Elasticsearch is a powerful search and analytics engine widely used for handling large-scale data efficiently. Whether you’re a beginner or an experienced professional, understanding its core concepts is crucial for optimizing performance and scalability.
In this article, we present 100 essential questions covering everything from indexing, querying, and aggregations to security, scalability, and performance tuning. These questions will help you deepen your knowledge, prepare for interviews, and enhance your Elasticsearch expertise.
1. What is Elasticsearch?
A distributed search and analytics engine based on Lucene, used for full-text search and log analysis.
2. How does Elasticsearch differ from traditional databases?
It is schema-less, document-based, and optimized for search and analytics rather than transactional operations.
3. What is an index in Elasticsearch?
A collection of documents with similar characteristics, equivalent to a database in relational systems.
4. What is a document in Elasticsearch?
A JSON object that holds the actual data you want to index.
5. How does Elasticsearch store data?
It stores data as documents in indices, internally creating inverted indices for efficient searching.
6. What is the role of shards in Elasticsearch?
Shards divide an index into pieces for parallel processing and scalability.
7. What are replicas in Elasticsearch?
Copies of primary shards used for fault tolerance and load balancing.
8. How does Elasticsearch achieve high availability?
Through replication of shards across nodes and automatic failover.
9. What is the Elasticsearch Query DSL?
A JSON-based query language for writing complex and powerful queries.
10. What is the difference between match and term queries in Elasticsearch?
`match` analyzes text, `term` does exact matching without analysis.
11. How does full-text search work in Elasticsearch?
By analyzing text into tokens and matching them using inverted indices.
12. What is an analyzer in Elasticsearch?
A component that breaks text into tokens during indexing and searching.
13. What are tokenizers in Elasticsearch?
They split text into individual terms (tokens) for indexing.
14. What is the purpose of filters in Elasticsearch?
Used to refine queries without affecting scoring, often cached for performance.
15. How does Elasticsearch handle relevance scoring?
Using the TF/IDF or BM25 algorithm to rank documents by relevance.
16. What is the inverted index in Elasticsearch?
A data structure that maps terms to the documents containing them.
17. How does Elasticsearch handle large-scale data?
Through horizontal scaling using shards and distributed architecture.
18. What is the difference between Elasticsearch and Apache Solr?
Elasticsearch is more cloud-native and RESTful, while Solr offers more traditional configuration.
19. What is Kibana, and how does it integrate with Elasticsearch?
A visualization tool for data stored in Elasticsearch.
20. What is Logstash, and how does it work with Elasticsearch?
A data processing pipeline that ingests and forwards data to Elasticsearch.
21. What is Beats in the Elastic Stack?
Lightweight data shippers that send logs and metrics to Logstash or Elasticsearch.
22. How does Elasticsearch handle data ingestion?
Via REST API, Logstash, Beats, or custom ingestion pipelines.
23. What is the role of mappings in Elasticsearch?
Define how documents and their fields are stored and indexed.
24. How do you define field types in Elasticsearch?
By specifying the data types (e.g., text, keyword, date) in the mapping.
25. What is dynamic mapping in Elasticsearch?
Automatically assigns data types to new fields when documents are indexed.
26. How can you manually define mappings in Elasticsearch?
Using the `PUT` mapping API or during index creation.
27. What is the difference between keyword and text fields in Elasticsearch?
`keyword` is for exact matches; `text` is for full-text search.
28. How does Elasticsearch handle nested objects?
Through `nested` field type which enables querying nested JSON objects.
29. What is the purpose of parent-child relationships in Elasticsearch?
Models one-to-many relationships without denormalization.
30. How do you update a document in Elasticsearch?
Using the `POST _update` API or reindexing the document.
31. How do you delete a document in Elasticsearch?
By calling the `DELETE` API with the index and document ID.
32. What is the difference between PUT and POST in Elasticsearch?
`PUT` creates or replaces; `POST` is for partial updates or searches.
33. How does Elasticsearch handle bulk operations?
Via the `_bulk` API which allows batch indexing, updating, or deleting.
34. What is the \_source field in Elasticsearch?
Stores the original JSON document for retrieval.
35. How do you retrieve specific fields from a document in Elasticsearch?
Use the `_source` filtering feature in the query.
36. What is the purpose of the \_id field in Elasticsearch?
A unique identifier for each document within an index.
37. How does Elasticsearch handle pagination?
Using `from` and `size` parameters in search queries.
38. What is the scroll API in Elasticsearch?
Used for retrieving large result sets in chunks.
39. What is the difference between search\_after and scroll API?
`search_after` is for real-time sorted pagination; `scroll` is for deep pagination of static data.
40. How does Elasticsearch handle aggregations?
It summarizes data through a powerful aggregation framework.
41. What are bucket aggregations in Elasticsearch?
Group documents into buckets based on field values.
42. What are metric aggregations in Elasticsearch?
Compute metrics like sum, average, min, and max over data.
43. What is the difference between terms and histogram aggregations?
`terms` aggregates by unique values; `histogram` groups numeric values into ranges.
44. How does Elasticsearch handle date-based aggregations?
Using `date_histogram` and `date_range` aggregations.
45. What is the composite aggregation in Elasticsearch?
Combines multiple sources for pagination and efficient aggregation.
46. How does Elasticsearch handle geo-based queries?
Using geo\_point and geo\_shape fields with special queries.
47. What is the geo\_point field type in Elasticsearch?
Stores latitude and longitude for location-based search.
