Difference between Elasticsearch and Hadoop

Elasticsearch debuted on February 8, 2010. Programmers primarily utilize Java. Elasticsearch has an HTTP web interface and JavaScript Object Notation documents. Shay Banon created "Compass" in 2004 as a precursor to Elasticsearch. Shay Banon renamed Compass Elasticsearch and created a common interface called JavaScript Object Notation (HTTP). JSON is a better programming language than Java.

On April 1, 2006, Doug Cutting and Mike Cafarella created Hadoop. It is an open-source software developed by Apache Software Foundation. Hadoop's core has two parts. First is the processing part, then storage. Hadoop's storage and processing segments are HDFS and MapReduce, respectively. Hadoop divides huge files into smaller blocks that are randomly dispersed across the system's nodes. After that, it inserts code into nodes to filter data in parallel.

What is Elasticsearch?

Elasticsearch is a distributed search and analytics engine that leverages RESTful protocol. It's the foundation of the open-source, free Elastic Stack and is responsible for centralised data storage, blazing-fast searches, and scalable analytics. It started as a full-text search engine but is becoming an analytical engine that supports complex aggregations.

Despite originally being a full-text search engine, it's built on Lucene, an Apache Software Foundation-backed Java search engine framework. Apache Lucene is a search library. Elasticsearch is organically distributed and easy to use, so it's easy to get started and grow as more data is added. Due to its extensive aggregation mechanism and data storage capabilities, it can also serve as an analytics framework.

Basic Concepts of Elasticsearch

To better understand how Elasticsearch works, let's go over how it organizes data and what its backend parts are.

  • Documents − Elasticsearch indexes files. JSON is the internet data format. Records are like documents. Relational database records represent entities. Elasticsearch documents are structured JSON data. Dates, strings, and integers are data. Each document has an ID and data type. Encyclopedia entries and server logs are documents.

  • Indices − Indexes compare documents. Elasticsearch queries indexes. Relational database schemas include indexes. Most indexes are linked. Customers, Products, Orders, etc. can be indexed on e-commerce websites. Name is used while indexing, searching, updating, or removing documents.

  • Inverted Index − It monitors words or numbers in a document or series. It ties words to documents like a HashMap. Strings aren't stored in inverted indexes. Instead, it links search words to documents.

Backend Components

  • Node − A node stores data and takes part in the cluster's ability to index and search. There are different ways to set up an Elasticsearch node

  • Master Node − This node runs the Elasticsearch cluster and is in charge of all cluster-wide tasks.

  • Data Node − A node that stores data and runs operations on it, like searching and grouping.

  • Client Node − Sends requests about the cluster to the master node and requests about data to the data nodes.

  • Cluster − A collection of one or more Elasticsearch node instances that are interconnected to form a cluster is known as Elasticsearch.

  • Shards − Elasticsearch lets you break the index up into smaller pieces called "shards." Each shard is its own fully functional and independent "index" that can be hosted on any node in a cluster.

  • Replicas − You can make as many copies of your index's shards as you want with Elasticsearch. These copies are called "replica shards" or just "replicas." A replica shard is, in essence, a copy of a primary shard.

What is Hadoop?

Apache Hadoop is an open-source Java platform. It manages the processing and storage needs of data-intensive applications. The Hadoop platform must first distribute large data and analytics jobs among the computer cluster's nodes. These tasks are then separated into reasonable workloads that can be completed simultaneously.

Hadoop can process structured and unstructured data and scale from one server to thousands without sacrificing reliability. HADOOP-based programs run on clusters of commodity machines with massive data collections. These machines are cheap and numerous. They offer more processing power at a lower cost. Hadoop employs a distributed file system called the Hadoop Distributed File system to store its data. This is like saving data on a PC's local file system.

At its base, Hadoop is composed of two primary layers, which are −

  • The Processing and Computation layer, also known as the Map Reduce layer.

  • The Storage layer also known as Hadoop Distributed File System.

Map Reduce Layer

Google developed MapReduce for creating distributed applications. It was intended for dependable and fault-tolerant processing of multi-terabyte data sets on huge clusters (thousands of nodes) of commodity hardware. Hadoop is an Apache-managed open-source platform where MapReduce is implemented. This is like saving data on a PC's local file system.

Hadoop Distributed File System

Hadoop Distributed File System (HDFS) is based on Google File System (GFS), which operates on commodity hardware. It's like other distributed file systems. However, this system differs significantly from others. It's error-tolerant and runs on low-cost hardware. It delivers high throughput for accessing application data and is suitable for large datasets.

In addition to the two primary components, the Hadoop framework includes the two modules below.

  • Yet Another Resource Negotiator (YARN) − It manages the cluster's nodes and resources. It schedules work.

  • Hadoop Common − Offers standard Java libraries that are applicable to all modules and can be used by any of them.

Comparison between Elasticsearch and Hadoop

Hadoop is different from Elasticsearch in that Elasticsearch is just a kind of search engine. Hadoop, on the other hand, has a distributed filesystem that is mostly used to validate data in parallel.

Basis of Comparison
Elasticsearch is built on REST and provides API endpoints for HTTP CRUD and cluster monitoring. It expands our options for managing, integrating, and querying indexed data.
Hadoop is a free software platform that stores and processes data using a masterslave architecture and the MapReduce programming methodology. HDFS was designed to handle Big Data. Highperforming.
Use case
Elasticsearch searches full-text. Advanced aggregation supports analytics. It can do realtime queries offline or in batches. It handles search and groups.
Hadoop stores data and runs applications on commodity hardware clusters. Highest-level file storage of credibility in the world, HDFS.
A complete query on Digital Subscriber Line can be obtained through Elasticsearch by using JavaScript Object Notation.
When conducting analyses on massive amounts of data, Hadoop makes use of the MapReduce programming model.
Elasticsearch is compatible with any and all operating systems that contain Java Virtual Machine.
Hadoop may run on Unix, Linux, and even Windows operating systems.
Elasticsearch runs a fulltext search engine and an analytics framework. Users have both options.
Hadoop is a system that may be leveraged to store data and to execute programs in clusters.
Prior to the data being uploaded, it is recommended by Elasticsearch that the data be in a generic key-value format.
Hadoop's NoSQL technology makes it easy to upload keyvalue data.


Elasticsearch is a powerful tool for full text search and document indexing that was built on top of Lucene, which is a search engine software library that was entirely written in Java. On the other hand, Hadoop is a data processing framework that can handle large volumes of data in a fraction of a second, whereas Elasticsearch is a search engine software library that was entirely written in Java.

Updated on: 05-Jul-2022


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