Elasticsearch Interview Questions and Answers for 2025

An index is a collection of documents that are similar in some way. As an example, we can have a customer data index, a product catalogue index, and an order data index. When indexing, searching, updating, and deleting documents contained within an index, the name (which must be all lowercase) serves as an identifier for the index.

To stop or disable Elasticsearch service on a Linux server, you must 'kill' the running process. It is accomplished by sending the process a SIGTERM request, which ends or terminates it.

To start the shutdown process, you must first find the process identifier (PID) for the Elasticsearch service you want to stop. The Grep command can be used to quickly locate processes. If you want to find all Elasticsearch-related processes running on a server, use this command.

We know more about our data than Elasticsearch, so while dynamic mapping is useful for getting started, we will eventually want to specify our own explicit mappings.

When we create an index or add fields to an existing index, we can create field mappings.

This is one of the most frequently asked technical Elasticsearch interview questions. Let's 

To create a new index with an explicit mapping, we can use the create index API. 

PUT /my-index-000001 
{ 
  "mappings": { 
    "properties": { 
      "age":    { "type": "integer" },   
      "email":  { "type": "keyword"  },  
      "name":   { "type": "text"  }      
    } 
  } 
} 

Here, age creates an integer field, email is a keyword field, and name is a text field. 

The update mapping API can be used to add one or more new fields to an existing index. 

The following example adds employee-id, a keyword field with a value of false for the index mapping parameter. This means that employee-id field values are saved but not indexed or searchable. 

PUT /my-index-000001/_mapping 
{ 
  "properties": { 
    "employee-id": { 
      "type": "keyword", 
      "index": false 
    } 
  } 
} 

Match query creates simple queries after analyzing the input request. Exact matching is conducted while in term inquiry. For instance, if we search for a document that has the name Anurag, and if any document also contains the name Anupriya, then that document will also appear in the search results when using a Match query. 

On the other hand, a word query uses exact matching. Therefore, the document with the name: Anupriya won't be returned. 

Elasticsearch may indeed be integrated with other programmers and systems. The two tools that make up ELK stack, Logstash and Kibana, are the most widely used ones. Other tools that Elasticsearch can integrate with are listed below: 

Services provided by: 

• Amazon  
• Elasticsearch  
• Couchbase  
• Contentful  
• Datadog 

We can get the following details of the cluster in Elasticsearch by using the command 

GET/_<index_name>  

We can add the Mapping in an index by using the command 

POST/_<index_name>/_type/_id 

We can retrieve a document by ID by using the command 

GET <index_name>/_doc/<_id>

The skills required to become an agile coach may vary significantly depending on what your company wants, but in general, you will need the following:

• Soft skills:

A lot of soft skills are required to be an effective agile coach. These qualities include being able to communicate clearly and effectively, building relationships and trust, encouraging and inspiring others, and supporting learning and change. An agile coach must also be patient, adaptive, and open-minded, as well as believe in agile concepts and values. To effectively educate agile teams, you must possess three critical soft skills.

• Hard Skills:

Becoming a good agile coach necessitates a wide range of soft skills. These qualities include the ability to motivate and inspire others, build relationships and trust, and support learning and change. An agile coach must be patient, versatile, and open-minded, in addition to having a strong commitment to the concepts and values of agile. These critical soft skills are required for coaching agile teams effectively.

• Additional Competencies:

Strong facilitation skills: Agile coaches must be able to effectively conduct talks and workshops in order to aid teams in discovering and resolving challenges.

Effective communication and interpersonal skills: Agile coaches must interact clearly and effectively with team members, stakeholders, and sponsors. They must also be able to build rapport and trust with the rest of the team.

Strong problem-solving abilities: Agile coaches must be able to recognize and address problems in a timely and effective manner.

Strong organizational and time management skills: To fulfill deadlines and deliverables, agile coaches must be able to efficiently manage their time and resources.

Yes, we must install X-Pack if you use Elasticsearch. X-Pack is essentially an Elastic Stack extension that combines or bundles alerting, reporting, monitoring, security, and graphing capabilities into a single package that can be quickly and easily installed. Although the X-components Pack's work seamlessly together, we can enable or disable the features we require. Because X-Pack is an Elastic Stack extension, we must first install Elasticsearch and Kibana before we can install X-Pack. The version of X-Pack must match the versions of Elasticsearch and Kibana. 

The following X-Pack commands can assist you in configuring security and performing other tasks: 

• elasticsearch-certgen 
• elasticsearch-certutil  
• elasticsearch-reset-password 
• elasticsearch-setup-passwords 
• elasticsearch-syskeygen

Yes, Elasticsearch has the ability to have a schema. The schema is a description of one or more fields in a document that describe the type of document and how different fields in the document are to be handled. A schema describes the fields in JSON documents, their data types, and how they should be indexed in the Lucene indexes in Elasticsearch. As a result, this schema is referred to as a "mapping" on Elasticsearch.ch.

However, Elasticsearch can be schema-less, which means that documents can be indexed without explicitly specifying a schema. If no mapping is specified, Elasticsearch will generate one by default when newly added fields are detected during indexing. 

Here are a few ways to search in Elasticsearch: 

• Using the search API: You can use the search API to search and aggregate data stored in Elasticsearch data streams and indices. 
• URI (Uniform Resource Identifier) search: By providing request parameters, the search request is executed using a URI (Uniform Resource Identifier). 
• Request a search of the body: Within the body, the search request should be carried out using DSL (Domain Specific Language). 

To define queries, Elasticsearch typically provides a query Domain Specific Language (DSL) based on JSON. There are two types of clauses in Query DSL: 

• A leaf query clause searches for specific values in a field or fields. They can be used separately. These queries include matches, terms, and range queries.
• A compound query clause is made up of a leaf query and several other compound queries. These queries combine several queries to produce the desired results. 

