Elasticsearch Essentials

Packt Publishing Limited
  • 1. Auflage
  • |
  • erschienen am 30. Januar 2016
  • |
  • 240 Seiten
E-Book | ePUB mit Adobe DRM | Systemvoraussetzungen
978-1-78439-088-4 (ISBN)
Harness the power of ElasticSearch to build and manage scalable search and analytics solutions with this fast-paced guideAbout This BookNew to ElasticSearch? Here's what you need-a highly practical guide that gives you a quick start with ElasticSearch using easy-to-follow examples; get up and running with ElasticSearch APIs in no timeGet the latest guide on ElasticSearch 2.0.0, which contains concise and adequate information on handling all the issues a developer needs to know while handling data in bulk with search relevancyLearn to create large-scale ElasticSearch clusters using best practicesLearn from our experts-written by Bharvi Dixit who has extensive experience in working with search servers (especially ElasticSearch)Who This Book Is ForAnyone who wants to build efficient search and analytics applications can choose this book. This book is also beneficial for skilled developers, especially ones experienced with Lucene or Solr, who now want to learn Elasticsearch quickly.What You Will LearnGet to know about advanced Elasticsearch concepts and its REST APIsWrite CRUD operations and other search functionalities using the ElasticSearch Python and Java clientsDig into wide range of queries and find out how to use them correctlyDesign schema and mappings with built-in and custom analyzersExcel in data modeling concepts and query optimizationMaster document relationships and geospatial dataBuild analytics using aggregationsSetup and scale Elasticsearch clusters using best practicesLearn to take data backups and secure Elasticsearch clustersIn DetailWith constantly evolving and growing datasets, organizations have the need to find actionable insights for their business. ElasticSearch, which is the world's most advanced search and analytics engine, brings the ability to make massive amounts of data usable in a matter of milliseconds. It not only gives you the power to build blazing fast search solutions over a massive amount of data, but can also serve as a NoSQL data store.This guide will take you on a tour to become a competent developer quickly with a solid knowledge level and understanding of the ElasticSearch core concepts. Starting from the beginning, this book will cover these core concepts, setting up ElasticSearch and various plugins, working with analyzers, and creating mappings. This book provides complete coverage of working with ElasticSearch using Python and performing CRUD operations and aggregation-based analytics, handling document relationships in the NoSQL world, working with geospatial data, and taking data backups. Finally, we'll show you how to set up and scale ElasticSearch clusters in production environments as well as providing some best practices.Style and approachThis is an easy-to-follow guide with practical examples and clear explanations of the concepts. This fast-paced book believes in providing very rich content focusing majorly on practical implementation. This book will provide you with step-by-step practical examples, letting you know about the common errors and solutions along with ample screenshots and code to ensure your success.
  • Englisch
  • Birmingham
  • |
  • Großbritannien
978-1-78439-088-4 (9781784390884)
1784390887 (1784390887)
weitere Ausgaben werden ermittelt
Bharvi Dixit is an IT professional with an extensive experience of working on the search servers (especially Elasticsearch) and NoSQL databases. He is currently working as a technology and search expert with GrownOut, a SAAS-based referral hiring solution provider company. He is the organizer and speaker of Delhi's Elasticsearch Meetup Group, which is one of the fastest growing Elasticsearch communities in India.
He also works as a freelance Elasticsearch consultant and has helped many small to medium size organizations in adapting Elasticsearch for different use cases such as, creating search solutions for big data-automated intelligence platforms in the area of counter-terrorism and risk management as well as in other domains such as recruitment, e-commerce, finance and log monitoring.
He holds a master's degree in computer science from LBSIM - Delhi, India, and has a keen interest in creating scalable backend platforms. His other interest area are data analytics, distributed computing, automations, and DevOps. Java and Python are the primary languages in which he loves to write code, and he has already built a proprietary software for consultancy firms.
In his spare time, he loves writing blogs and reading the latest technology books. He can be connected through LinkedIn at https://in.linkedin.com/in/bharvidixit.
  • Cover
  • Copyright
  • Credits
  • About the Author
  • Acknowledgments
  • About the Reviewer
  • www.PacktPub.com
  • Table of Contents
  • Preface
  • Chapter 1: Getting Started with Elasticsearch
  • Introducing Elasticsearch
  • The primary features of Elasticsearch
  • Understanding REST and JSON
  • What is REST?
  • What is JSON?
