Top 50 ELK Stack Interview Question and Answers

1. What is the ELK Stack?
   ELK Stack is a collection of three open-source tools: Elasticsearch, Logstash, and Kibana, used for centralized logging, data analysis, and visualization.

   • Elasticsearch: Search and analytics engine.
   • Logstash: Data collection and transformation pipeline.
   • Kibana: Visualization and dashboard interface.

2. Why is the ELK Stack popular?

   • Centralized logging for better monitoring and troubleshooting.
   • Scalable and real-time analytics.
   • Open-source and cost-effective.

3. Explain the architecture of the ELK Stack.

   • Logstash collects and processes data.
   • Elasticsearch stores and indexes the processed data.
   • Kibana visualizes the data using dashboards.

4. What is Elasticsearch?

   • It’s a distributed search engine based on Lucene that supports full-text search and analytics on large datasets.

5. What is Logstash?

   • A data pipeline tool used to collect, process, and forward data into Elasticsearch or other outputs.

6. What is Kibana?

   • A visualization tool for Elasticsearch data. It allows users to create interactive dashboards and visualizations.

7. What are some use cases of the ELK Stack?

   • Log analysis and monitoring.
   • Application performance monitoring (APM).
   • Security and compliance.
   • Business intelligence and analytics.

8. What is the difference between ELK and EFK?

   • EFK Stack replaces Logstash with Fluentd, which is lighter and more resource-efficient.

9. What are Beats in the ELK ecosystem?

   • Beats are lightweight data shippers that send data directly to Elasticsearch or Logstash. Examples include Filebeat, Metricbeat, and Packetbeat.

10. What is the purpose of Filebeat?

    • It collects log files and forwards them to Logstash or Elasticsearch.

11. What is an Elasticsearch index?

    • An index is a collection of documents in Elasticsearch. It’s similar to a database in relational systems.

12. What is a document in Elasticsearch?

    • A document is the basic unit of data, stored in JSON format.

13. Explain Elasticsearch shards.

    • Shards split an index into smaller parts to distribute and process data across multiple nodes.

14. What are replicas in Elasticsearch?

    • Replicas are copies of primary shards used for fault tolerance and load balancing.

15. How does Elasticsearch handle full-text search?

    • Elasticsearch uses the Inverted Index data structure and Lucene Query for full-text search.

16. What are Elasticsearch analyzers?

    • Analyzers process text data for indexing and searching. Examples: standard, whitespace, and custom analyzers.

17. What is a query DSL in Elasticsearch?

    • Query DSL (Domain Specific Language) is used to write queries, such as match, term, or range.

18. How does Elasticsearch differ from traditional databases?

    • It is schema-free, supports distributed architecture, and excels in full-text search.

19. What is a mapping in Elasticsearch?

    • Mapping defines how a document’s fields are stored and indexed.

20. What is the purpose of Elasticsearch aggregations?

    • Aggregations allow advanced analytics, such as data summarization and statistical calculations.

21. What are Logstash pipelines?

    • A pipeline is a sequence of stages: input, filter, and output, used to process data.

22. What is the purpose of the Logstash config file?

    • The config file defines input sources, filters, and output destinations.

23. What are Logstash plugins?

    • Plugins are modular components in Logstash:
    • Input Plugins: Fetch data (e.g., file, beats).
    • Filter Plugins: Process data (e.g., grok, mutate).
    • Output Plugins: Send data (e.g., elasticsearch, stdout).

24. What is the grok filter in Logstash?

    • Grok filters parse unstructured data into structured data using predefined patterns.

25. How do you monitor Logstash performance?

    • Use monitoring APIs, log files, and pipeline metrics in Kibana.

26. How does Logstash handle failures?

    • It retries events and uses a dead letter queue for failed events.

27. What is a multiline filter in Logstash?

    • It combines multiple log lines into a single event, often used for stack traces.

28. What is the use of conditionals in Logstash pipelines?

    • Conditionals apply filters or outputs based on specific criteria.

29. Can Logstash output to multiple destinations?

    • Yes, it supports multiple outputs like Elasticsearch, S3, and Kafka.

30. How do you secure Logstash?

    • Use SSL/TLS encryption, authentication, and pipeline-specific access controls.

31. What is Kibana used for?

    • It visualizes Elasticsearch data with charts, maps, and dashboards.

32. How do you create a dashboard in Kibana?

    • Use visualizations (e.g., bar charts, tables) and combine them into a dashboard.

33. What are index patterns in Kibana?

    • Index patterns connect Kibana to specific Elasticsearch indices.

34. What is the Discover tab in Kibana?

    • It provides a raw view of Elasticsearch data with search and filter capabilities.

35. What is Kibana Lens?

    • A user-friendly interface for creating visualizations with drag-and-drop functionality.

36. How can you secure Kibana?

    • Use role-based access control, TLS encryption, and secure Elasticsearch integration.

37. What is Canvas in Kibana?

    • A tool for creating custom, infographic-style visualizations and reports.

38. What are Kibana alerts?

    • Alerts notify users about data changes based on defined conditions.

39. What is Kibana Timelion?

    • A tool for time-series visualizations and analysis.

40. Can you embed Kibana visualizations into external applications?

    • Yes, using iframe links or APIs.

41. How do you scale the ELK Stack?

    • Scale Elasticsearch with more nodes and shards, use Kafka with Logstash for high-volume data, and implement Kibana load balancing.

42. What is X-Pack in Elasticsearch?

    • A plugin offering features like security, monitoring, and machine learning.

43. What is the Elastic Common Schema (ECS)?

    • A standard schema for structured logging across the ELK ecosystem.

44. How does Elasticsearch ensure data consistency?

    • By using write-ahead logging (WAL) and primary-replica synchronization.

45. How can you optimize Elasticsearch queries?

    • Use filters instead of queries, minimize wildcard usage, and ensure proper indexing.

46. What is hot-warm architecture in Elasticsearch?

    • A tiered storage approach where hot nodes handle frequent queries, and warm nodes store older data.

47. How do you back up Elasticsearch data?

    • Use the snapshot and restore feature to store data in repositories like S3.

48. What are common performance bottlenecks in ELK?

    • High CPU usage in Logstash, insufficient heap size in Elasticsearch, and slow Kibana visualizations.

49. How do you secure the entire ELK Stack?

    • Use authentication, encryption, role-based access, and network isolation.

50. What are some alternatives to the ELK Stack?

    • EFK Stack, Splunk, Graylog, Datadog, and Sumo Logic.