Elasticsearch Interview Questions and Answers for 2 years experience

1. What is Elasticsearch?

   • Answer: Elasticsearch is a distributed, RESTful search and analytics engine capable of handling large volumes of data and providing near real-time search capabilities. It's built on top of Apache Lucene and is highly scalable and fault-tolerant.

2. Explain the concept of an index in Elasticsearch.

   • Answer: An index in Elasticsearch is a logical namespace that holds a collection of documents. Think of it as a database table. Each index has its own schema and settings. You can have multiple indices within a single Elasticsearch cluster.

3. What is a document in Elasticsearch?

   • Answer: A document is a single unit of data in Elasticsearch. It's a JSON object containing fields representing attributes of the data. For example, a document might represent a customer with fields like `name`, `email`, and `address`.

4. What are mappings in Elasticsearch?

   • Answer: Mappings define the structure of your documents within an index. They specify the data type of each field (e.g., text, keyword, integer, date) and how it should be indexed and analyzed. Proper mappings are crucial for efficient search and aggregation.

5. Explain the concept of shards and replicas in Elasticsearch.

   • Answer: Shards are horizontal partitions of an index, allowing you to distribute data across multiple nodes. Replicas are copies of a shard, providing redundancy and high availability. If one shard fails, a replica can take over.

6. What is the difference between `keyword` and `text` data types in Elasticsearch?

   • Answer: `keyword` fields are not analyzed; they are stored as is. They are suitable for exact-match searches (e.g., usernames, IDs). `text` fields are analyzed, meaning Elasticsearch breaks them down into terms for full-text search (e.g., descriptions, articles).

7. What are analyzers in Elasticsearch?

   • Answer: Analyzers are pipelines that process text fields, breaking them down into individual terms (words, tokens). They typically involve tokenization, filtering (e.g., stop word removal), and stemming/lemmatization.

8. Explain the different types of queries in Elasticsearch.

   • Answer: Elasticsearch offers various query types, including term queries (exact match), match queries (full-text search), wildcard queries, range queries, bool queries (combining multiple queries with AND/OR/NOT), and more. The choice depends on the specific search needs.

9. What are aggregations in Elasticsearch?

   • Answer: Aggregations allow you to perform statistical analysis on your data, such as calculating counts, averages, sums, percentiles, and creating histograms and other groupings. They are used for summarizing and understanding your data.

10. Explain the concept of a scroll in Elasticsearch.

    • Answer: A scroll allows you to retrieve large result sets efficiently without loading them all into memory at once. It's a cursor-based approach that lets you fetch data in batches.

11. How do you handle nested objects in Elasticsearch?

    • Answer: Nested objects are handled using the `nested` data type in mappings. This allows you to index and search within arrays of objects. Queries require specific `nested` query clauses to search within these nested structures.

12. What is the difference between `term` and `match` queries?

    • Answer: `term` queries search for an exact match on a non-analyzed field (keyword). `match` queries search for terms within analyzed text fields, taking into account analyzers and allowing for partial matches.

13. Explain the concept of a filter in Elasticsearch.

    • Answer: Filters are used to pre-filter documents before applying queries. They are cached, making them more efficient for frequently used filters. They are primarily used for performance optimization, not for text matching like queries.

14. What are some common performance tuning techniques for Elasticsearch?

    • Answer: Performance tuning involves optimizing mappings, analyzers, query structure, shard allocation, hardware resources (CPU, RAM, disk I/O), and using appropriate caching strategies. Regular monitoring and analysis of query performance are also crucial.

15. How do you handle updates and deletes in Elasticsearch?

    • Answer: Elasticsearch doesn't directly update documents in place. Updates are done by re-indexing the document with modified fields. Deletes remove documents from the index. Both actions are typically done using the Elasticsearch API.

16. What is Kibana, and how does it relate to Elasticsearch?

    • Answer: Kibana is a visualization and management tool for Elasticsearch. It allows you to explore, analyze, and visualize data stored in Elasticsearch indices. It provides dashboards, visualizations, and monitoring capabilities.

17. Explain the concept of a cluster in Elasticsearch.

    • Answer: A cluster is a collection of one or more Elasticsearch nodes working together. They share data and resources, providing scalability and fault tolerance. Data is distributed across the nodes in the cluster.

18. What are the different types of nodes in an Elasticsearch cluster?

    • Answer: Common node types include master-eligible nodes (responsible for cluster management), data nodes (store data), and ingest nodes (pre-process data before indexing).

