Elasticsearch Interview Questions and Answers

Top 50 Elasticsearch Frequently Asked Interview Questions are collected based on my Interview Experience on ELK (Elasticsearch, Logstash and Kibana) with different Organization. I have divided these question in three categories  as below .

  • Elasticsearch Overview Questions and Answers.
  • Basic Concepts and Terminology Questions and Answers.
  • Advance and Practical Questions and Answers.

Elasticsearch Overview Questions and Answers

1.What is Elasticsearch?

“Elasticsearch is Open source, cross-paltform, scalable, full-text search and analytical engine based on Apache Lucene technology. It help in NRT (Near Real Time) analysis and full text search on big volume of data on distributed clustered environment.”

  • Elasticsearch is developed by Apache in Java Language.
  • Elasticsearch store records in form of JSON documents as key and value.
  • By Default Schema free if required schema can added by mapping from client app.
  • Access by HTTP over the browser, by application through Elasticsearch REST Client API or Elasticsearch Transport Client.
  • Elasticsearch Organization provide some application and plug-in for making Elasticsearch more useful like Kibana for doing search and Analysis by different charts and Dashboard.

2. What are the advantages of Elasticsearch?

  • Elasticsearch is implemented on Java, which makes it compatible on almost every platform.
  • Elasticsearch is Near Real Time (NRT), in other words after one second the added document is searchable in this engine.
  • Elasticsearch cluster is distributed, which makes it easy to scale and integrate in any big organizations.
  • Creating full backups of data are easy by using the concept of gateway, which is present in Elasticsearch.
  • Elasticsearch REST uses JSON objects as responses, which makes it possible to invoke the Elasticsearch server with a large number of different programming languages.
  • Elasticsearch supports almost every document type except those that do not support text rendering.
  • Handling multi-tenancy is very easy in Elasticsearch when compared to Apache Solr.

3. What are the Disadvantages of Elasticsearch?

  • Elasticsearch does not have multi-language support in terms of handling request and response data in JSON while in Apache Solr, where it is possible in CSV, XML and JSON formats.
  • Elasticsearch have a problem of Split Brain situations, but in rare cases.

4. What is difference and Similarities between NoSQL MongoDB and Elasticsearch?

Elasticsearch is Apache Lucene based RESTful NRT(Near Real Time) search and analytics engine while MongoDB is an open source document-oriented Database Management System.


Certain features are common between both products like Document-oriented Store, Schema free, Distributed Data Storage, High-Availability, Sharding, Replication etc.


 There are many differences between both products as below

Type Elasticsearch MongoDB
  • Uses Apache Lucene for indexing.
  • Real-time indexing and searching power from Lucene, which allows creation of index on every field of a document by default.
  • Based on traditional B+ Tree.
  • Define the index, which improves query performance, but affects write operations.


Language Implemented in Java Implemented in C++
Documents Stores JSON documents Stores them in BSON (Binary JSON) format. (though, it looks same like a JSON document to the end user)
REST Interface RESTful Not RESTful
Map Reduce Not Support MapReduce Allow Map Reduce Operation
Huge Data Store and Retrieve Huge Data Store and Search Huge Data

5. What are common area of use Elasticsearch?

  • It’s useful in application where need to do analysis, statics and need to find out anomalies on data based on pattern.
  • It’s useful where need to send alerts when particular condition matched like stock market, exception from logs etc.
  • It’s useful with application where log analysis and issue solution provide because of full search in billions of records in milliseconds.
  • It’s compatible with application like Filebeat, Logstash and Kibana for storage of high volume data for analysis and visualize in form of chart and dashboards.

6. What are operations can be performed on Elasticsearch Documents?

Elasticsearch perform some basic operations like:

  • Indexing
  • Searching
  • Fetching
  • Updating
  • Delete Documents.

Basic Concepts and Terminology Questions and Answers

7. What is Elasticsearch Cluster ?

Cluster is a collection of one or more nodes which provide capabilities to search text on scattered data on nodes. It’s identified by unique name with in network so that all associated nodes will join together by cluster name.

Operation Persistent : Cluster also maintain keep records of all transaction level changes for schema if anything get change in data for index and track of availability of Nodes in cluster so that make data easily available if any fail-over of any node.

