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The Ultimate Guide for ElasticSearch Interview Questions

Elasticsearch is a powerful search and analytics engine built on Apache Lucene. Since it was launched in 2010, it has become incredibly popular and is widely used for various purposes, including log analytics, full-text search, security intelligence, business analytics, and operational intelligence.

However, on January 21, 2021, Elastic NV, the company behind Elasticsearch, announced a major change in their software licensing. They decided to stop releasing new versions of Elasticsearch and Kibana under the permissive Apache License, Version 2.0 (ALv2). Instead, future versions would be available under the Elastic License or SSPL, which are not open-source licenses and do not provide the same freedoms to users. In response to this change, to ensure that the open-source community and customers continue to have access to a secure, high-quality, fully open-source search and analytics suite, the OpenSearch project was introduced. OpenSearch is a community-driven fork of Elasticsearch and Kibana, licensed under the open-source ALv2 license.

ElasticSearch Interview Questions

How Elasticsearch work?
You can send data to Elasticsearch as JSON documents using its API or tools like Logstash and Amazon Data Firehose. Elasticsearch will store the original document and create a searchable reference to it in the cluster’s index. You can then search for and retrieve the document using the Elasticsearch API. Additionally, you can use Kibana, a visualization tool, to view your data and create interactive dashboards.

Q1. What is a shard in Elasticsearch? What are the different types of shards in Elasticsearch?
Ans: In Elasticsearch, a shard is a basic unit of storage and indexing. An index is divided into smaller pieces called shards, which are distributed across the nodes in a cluster.

Types of Shards:

  • Primary Shards: These are the original shards that store the data.
  • Replica Shards: These are copies of the primary shards that provide redundancy and high availability.

Q2. What is Elasticsearch Mapping?
Ans: Elasticsearch Mapping is the process of defining how documents and their fields are stored and indexed in an Elasticsearch index. It includes setting the data types (e.g., text, keyword, date) and configuring analyzers.


  "mappings": {
    "properties": {
      "title": { "type": "text" },
      "date": { "type": "date" },
      "views": { "type": "integer" }

Q3. What is Elasticsearch fuzzy search?
Ans: Elasticsearch fuzzy search is a query that matches documents containing terms similar to the specified search term, accommodating for typos and misspellings. It uses the Levenshtein edit distance algorithm.

Example: A fuzzy search for “roam” could match “roam,” “foam,” and “roams.”

Q4. What is an Elasticsearch index?
Ans: An Elasticsearch index is a collection of documents that are related to each other. It is similar to a database in traditional relational databases and can contain multiple types of documents.

Example: An index named “blog” might contain documents of types “posts” and “comments.”

Q5. What is Elasticsearch used for?
Ans: Elasticsearch is used for full-text search, analytics, and logging. It allows for quick and scalable searches across large volumes of data.

Example Uses:

  • Search Engines: Efficiently indexing and searching large datasets.
  • Log and Event Data Analysis: Real-time monitoring and analysis.

Q6. What is a document in Elasticsearch?
Ans: A document in Elasticsearch is a JSON object that contains data stored in an index. Each document is a basic unit of information and is analogous to a row in a table of a relational database.


  "title": "Elasticsearch Basics",
  "author": "John Doe",
  "published_date": "2024-05-18"

Q7. What is an Analyzer in Elasticsearch?
Ans: An Analyzer in Elasticsearch is a component that processes text data during indexing and searching. It consists of a tokenizer and a series of token filters that transform the text into indexed terms.

Example: Standard Analyzer: Tokenizes text into lowercase terms, removing most punctuation.

Q8. What is the process of deleting an index in Elasticsearch?
Ans: To delete an index in Elasticsearch, you use the DELETE HTTP request.

Example Command:

DELETE /my_index

This command deletes the index named “my_index” and all its data.

Q9. What is a node in Elasticsearch? What are the different types of nodes in Elasticsearch?
Ans: A node in Elasticsearch is a single server that is part of a cluster and holds data and provides indexing and search capabilities.

Types of Nodes:

  • Master Node: Manages the cluster by handling cluster-wide operations like creating or deleting an index.
  • Data Node: Stores data and performs data-related operations like search and aggregation.
  • Client Node: Routes requests to appropriate data nodes and reduces the load on master and data nodes.

Q10. What configuration management tools does Elasticsearch support?
Ans: Elasticsearch supports several configuration management tools for automating deployment and management:

  • Ansible
  • Chef
  • Puppet
  • Terraform

These tools help manage configurations and orchestrate deployments across multiple environments.

Q11. Does Elasticsearch have a schema?
Ans: Yes, Elasticsearch uses a schema called Mapping. This defines how data fields are stored and indexed. It can be explicit (user-defined) or dynamic (generated by Elasticsearch based on incoming data).

Q12. What types of queries does Elasticsearch support?
Ans: Elasticsearch supports various types of queries, including:

  • Match Query: Full-text search.
  • Term Query: Exact match.
  • Range Query: Range-based searches (e.g., dates or numbers).
  • Boolean Query: Combines multiple queries with boolean operators.

Q13. What do you mean by NRT (Near Real-Time Search) in Elasticsearch?
Ans: NRT (Near Real-Time Search) in Elasticsearch refers to the ability to search data almost immediately after it is indexed. Typically, there is a slight delay (1 second by default) for newly indexed documents to become searchable.

