Basic Elasticsearch Interview Questions
1. What is Elasticsearch?
Elasticsearch is an open-source, distributed search and analytics engine built on Apache Lucene. It provides near real-time search capabilities with a latency of typically one second between indexing a document and it becoming searchable.[2][5]
2. What are the primary use cases of Elasticsearch?
Primary use cases include application search, enterprise search, website search, analyzing log data in near-real-time, business analytics, security analytics, geospatial data analysis, application performance monitoring, and infrastructure metrics monitoring.[2][5]
3. What is an Elasticsearch cluster?
An Elasticsearch cluster is a collection of one or more nodes (servers) that work together to store data and provide federated indexing and search capabilities. Nodes can be added or removed without affecting data availability.[5][6]
4. What is an Elasticsearch index?
An Elasticsearch index is a collection of documents that have similar characteristics. Each index can be thought of as a database in traditional database terms, containing multiple shards for distributed storage.[5][6]
5. What is a document in Elasticsearch?
A document is the basic unit of information that can be indexed into Elasticsearch. Documents are JSON objects that contain one or more fields, each with its corresponding values.[4]
6. What do you mean by ‘type’ in Elasticsearch?
Types are logical categories or parts of an index whose semantics are determined by the application. A document type defines the schema or mapping for all fields in documents of that type.[2][4]
7. What is the inverted index in Elasticsearch?
An inverted index is a data structure used by Elasticsearch to store mappings of content (words) to their locations in documents. This enables fast full-text searches.[7]
8. How do you check the version of Elasticsearch you are working with?
You can check the Elasticsearch version using the REST API with:
GET /This returns cluster information including the version.[5]
9. What are ingest nodes in Elasticsearch?
Ingest nodes preprocess documents before actual indexing. They intercept bulk and index requests, apply transformations, and pass documents back to the bulk API.[4]
10. What command shows disk allocation across nodes?
Use
GET _cat/allocation?vto display disk allocation, node attributes, and storage usage across the Elasticsearch cluster.[2]
Intermediate Elasticsearch Interview Questions
11. What is the difference between full-text queries and term-level queries?
Full-text queries analyze the query string before execution and work on full-text fields, while term-level queries operate directly on exact terms stored in the inverted index without analysis.[2][4]
12. What are some examples of full-text queries in Elasticsearch?
Full-text queries include match, match_phrase, multi_match, query_string, match_phrase_prefix, and simple_query_string queries.[2][4]
13. What are some examples of term-level queries?
Term-level queries include term, exists, wildcard, prefix, range, ids, and fuzzy queries.[2][4]
14. What is a fuzzy query in Elasticsearch?
A fuzzy query returns documents containing terms similar to the search terms within a specified edit distance. It creates variations of search terms to find approximate matches.[4]
15. How does Elasticsearch handle mappings?
Elasticsearch supports mappings to enforce a schema on documents, defining field data types and how they should be analyzed or stored.[4][6]
16. What is the role of tokenizers and token filters in Elasticsearch?
Tokenizers break text into tokens, while token filters modify, add, or delete tokens from the token stream. They work together in analyzers for text processing.[1][4]
17. What is the refresh operation in Elasticsearch?
The refresh operation makes recently indexed documents available for search. By default, Elasticsearch refreshes every second, enabling near real-time search.[6]
18. How do you view information about indices using cat APIs?
Use
GET _cat/indices?vto display index information including size, shard count, and status.[2]
19. What field data monitoring command shows memory usage per field?
The command
GET _cat/fielddata?vshows memory usage of each field per node.[2]
20. Can Elasticsearch handle schema enforcement?
Yes, Elasticsearch can have explicit mappings that enforce schemas on documents, defining field types and analysis rules.[4]
Advanced Elasticsearch Interview Questions
21. How do you implement custom analyzers in Elasticsearch?
Custom analyzers combine tokenizers, token filters, and character filters defined in index mappings to meet specific text processing requirements.[1]
22. What steps ensure data consistency across a distributed Elasticsearch cluster?
Data consistency is maintained through primary and replica shards, quorum writes, and version-based optimistic concurrency control during indexing.[3]
23. When is reindexing necessary in Elasticsearch?
Reindexing is needed when schema changes make existing indices incompatible. Create a new index with updated mappings and migrate data from the old index.[3]
24. How do you design an efficient Elasticsearch schema at Atlassian?
Efficient schema design involves defining precise field mappings, selecting appropriate data types, using analyzers effectively, minimizing dynamic mapping, and optimizing nested fields.[3]
25. What are best practices for bulk indexing performance?
Best practices include optimizing shard settings for expected data volume, using bulk API endpoints, batching appropriately, and monitoring cluster load during indexing.[3]
26. How do you handle different data types in Elasticsearch mappings?
Create specific mappings for text, keyword, date, numeric fields, and objects to ensure efficient processing and querying of structured and unstructured data.[3][6]
27. What strategies work for multi-tenant Elasticsearch at Paytm?
Implement index isolation per tenant, resource allocation controls, tenant-specific analyzers, and query routing to maintain performance isolation.[1][3]
28. How do you optimize queries in Elasticsearch?
Query optimization uses filter context instead of query context, caches frequent queries, leverages boolean queries efficiently, and avoids deep pagination.[1]
29. What approach optimizes an Elasticsearch cluster for high-load scenarios at Zoho?
Optimization includes proper shard allocation, heap size tuning, cache management, segment merging control, and monitoring query patterns for load balancing.[1]
30. How do you implement real-time analytics pipelines in Elasticsearch?
Use ingest pipelines for data transformation, near-real-time indexing, optimized aggregations, and rolling indices to handle high-velocity data streams efficiently.[1]
