Elasticsearch Important Concept Understanding

Elasticsearch is a distributed, extensible, real-time search and data analysis engine. Elasticsearch is not just full-text search, we will also introduce structured search, data analysis, complex language processing, geographic location, and inter-object correlation.

Elasticsearch is an open source search engine based on Apache Lucene, a full-text search engine library. Lucene can be said to be the most advanced, high-performance, full-featured search engine library-whether it's open source or private.

But Lucene is just a library. To get the most out of its functionality, you need to use Java and integrate Lucene directly into your application. Lucene is very complex.

Elasticsearch is also written in Java, which uses Lucene to index and search internally, but its purpose is to make full-text retrieval simple, by hiding the complexity of lucene and instead providing a simple, consistent set of RESTful APIs.

Elasticsearch is more than Lucene, and it's not just a full-text search engine. It can be described as follows: a distributed real-time document storage, each field can be indexed and searched for a distributed real-time analysis search engine capable of hundreds of service node expansion, and support PB-level structured or unstructured data

Elasticsearch all the features into a single service so that you can communicate with the simple RESTful APIs it provides, use your favorite programming language to act as a Web client, and even use the command line (to act as a client).

As far as Elasticsearch is concerned, the start is simple. For starters, it presets some of the appropriate defaults and hides complex search theory knowledge. It is available out of the box. With a minimum of understanding, you will soon be productive.

With the accumulation of your knowledge, you can take advantage of Elasticsearch more advanced features, its entire engine is configurable and flexible. Choose from a wide range of advanced features to properly modify the Elasticsearch, so that it can solve your local problems. Elasticsearch is document-oriented, meaning it stores the entire object or document _. Elasticsearch not only stores documents, but also indexes the contents of each document so that it can be retrieved. In Elasticsearch, you index, retrieve, sort, and filter the document--not the row and column data. This is a completely different way of thinking about data and the reason why Elasticsearch can support complex full-text searches.

An index is similar to a database in a traditional relational database and is a place to store relational documents. The plural word of index is indices or indexes.

Indexing a document is to store a document in an index (noun) so that it can be retrieved and queried. This is very similar to the INSERT keyword in an SQL statement, except that a new document already exists that replaces the old document.

Inverted index: A relational database improves data retrieval speed by adding an index such as a B-tree (b-tree) index to the specified column. Elasticsearch and Lucene use a structure called an inverted index to achieve the same purpose.

The object model of the

Elasticsearch, compared to the relational Database model: index: the equivalent of a database that defines the storage for a document type; In the same index, only one data type can be defined in the same field; Document type: equivalent to a relational table, Used to describe the definitions of each field in the document, different document types, the ability to store different fields, serve different query requests, document: A data row equivalent to a relational table, a carrier that stores data, and a field containing one or more data; field: A key to a document /value; a word (term): Represents a word in a text; token: Represents the word that appears in a field, consisting of the word's text, offset (start and end), and type;