Elasticsearch Cluster

Elasticsearch Cluster

In above screen Elasticsearch cluster “FACING_ISSUE_IN_IT” having three master and four data node.

8.What is Elasticsearch Node?

Node is a Elasticsearch server which associate with in a cluster. It’s store data , help cluster for indexing data and search query. It’s identified by unique name in Cluster if name is not provided in elasticsearch will generate random Universally Unique Identifier(UUID) on time of server start.

A Cluster can have one or more Nodes .If first node start that will have Cluster with single node and when other node will start will add with that cluster.

Data Node storage

Data Node Documents Storage

In above screen trying to represent data of two indexes like I1 and I2. Where Index I1 is having two type of documents T1 and T2 while index I2 is having only type T2 and these shards are distributes over all nodes in cluster. This data node is having documents of shard (S1) for  Index I1 and shard (S3) for Index I2. It’s also keeping replica of documents of shards S2 of Index I2 and I1 which are store some other nodes in cluster.

9. What are types of Node in Elasticsearch?

With in Elasticsearch Cluster each Node know others Node based on configuration decide role/responsibility of each individual Node.  Below are Elasticsearch Node Types.

  • Master-Eligible Node.
  • Data Node.
  • Ingest Node.
  • Tribe Node/Coordinating Node.

10. What is Master Node and Master Eligible Node in Elasticsearch?

Master Node control cluster wide operations like creating or deleting an index, tracking which nodes are part of the cluster, and deciding which shards to allocate to which nodes. It is important for cluster health to have a stable master node. Master Node elected based on configuration properties node.master=true (Default).

Master Eligible Node decide based on below  configuration

discovery.zen.minimum_master_node : number (default 1)

and above number decide based (master_eligible_nodes / 2) + 1

11. What is Data Node in Elasticsearch?

Data nodes hold the shards/replica that contain the documents that was indexed. Data Nodes perform data related operation such as CRUD, search aggregation etc. Set node.data=true (Default) to make node as Data Node.

Data Node operations are I/O-, memory-, and CPU-intensive. It is important to monitor these resources and to add more data nodes if they are overloaded.The main benefit of having dedicated data nodes is the separation of the master and data roles.

12. What is Ingest Node in Elasticsearch?

Ingest nodes can execute pre-processing an ingest pipeline to a document in order to transform and enrich the document before indexing. With a heavy ingest load, it makes sense to use dedicated ingest nodes and to mark the master and data nodes  as false and node.ingest=true.

13. What is Tribe Node and Coordinating Node in Elasticsearch?

Tribe node, is special type of node that coordinate to connect to multiple clusters and perform search and  others operation across all connected clusters. Tribe Node configured by settings tribe.*.

Coordinating Node behave like Smart Load balancer which able to handle master duties, to hold data, and pre-process documents, then you are left with a coordinating node that can only route requests, handle the search reduce phase, and distribute bulk indexing.

Every node is implicitly a coordinating node. This means that a node that has all three node.masternode.data and node.ingest set to false will only act as a coordinating node, which cannot be disabled. As a result, such a node needs to have enough memory and CPU in order to deal with the gather phase.

14. What is Index in Elasticsearch?

An Index is collection of documents with same characteristics which stores on nodes in distributed fashion and its identify by unique name on which perform different operations like insert , search query, update and delete for documents. A cluster can have as many indexes with unique name.

A document store in Index and assigned a type to it and an Index can have multiple types of documents.

15. What is Shards in Elasticsearch?

Shards are partitions of indexes scattered on nodes in order to make  scal. It provide capability to store large amount (billions) of documents for same index to store in cluster even one disk of node is not capable to store it. Shards also maintain Inverted Index of documents token to make full-text search fast.

16. What is Replica in Elasticsearch?

Replica is copy of shard which store on different node or same node. A shard can have zero or more replica. If shard on one node then replica of shard will store on another node.

17. What are Benefits of Shards and Replica in Elasticsearch?

  • Shards splits indexes in horizontal partition for high volumes of data.
  • It perform operations parallel to each shards or replica on multiple node for index so that increase system performance and throughput.
  • Recovered easily in case of fail-over of node because data replica exist on another node because replica always store on different node where shards exist.

