In today’s world of technology, large amounts of data are generated every day at approximately 2.5 quintillion bytes (about 2.5 billion bytes). This information comes mainly from a variety of sources, for example, mass communication sites, video sharing sites, and media for large organizations. This data is called the ocean of data or, more generally, the Big Data. A significant portion of this data is unstructured and fragmented, and one needs analytical tools to understand it. Many analytical tools on the market can review, record, analyze and process this data. One of the most widely used tools is Elasticsearch. So in this article, we want to know what it is Elasticsearch? Talk.
What is Elasticsearch?
Elasticsearch is a product of a company called Elastic, which was founded in 2012. Elasticsearch is a full-featured open-source search engine developed in Java. It takes unstructured data from various sources and stores it in a complex format that is highly optimized for text search. Elasticsearch uses Lucene Apache at its core for indexing and searching. Lucene is a complicated library to work with, But you do not have to worry because Elasticsearch eliminates all the hassle by providing API usability. The API is a RESTful HTTP API that uses JSON as the data exchange format; As a result, using Elasticsearch, a large amount of data can be stored and analyzed quickly and efficiently. This is especially useful when dealing with semi-structured natural language data.
Where is Elasticsearch used?
Elasticsearch scalability and speed are high and can be used for:
- Search the app
- Website Search
- Organizational search
- Analysis of input information
- Check program performance
- Analysis and visualization of data
- Security analysis
- Business analysis
Concepts used by Elasticsearch
To better understand and understand what Elasticsearch is? There are some concepts we need to be familiar with:
Near Real-Time
Elasticsearch is a real-time search platform; it can continuously program a new state of searchable documents. The default rate is any situation per second. Therefore, the amount of time from searching the document for the user to the moment of announcing the results is small and insignificant.
Index
Elasticsearch is a collection of documents that have similar properties. This data is stored in one or more indexes using SQL statements, and the indexes are used to store and read documents. In Elasticsearch, an index with a unique name is identified, and all letters must be lowercase. The name is then used to refer to a specific index when performing various activities on existing documents.
Document
In Elasticsearch, a document is the primary unit of information that can be indexed. These documents contain different fields, and each of these fields is identified by its name and can contain one or more values. Documents are also completely free, and multiple documents can be stored in one index.
Type
In Elasticsearch, a type is defined for documents consisting of sets of fields. The document type is user-defined, and more than one type can be specified in an index.
Node
A node is an instance of an Elasticsearch server that stores data. A node has a name and is identified by it. By default, a unique random identifier is assigned to the node at startup. This name is used for administrative purposes.
Cluster
A cluster is a collection of one or more nodes or servers that work together. The cluster holds all the data, allows easy search in all nodes, and makes it easy to control the information for each node. As a node, a cluster is identified by a unique name called “elastic search” by default. The cluster name is used to connect multiple nodes to a cluster, so the cluster name is so important.
Shards
Storing large amounts of information can go beyond the capabilities of a single server. Elasticsearch allows the index to be split into several sections called shards to solve this problem. The number of sections required can be specified while creating an index. Each shard is completely independent of each index and can host any node within the cluster.
Replicas
To prevent any random errors, such as sharing an offline node, Elasticsearch offers a copy-like concept. Replicas are essentially just another copy of a snippet and can be used as a snippet for queries.
What is the Elasticsearch mechanism?
Elasticsearch is a collection of related documents that stores data as JSON documents. Each document associates a set of keys with its corresponding values, such as strings, numbers, Booleans, dates, arrays containing values, or other data types.
Elasticsearch also uses a reverse index data structure, which is designed for full-text searches. An inverse index lists each unique word that appears in each document and identifies all the documents in which that word occurs.
During the indexing process, Elasticsearch stores the documents and creates a reverse index to search the data in real-time. Indexing begins with the API, through which a JSON document can be updated or added to a specific index. The proposed APIs are:
- Index API: Used to record and index.
- Get API: Used to retrieve a document.
- Search API: Used to send requests and receive results.
- Put API: Used to select default options and define a map.
Other APIs can also be tested and created as needed; Because real-world projects require different queries in different fields by applying their conditions.
