Elasticsearch: A Complete Guide by Saurabh Chhajed

Key FeaturesSolve your data analytics problems with the Elastic StackImprove your user search experience with Elasticsearch and develop your own Elasticsearch pluginsDesign your index, configure it, and distribute it — youll also learn how it worksBook DescriptionElasticsearch is a modern, fast, distributed, scalable, fault tolerant, open source search and analytics engine. It provides a new level of control over how you can index and search even huge sets of data. This course will take you from the basics of Elasticsearch to using Elasticsearch in the Elastic Stack and in production.Youll start with the very basics: Elasticsearch terminology, installation, and configuring Elasticsearch. After this, youll take a look at analytics and indexing, search, and querying. Youll learn how to create maps and visualizations. Youll also be briefed on cluster scaling, search and bulk operations, backups, and security.Then youll be ready to get into Elasticsearchs internal functionalities including caches, Apache Lucene library, and its monitoring capabilities. Youll learn about the practical usage of Elasticsearch configuration parameters and how to use the monitoring API. Youll discover how to improve the user search experience, index distribution, segment statistics, merging, and more.Once you have mastered this, youll dive into end-to-end visualize-analyze-log techniques with Elastic Stack (also known as the ELK stack). Youll explore Elasticsearch, Logstash, and Kibana and see how to make them work together to build fresh insights and business metrics out of data. Youll be able to use Elasticsearch with other de facto components in order to get the most out of Elasticsearch. By the end of this course, youll have developed a full-fledged data pipeline.This Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products:Elasticsearch EssentialsMastering Elasticsearch, Second EditionLearning ELK StackWhat you will learnInstall and configure Elasticsearch, Logstash, and KibanaWrite CRUDE operations and other search functionalities using the Elasticsearch Python and Java ClientsBuild analytics using aggregationsSet up and scale Elasticsearch clusters using best practicesMaster document relationships and geospatial dataBuild your own data pipeline using Elastic StackChoose the appropriate amount of shards and replicas for your deploymentBecome familiar with the Elasticsearch APIsAbout the AuthorBharvi Dixit is an IT professional with extensive experience working on 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 Delhis 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 masters degree in computer science from LBSIM - Delhi, India, and has a keen interest in creating scalable backend platforms. His other areas of interest 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/bharvidixitRafal Kuc is a born team leader and software developer. Currently, he is working as a consultant and a software engineer at Sematext Group, Inc., where he concentrates on open source technologies, such as Apache Lucene, Solr, Elasticsearch, and the Hadoop stack. He has more than 13 years of experience in various software branches — from banking software to e-commerce products. He is mainly focused on Java but is open to every tool and programming language that will make the achievement of his goal easier and faster.Rafal is one of the founders of the solr.pl website, where he tries to share his knowledge and help people with their problems related to Solr and Lucene. He is also a speaker at various conferences around the world, such as Lucene Eurocon, Berlin Buzzwords, ApacheCon, Lucene Revolution, and DevOps Days. He began his journey with Lucene in 2002, but it wasnt love at first sight. When he came back to Lucene in late 2003, he revised his thoughts about the framework and saw the potential in search technologies. Then came Solr, and that was it. He started working with Elasticsearch in the middle of 2010. Currently, Lucene, Solr, Elasticsearch, and information retrieval are his main points of interest.Rafal is the author of Solr 3.1 Cookbook, its update Solr 4.0 Cookbook, and its third release Solr Cookbook, Third Edition. He is also the author of Elasticsearch Server and its second edition, along with the first edition of Mastering Elasticsearch — all published by Packt.Marek Rogozinski is a software architect and consultant with over 10 years of experience. He specializes in solutions based on open source search engines, such as Solr and Elasticsearch, and software stack for Big Data analytics, including Hadoop, Hbase, and Twitter Storm. He is also a cofounder of the solr.pl website, which publishes information and tutorials about Solr and Lucene libraries. He is the coauthor of Mastering ElasticSearch, ElasticSearch Server, and Elasticsearch Server Second Edition — all published by Packt.Currently, he holds the position of chief technology officer and lead architect at ZenCard, a company processing and analyzing large amounts of payment transactions in real time, allowing automatic and anonymous identification of retail customers on all retailer channels (m-commerce / e-commerce / brick and mortar) and giving retailers a customer retention and loyalty tool.Saurabh Chhajed is a technologist with vast professional experience in building Enterprise applications that span across product and service industries. He has experience building some of the largest recommender engines using big data analytics and machine learning, and also enjoys acting as an evangelist for big data and NoSQL technologies. With his rich technical experience, Saurabh has helped some of the largest financial and industrial companies in USA build their large product suites and distributed applications from scratch. He shares his personal experiences with technology at: http://saurzcode.inSaurabh has also reviewed books by Packt — Apache Camel Essentials and Java EE 7 Development with NetBeans 8 — in the past.Table of ContentsGetting Started with ElasticsearchUnderstanding Document Analysis and Creating MappingsPutting Elasticsearch into ActionAggregations for AnalyticsData Looks Better on Maps: Master Geo-SpatialityDocument Relationships in NoSQL WorldDifferent Methods of Search and Bulk OperationsControlling RelevancyCluster Scaling in Production DeploymentsBackups and SecurityIntroduction to ElasticsearchPower User Query DSLNot Only Full Text SearchImproving the User Search ExperienceThe Index Distribution ArchitectureLow-level Index ControlElasticsearch AdministrationImproving PerformanceDeveloping Elasticsearch PluginsIntroduction to ELK StackBuilding Your First Data Pipeline with ELKCollect, Parse and Transform Data with LogstashCreating Custom Logstash PluginsWhy Do We Need Elasticsearch in ELK?Finding Insights with KibanaKibana – Visualization and DashboardPutting It All TogetherELK Stack in ProductionExpanding Horizons with ELKBibliography