19. How do you manage and monitor your Elasticsearch cluster?

    • Answer: Monitoring involves using tools like Kibana, Elasticsearch's built-in monitoring features, and third-party tools. Management includes tasks such as adding/removing nodes, configuring settings, managing indices, and handling data backups and recovery.

20. Explain the role of the `_settings` API in Elasticsearch.

    • Answer: The `_settings` API allows you to retrieve and update index-level and cluster-level settings. This includes settings for things like shard allocation, refresh intervals, and other performance-related configurations.

21. What is the difference between a primary shard and a replica shard?

    • Answer: A primary shard is the original copy of a partition of the index. Replica shards are copies of the primary shard, providing redundancy and high availability. Reads can be served from replica shards.

22. How do you handle large datasets in Elasticsearch?

    • Answer: Handling large datasets involves strategies like using more shards and replicas, optimizing mappings and analyzers, efficient query design, and leveraging scroll for retrieving results, as well as potentially using multiple Elasticsearch clusters.

23. Explain the concept of a pipeline in Elasticsearch.

    • Answer: Ingest pipelines process documents before they are indexed. They allow you to perform transformations, enrichments, and other operations on the data stream, such as adding fields, changing data types, or removing sensitive information.

24. What is the role of the `_mapping` API in Elasticsearch?

    • Answer: The `_mapping` API allows you to define, retrieve, and update the mappings for an index. Mappings define how the fields of your documents are structured and indexed, influencing search and aggregation performance.

25. How do you use wildcard queries in Elasticsearch?

    • Answer: Wildcard queries use wildcards such as `*` (matches zero or more characters) and `?` (matches a single character) to search for patterns in text fields. They are useful for flexible searches, but can be less efficient than other query types for large datasets.

26. Explain the concept of a "must", "should", and "must_not" clause in a bool query.

    • Answer: In a bool query, "must" clauses are required for a document to match, "should" clauses are optional but increase the relevance score if matched, and "must_not" clauses exclude documents that match the specified criteria.

27. How do you handle geo-spatial data in Elasticsearch?

    • Answer: Geo-spatial data is handled using the `geo_point` data type. This allows you to store latitude and longitude coordinates. Elasticsearch offers various geo-spatial queries to search based on distance, bounding boxes, and other spatial relationships.

28. What are some common security considerations when working with Elasticsearch?

    • Answer: Security involves configuring authentication (e.g., using X-Pack/Elasticsearch Security features), authorization (controlling access to indices and data), network security (firewall rules, secure communication), and regular security updates and patching.

29. How do you handle data backups and recovery in Elasticsearch?

    • Answer: Backup strategies involve using snapshot and restore functionality, or utilizing external backup solutions. Recovery involves restoring snapshots or using replicas to recover from node failures.

30. Explain the concept of a refresh interval in Elasticsearch.

    • Answer: The refresh interval determines how often Elasticsearch makes newly indexed documents searchable. A shorter interval improves search responsiveness but increases resource consumption. A longer interval reduces resource usage but increases latency.

31. What is the purpose of the `_search` API in Elasticsearch?

    • Answer: The `_search` API is the primary way to perform searches in Elasticsearch. It accepts search requests including queries, aggregations, sorting, and other parameters, and returns search results.

32. How do you use range queries in Elasticsearch?

    • Answer: Range queries filter documents based on a range of values for a specific field. You specify the lower and upper bounds (inclusive or exclusive) of the range.

33. Explain the concept of term vectors in Elasticsearch.

    • Answer: Term vectors provide detailed information about the terms within a document, including their positions and frequencies. They are useful for advanced search features like phrase searches and highlighting.

34. What are some common troubleshooting techniques for Elasticsearch issues?

    • Answer: Troubleshooting involves checking logs for errors, monitoring resource usage (CPU, memory, disk I/O), analyzing slow queries, verifying mappings and analyzers, and using the Elasticsearch monitoring tools to identify bottlenecks.

35. How do you handle different languages in Elasticsearch?

    • Answer: Language support is handled using analyzers specific to different languages. You can configure analyzers to handle tokenization, stemming, and stop words specific to each language.

36. What are some best practices for designing Elasticsearch indices?

    • Answer: Best practices include defining clear mappings, using appropriate data types, choosing suitable analyzers, properly configuring shards and replicas based on data size and expected load, and regularly reviewing index performance.

37. Explain the concept of index lifecycle management (ILM) in Elasticsearch.

    • Answer: ILM automates the management of indices over their lifecycle. This includes managing phases like hot, warm, and cold, where policies define actions such as rollover (creating new indices), shrinking (reducing the number of shards), and deletion, optimizing storage costs and performance.

