Elasticsearch Interview Questions

Checkout Vskills Interview questions with answers in Elasticsearch to prepare for your next job role. The questions are submitted by professionals to help you to prepare for the Interview.    

Q.1 What is Elasticsearch?

Elasticsearch is a distributed, free and open search and analytics engine for all types of data, including textual, numerical, geospatial, structured data. Elasticsearch is a search engine based on the Lucene library. It provides a distributed, multitenant-capable full-text search engine with an HTTP web interface and schema-free JSON documents.

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Q.2 What is Elasticsearch used for?

Elasticsearch is a highly scalable open-source full-text search and analytics engine. It can store, search, and analyze big volumes of data quickly and in near real time. It is generally used to power applications that have complex search features and requirements.

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Q.3 What is an Elasticsearch cluster?

An Elasticsearch cluster is a group of nodes that have the same cluster.name attribute. As nodes join or leave a cluster, the cluster automatically reorganizes itself to evenly distribute the data across the available nodes. If you are running a single instance of Elasticsearch, you have a cluster of one node.

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Q.4 What is an Elasticsearch Index?

An Elasticsearch index is a collection of documents that are related to each other. Elasticsearch stores data as JSON documents.

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Q.5 What does the term document mean in Elasticsearch?

Documents are JSON objects that are stored within an Elasticsearch index and are considered the base unit of storage.

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Q.6 What is ELK?

ELK is the acronym for three open source projects: Elasticsearch, Logstash, and Kibana. Logstash is a server‑side data processing pipeline that ingests data from multiple sources simultaneously, transforms it, and then sends it to a "stash" like Elasticsearch.

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Q.7 What is Logstash?

Logstash is a light-weight, open-source, server-side data processing pipeline that allows you to collect data from a variety of sources, transform it on the fly, and send it to your desired destination. It is most often used as a data pipeline for Elasticsearch, an open-source analytics and search engine.

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Q.8 What is Kibana?

Kibana lets you visualize your Elasticsearch data and navigate the Elastic Stack. Kibana is a proprietary data visualization dashboard software for Elasticsearch, whose open source successor in OpenSearch is OpenSearch Dashboards.

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Q.9 What are the advantages of using Elasticsearch?

Some of the key benefits users of Elasticsearch commonly cite include: Lightning-fast performance even when working with massive-scale datasets, ability to scale, Schema free, has Extensive API and Multilingual

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Q.10 What are the advantages of using Logstash?

The key features that users of Logstash find beneficial include: easily load data from a variety of data sources including system logs, website logs, and application server logs, has pre-built filters, Process unstructured data, Pre-built and custom filters, Built custom data processing pipelines and Works as an extract, transform & load (ETL) tool

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Q.11 What are the advantages of using Kibana?

Few advantages of Kibana are: can create interactive data dashboards using realtime data, contains open source browser based visualization tool mainly used to analyse large volume of logs in the form of line graph, bar graph, pie charts, heat maps etc., simple and easy for beginners to understand, Real-time observability and Integration with Elasticsearch

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Q.12 How do you check the version of Elasticsearch you are working with?

The version of Elasticsearch running locally can be listed by the following curl command from command line: curl -XGET 'http://localhost:9200'

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Q.13 What is bucketing in Elasticsearch and Kibana?

Bucket aggregations in Elasticsearch create buckets or sets of documents based on certain criteria. Depending on the aggregation type, you can create filtering buckets, that is, buckets representing different value ranges and intervals for numeric values, dates, IP ranges, and more.

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Q.14 How do you create an index in Elasticsearch?

The following command creates a new Elasticsearch index: PUT /my-index-000001

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Q.15 How do you load data into Elasticsearch?

We can use one of the many available Beats (Elastic’s own log shippers) to load data into Logstash for further processing within Elasticsearch. Popular Beats include the following: Filebeat, Metricbeat, Heartbeat, Auditbeat, Packetbeat and WinLogBeat

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Q.16 Where is Elasticsearch data stored?

By default , Elasticsearch keeps a copy of all the JSON documents you offer it for indexing in a field called _source . You get a copy of this stored data on each query that matches the document.

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Q.17 Where does Elasticsearch store data?

