As the time people spend on the internet increases, the amount of data accumulated on websites also increases. Huge amounts of data are produced on many websites and they are called big data. Most of this data is scattered, meaningless and unstructured on their own. To make these data meaningful for businesses, to be accessible and analyzed quickly and accurately; it is vital in many aspects such as providing customer loyalty, developing a marketing strategy, and seeing problems. Many of the search tools do not work in such large, dispersed, and stand-alone data collections. ElasticSearch is a search tool developed to meet such needs of businesses. As GTech, we shared various information about ElasticSearch in this article.
How was ElasticSearch born?
ElasticSearch is a full text search engine and analysis tool developed on the Apache Lucene infrastructure using Java programming language. Lucene, developed to perform searches on huge text files on a single machine, was insufficient for searches on instant data and distributed systems; so ElasticSearch was born and it has gained popularity in a short time with its flexible structure and capabilities such as working with real-time data in distributed systems.
What are the Advantages of ElasticSearch?
- It can operate in a distributed and highly scalable structure.
- It is open sourced.
- It has RestfullAPI support.
- It is close to the reality. This way, datas can be found via searches with SearchElastic seconds after they have been saved.
- It can be backed up easily.
- It works using very few resources compared to its alternatives.
- It has a simple cluster structure.
- Since it indexes, it returns search results quickly.
- It indexes documents as JSON, it supports many programming languages.
- It can be mapping according to the data type.
- It has fast and easy installation.
- It allows data transfer from databases such as MongoDB, NoSQL, Cassandra and HBase to ElasticSearch.
- It has an auto-complete feature.
How does ElasticSearch Works?
While saving any data to ElasticSearch, previously determined fields in the data are indexed. Since ElasticSearch performs this process at the first moment of the data record and classifies the data according to the index list, it can reach the search results quickly.
What are ElasticSearch’s Basic Concepts?
- Indice: While searches are performed through databases in classical search engines, ElasticSearch searches are performed over index files called “Indice”. There could be more than one database (Indice) in ElasticSearch Cluster.
- Document: It represents the rows in Type structures in ElasticSearch and Type’s are made up of these structures.
- Mapping: It is the process of defining the data type of the indexed data.
- Field: Column in other database types is called Field in ElasticSearch. Each Document can contain more than one Field.
- Cluster: It is the sum of one or more nodes. Thanks to the cluster, index creation and search capabilities that include all data are created.
GTech Big Data and Advanced Analytics services providing consultancy services to determine the most suitable big data platform and capacity for the big data needs of the enterprises. It also undergoes a health screening of the existing big data ecosystems and makes the most appropriate development recommendations.
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