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Introduction to Elasticsearch Features

The following article provides an outline for Elasticsearch Features. A search engine which has a web interface and JSON documents provided for free using Lucene Library is called Elasticsearch. It was developed in 2010 by Apache Lucene in Java programming language with a full capable text engine in distributed and multi-tenant levels. Any kind of data, be it diagram or text, structured or unstructured, can be searched with the help of Elasticsearch, which we can consider as a server processing JSON data and returning JSON output to the users. Performance is good when compared with its peers as it uses horizontal scalability and single text query for any searches.

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Various Elasticsearch Features

Elasticsearch has a license in source-available server-side public license and Elastic license, making it a big enterprise search engine with the proprietary license. We can do any type of search in Elasticsearch that delivers results faster for all the clients at the same time, even for the same search word or data. Data collection and index creation of the data along with log parsing forms Elastic Stack or ELK. We have four products in ELK such as data collection, log parsing, visualization platform, and lightweight data shippers. Users can use the filtering technique to filter out all the available options and reach their specific answers for the search options.

All the features are made available through Java API, which notifies users about the upload of new documents. This also helps to retrieve the index data when there is a server crash making it easy to search via JSON scripts. We can call Elasticsearch a NoSQL datastore where we can store data and retrieve the same, but we cannot make any transactions considering it as a database.

Several security features are enabled in Elastic stack, making it encrypted for any type of communications, managing data and users within the database, and controlling user access in the server using RBAC control (Role-Based Access Control). Furthermore, security alerts are generated in the database that gives information to the infrastructure team about the security threats which they can monitor and fix in real-time. In addition, modules in security and machine learning are offered that help users to learn about the same and apply it in Elasticsearch.

We have a cloud option in Elasticsearch called Elastic cloud, making the users to store information in the cloud platform and retrieve it whenever needed. This is used as a managing service so that people can store, retrieve and create data into the cloud platform. Clusters are created easily and managed with the help of Java API. Mounts are developed to connect with the storage with the help of Java API.

Elasticsearch is highly available. We have different clusters in the application which hold the server, and with one or two nodes being down, other nodes are held up and do the searching, providing prompt response to the users. This makes the search engine highly available. The data in the node is not lost but rather is stored in the database as shards. This can be retained by the application if needed by the user.

Horizontal scalability is provided by Elasticsearch, which the user from their end can do. With all the searches, if they run out of resources, they can add nodes so that clusters will add related resources automatically, making the server scalable in horizontal form. This will not burden the server as the clusters and nodes manage the load.

In the cloud storage, we have data tiers where we can decide how long to store the data and based on which cost will be incurred. This policy is available in different cloud platforms making users easy to understand the data distribution. The performance will be increased, and the process can be automated to easily move the data from one tier to another tier. For example, when we search the same data often, we can store the data in a hot tier, whereas if the data is not queried frequently, we can store the data in the warm tier. If the data is not queried and we want to store the data, cold tier is the better option, reducing the cost and improving performance.

It is good to have historical data in place so that we can do the analysis of data and make necessary changes in the server management for a better future. But this will always incur costs. Cold tier is the best option to avoid data storage costs. Also, Elasticsearch has data streams that help us to search time-bound data within different time frames. Elasticsearch also has transformed helps to transform data into a different tabular format. This helps in the aggregation and visualization of data as per user’s requirements so that they can do data analysis easily.

Java API helps users in various methods. We have Assistant API, which informs users about the upgrade of indexes and nodes so that they will not miss the timeline. The key management is present in API, but it is limited to users so that unauthenticated logins will not happen to mess with the data. A Keystore is provided, which can be locked with a security key so that all users cannot see the keys and tamper with the values. The traffic attack can be avoided using encryption in SSL or TLS, making it more useful for the developers to identify the hackers.

We also have ABAC (Attribute-Based Access Control) in Elasticsearch so that users with specific attributes can access the servers for their specific needs. Document-level security is present, making the read access enabled users to lock their documents stored in the cloud storage. In addition, auditing can be done to mark the errors in the server so that these can be avoided in the later stage.


The features of Elasticsearch are huge, that users are happy to use the search engine basically for the feature of searching any data in the HTTP enabled engine. The requests are processed faster with less lead time, and the cloud options are also provided. Authentication systems are in place with all the security features enabled.

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This is a guide to Elasticsearch Features. Here we discuss the introduction and various elasticsearch features for better understanding. You may also have a look at the following articles to learn more –

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