38. How do you use the painless scripting language in Elasticsearch?

    • Answer: Painless is a scripting language used for various tasks in Elasticsearch, including data transformation within ingest pipelines, generating dynamic values during indexing, and creating custom aggregations. It is a secure scripting language designed for Elasticsearch.

39. What are some common use cases for Elasticsearch?

    • Answer: Common use cases include log analytics, website search, e-commerce search, security information and event management (SIEM), and real-time data analytics.

40. How do you optimize Elasticsearch queries for performance?

    • Answer: Query optimization involves using efficient query types, avoiding wildcard queries where possible, utilizing filters, properly structuring bool queries, analyzing query execution plans, and ensuring that appropriate analyzers are used.

41. Explain the concept of a "type" in Elasticsearch (in older versions).

    • Answer: In older versions of Elasticsearch, "types" were used to define different document structures within a single index. This is now deprecated; modern Elasticsearch uses a single type per index.

42. What is the significance of the `_source` field in Elasticsearch?

    • Answer: The `_source` field contains the original JSON document as indexed. It's used to retrieve the complete document when needed but can be excluded from search results for performance optimization.

43. How do you configure and manage roles and users in Elasticsearch?

    • Answer: Role-Based Access Control (RBAC) is managed through the Elasticsearch Security features (X-Pack or equivalent). This allows defining roles with specific permissions and assigning those roles to users to control access to indices and data.

44. What is the purpose of the `index` API in Elasticsearch?

    • Answer: The `index` API is used to add or update documents in an Elasticsearch index.

45. What are some common tools for interacting with Elasticsearch?

    • Answer: Common tools include the Elasticsearch command-line client, various API clients in different programming languages (Java, Python, Node.js, etc.), and Kibana.

46. How do you handle data inconsistencies in Elasticsearch?

    • Answer: Data inconsistencies can be handled through careful data validation before indexing, using proper mappings, employing versioning for optimistic locking, and using tools to detect and resolve inconsistencies.

47. Explain the concept of a segment in Elasticsearch.

    • Answer: Segments are the fundamental units that make up an index. They are self-contained files containing indexed documents and metadata. The Lucene index is made up of many segments, which are merged over time for better performance.

48. How do you monitor the health of an Elasticsearch cluster?

    • Answer: Cluster health is monitored using Kibana's monitoring dashboards, the Elasticsearch cluster health API, and by observing metrics such as CPU usage, memory consumption, disk space, and shard allocation.

49. What is the difference between a search and a query in Elasticsearch?

    • Answer: While often used interchangeably, a "search" usually refers to the entire process of retrieving documents based on specified criteria, including query parameters, aggregations, sorting, etc. A "query" is a specific part of the search request that defines the criteria to match documents.

50. Explain the concept of fielddata in Elasticsearch.

    • Answer: Fielddata loads field values into memory for sorting and aggregations. It should be used judiciously for large fields since it can consume significant memory. Careful mapping design and query optimization are key to avoiding fielddata issues.

51. How do you handle schema changes in Elasticsearch?

    • Answer: Schema changes usually involve creating a new index with the updated mappings and re-indexing your data. Alternatively, dynamic mappings allow for some flexibility, but careful planning is essential to avoid performance degradation.

52. What is the role of the `get` API in Elasticsearch?

    • Answer: The `get` API retrieves a specific document from an index by its ID.

53. What are some common challenges faced when working with Elasticsearch?

    • Answer: Challenges include performance tuning, optimizing queries for large datasets, managing schema changes, securing the cluster, monitoring and troubleshooting issues, and understanding complex interactions between analyzers and mappings.

54. How do you use date histograms in Elasticsearch?

    • Answer: Date histograms are used to group data based on time intervals (e.g., daily, hourly, weekly). They are a type of aggregation that allows you to visualize trends and patterns over time.

55. What is the purpose of the `delete` API in Elasticsearch?

    • Answer: The `delete` API removes a specific document from an index by its ID.

56. How do you handle different data formats in Elasticsearch?

    • Answer: Elasticsearch primarily works with JSON data. For other formats (CSV, XML, etc.), you usually need to pre-process the data using other tools and convert it to JSON before indexing in Elasticsearch.

57. Explain the concept of a "completion suggester" in Elasticsearch.

    • Answer: A completion suggester provides auto-completion functionality for text fields. It's often used for search boxes that offer suggestions as the user types.

58. How do you use terms aggregations in Elasticsearch?

    • Answer: Terms aggregations group documents based on the unique values of a specific field, counting the number of documents for each unique value. This is useful for understanding the distribution of values in a field.

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