Elasticsearch stores data under its default paths. For Debian/Unbuntu this will be located at /var/lib/elasticsearch/data & for RHEL/CentOS this will be located at /var/lib/elasticsearch.

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Q.18 How do you stop Elasticsearch?

To stop the Elasticsearch service on Linux you will want to change the directory in a terminal window to ES_HOME/bin. and use kill to stop Elasticsearch search, you can find the process ID (pid) by using the following command ps -ef | grep elas.

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Q.19 Does Elasticsearch have a database?

Elasticsearch is based on Apache Lucene. Elasticsearch is a NoSQL database and it stores data in an unstructured way.

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Q.20 How do you delete an index in Elasticsearch?

By using the DELETE /index name. Command.

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Q.21 How do I add storage to Elasticsearch?

If you can tolerate the downtime, the simplest approach is to shut down Elasticsearch, move the data folder onto the larger disk, adjust the path. data setting in elasticsearch. yml and then start the node back up again.

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Q.22 Where is Logstash config file?

On deb and rpm, you place the pipeline configuration files in the /etc/logstash/conf. d directory. Logstash tries to load only files with . conf extension in the /etc/logstash/conf.

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Q.23 How can you use Logstash GeoIP?

Logstash uses a GeoIP database to convert IP addresses into a latitude and longitude coordinate pair, i.e. the approximate physical location of an IP address. The coordinate data is stored in Elasticsearch in geo_point fields, and also converted into a geohash string.

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Q.24 What do you understand by Filebeat in Elasticsearch?

Filebeat is a lightweight shipper for forwarding and centralizing log data. Installed as an agent on your servers, Filebeat monitors the log files or locations that you specify, collects log events, and forwards them either to Elasticsearch or Logstash for indexing.

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Q.25 What do you understand by Metricbeat in Elasticsearch?

Metricbeat is a lightweight shipper that you can install on your servers to periodically collect metrics from the operating system and from services running on the server. Metricbeat takes the metrics and statistics that it collects and ships them to the output that you specify, such as Elasticsearch or Logstash.

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Q.26 What do you understand by Journalbeat in Elasticsearch?

Journalbeat is a lightweight shipper for forwarding and centralizing log data from systemd journals. Installed as an agent on your servers, Journalbeat monitors the journal locations that you specify, collects log events, and forwards them to either to Elasticsearch or Logstash. Journalbeat is an Elastic Beat.

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Q.27 What do you understand by Heartbeat in Elasticsearch?

Heartbeat holds onto incoming data and then ships it all to Elasticsearch or Logstash when things are back to normal. Heartbeat is a lightweight shipping agent that was created to allow observability of the health of services running on a specified host, its results can then be forwarded to Logstash for further processing. Heartbeat is notable for the fact that it is the only member of the Beats family that Elastic themselves recommend you to install on a separate network/machine external to the one you are currently wishing to monitor.

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Q.28 What do you understand by Packetbeat in Elasticsearch?

Packetbeat is a real-time network packet analyzer that you can use with Elasticsearch to provide an application monitoring and performance analytics system. Packetbeat completes the Beats platform by providing visibility between the servers of your network.

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Q.29 What do you understand by WinLogBeat in Elasticsearch?

Winlogbeat reads from one or more event logs using Windows APIs, filters the events based on user-configured criteria, then sends the event data to the configured outputs (Elasticsearch or Logstash). Winlogbeat watches the event logs so that new event data is sent in a timely manner.

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Q.30 What do you understand by Auditbeat in Elasticsearch?

Auditbeat allows you to carefully watch lists of directories for any funny business on Linux, macOS, and Windows. File changes are sent in real time to Elasticsearch, each message containing metadata and cryptographic hashes of the file contents for further analysis.

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Q.31 What do you understand by reindexing in Elasticsearch?

Reindex is the concept of copying existing data from a source index to a destination index which can be inside the same or a different cluster. Elasticsearch has a dedicated endpoint _reindex for this purpose. A reindexing is mostly required for updating mapping or settings.

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Q.32 How you keep yourself updated of new trends in Elasticsearch management?

Elasticsearch and Log management is seeing newer development every year and I update myself by attending industry seminars, conferences as available online or offline.

