Welcome to Elasticsearch Server, Third Edition. This is the third instalment of the book dedicated to yet another major release of Elasticsearch—this time version 2.2. In the third edition, we have decided to go on a similar route that we took when we wrote the second edition of the book. We not only updated the content to match the new version of Elasticsearch, but also restructured the book by removing and adding new sections and chapters. We read the suggestions we got from you—the readers of the book, and we carefully tried to incorporate the suggestions and comments received since the release of the first and second editions.
While reading this book, you will be taken on a journey to the wonderful world of full-text search provided by the Elasticsearch server. We will start with a general introduction to Elasticsearch, which covers how to start and run Elasticsearch, its basic concepts, and how to index and search your data in the most basic way. This book will also discuss the query language, so called Query DSL, that allows you to create complicated queries and filter returned results. In addition to all of this, you'll see how you can use the aggregation framework to calculate aggregated data based on the results returned by your queries. We will implement the autocomplete functionality together and learn how to use Elasticsearch spatial capabilities and prospective search.
Finally, this book will show you Elasticsearch's administration API capabilities with features such as shard placement control, cluster handling, and more, ending with a dedicated chapter that will discuss Elasticsearch's preparation for small and large deployments— both ones that concentrate on indexing and also ones that concentrate on indexing.
Chapter 1, Getting Started with Elasticsearch Cluster, covers what full-text searching is, what Apache Lucene is, what text analysis is, how to run and configure Elasticsearch, and finally, how to index and search your data in the most basic way.
Chapter 2, Indexing Your Data, shows how indexing works, how to prepare index structure, what data types we are allowed to use, how to speed up indexing, what segments are, how merging works, and what routing is.
Chapter 3, Searching Your Data, introduces the full-text search capabilities of Elasticsearch by discussing how to query it, how the querying process works, and what types of basic and compound queries are available. In addition to this, we will show how to use position-aware queries in Elasticsearch.
Chapter 4, Extending Your Query Knowledge, shows how to efficiently narrow down your search results by using filters, how highlighting works, how to sort your results, and how query rewrite works.
Chapter 5, Extending Your Index Structure, shows how to index more complex data structures. We learn how to index tree-like data types, how to index data with relationships between documents, and how to modify index structure.
Chapter 6, Make Your Search Better, covers Apache Lucene scoring and how to influence it in Elasticsearch, the scripting capabilities of Elasticsearch, and its language analysis capabilities.
Chapter 7, Aggregations for Data Analysis, introduces you to the great world of data analysis by showing you how to use the Elasticsearch aggregation framework. We will discuss all types of aggregations—metrics, buckets, and the new pipeline aggregations that have been introduced in Elasticsearch.
Chapter 8, Beyond Full-text Searching, discusses non full-text search-related functionalities such as percolator—reversed search, and the geo-spatial capabilities of Elasticsearch. This chapter also discusses suggesters, which allow us to build a spellchecking functionality and an efficient autocomplete mechanism, and we will show how to handle deep-paging efficiently.
Chapter 9, Elasticsearch Cluster in Detail, discusses nodes discovery mechanism, recovery and gateway Elasticsearch modules, templates, caches, and settings update API.
Chapter 10, Administrating Your Cluster, covers the Elasticsearch backup functionality, rebalancing, and shards moving. In addition to this, you will learn how to use the warm up functionality, use the Cat API, and work with aliases.
Chapter 11, Scaling by Example, is dedicated to scaling and tuning. We will start with hardware preparations and considerations and a single Elasticsearch node-related tuning. We will go through cluster setup and vertical scaling, ending the chapter with high querying and indexing use cases and cluster monitoring.