Some Important Points:

When we create index by default elasticseach index configure as 5 shards and 1 replica but we can configure it from config/elasticsearch.yml file or by passing shards and replica values in mapping when index create.

Once index created we can’t change shards configuration but modify in replica. If need to update in shards only option is re-indexing.

Each Shard itself a Lucene index and it can keep max 2,147,483,519 (= Integer.MAX_VALUE – 128) documents. For merging of search results and failover taken care by elasticsearch cluster.

18. What is Document in Elasticsearch?

Each Record store in index is called a document which store in JSON object. Document is Similar to row in term of RDBMS only difference is that each document will have different number of fields and structure but common fields should have same data type.

19. What is a Type in Elasticsearch ?

Type is logical category/grouping/partition of index whose semantics is completely up to user and type will always have same number of columns for each documents.

ElasticSearch => Indices => Types => Documents with Fields/Properties

20. What is a Document Type in Elaticsearch?

A document type can be seen as the document schema / mapping definition, which has the mapping of all the fields in the document along with its data types.

21. What is indexing in ElasticSearch ?

The process of storing data in an index is called indexing in ElasticSearch. Data in ElasticSearch can be dividend into write-once and read-many segments. Whenever an update/modification is attempted, a new version of the document is written to the index.

22. What is inverted index in Elasticsearch ?

Inverted Index is backbone of Elasticsearch which make full-text search   fast.  Inverted index consists of a list of all unique words that occurs in  documents and for each word, maintain a list of documents number and positions in which it appears.

For Example  : There are two documents and having content as :

1: FacingIssuesOnIT is for ELK.

2: If ELK check FacingIssuesOnIT.

To make inverted index each document will split in words (also called as terms or token) and create below sorted index .

Term                   Doc_1  Doc_2
FacingIssuesOnIT    |   X   |  X
is                  |   X   |
for                 |   X   |  
ELK                 |   X   |  X
If                  |       |  X
check               |       |  X

Now when we do some full-text search for String will sort documents based on existence and occurrence of matching counts .

Usually in Books we have inverted indexes on last pages. Based on the word we can thus find the page on which the word exists.

23. What is an Analyzer in ElasticSearch ?

While indexing data in Elastic Search, data is transformed internally by the Analyzer defined for the index, and then indexed. An analyzer is building block of  character filters, tokenizers and token filters. Following types of Built-in Analyzers are available in Elasticsearch 5.6.


Standard Analyzer

Divides text into terms on word boundaries, as defined by the Unicode Text Segmentation algorithm. It removes most punctuation, lower cases terms, and supports removing stop words.

Simple Analyzer

Divides text into terms whenever it encounters a character which is not a letter. It lower cases all terms.

White space Analyzer

Divides text into terms whenever it encounters any white space character. It does not lowercase terms.

Stop Analyzer

It is like the simple analyzer, but also supports removal of stop words.

Keyword Analyzer

A “noop” analyzer that accepts whatever text it is given and outputs the exact same text as a single term.

Pattern Analyzer

Uses a regular expression to split the text into terms. It supports lower-casing and stop words.

Language Analyzer

Elasticsearch provides many language-specific analyzers like English or French.

Finger Print Analyzer

A specialist analyzer which creates a fingerprint which can be used for duplicate detection.

24. What is a Tokenizer in ElasticSearch ?

tokenizer receives a stream of characters, breaks it up into individual tokens (usually individual words), and outputs a stream of tokens. Inverted indexes are created and updates using these token values by recording the order or position of  each term and the start and end character offsets of the original word which the term represents.

An analyzer must have exactly one Tokenizer.

25. What is Character Filter in Elasticsearch Analyzer?

character filter receives the original text as a stream of characters and can transform the stream by adding, removing, or changing characters. For instance, a character filter could be used to convert Hindu-Arabic numerals (٠‎١٢٣٤٥٦٧٨‎٩‎) into their Arabic-Latin equivalents (0123456789), or to strip HTML elements like  from the stream.

An analyzer may have zero or more character filters, which are applied in order.