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Q.33 What is Elasticsearch?

Elasticsearch is an open-source, distributed search and analytics engine designed for real-time data exploration.

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Q.34 What is the role of Elasticsearch in the Elastic Stack?

Elasticsearch is the core search and analytics engine in the Elastic Stack, working alongside Kibana, Beats, and Logstash.

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Q.35 Explain the distributed nature of Elasticsearch.

Elasticsearch distributes data across multiple nodes, allowing for scalability, fault tolerance, and high availability.

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Q.36 What types of data can you store and search in Elasticsearch?

Elasticsearch can index and search structured and unstructured data, including text, numbers, and geospatial data.

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Q.37 What is an index in Elasticsearch?

An index is a collection of documents that share a common data structure and are stored together for efficient retrieval.

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Q.38 How is data organized within an index in Elasticsearch?

Data in an index is organized into shards, which are further divided into smaller units called segments for indexing and querying.

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Q.39 What is a document in Elasticsearch?

A document is a basic unit of data in Elasticsearch, represented in JSON format and stored in an index.

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Q.40 What is a shard in Elasticsearch?

A shard is a single, self-contained index that holds a subset of data, enabling parallel processing and distribution.

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Q.41 What is a node in Elasticsearch?

A node is a single instance of Elasticsearch running on a server, which can be part of a cluster of nodes.

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Q.42 How does Elasticsearch ensure high availability and fault tolerance?

Elasticsearch replicates data across nodes and shards, providing redundancy and automatic failover.

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Q.43 What is a cluster in Elasticsearch?

A cluster is a collection of nodes that work together, forming a single Elasticsearch environment to store and search data.

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Q.44 How does Elasticsearch handle distributed searching?

Elasticsearch distributes search requests across nodes and merges the results for efficient distributed searching.

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Q.45 What is a query in Elasticsearch?

A query is a request for searching and retrieving data from Elasticsearch, specified in JSON format.

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Q.46 What is a filter in Elasticsearch?

A filter is used to narrow down search results and retrieve documents that meet specific criteria, such as range or term filters.

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Q.47 How does Elasticsearch support full-text search?

Elasticsearch uses an inverted index to efficiently perform full-text searches on text data.

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Q.48 What is a token in Elasticsearch's text analysis?

A token is a single unit of text, typically a word or part of a word, generated during the text analysis process.

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Q.49 What is stemming in Elasticsearch's text analysis?

Stemming reduces words to their root form, allowing for more flexible and accurate text searches (e.g., "running" → "run").

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Q.50 How does Elasticsearch support multilingual text analysis?

Elasticsearch provides built-in analyzers and tokenizers for various languages, enabling multilingual search.

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Q.51 What is relevance scoring in Elasticsearch?

Relevance scoring is the algorithmic calculation of how well a document matches a query, allowing for ranked search results.

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Q.52 What is an Elasticsearch index mapping?

An index mapping defines the data structure and properties for documents within an index, including field types and analyzers.

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Q.53 How do you create an index mapping in Elasticsearch?

You can define an index mapping when creating an index or by updating the mapping of an existing index.

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Q.54 What is dynamic mapping in Elasticsearch?

Dynamic mapping allows Elasticsearch to automatically detect and assign field data types based on the incoming data.

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Q.55 How can you change the mapping of an existing field in Elasticsearch?

You cannot change the mapping of an existing field directly; instead, you need to create a new index with the desired mapping.

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Q.56 What are analyzers in Elasticsearch?

Analyzers define how text data is processed during indexing and searching, including tokenization and filtering.

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Q.57 Explain the purpose of Elasticsearch's aggregations.

Aggregations allow you to perform complex data analysis, including metrics, grouping, and data summarization.

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Q.58 What is a term query in Elasticsearch?

A term query matches documents that contain an exact term in a specific field, suitable for keyword searching.

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Q.59 How does Elasticsearch support fuzzy matching?

Fuzzy matching allows Elasticsearch to find documents that are similar to a specified term, accommodating spelling errors or variations.

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Q.60 What is a match query in Elasticsearch?

A match query analyzes the input text and searches for documents containing any matching terms, not just exact matches.