All this complexity can be facilitated through queries. DSL is a powerful query designed to handle complexity through a query. Elasticsearch APIs are directly related to Lucene and use the same name to work with Lucene. DSL also uses Lucene TermQuery to run.
What are the benefits of using Elasticsearch?
There are many benefits to using Elasticsearch; These benefits include the following:
- Scalability: As the volume of data increases, the performance remains very simple, and the results are reliable. This is a very important feature that helps to simplify complex architectures and save time during project execution.
- Speed: Elasticsearch uses reverse indexing. As we learned in the previous section, invert indexing is a word-based method used to search for documents containing a particular word quickly. As a result, it is very fast, even when searching in very large datasets.
- Using the API: Elasticsearch Provides simple RESTful APIs and uses template-free JSON documents that make indexing, searching, and querying data very easy.
- Multilingual: One of the features of Elasticsearch is that it is multilingual. The engine supports extensive text in various languages such as Arabic, Brazilian, Chinese, English, French, Korean, and more.
- Document optimization: Elasticsearch structures the complex nature of the real world as JSON documents and gathers all concepts into an index by default so that data can be searched. Since there are no rows and columns of data, it is easy to search the full text.
- AutoComplete: Elasticsearch speeds up human-computer interaction by predicting the word (even if it contains very few characters).
- Free schema: Elasticsearch is free of schema, even though it accepts JSON documents. Elasticsearch tries to identify the structure of the data, index the data, and finally be able to search the data.
What are the disadvantages of Elasticsearch?
It should be noted that Elasticsearch should be used when the data under review has properties that make it possible to use Elasticsearch’s strengths. Because if it is not, the result will be the opposite, and those aspects expressed as advantages will become disadvantages. However, Elasticsearch is not without its drawbacks. Some of these disadvantages are:
- Unlike some systems that work in CSV, XML, and JSON formats, Elasticsearch does not have multilingual support in request and response management when working with services.
- Elasticsearch also has a split-brain problem. This problem occurs when communication between servers is lost, and it is difficult to maintain two separate datasets that overlap in the same subject.
- If one is not fluent in it, using tools like Algolia is not easy. Of course, as we said, Elasticsearch is a more powerful and flexible method than normal, but it still takes time to learn.
Install Elasticsearch and get started
We mentioned earlier that Elasticsearch was developed in Java; therefore, you must install the latest version of Java in the first step. If Java is installed, check its version using the Java grammar in cmd. Note, however, that many libraries have been developed to use Elasticsearch with other programming languages such as .NET (C #), Python, JavaScript, PHP, Ruby, Perl, and others.
You have to go to the Elasticsearch site and download it in the second step. Unzip the file, place it on the server, and double-click the .bat file to run it in the elasticsearch-XYZ / bin path.
In the third step, could you wait for it to start working? Type in the browser: localhost: 9200 or 127.0.0.1:9200 because, by default, elasticsearch runs on the local IP and port 9200 and can be used.
If you can see the message specified in the browser, everything is correct. If you have a question or got an error in the installation, you can share it in the comments section.
What are Elasticsearch-related tools?
Elasticsearch recommends several tools to make working with Elasticsearch easier, including:
- Logstash: is one of the main elastic products used to collect and process data and send it to Elasticsearch. Logstash is a server-based data processing pipeline that allows data to be recruited from multiple sources simultaneously and converted to Elasticsearch before indexing.
- Kibana: A visualization and management tool for Elasticsearch that provides histograms, line charts, pie charts, and maps. Kibana also includes advanced applications that allow users to create custom infographics based on data to visualize spatial data better.
Conclusion:
Elasticsearch is a powerful engine with high capabilities for data search and analysis. What is in the Elasticsearch article? We first reviewed the related terms and then discussed the Elasticsearch mechanism, its advantages and disadvantages, and how to install it. Finally, we introduced two application tools that interact with Elasticsearch. Many libraries have been developed to work with Elasticsearch that you can use depending on your needs and programming language. To learn more and work with Elasticsearch, you can read the Laravel Elastic Search Training article. If you have a question or experience in this area, let us know.