26.What is Token filters in Elasticsearch Analyzer?

token filter receives the token stream and may add, remove, or change tokens. For example, a lowercase token filter converts all tokens to lowercase, a stop token filter removes common words (stop words) like the from the token stream, and a synonym token filter introduces synonyms into the token stream.

Token filters are not allowed to change the position or character offsets of each token.

An analyzer may have zero or more token filters, which are applied in order.

27. What are Type of Token Filters in Elasticsearch Analyzer?

Elasticsearch have number of built in Token filters which can use in custom filters.

28.  What is the is use of attributes- enabled, index and store ?

The enabled attribute applies to various ElasticSearch specific/created fields such as _index and _size. User-supplied fields do not have an “enabled” attribute.

Store means the data is stored by Lucene will return this data if asked. Stored fields are not necessarily searchable. By default, fields are not stored, but full source is. Since you want the defaults (which makes sense), simply do not set the store attribute.

The index attribute is used for searching. Only indexed fields can be searched. The reason for the differentiation is that indexed fields are transformed during analysis, so you cannot retrieve the original data if it is required.

29.What is the query language of ElasticSearch ?

Elasticsearch uses the Apache Lucene query language, which is called as Query DSL.

30. Does Elasticsearch have a schema ?

Yes, Elasticseach can have mappings which can be used to enforce schema on documents. We define Elasticsearch Index Schema by defining Mappings.

Advance and Practical Interview Questions and Answers 

31.What are Scripting Languages Support by Elasticsearch?

Elasticsearch supports custom scripting available in Lucene Expression, Groovy, Python,Java Script and Painless.

32. What is Painless and their benefits in Elasticsearch?

Painless is a simple, secure scripting language designed specifically for use with Elasticsearch 5.XX . It is the default scripting language for Elasticsearch  and can safely be used for inline and stored scripts. Painless use anywhere scripts can be used in Elasticsearch.

Benefits of Painless :

  • Fast performance: Painless scripts run several times faster than the alternatives.
  • Safety: Fine-grained whitelist with method call/field granularity.
  • Optional typing: Variables and parameters can use explicit types or the dynamic def type.
  • Syntax: Extends Java’s syntax to provide Groovy-style scripting language features that make scripts easier to write.
  • Optimizations: Designed specifically for Elasticsearch scripting.

33. How to store Elasticsearch Node Data to external Directory?

By default in Elasticsearch  data path location is $ES_HOME/data.  Keeping data in external path from Elasticsearch directory is beneficial while doing upgrade or any modification of Elasticsearch so that no any data loss.

For pointing to external path there are two ways to do :

First :   Set static path on elasticsearch.yml file as below .

path.data: /opt/app/FacingIssuesOnIT/data

Second : By Passing argument from command line while starting Elasticsearch.

./bin/elasticsearch Epath.data=/opt/app/FacingIssuesOnIT/data

33. What is Restore and Snapshot in Elasticsearch?

Snapshot : Snapshot is copy or backup of individual indices or an entire cluster into a  remote repository like shared file system, S3, or HDFS. Snapshots are not archival because they can only be restored to versions of Elasticsearch that can read the index.

Steps to create Snapshot:

  • Setup Backup directory
PUT /_snapshot/facingIssueOnIT_bkp
"type": "fs",  
"settings": { 
"compress": true, 
"location": "/mount/backups/facingIssueOnIT_bkp" 
  • Check status
GET /_snapshot/facingIssueOnIT_bkp
GET /_snapshot/_all
"facingIssueOnIT_bkp": { 
"type": "fs", 
"settings": { 
 "compress": true, 
 "location": "/mount/backups/facingIssueOnIT_bkp" 
  • After registering repository create Snapshot of Cluster or Index as Below
For Cluster
PUT /_snapshot/facingIssueOnIT_bkp/snapshot_1?wait_for_completion=true

For indexes
PUT /_snapshot/facingIssueOnIT_bkp/snapshot_1
  "indices": "index_1,index_2",
  "ignore_unavailable": true,
  "include_global_state": false

wait_for_completion=true is use to complete prompt then only you can execute any other action for doing it in background use as false.

Restored : Restored is used to retrieve backup/snapshot indexes again in cluster. Restore can we done on cluster level and index level.