  • Elasticsearch common terms
  • Understanding Elasticsearch structure with respect to relational databases
  • Installing and configuring Elasticsearch
  • Installing Elasticsearch on Ubuntu through Debian package
  • Installing Elasticsearch on Centos through the RPM package
  • Understanding the Elasticsearch installation directory layout
  • Configuring basic parameters
  • Adding another node to the cluster
  • Installing Elasticsearch plugins
  • Checking for installed plugins
  • Installing the Head plugin for Elasticsearch
  • Installing Sense for Elasticsearch
  • Basic operations with Elasticsearch
  • Creating an Index
  • Indexing a document in Elasticsearch
  • Fetching documents
  • Get a complete document
  • Getting part of a document
  • Updating documents
  • Updating a whole document
  • Updating documents partially
  • Deleting documents
  • Checking documents' existence
  • Summary
  • Chapter 2: Understanding Document Analysis and Creating Mappings
  • Text search
  • TF-IDF
  • Inverted indexes
  • Document analysis
  • Introducing Lucene analyzers
  • Creating custom analyzers
  • Changing a default analyzer
  • Putting custom analyzers into action
  • Elasticsearch mapping
  • Document metadata fields
  • Data types and index analysis options
  • Configuring data types
  • Indexing the same field in different ways
  • Putting mappings in an index
  • Viewing mappings
  • Updating mappings
  • Summary
  • Chapter 3: Putting Elasticsearch into Action
  • CRUD operations using elasticsearch-py
  • Setting up the environment
  • Installing Pip
  • Installing virtualenv
  • Installing elasticsearch-py
  • Performing CRUD operations
  • Request timeouts
  • Creating indexes with settings and mappings
  • Indexing documents
  • Retrieving documents
  • Updating documents
  • Appending a value in an array
  • Updates using doc
  • Checking document existence
  • Deleting a document
  • CRUD operations using Java
  • Connecting with Elasticsearch
  • Indexing a document
  • Fetching a document
  • Updating a document
  • Deleting documents
  • Creating a search database
  • Elasticsearch Query-DSL
  • Understanding Query-DSL parameters
  • Query types
  • Full-text search queries
  • match_all
  • match query
  • multi match
  • query_string
  • Term-based search queries
  • Term query
  • Terms query
  • Range queries
  • Exists queries
  • Missing queries
  • Compound queries
  • Bool queries
  • Not queries
  • Search requests using Python
  • Search requests using Java
  • Parsing search responses
  • Sorting your data
  • Sorting documents by field values
  • Sorting on more than one field
  • Sorting multivalued fields
  • Sorting on string fields
  • Document routing
  • Summary
  • Chapter 4: Aggregations for Analytics
  • Introducing the aggregation framework
  • Aggregation syntax
  • Extracting values
  • Returning only aggregation results
  • Metric aggregations
  • Computing basic stats
  • Combined stats
  • Computing stats separately
  • Computing extended stats
  • Finding distinct counts
  • Bucket aggregations
  • Terms aggregation
  • Range aggregation
  • Date range aggregation
  • Histogram aggregation
  • Date histogram aggregation
  • Filter-based aggregation
  • Combining search, buckets, and metrics
  • Memory pressure and implications
  • Summary
  • Chapter 5: Data Looks Better on Maps: Master Geo-Spatiality
  • Introducing geo-spatial data
  • Working with geo-point data
  • Mapping geo-point fields
  • Indexing geo-point data
  • Querying geo-point data
  • Geo distance query
  • Geo distance range query
  • Geo bounding box query
  • Sorting by distance
  • Geo-aggregations
  • Geo distance aggregation
  • Using bounding boxes with geo distance aggregation
  • Geo-shapes
  • Point
  • Linestring
  • Circles
  • Polygons
  • Envelops
  • Mappings geo-shape fields
  • Indexing geo-shape data
  • Querying geo-shape data
  • Summary
  • Chapter 6: Document Relationships in NoSQL World
  • Relational data in the document-oriented NoSQL world
  • Managing relational data in Elasticsearch
  • Working with nested objects
  • Creating nested mappings
  • Indexing nested data
  • Querying nested type data
  • Nested aggregations
  • Nested aggregation
  • Reverse nested aggregation
  • Parent-child relationships
  • Creating parent-child mappings
  • Indexing parent-child documents
  • Querying parent-child documents
  • has_child query
  • has_parent query
  • Considerations for using document relationships
  • Summary
  • Chapter 7: Different Methods of Search and Bulk Operations
  • Introducing search types in Elasticsearch
  • Cheaper bulk operations
  • Bulk create
  • Bulk indexing
  • Bulk updating
  • Bulk deleting
  • Multi get and multi search APIs
  • Multi get
  • Multi searches
  • Data pagination
  • Pagination with scoring
  • Pagination without scoring
  • Scrolling and re-indexing documents using scan-scroll
  • Practical considerations for bulk processing
  • Summary
  • Chapter 8: Controlling Relevancy
  • Introducing relevant searches
  • The Elasticsearch out-of-the-box tools
  • An example: why defaults are not enough
  • Controlling relevancy with custom scoring
  • The function_score query
  • weight
  • field_value_factor
  • script_score
  • Decay functions - linear, exp, and gauss
  • Summary
  • Chapter 9: Cluster Scaling in Production Deployments
  • Node types in Elasticsearch
  • Client node
  • Data node
  • Master node
  • Introducing Zen-Discovery
  • Multicasting discovery
  • Unicasting discovery
  • Configuring unicasting discovery
  • Node upgrades without downtime
  • Upgrading Elasticsearch version
  • Best ElasticSearch practices in production
  • Creating a cluster
  • Scaling your clusters
  • When to scale
  • Metrics to watch
  • How to scale
  • Summary
  • Chapter 10: Backups and Security
  • Introducing backup and restore mechanisms
  • Backup using snapshot API
  • Creating an NFS drive
  • Creating a snapshot
  • Restoring snapshots
  • Restoring multiple indices
  • Renaming indices
  • Partial restore
  • Changing index settings during restore
  • Restoring to a different cluster
  • Manual backups
  • Manual restoration
  • Securing Elasticsearch
  • Setting up basic HTTP authentication
  • Setting up Nginx
  • Securing critical access
  • Restricting DELETE requests
  • Restricting endpoints
  • Load balancing using Nginx
  • Summary
  • Index

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