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Q.61 How does Elasticsearch handle geospatial data?

Elasticsearch supports geospatial queries and data types like geo_point to perform location-based searches.

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Q.62 What is the purpose of an Elasticsearch filter context?

A filter context narrows down the set of documents but does not affect the relevance score, making it suitable for precise filtering.

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Q.63 What is a bool query in Elasticsearch?

A bool query combines multiple queries and filter clauses using boolean logic (AND, OR, NOT) to retrieve matching documents.

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Q.64 How does Elasticsearch handle large datasets?

Elasticsearch can efficiently handle large datasets by sharding data across nodes and allowing for distributed queries.

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Q.65 What is the purpose of Elasticsearch's scoring algorithm?

Elasticsearch's scoring algorithm determines the relevance of documents to a query, helping rank search results.

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Q.66 How does Elasticsearch handle pagination of search results?

Elasticsearch supports pagination using the "from" and "size" parameters in queries, allowing you to retrieve specific result subsets.

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Q.67 What is the role of the Elasticsearch REST API?

The REST API allows users to interact with Elasticsearch by sending HTTP requests for indexing, searching, and managing data.

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Q.68 How can you secure Elasticsearch clusters?

Elasticsearch can be secured using features like role-based access control (RBAC), encryption, and authentication mechanisms.

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Q.69 What is an Elasticsearch node role?

Each Elasticsearch node can have one or more roles (e.g., master, data, ingest) that define its responsibilities within the cluster.

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Q.70 How does Elasticsearch handle versioning of documents?

Elasticsearch uses a versioning system to track changes to documents, enabling conflict resolution and updates.

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Q.71 What is Elasticsearch's query DSL (Domain-Specific Language)?

The query DSL is a JSON-based language used to define complex queries and aggregations in Elasticsearch.

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Q.72 What is the "_source" field in Elasticsearch documents?

The "_source" field contains the original JSON document that was indexed, allowing retrieval of the full document.

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Q.73 How can you perform a "match all" query in Elasticsearch?

You can use the "match_all" query to retrieve all documents in an index without specifying any search criteria.

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Q.74 What is an "alias" in Elasticsearch?

An alias is an alternative name for an index, allowing you to refer to the same data with different names.

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Q.75 How can you optimize index performance in Elasticsearch?

Performance can be improved by using proper mappings, sharding strategies, and query optimizations.

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Q.76 What is the role of the "refresh" setting in Elasticsearch?

The "refresh" setting controls how frequently changes to an index become visible to search operations, impacting indexing speed.

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Q.77 How can you enable cross-origin resource sharing (CORS) in Elasticsearch?

CORS can be configured in Elasticsearch to allow requests from specific domains or origins.

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Q.78 What is the purpose of the "minimum_should_match" parameter?

The "minimum_should_match" parameter in a bool query controls how many "should" clauses must match for a document to be considered a match.

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Q.79 How does Elasticsearch support near real-time (NRT) indexing?

Elasticsearch offers near real-time indexing, meaning documents are searchable shortly after being indexed.

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Q.80 What is the "routing" parameter in Elasticsearch?

The "routing" parameter allows you to specify which shard should handle a document based on a routing value, optimizing data distribution.

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Q.81 How can you optimize storage in Elasticsearch?

Storage optimization can be achieved through techniques like data compression, index optimization, and appropriate shard sizing.

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Q.82 What is the purpose of the "field collapsing" feature in Elasticsearch?

Field collapsing allows you to group and collapse search results based on a specific field, useful for aggregation and presentation.

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Q.83 How does Elasticsearch handle distributed join operations?

Elasticsearch provides the "nested" and "parent-child" data modeling approaches to handle complex join operations.

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Q.84 What is the "_id" field in Elasticsearch documents?

The "_id" field uniquely identifies a document within an index and can be manually assigned or automatically generated.

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Q.85 What is a "node data role" in Elasticsearch?

A node with a "data" role stores and manages the actual data and documents in Elasticsearch, including the primary and replica shards.

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Q.86 How can you prevent mapping conflicts when indexing documents?

Mapping conflicts can be avoided by defining an explicit index mapping or using dynamic templates to handle incoming data.

