we are flooded with loads of data so we should know how to handle those data and transform them to fetch meaningful information. This book is here to help you manage the data using Elasticsearch.
The book starts by covering the fundamentals of Elasticsearch and the concept behind it. After the introduction, you will learn how to install Elasticsearch on different platforms. You will then get to know about Index Management where you will learn to create, update, and delete Elasticsearch indices. Then you will understand how the Query DSL works and how to write some complex search queries using the Query DSL. After completing these basic features, you will move to some advanced topics. Under advanced topics, you will learn to handle Geodata which can be used to plot the data on a map. The book then focuses on Data Analysis using Aggregation. You will then learn how to tune Elasticsearch performance. The book ends with a chapter on Elasticsearch administration.
What will you learn
- Learn how to create and manage a cluster
- Work with different components of Elastic Stack
- Review the list of top Information Security certifications.
- Get to know more about Elasticsearch Index Management.
- Understand how to improve the performance by tuning Elasticsearch.
Who this book is for
This book is for developers, architects, DBA, DevOps, and other readers who want to learn Elasticsearch efficiently and want to apply that in their application whether it is a new one or an existing one. It is also beneficial to those who want to play with their data using Elasticsearch. Basic computer programming is a prerequisite.
About the Authors
Anurag Srivastava works as Deputy Manager in the R&D centre of an air conditioning company. With 14+ years of experience in the software industry, he has led and handled teams and clients for more than 7 years. He is well experienced with the Elastic Stack for creating dashboards using system metrics data, log data, application data, or relational databases.
He is a regular blogger on technical subjects, which can be found at bqstack or medium.
Linkedin profile: https://www.linkedin.com/in/anubioinfo/
Cover Page Title Page Copyright Page Dedication Page About the Author About the Reviewer Acknowledgement Preface Errata Table of Contents 1. Getting Started with Elasticsearch Introduction Structure Objectives Introduction to Elasticsearch What is Elasticsearch The basic concepts of Elasticsearch Node Master node Data node Ingest node Machine learning node Cluster Documents Index Shard Use cases of Elasticsearch Data search Data logging and analysis Application performance monitoring System performance monitoring Data Visualization Different clients for Elasticsearch Java PHP Perl Python .NET Ruby JavaScript How to use Elasticsearch Elasticsearch as a primary data source Elasticsearch as a secondary data source for searching Elasticsearch as a standalone system Conclusion Questions 2. Installing Elasticsearch Introduction Structure Objectives What’s new in Elasticsearch 7.x Adaptive replica selection Skip shard refreshes One shard per index by default Support for small heap Installing Elasticsearch Installing Elasticsearch on Linux or macOS Installing Elasticsearch on Linux Installing Elasticsearch on macOS Installing Elasticsearch using the Debian package Installing the Debian package manually Installing Elasticsearch using the RPM package Installing the RPM package manually Start the Elasticsearch service and verify Elasticsearch REST APIs cat APIs cat API parameters Verbose Help Headers Response formats Sort cat count API cat health API cat indices API cat master API cat nodes API cat shards API Cluster APIs Cluster health API Cluster stats API Conclusion Questions 3. Working with Elastic Stack Introduction Structure Objectives What is Elastic Stack Elasticsearch Logstash Logstash input plugin Logstash filter plugin Logstash output plugin Fetch Apache logs using logstash Kibana Beats Filebeat Configure input Configure output Metricbeat Configure Metricbeat Enabling the required modules Output configuration Packetbeat Configuring Packetbeat Winlogbeat Configure Winlogbeat Auditbeat Configuring Auditbeat Heartbeat Configuring Heartbeat Functionbeat Configuring Functionbeat Conclusion Questions 4. Preparing Your Data Introduction Structure Objectives Why it is important to prepare the data before indexing An introduction to Elasticsearch analyzers Built-in analyzer Standard analyzer Simple analyzer Whitespace analyzer Stop analyzer Keyword analyzer Pattern analyzer Language analyzers Fingerprint analyzer Custom analyzer