48. How does Elasticsearch handle geospatial indexing?
By indexing geo\_point or geo\_shape fields with spatial algorithms.
49. What is the difference between geo\_distance and geo\_bounding\_box queries?
`geo_distance` filters by radius; `geo_bounding_box` by a rectangular area.
50. How does Elasticsearch handle synonyms?
Through synonym filters in the analyzer configuration.
51. What is the role of the synonym filter in Elasticsearch?
Expands or replaces words with synonyms during indexing or querying.
52. How do you configure stop words in Elasticsearch?
Using the stop filter in custom analyzers.
53. What is the role of stemming in Elasticsearch?
Reduces words to their root forms to improve matching.
54. How does Elasticsearch handle multilingual search?
By using language-specific analyzers.
55. What is the role of the ICU plugin in Elasticsearch?
Enhances international text handling, including Unicode support.
56. How does Elasticsearch handle fuzzy search?
Allows matching terms with slight differences using edit distance.
57. What is the difference between fuzzy and wildcard queries?
`fuzzy` allows typo tolerance; `wildcard` matches patterns.
58. How does Elasticsearch handle autocomplete functionality?
Using edge n-grams or the completion suggester.
59. What is the completion suggester in Elasticsearch?
A specialized suggester optimized for type-ahead search.
60. How does Elasticsearch handle phrase matching?
Using `match_phrase` queries to find exact sequences of words.
61. What is the difference between match\_phrase and match queries?
`match_phrase` matches word sequences; `match` checks for individual terms.
62. How does Elasticsearch handle highlighting?
Marks and returns matching terms in the result snippets.
63. What is the purpose of the highlight query in Elasticsearch?
To visually emphasize matches in search results.
64. How does Elasticsearch handle security?
Through X-Pack features like authentication, encryption, and RBAC.
65. What is the role of Elasticsearch authentication?
Verifies user identity using credentials or tokens.
66. How does Elasticsearch handle role-based access control?
Assigns permissions to users based on roles.
67. What is the difference between API keys and basic authentication in Elasticsearch?
API keys are secure tokens; basic uses username/password.
68. How does Elasticsearch handle encryption?
Supports TLS for encrypting data in transit.
69. What is the role of TLS in Elasticsearch?
Secures communication between nodes and clients.
70. How does Elasticsearch handle logging?
Uses log files to track activity, errors, and performance.
71. What is the role of slow logs in Elasticsearch?
Logs slow queries or indexing operations for troubleshooting.
72. How does Elasticsearch handle monitoring?
Through built-in APIs and tools like Kibana or Elastic Observability.
73. What is the role of the Elasticsearch Monitoring API?
Provides stats on cluster health, indexing, and search performance.
74. How does Elasticsearch handle backups?
Uses snapshots stored in remote repositories.
75. What is the snapshot and restore feature in Elasticsearch?
Saves and restores index data for backup and disaster recovery.
76. How does Elasticsearch handle scaling?
By adding nodes (horizontal) or increasing resources (vertical).
77. What is the difference between vertical and horizontal scaling in Elasticsearch?
Vertical increases hardware power; horizontal adds nodes.
78. How does Elasticsearch handle cluster management?
Manages node coordination, shard allocation, and metadata.
79. What is the role of the master node in Elasticsearch?
Manages cluster-wide settings and metadata.
80. How does Elasticsearch handle node discovery?
Uses multicast or unicast to find and connect nodes.
81. What is the role of the transport layer in Elasticsearch?
Handles internal communication between nodes.
82. How does Elasticsearch handle indexing performance?
Uses refresh intervals, bulk indexing, and resource tuning.
83. What is the role of refresh intervals in Elasticsearch?
Determines how often new data becomes searchable.
84. How does Elasticsearch handle query performance?
Through caching, filters, and optimized data structures.
85. What is the role of caching in Elasticsearch?
Speeds up repeated queries by storing results or filters.
86. How does Elasticsearch handle circuit breakers?
Prevents resource exhaustion by limiting memory usage.
87. What is the role of thread pools in Elasticsearch?
Manages concurrency for indexing, search, and management tasks.
88. How does Elasticsearch handle garbage collection?
Uses JVM garbage collection to free up unused memory.
89. What is the role of JVM tuning in Elasticsearch?
Optimizes memory and performance through heap and GC settings.
90. How does Elasticsearch handle cluster state updates?
Propagates changes from master to all nodes.
91. What is the role of the cluster state API in Elasticsearch?
Returns current cluster state including nodes, settings, and indices.
92. How does Elasticsearch handle reindexing?
Copies documents from one index to another with modifications if needed.
93. What is the role of the reindex API in Elasticsearch?
Automates reindexing of documents using source and destination indices.
94. How does Elasticsearch handle index lifecycle management?
Automates index creation, rollover, deletion, and retention policies.
95. What is the role of index templates in Elasticsearch?
Predefines settings and mappings for new indices.
96. How does Elasticsearch handle aliasing?
Uses index aliases to abstract index names and support zero-downtime changes.
97. What is the role of index aliases in Elasticsearch?
Acts as virtual names pointing to one or more indices.
98. How does Elasticsearch handle cross-cluster search?
Enables querying multiple remote clusters from a single node.
99. What is the role of remote clusters in Elasticsearch?
Facilitate cross-cluster search and replication.
100. How does Elasticsearch handle machine learning?
Provides features for anomaly detection, classification, and forecasting via X-Pack.
