What is Elasticsearch? How it works?

Some auxiliary tools are needed in the processes of storing, storing and analyzing Big Data in the most accurate way. Elasticsearch is used for Full text search needs among Big Data.

ElasticSearch is a search tool developed with R&D studies that meets the search needs of businesses. Elasticsearch's working system carries out its searches through indexes instead of directly searching and querying over texts (Texts). 

This method, which gives much faster results than classical searches, also allows statistical analysis and scoring on queries. When data is saved to Elasticsearch, the fields determined in the data begin to be indexed with the use of Apache Lucene infrastructure. 

Elasticsearch indexes in which document the defined word is included in ROW (Rest Of the World ) during the recording process of the data. When a word query is made later, instead of processing all the data, the results are quickly found based on the index list created before.

What is Elasticsearch? What Does It Do?

In today's technology age, the increase in the duration of people's internet use also causes an increase in the amount of data on Web sites. Huge amounts of data are produced on Web sites around the world, and these are referred to as Big Data . A high proportion of big data data is more scattered, meaningless on its own, and unstructured. 

In terms of businesses, it is necessary to make the existing data meaningful, to be easily and quickly accessible, and to be able to analyze. processing of data; It is important in many ways such as creating customer satisfaction and loyalty, developing marketing strategies, and identifying problems. 

In most search engines, such dispersed, high-capacity, meaningless data collections on their own are not functional. Elasticsearch (Flexible Search) was developed at this point to recognize and meet the accurate search needs of businesses. The Elasticsearch search tool has the capacity to provide businesses with the specific data they need. 

Elasticsearch is a text search engine and analysis tool built on the Apache Lucene infrastructure and developed with the Java programming language. Elasticsearch has emerged as a result of Lucene's inability to search in instant data groups and scattered systems. Elasticsearch has managed to become popular in a short time with its flexible working structure, its ability to process data in real time in all dispersed systems.

What are the Advantages of Elasticsearch (Flexible Search)?

The advantages of Elasticsearch, which perfectly performs searches that are vital for businesses' digital marketing strategies, are indicated as follows;

It is capable of working on highly scalable and distributed ordered structures. 

It has open source software. 

Restful API (API that uses an architectural approach capable of communicating over HTTP protocol) is supported. 

Real-time, data is listed in Elasticsearch searches immediately after recording. 

Backup processes are quick and easy. 

It works with less resource usage. 

It has a simple Cluster structure. 

With the indexing feature, the search results are transmitted quickly. 

Documents are indexed as JSON ( Java S cript Object Notation ) "Javascript Object Notation" and supports different programming languages. 

It can perform data type-specific mapping. 

Installation is quick and easy. 

It has auto-complete feature. 

What is Elasticsearch Template? How to use?

Elasticsearch is a popular text search engine used by all small and large businesses in the business world. Elasticsearch also offers businesses the opportunity to scale and analyze. It also provides the determination of the words, fields and result lists to be searched. Elasticsearch completes the data transfer process in a few seconds. Being fast and easy to use is among the reasons why Elasticsearch is preferred. How and in which areas Elasticsearch is used is stated as follows;

The use of Elasticsearch in text searches: It is especially used in searches of one or more texts and is the right choice to create the most appropriate match with the specified phrases. 

The use of Elasticsearch in contact searches between the product and the customer: Elasticsearch enables the creation of customer / contact records more quickly with the use of text-based search and structured data. With Elasticsearch, the recorded data can be accessed directly from the search results. 

Use of Elasticsearch in data collection: Elasticsearch supports the creation of a list of collected data related to the search query. The detail information of the result page is customized with Elasticsearch. 

Using Elasticsearch in multi-language option: Elasticsearch can be integrated into the specified language or languages ​​when requested. Elasticsearch optimization can be provided with the selected language.

What is Elasticsearch Analyzer?

Elasticsearch Analyzer packages are used in the processing of the searched words or phrases. There are Elasticsearch Analyzer alternatives according to the status of the operations to be performed or the language structure. Generally, Standard Analyzer is used by default and index is taken as analyzer in the default definition.

It means that the defined area is processed and indexed accordingly. In the areas that should not be processed, "Not analyzer" is recommended, it means to be excluded from the process. Default Analyzer API support is used to determine whether the Analyzer meets the needs and to determine alternative Analyzer selections. 

When Elasticsearch detects a field, it defines it as a Full Text String (the term used to search for group words) and performs its operations using the Default Standard Analyzer. As soon as the command is run, the words are parsed.

In the use of Elasticsearch Analyzer, the indexing of the word " Country " instead of the word " Country " and the word " Internet " instead of "my internet " is determined by the command.

While working with Elasticsearch Analyzer, which also shows high performance on specific words and phrases, some words or phrases are exempted from searches if desired. The choice of Elasticsearch Analyzer directly affects the results. It is possible to get the best results by choosing the appropriate Elasticsearch Analyzer.

What is Elasticsearch Index? What Does It Cover?

Elasticsearch carries out its searches through indexes (Indexes) instead of searching directly within texts (Texts). The definition of Elasticsearch Index introduces two new concepts involved in transactions. These; Shared and Replica .

1. Shared

Elasticsearch search tool has Cluster infrastructure. Thanks to this feature, scaling is provided within the system in the processes of recording Big data data and when high traffic is received. 

There are nodes (Nodes) in cluster structures and Elasticsearch processes on each node. The feature of nodes is that they communicate with each other. Shards also work inside the nodes. Shards represent the smallest units in which documents are located. 

Shards that run periodically inside the nodes are essentially Apache Lucene (Elasticsearch infrastructure) applications and are held responsible for indexing (Directory processing) of data.

2. Replica 

In Elasticsearch, there is a Replica Shard structure, which is developed against the possibility of the Shards being disabled, which allows one or more copies to be created. Replicas are important for high availability. Shared and Replica concepts are very important for Elasticsearch architecture. 

Other Concepts of Elasticsearch 

What is Elasticsearch Aggregation?

Aggregation refers to the structures that provide data collection and analysis of the collected data on the documents processed with the search results. For example; When a search is made on a specific product, it is not always possible to deduce which categories the product is in and how many products there are.

However, with Elasticsearch Aggregation, it is possible to perform all kinds of queries and analyzes over category information.

What is Elasticsearch Logstash Kibana?

Elasticsearch is a search engine tool that enables text searches through applications and analysis of collected data. Logstash is the name given to the tool that enables the data collected with Elasticsearch to be organized and made meaningful. Kibana, on the other hand, is responsible for performing the visualization processes after the analysis of the collected and meaningful data. 

What is Elasticsearch Mapping?

Elasticsearch mapping refers to the structure of the data involved in search operations. It is also known as the schema that creates the database. It shows in which fields the Elasticsearch data is formed, the types and characteristics of these fields. It also determines which fields will be included in indexing and which fields will be kept in Lucene (Elasticsearch infrastructure).

What is Elasticsearch Integration?

Elasticsearch and Apache Spark (An open source analytics engine for processing large-scale data) are among the most popular tools used in the Big data world. With the integration of Apache Spark and Elasticsearch, it is possible to perform enormous tasks. While Big data data is processed with Apache Spark analysis engine, it is possible to search, analyze and visualize this data with Elasticsearch. There are also storage options.