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Q.87 What is the purpose of the "_index" field in Elasticsearch search results?

The "_index" field indicates the name of the index where a document was found when retrieving search results.

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Q.88 How does Elasticsearch support in-place updates of documents?

Elasticsearch provides the "update" API, which allows you to modify specific fields within a document without reindexing the entire document.

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Q.89 What is the role of the "bulk" API in Elasticsearch?

The "bulk" API allows you to efficiently perform multiple indexing, updating, or deleting operations in a single request.

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Q.90 How does Elasticsearch handle time-based data, such as log data?

Elasticsearch can use time-based indices and rollover strategies to manage and optimize time-series data like logs.

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Q.91 What is the purpose of the "fielddata" cache in Elasticsearch?

The "fielddata" cache stores field values for aggregations and sorting, improving performance for these operations.

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Q.92 How can you delete an index in Elasticsearch?

You can delete an index using the "_delete" index API, specifying the index name to be removed.

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Q.93 What is a "geo-shape" field in Elasticsearch?

A "geo-shape" field is used to store complex geospatial shapes, such as polygons, for geospatial queries and mapping.

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Q.94 What is the purpose of the "highlight" feature in Elasticsearch?

The "highlight" feature in Elasticsearch allows you to highlight matching text fragments within search results for better user experience.

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Q.95 How does Elasticsearch handle data replication?

Elasticsearch replicates data by creating one or more replica shards for each primary shard, ensuring data availability and reliability.

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Q.96 What is "index aliasing" in Elasticsearch?

Index aliasing allows you to create a named alias for one or more indices, simplifying queries and index management.

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Q.97 How can you use Elasticsearch's "percolator" feature?

The percolator feature enables you to register queries and match documents against them, essentially reversing the search process.

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Q.98 What is the "cross-cluster replication" feature in Elasticsearch?

Cross-cluster replication allows you to replicate data across multiple Elasticsearch clusters, facilitating data redundancy and distribution.

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Q.99 How does Elasticsearch handle security roles and privileges?

Elasticsearch uses role-based access control (RBAC) to define user roles and their associated privileges for cluster and index access.

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Q.100 What is the "refresh interval" in Elasticsearch index settings?

The refresh interval specifies how often Elasticsearch refreshes the index for new data, affecting indexing and search performance.

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Q.101 How does Elasticsearch support data transformation using "ingest nodes"?

Ingest nodes allow you to perform data transformation and enrichment within Elasticsearch before indexing.

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Q.102 What is the role of the "node ingest" setting in Elasticsearch?

The "node.ingest" setting determines whether a node can be used for ingest purposes, enabling or disabling data transformation.

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Q.103 What is a "nested datatype" in Elasticsearch?

A nested datatype allows you to index and query arrays of objects as separate, nested documents, preserving the relationships between them.

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Q.104 How does Elasticsearch handle data backups and snapshots?

Elasticsearch provides the snapshot and restore API for creating and restoring backups of indices and cluster data.

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Q.105 What is "index lifecycle management" (ILM) in Elasticsearch?

ILM allows you to automate the management of index lifecycle events, such as rollover, retention, and deletion.

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Q.106 How can you control access to Elasticsearch APIs using role-based access control (RBAC)?

RBAC allows you to define roles with specific privileges and assign them to users or applications, restricting API access.

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Q.107 What is the "cluster state" in Elasticsearch?

The cluster state contains information about the cluster's configuration, nodes, indices, and shard allocation, helping to coordinate operations.

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Q.108 How does Elasticsearch handle data consistency and replication?

Elasticsearch uses distributed consensus algorithms to ensure data consistency and maintain synchronized copies of data across nodes.

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Q.109 What is the purpose of the "scripting" feature in Elasticsearch?

Scripting enables you to write custom scripts to perform complex operations during query execution or data transformation.

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Q.110 How can you use "cross-origin resource sharing" (CORS) settings in Elasticsearch to enable web browsers to access Elasticsearch from different domains?

You can configure CORS settings in Elasticsearch to specify which domains are allowed to make cross-origin HTTP requests to the cluster, enhancing web application compatibility.

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