Cluster Level
POST /_snapshot/facingIssueOnIT_bkp/snapshot_1/_restore
Index Level
POST /_snapshot/facingIssueOnIT_bkp/snapshot_1/_restore
  "indices": "index_1,index_2",
  "ignore_unavailable": true,
  "include_global_state": true,
  "rename_pattern": "index_(.+)",
  "rename_replacement": "restored_index_$1"

34. What is Elasticsearch REST API and use of it?

Elasticsearch provides a very comprehensive and powerful REST API that you can use to interact with your cluster. Among the few things that can be done with the API are as follows:

  • Check your cluster, node, and index health, status, and statistics
  • Administer your cluster, node, and index data and metadata
  • Perform CRUD (Create, Read, Update, and Delete) and search operations against your indexes
  • Execute advanced search operations such as paging, sorting, filtering, scripting, aggregations, and many others

To learn more on Elasticsearch REST API follow link Elasticsearch Tutorial

35. How to check Elasticsearch Cluster Health?

To know about cluster health follow below URL over curl or on your browser.

GET /_cat/health?v

36. What are type of Cluster Health Status?

  • Green means everything is good (cluster is fully functional).
  •  Yellow means all data is available but some replicas are not yet allocated (cluster is fully functional)
  • Red means some data is not available for whatever reason.
  • Note: that even if a cluster is red, it still is partially functional (i.e. it will continue to serve search requests from the available shards) but you will likely need to fix it ASAP since you have missing data

37.  How to know Number of Nodes?

GET /_cat/nodes?v


ip        heap.percent ram.percent cpu load_1m load_5m load_15m node.role master name           10           5   5    4.46                        mdi      *      PB2SGZY

Here, we can see our one node named “PB2SGZY”, which is the single node that is currently in our cluster.

38. How to get list of available Indices in Elasticsearch Cluster?

GET /_cat/indices?v

39. How to create Indexes?

PUT /customer?pretty
GET /_cat/indices?v

39. How to delete Index and records?

DELETE /customer?pretty
GET /_cat/indices?v


PUT /customer
PUT /customer/external/1
  "name": "John Doe"
GET /customer/external/1
DELETE /customer

If we study the above commands carefully, we can actually see a pattern of how we access data in Elasticsearch. That pattern can be summarized as follows:

<REST Verb> //<Type>/<ID>

This REST access pattern is so pervasive throughout all the API commands that if you can simply remember it, you will have a good head start at mastering Elasticsearch.

40. How to update record and document fields value in Index?

We’ve previously seen how we can index a single document. Let’s recall that command again:

PUT /customer/external/1?pretty
  "name": "John Doe"

Again, the above will index the specified document into the customer index, external type, with the ID of 1. If we then executed the above command again with a different (or same) document, Elasticsearch will replace (i.e. reindex) a new document on top of the existing one with the ID of 1:

PUT /customer/external/1?pretty
  "name": "Jane Doe"

The above changes the name of the document with the ID of 1 from “John Doe” to “Jane Doe”. If, on the other hand, we use a different ID, a new document will be indexed and the existing document(s) already in the index remains untouched.

PUT /customer/external/2?pretty
  "name": "Jane Doe"

The above indexes a new document with an ID of 2.

When indexing, the ID part is optional. If not specified, Elasticsearch will generate a random ID and then use it to index the document. The actual ID Elasticsearch generates (or whatever we specified explicitly in the previous examples) is returned as part of the index API call.

This example shows how to index a document without an explicit ID:

POST /customer/external?pretty
  "name": "Jane Doe"

Note that in the above case, we are using the POST verb instead of PUT since we didn’t specify an ID.


Read More

To read more on Elasticsearch Configuration, Sample Elasticsearch REST Clients, Search Queries Types with example follow link Elasticsearch Tutorial and Elasticsearch Issues.

Hope this blog was helpful for you.

Leave you feedback to enhance more on this topic so that make it more helpful for others.

About Saurabh Gupta

My Name is Saurabh Gupta, I have approx. 10 Year of experience in Information Technology World manly in Java/J2EE. During this time I have worked with multiple organization with different client, so many technology, frameworks etc.
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