- Elasticsearch Tutorial
- Elasticsearch - Home
- Elasticsearch - Basic Concepts
- Elasticsearch - Installation
- Elasticsearch - Populate
- Migration between Versions
- Elasticsearch - API Conventions
- Elasticsearch - Document APIs
- Elasticsearch - Search APIs
- Elasticsearch - Aggregations
- Elasticsearch - Index APIs
- Elasticsearch - CAT APIs
- Elasticsearch - Cluster APIs
- Elasticsearch - Query DSL
- Elasticsearch - Mapping
- Elasticsearch - Analysis
- Elasticsearch - Modules
- Elasticsearch - Index Modules
- Elasticsearch - Ingest Node
- Elasticsearch - Managing Index Lifecycle
- Elasticsearch - SQL Access
- Elasticsearch - Monitoring
- Elasticsearch - Rollup Data
- Elasticsearch - Frozen Indices
- Elasticsearch - Testing
- Elasticsearch - Kibana Dashboard
- Elasticsearch - Filtering by Field
- Elasticsearch - Data Tables
- Elasticsearch - Region Maps
- Elasticsearch - Pie Charts
- Elasticsearch - Area and Bar Charts
- Elasticsearch - Time Series
- Elasticsearch - Tag Clouds
- Elasticsearch - Heat Maps
- Elasticsearch - Canvas
- Elasticsearch - Logs UI
- Elasticsearch Useful Resources
- Elasticsearch - Quick Guide
- Elasticsearch - Useful Resources
- Elasticsearch - Discussion
Elasticsearch - Basic Concepts
Elasticsearch is an Apache Lucene-based search server. It was developed by Shay Banon and published in 2010. It is now maintained by Elasticsearch BV. Its latest version is 7.0.0.
Elasticsearch is a real-time distributed and open source full-text search and analytics engine. It is accessible from RESTful web service interface and uses schema less JSON (JavaScript Object Notation) documents to store data. It is built on Java programming language and hence Elasticsearch can run on different platforms. It enables users to explore very large amount of data at very high speed.
General Features
The general features of Elasticsearch are as follows −
Elasticsearch is scalable up to petabytes of structured and unstructured data.
Elasticsearch can be used as a replacement of document stores like MongoDB and RavenDB.
Elasticsearch uses denormalization to improve the search performance.
Elasticsearch is one of the popular enterprise search engines, and is currently being used by many big organizations like Wikipedia, The Guardian, StackOverflow, GitHub etc.
Elasticsearch is an open source and available under the Apache license version 2.0.
Key Concepts
The key concepts of Elasticsearch are as follows −
Node
It refers to a single running instance of Elasticsearch. Single physical and virtual server accommodates multiple nodes depending upon the capabilities of their physical resources like RAM, storage and processing power.
Cluster
It is a collection of one or more nodes. Cluster provides collective indexing and search capabilities across all the nodes for entire data.
Index
It is a collection of different type of documents and their properties. Index also uses the concept of shards to improve the performance. For example, a set of document contains data of a social networking application.
Document
It is a collection of fields in a specific manner defined in JSON format. Every document belongs to a type and resides inside an index. Every document is associated with a unique identifier called the UID.
Shard
Indexes are horizontally subdivided into shards. This means each shard contains all the properties of document but contains less number of JSON objects than index. The horizontal separation makes shard an independent node, which can be store in any node. Primary shard is the original horizontal part of an index and then these primary shards are replicated into replica shards.
Replicas
Elasticsearch allows a user to create replicas of their indexes and shards. Replication not only helps in increasing the availability of data in case of failure, but also improves the performance of searching by carrying out a parallel search operation in these replicas.
Advantages
Elasticsearch is developed on Java, which makes it compatible on almost every platform.
Elasticsearch is real time, in other words after one second the added document is searchable in this engine
Elasticsearch is distributed, which makes it easy to scale and integrate in any big organization.
Creating full backups are easy by using the concept of gateway, which is present in Elasticsearch.
Handling multi-tenancy is very easy in Elasticsearch when compared to Apache Solr.
Elasticsearch uses JSON objects as responses, which makes it possible to invoke the Elasticsearch server with a large number of different programming languages.
Elasticsearch supports almost every document type except those that do not support text rendering.
Disadvantages
Elasticsearch does not have multi-language support in terms of handling request and response data (only possible in JSON) unlike in Apache Solr, where it is possible in CSV, XML and JSON formats.
Occasionally, Elasticsearch has a problem of Split brain situations.
Comparison between Elasticsearch and RDBMS
In Elasticsearch, index is similar to tables in RDBMS (Relation Database Management System). Every table is a collection of rows just as every index is a collection of documents in Elasticsearch.
The following table gives a direct comparison between these terms−
| Elasticsearch | RDBMS |
|---|---|
| Cluster | Database |
| Shard | Shard |
| Index | Table |
| Field | Column |
| Document | Row |