Q14. Explain the ELK stack and its architecture?
Ans: The ELK Stack consists of:

  • Elasticsearch: Search and analytics engine.
  • Logstash: Data processing pipeline that ingests data from various sources.
  • Kibana: Visualization tool for displaying Elasticsearch data.


  1. Logstash collects and processes data.
  2. Elasticsearch indexes and stores the data.
  3. Kibana visualizes and interacts with the data.

Q15. How do you stop the Elasticsearch search service from running on a Linux server?
Ans: To stop the Elasticsearch service on a Linux server, use the following command:

sudo systemctl stop elasticsearch

Q16. What do you mean by aggregation in Elasticsearch?
Ans: Aggregation in Elasticsearch is a way to summarize and analyze data. It allows you to calculate metrics such as totals, averages, and statistics.


  • Terms Aggregation: Count occurrences of distinct values.
  • Date Histogram: Group data by date intervals.

Q17. Explain Tokenizer in Elasticsearch?
Ans: A Tokenizer in Elasticsearch breaks down text into individual terms or tokens. It is a part of the analysis process.

Example: Standard Tokenizer: Splits text into tokens based on word boundaries.

Q18. Describe the functionality of the cat API in Elasticsearch?
Ans: The cat API in Elasticsearch provides compact, easy-to-read information about cluster and index health, state, and statistics. It is useful for quick diagnostics.

Example Commands:

GET /_cat/indices
GET /_cat/nodes

Q19. What is an inverted index in Elasticsearch?
Ans: An inverted index in Elasticsearch is a data structure that maps terms to the documents that contain them. It enables fast full-text searches.

Q20. What is a replica in Elasticsearch?
Ans: A replica in Elasticsearch is a copy of a primary shard. Replicas provide redundancy and improve search performance by distributing the load across multiple nodes.

Q21. Explain Query DSL in Elasticsearch?
Ans: Query DSL (Domain Specific Language) in Elasticsearch is a flexible and powerful way to build queries for searching data. It supports various query types, including match, term, range, and boolean queries.

Q22. What are the advantages of using Elasticsearch over traditional relational databases for search and analytics tasks?
Ans: Advantages of Elasticsearch:

  • Full-Text Search: Advanced text searching capabilities.
  • Scalability: Horizontally scalable across multiple nodes.
  • Real-Time Data: Near real-time indexing and searching.
  • Analytics: Powerful aggregation capabilities.

Q23. What is the role of the Elasticsearch cluster, and how does it function?
Ans: An Elasticsearch cluster is a collection of nodes that work together to manage data and perform search and indexing operations. Clusters distribute data across nodes and provide redundancy and failover.

Q24. How does Elasticsearch handle data redundancy and fault tolerance?
Ans: Elasticsearch handles redundancy and fault tolerance through replica shards. Each primary shard can have multiple replica shards, which ensure data is available even if some nodes fail.

Q25. What is a rollover index in Elasticsearch?
Ans: A rollover index is a feature that automatically creates a new index when an existing index reaches a certain size, age, or document count. This helps manage large datasets efficiently.

Q26. How can you perform a bulk operation in Elasticsearch, and what are its benefits?
Ans: To perform a bulk operation in Elasticsearch, use the _bulk API endpoint. This allows multiple indexing, updating, and deleting operations in a single request, improving performance and reducing overhead.


POST /_bulk
{ "index": { "_index": "test", "_id": "1" } }
{ "field1": "value1" }
{ "delete": { "_index": "test", "_id": "2" } }

Q27. What are the key differences between Elasticsearch and traditional SQL databases?
Ans: Key Differences:

  • Data Model: Elasticsearch uses JSON documents, while SQL databases use tables and rows.
  • Schema: Elasticsearch is schema-less with dynamic mapping, whereas SQL databases have a fixed schema.
  • Search Capabilities: Elasticsearch is optimized for full-text search, while SQL databases are optimized for transactional queries.
  • Scalability: Elasticsearch scales horizontally, SQL databases often scale vertically.

Q28. How does Elasticsearch implement full-text search capabilities?
Ans: Elasticsearch implements full-text search using an inverted index, analyzers, and various query types. It breaks down text into tokens, indexes them, and allows powerful querying across these tokens.

Q29. What is the purpose of index templates in Elasticsearch, and how are they used?
Ans: Index templates in Elasticsearch define settings, mappings, and aliases for indices that match a pattern. They are used to apply configurations automatically when new indices are created.


  "index_patterns": ["log-*"],
  "settings": { "number_of_shards": 1 },
  "mappings": {
    "properties": {
      "host_name": { "type": "keyword" },
      "created_at": { "type": "date" }

Q30. How does Elasticsearch optimize search performance through the use of caching mechanisms?
Ans: Elasticsearch uses several caching mechanisms to optimize search performance:

  • Query Cache: Caches the results of frequently run queries.
  • Field Data Cache: Caches field values for sorting and aggregations.
  • Node and Shard Request Cache: Caches search request results to improve response times for repeated queries.

These caches reduce the need to repeatedly compute expensive operations, thereby speeding up search queries.

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