Elasticsearch can handle a wide variety of queries. The query starts with a query keyword, then conditions and filters in the form of a JSON object. Here are some of the questions:

• Match All Query: This is a straightforward query that returns all of the documents in the specified index.
• Full-text queries: High-level queries are available for performing full-text searches over full-text fields. Full-text queries typically work based on the analyzer associated with a specific document or index. Full-text queries can be of various types, such as match queries, multi-match queries, query-string queries, and so on.

When we search Elasticsearch for a document (a record), we want to get the relevant information that we are looking for. The Lucene scoring algorithm calculates the probability of receiving relevant information based on relevance. 

The Lucene technology aids in the search of a specific record, i.e. a document, based on the frequency of the term in search appearing in the document, how frequently it appears across an index, and query which is designed using various parameters.

Token filters are used to compare text tokens for search conditions after receiving text tokens from tokenizers. These filters check the tokens with the stream being searched, yielding a true or false Boolean value.

The functionality of a master node centers around cluster-wide operations such index creation, index deletion, and monitoring or keeping track of the nodes that make up a cluster. As a result of steady Elasticsearch cluster health, these nodes also decide how many shards should be allocated to particular nodes.

The nodes that are elected to be Master Nodes are known as Master eligible nodes.

Elasticsearch's analyzer does not necessarily require a character filter. By substituting a text token with a value mapped to the key, these filters change the input stream of the string.

Character filters for mapping can be used; they employ parameters like mappings and mappings path.

REST API is a method of system-to-system communication that uses the hypertext transfer protocol to transmit data requests in both XML and JSON formats.

The statelessness of REST protocol and the separation of the user interface from the server and storage data make the user interface more portable across different platforms. Additionally, it enhances scalability by enabling the separate implementation of the components, making programmers more adaptable to use.

We can extract and condense data about the documents and terms in an Elasticsearch data stream or index using the graph explore API. 

Using the Graph UI to investigate connections is the simplest method to comprehend how this API behaves. The Last request panel allows you to see the most recent request sent to the _explore endpoint. See Getting Started with Graph for further details.

A seed query is included in the first request to the _explore API and identifies the relevant documents as well as the fields that will be used to define the vertices and links in the network. We can use subsequent _explore queries to spider out from more interest vertices. Vertices that have already been returned can be excluded.

Here, the features that Graph AP provides: 

1. Security will be activated automatically when you launch the Elastic Stack. 
2. Configure security manually.  
3. Creating passwords for both built-in and native users. 
4. Profiles of users.  
5. Security spheres.  
6. JWT verification.  
7. Searching for users without being authenticated. 
8. Examine the occurrences & securing integrations and clients. 

After Elasticsearch is updated to a newer version, a migration API is implemented. The X-Pack indices are updated into the most recent/newer version of the Elasticsearch cluster using this migration API.

We can implement more than simply full-text search with the help of the Elasticsearch search APIs. Additionally, they support the implementation of suggesters (term, phrase, completion, and more), rating evaluation, and even comments on why a document was or wasn't returned with the search.

The search API aids in locating data from certain shards of the index using a routing parameter. It returns the search hits that match the query defined in the request. Allows us to execute a search query and get back search hits that match the query. We can provide search queries using the q query string parameter or request body. 

Feature of searchAPI:  

• Search 
• MultiSearch 
• Async Search 
• Point in time 
• Suggesters 

Run the search with a search template 

 GET my-index/_search/template 
{ 
  "id": "my-search-template", 
  "params": { 
    "query_string": "hello world", 
    "from": 0, 
    "size": 10 
  } 
} 

We need to have the read index privilege for the destination data stream, index, or alias if the Elasticsearch security features are enabled. See Configure remote clusters with security for information on cross-cluster search. 

The ELK Stack, a log analysis system, includes Kibana. It is a free visualization tool that examines expanding logs in a variety of graph styles, including line, pie-bar, coordinate maps, etc. You can use Kibana to browse the Elastic Stack and give your data structure. Kibana allows you to: 

• Search, keep an eye on, and safeguard your data. Kibana is your doorway for accessing these features and more, including document discovery, log analysis, and security vulnerability detection. 
• Review your data. Find hidden insights, display them using charts, gauges, maps, graphs, and other visuals, and combine them into a dashboard. 
• Control, keep an eye on, and protect the elastic stack. Control who has access to what features, keep an eye on the health of your Elastic Stack cluster, and manage your data. 

All data formats are compatible with Kibana. Your data may include text that is organized or unstructured, numbers, time series, geospatial information, logs, metrics, security incidents, and more. Kibana can assist you in finding patterns and relationships in your data and in visualizing the findings. 

Best Kibana Dashboard Examples 

• Global Flight Data 

An overview of global flight data is provided by this dashboard. This dashboard includes the following data points among others: 

Elastic's dashboard displays information about flights. It can be used by airlines, airport staff, and those looking for flight information. 

Analysts that are examining aero plane, as well as passenger travel tendencies and patterns, activity as well as passenger travel tendencies and patterns can also use it. 

• E-commerce Revenue Dashboard 

Another Kibana visualization dashboard example is provided by Elastic (makers of Kibana). This is the dashboard we will use if we ran an online store and wanted to use Kibana to track all of the data, sales, and performance in one location.

We can use this dashboard to monitor the performance of our eCommerce company. We will be able to tell if we are making enough money and selling enough goods each day to reach our goals.

Data can be retrieved through the reporting API in PDF, PNG, and CSV spreadsheet formats, which can then be shared or saved as needed.