Elasticsearch Interview Questions

1. What is Elasticsearch?

Ans: Elasticsearch is an open-source and distributed search engine built on top of Lucene. It is used for storing, searching and analyzing data in bulk. The data can be structured, unstructured, geo-spatial, etc. It stores data in the form of JSON and supports full-text searches. It provides query results in near real-time. It is widely used by companies such as Netflix, Linkedin, Stack Overflow, etc 

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2. What are the advantages of Elasticsearch?

Ans: The advantages of Elasticsearch are,

• It is document-oriented
• RESTful APIs are provided for accessing data
• Customized splitting, stemming and auto-completion of words
• Since the data is distributed, the queries execution will be faster
• It is horizontally scalable

3. Explain the difference between term-based and full-text queries

Ans: The term-based query will search for an exact match. The full-text query splits the sentences into terms and checks for their proximity matches.

4. What is the use of Kibana for Elasticssearch?

Ans: Kibana is an open-source data visualization tool for Elasticsearch. Using Kibana, we can perform data operations on the indexed data in an Elasticsearch cluster. We can even create dashboards with pie charts, bar graphs, scatter plots, etc.

5. What is the use of Log stash for Elasticsearch?

Ans: Logstash is an open-source data processing tool. It is mainly used to collect data that might be in the form of logs, chat messages, order details, etc, process it and send it to a destination, which in our case, an Elasticsearch cluster.

6. What are the operations that you can perform on the data stored in Elasticsearch?

Ans: We can perform four main operations on the data stored in Elasticsearch

• Create an index for the document
• Retrieve the documents
• Updating the fields of existing documents
• Delete a document

7. Does Elasticsearch have a schema?

Ans: Yes, Elasticsearch does have a schema. The schema is nothing but mapping the fields in a document to its type. Elasticsearch can also be schema-less. When documents are indexed without specifying the schema, it will generate dynamic schema by default.

8. Define type in Elasticsearch

Ans: A type in Elasticsearch represents a set of similar documents. The type name of a document will be stored in the ‘_type’ metadata field. When we want to search for similar documents, we can just search based on the ‘_type’ field.

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9. What is an index?

Ans: An index in Elasticsearch is equivalent to a table in RDBMS. It is a collection of documents and contains mappings of fields that can be of multiple types. All the operations on data in Elasticsearch are based on the indices.

10. What is a document?

Ans: A document in Elasticsearch is equivalent to a row of a table. It contains all the fields of an entry. The data in a document will be in the form of JSON i.e key-value pairs.

11. What is a node in Elasticsearch?

Ans: A node can be represented as a server that is part of a cluster. The data stored in Elasticsearch will actually be stored in the nodes. It has indexing and search capabilities.

12. What is a cluster?

Ans: A collection of nodes that are connected together becomes a cluster. When we install Elasticsearch in our system, by default it will create a cluster of one node with the name 'elasticsearch'. When data is stored in Elasticsearch, the data is evenly distributed across the available nodes.

13. Explain about mapping in Elasticsearch

Ans: Mapping is nothing but metadata i.e the definition of the fields that the document contains. Each index has mapping types, so similar documents can be grouped together. We can define custom rules of mapping for the dynamically added fields in a document.

14. What is text analysis?

Ans: Text analysis is a process of converting text into a structured format. To make the data better available for search, text analysis is used. It enables full-text searches so the results returned are more relevant to the query.

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15. Explain about index modules

Ans: Modules are for monitoring the functionality of indices. Modules consist of two types of settings,

• Static Settings - set only at the time of index creation or on a closed index
• Dynamic Settings - set when the index is live on Elasticsearch

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16. What are the ways for searching data in Elasticsearch?

Ans: We can perform searching in three different ways

• Search API - search a document across multiple indices
• Uniform Resource Identifier - search with a URI along with parameters
• Query DSL - specify the search parameters in the request body 

17. Write the syntax for creating an index

Ans: 

To add or create an index, we have to use the following syntax

PUT

IndexName will be the name of the index that you want to create

18. How can we retrieve a document in Elasticsearch?

Ans: To retrieve a document, we have to use a GET API like below,

GET /_doc/

DocumentID is the ID of the document 

IndexName will be the name of the index that you want to perform the search in

19. How can we delete a document in Elasticsearch?

20. What is the query language of Elasticsearch based on?

Ans: The query language of Elasticsearch is based on Lucene query language which is also called Query DSL.

21. Define Query DSL?

Ans: Query DSL (Domain Specific Language) is used to define JSON based queries on Elasticsearch. It has 2 clauses,

• Leaf Query Clause - to find a specific value through match, term or range
• Compound Query Clause - multiple queries, basically a combination of leaf query clauses

22. Explain about sharding

Ans: A shard is a bucket that stores data. When an index is created in Elasticsearch, it will split the data into buckets and stores them across the nodes. An index can be made up of a single shard or multiple. We can specify the number of shards that our index can have at the time of creating an index.

23. Explain about replicas in Elasticsearch

Ans: A replica is a copy of a shard in Elasticsearch. To avoid data loss in case of a failure, shards are copied and stored across different nodes to make the data available always. The default unit of a replica in Elastcsearch is 1. We can change this factor manually to have more replicas of data

24. What is the advantage of creating replicas in Elasticsearch?

Ans: 

• Data from primary shard is always synchronized with its replica
• When there is a hardware failure, data will not be lost
• It increases the capacity to read requests thus improving performance

25. What is an analyzer in Elasticsearch?

Ans: An analyzer is an algorithm that encompasses text analysis i.e it defines how a search sentence should be divided into terms. Elasticsearch provides built-in analyzers. We can even create a custom analyzer by combining character filters, tokenizers, etc.

26. What is ECS in Elasticsearch?

Ans: ECS (Elastic Common Schema) is developed by the Elastic user community to have a common schema for all the developers to follow. It defines a set of fields to be used when storing a document in Elastisearch.

27. What is an inverted index?

Ans: Inverted index is a data structure that enables full-text search. It is a hashmap of unique words of all the documents. It also stores the document name in which it appears for each word. So when we perform a search based on the text field, it will first refer to this inverted index to find the matching search terms.

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28. What is a filter in Elasticsearch?

Ans: Filters are used on structured data to optimize search results. Some of the filters provided by Elasticsearch are AND, OR, EXISTS, etc.

29. What are the loops available in Elasticsearch?

Ans: Elasticsearch supports For, While and D-While loops to loop through the documents.

30. Explain about relevancy and scoring

Ans: When we search for a document, Elasticsearch uses the Lucene scoring algorithm which calculates a weight for each document based on some search parameters and term frequency. When a search is performed, the weight i.e score is stored in the ‘_score’ represented by a positive floating number for each document. So based on this score, relevant results are returned for a search.