---
title: "Short note: Elastic Search"
subject: "Big Data Technologies"
chapter: 5
key: "semester_8/big_data_technologies:short-note-elastic-search"
source: https://www.bctengineer.com/q/semester_8/big_data_technologies/short-note-elastic-search
site: BCT Engineering
---

# Short note: Elastic Search

Big Data Technologies · Chapter 5: Searching and Indexing Big Data

- Asked in: 2074 Bhadra, 5 marks
- Video: [What is Elasticsearch?](https://www.youtube.com/watch?v=ZP0NmfyfsoM) — IBM Technology
- Answer checked by Claude Opus 5.5 (high effort) on 2026-10-14
- Page: https://www.bctengineer.com/q/semester_8/big_data_technologies/short-note-elastic-search
- Whole chapter: https://www.bctengineer.com/md/semester_8/big_data_technologies/chapter-5

## Question

Write a short note on Elastic Search.

## Short answer

Elasticsearch is a distributed, RESTful search engine on Apache Lucene that stores JSON documents in sharded and replicated indices. It provides full-text search, real-time analytics, aggregations, and near real-time indexing, often used in the ELK stack for search and log analysis.

## Answer

**Elasticsearch** is an open-source, distributed, RESTful search and analytics engine built on top of **Apache Lucene**. It stores data as JSON documents and makes them searchable in near real time. It is the core of the ELK stack (Elasticsearch, Logstash, Kibana).

#### Key concepts

| Elasticsearch | Relational analogy |
|---|---|
| Index | Database |
| Document (JSON) | Row |
| Field | Column |
| Shard | Partition of an index |

- **Cluster and node:** a cluster is a group of nodes that together hold the data; a node can have the master, data or client (coordinating) role.
- **Index:** a collection of similar documents.
- **Shards and replicas:** an index is split into primary shards spread over the nodes for scaling; each shard can have replica copies for fault tolerance and faster reads.

#### Features

- Full-text search with relevance ranking, fuzzy matching and auto-complete
- Distributed, horizontally scalable and highly available
- Schema-free JSON documents, with dynamic mapping
- REST API over HTTP: `GET /books/_search?q=hadoop`
- Powerful aggregations for analytics
- Near real-time indexing and search

Example:

```text
PUT /books/_doc/1
{ "title": "Hadoop Guide", "author": "Tom White" }

GET /books/_search
{ "query": { "match": { "title": "hadoop" } } }
```

#### Uses

Website and e-commerce search, log and event analysis (with Logstash and Kibana), monitoring, and security analytics. Large organisations such as Wikipedia, GitHub and Netflix use it for search.

## Check yourself

The answer's key points as multiple-choice questions, with the correct option and why.

1. What is Elasticsearch?
   A. A relational database that uses SQL tables
   B. A batch tool for moving data from RDBMS to HDFS
   C. A resource manager for Hadoop
   D. A distributed, RESTful search and analytics engine built on Apache Lucene that stores JSON documents
   Answer: D. Elasticsearch is built on Lucene and stores data as JSON documents, searchable in near real time. (Key point: What Elasticsearch is)

2. What is the purpose of shards and replicas in Elasticsearch?
   A. Shards give fault tolerance; replicas split data for scaling
   B. Primary shards split an index across nodes for scaling; replicas give fault tolerance and faster reads
   C. Both are used only for backup of the schema
   D. Shards are rows and replicas are columns
   Answer: B. An index is split into primary shards over nodes for scale, and replica copies provide fault tolerance and read speed. (Key point: Shards and replicas)

3. In the relational analogy, a JSON document in Elasticsearch corresponds to what?
   A. A database
   B. A column
   C. A row
   D. A table partition
   Answer: C. Index is like a database, document like a row, field like a column, and shard like a partition. (Key point: Relational analogy)