Tokenizers Word oriented tokenizers Standard tokenizer Letter tokenizer Lowercase tokenizer Whitespace tokenizer UAX URL email tokenizer Classic tokenizer Partial word tokenizers N-gram tokenizer Edge n-gram tokenizer Structured text tokenizers Keyword tokenizer Pattern tokenizer Token filters Character filters HTML strip character filter Mapping the char filter Pattern replace character filter Normalizers Conclusion Questions 5. Importing Data into Elasticsearch Introduction Structure Objectives Why is data so important for business Data shipping Data ingestion Data storage Data visualization Importing data into Elasticsearch using different Beats Pull Apache logs using Filebeat Pull server metrics using Metricbeat Pulling network data using Packetbeat Pulling CSV data using logstash Conclusion Questions 6. Managing Your Index Introduction Structure Objectives Creating index along with mapping Creating an index without any document Creating index along with the documents Get mapping of the index Create a mapping of the index Index management Performing index-level operations Close index Delete index Freeze index Refresh index Force merge index Clear index cache Flush index Add lifecycle policy Index APIs Index management Creating an index Delete index Get index Close index Open index Index Exist API Shrink index Freeze index Unfreeze index Split index Clone index Rollover index Index settings Update index settings Get index settings Manage index templates Creating an index template Get index template Delete index template Index lifecycle management Conclusion Questions 7. Applying Search on Your Data Introduction Structure Objective URI search Empty search Field search Request body search Query versus filter Query Query types Full-text search Term-level queries Compound queries Multi-search Multi-search API Multi search template Explain API Profile API Conclusion Questions 8. Handling Geo with Elasticsearch Introduction Structure Objective Geodata type Geo point data Creating mapping Saving geo point data Geo shape data Creating mapping Saving geo point data Point LineString Polygon MultiPoint MultiLineString MultiPolygon Geometry collection Envelope Circle Geo queries Geo-distance queries Geo-polygon queries Geo-bounding box queries Geo-shape queries Use case Restaurant search Aggregate restaurant based on the distance Conclusion Questions 9. Aggregating Your Data Introduction Structure Objective Introduction to Elasticsearch aggregation Bucket aggregation Range aggregation Composite aggregation Terms Histogram Date histogram Terms aggregation Filter aggregation Filters aggregation Geo distance aggregation Metrics aggregation Min aggregation Max aggregation Avg aggregation Sum aggregation Value count aggregation Stats aggregation Extended stats aggregation Percentiles aggregation Matrix aggregation Matrix stats aggregation Pipeline aggregation Avg bucket aggregation Max bucket aggregation Sum bucket aggregation Conclusion Questions 10. Improving the Performance Introduction Structure Objectives Introduction Tuning Elasticsearch indexing speed Bulk R equests instead of a single request Smart use of the Elasticsearch Cluster Increasing the refresh interval Disable replicas Using auto-generated ids Tweaking the indexing buffer size Use of faster hardware Allocating memory to the filesystem cache Tuning Elasticsearch search speed Document modelling Search a few fields if possible Pre-index data Mapping of identifiers as keyword We should force merge the read-only indices Use filter instead of the query Increase the replica count Fetch only the required fields Use of faster hardware Allocate memory to the filesystem cache Avoid including stop words in the search Avoid the script in the query Tuning Elasticsearch for disk usage Shrink index Force merge Disable the unrequired features Avoid dynamic string mappings Disable_source Use the smallest numeric type Elasticsearch best practices Always define the mapping Do your capacity planning Avoid split-brain problem Enable the slow query log Conclusion Questions 11. Administering Elasticsearch Structure Objectives Elasticsearch security Configuring TLS Elasticsearch cluster passwords Configuring role-based access using Kibana Creating users Creating roles Index aliases Repository and snapshot Creating the repository Taking the snapshot Restoring a snapshot Elastic common schema Why do we need a common schema Introduction to Elastic common schema ECS general guidelines ECS field name guidelines Getting started with ECS Conclusion Questions Index
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