50 MongoDB Interview Questions and Answers

MongoDB: A Deep Dive into Common Interview Questions and Answers

MongoDB, a high-performance, scalable NoSQL database, stores data in flexible, JSON-like documents (BSON). Its document-oriented model and dynamic schema make it a popular choice for modern applications. This article explores frequently asked MongoDB interview questions.

Core Concepts and Differences:

1. What is MongoDB? A document-oriented NoSQL database using BSON for data storage, prioritizing scalability and performance.

2. Documents vs. Collections: Documents are the fundamental data units (like JSON objects), while collections group related documents (similar to tables).

3. MongoDB vs. Relational Databases: Key differences include flexible schemas (MongoDB) vs. fixed schemas (relational), easier horizontal scaling (MongoDB), document-based query language (MongoDB) vs. SQL, and denormalization (MongoDB) vs. joins (relational).

4. NoSQL Database Types: MongoDB falls under Document Databases. Other types include Key-Value, Column-Store, and Graph databases.

5. Advantages of MongoDB: Handles large datasets efficiently, offers cross-platform compatibility, provides high performance and scalability, simplifies data modeling, supports horizontal and vertical scaling, and integrates well with cloud platforms.

Advanced Features and Operations:

6. Sharding: Distributes data across multiple machines for extreme scalability and high throughput.

7. Indexing: Improves query performance by creating optimized data structures for faster retrieval. Various index types exist (single field, compound, etc.).

8. Replica Sets: Groups of MongoDB servers replicating data for redundancy and high availability, featuring primary and secondary nodes with automatic failover.

9. Aggregation Framework: A suite of analytical tools using a pipeline approach to process data and produce computed results.

10. Data Consistency: Achieved through write concerns, journaling, and multi-document transactions (since version 4.0).

11. Capped Collections: Fixed-size collections maintaining insertion order, useful for logs or caching. Old documents are overwritten when the limit is reached.

12. $lookup Operator: Performs left outer joins with other collections within the same database during aggregation.

13. ObjectId: A 12-byte unique identifier automatically generated for each document (unless user-specified).

14. Schema-less Design: Documents within a collection can have different fields, offering flexibility in data modeling.

15. save() vs. insert(): insert() adds new documents, while save() inserts if new or updates if the _id matches an existing document.

16. High Availability: Ensured through replica sets, automatic failover, data redundancy, and read scaling via secondary reads.

17. explain() Method: Provides details on query execution plans for optimization and performance analysis.

18. Storage Engines: MongoDB supports WiredTiger (default), MMAPv1 (deprecated), and In-Memory engines.

19. Transactions (ACID): Supported since version 4.0 for multi-document operations across collections and databases.

20. $where vs. $expr: $where uses JavaScript expressions (slower, less secure), while $expr uses aggregation expressions (faster, more secure).

21. TTL (Time-To-Live) Indexes: Automatically remove documents after a specified time, ideal for temporary data.

22. BSON (Binary JSON): MongoDB's binary-encoded format for document storage and transfer.

23. Database Creation: Use the command use database_name (creates if it doesn't exist).

24. _id Field: The primary key, automatically generated if not specified, and must be unique within a collection.

25. $set vs. $unset: $set updates or adds fields, while $unset removes fields.

26. $push Operator: Adds elements to array fields.

27. Covered Queries: Queries where all fields are indexed, allowing results to be returned directly from the index without accessing documents.

28. $lookup in Aggregation: Performs left outer joins within aggregation pipelines.

29. Map-Reduce: Though supported, aggregation pipelines are generally preferred for most data processing tasks.

30. $and vs. $all: $and performs logical AND on multiple expressions, while $all matches arrays containing all specified elements.

Optimization and Advanced Topics:

31. Query Optimization: Achieved through proper indexing, covered queries, avoiding large documents, and using explain().

32. $match Stage: Filters documents in aggregation pipelines.

33. Write Concerns: Control the level of acknowledgment for write operations, affecting data durability and latency.

34. $inc vs. $mul: $inc increments, $mul multiplies a field's value.

35. Full-Text Search: Supported through text indexes and the $text operator.

36. $group Stage: Groups documents and performs aggregations on grouped data.

37. Change Streams: Allow real-time monitoring of database changes.

38. Data Insertion (insertOne, insertMany): Methods for adding single or multiple documents.

39. Document Validation: Enforced using schema validation rules with the $jsonSchema operator.

40. $elemMatch vs. $all: $elemMatch matches arrays with at least one element meeting criteria, while $all matches arrays containing all specified elements.

41. Replication Architecture: Primary node handles writes, logs changes to the oplog, and secondaries replicate this data.

42. $out Stage: Writes aggregation pipeline results to a collection.

43. Read Preferences: Control how read operations target replica set members.

44. $unwind vs. $flatten: $unwind deconstructs arrays, while $flatten flattens nested arrays.

45. Concurrency Control (MVCC): Uses multi-version concurrency control for concurrent reads and writes.

46. $graphLookup Stage: Performs recursive searches for graph-like data.

47. Oplog: A capped collection logging all data-modifying operations, essential for replication.

48. Soft Deletes: Implemented by adding a boolean field (e.g., isDeleted) instead of physically removing documents.

49. $merge Stage: Writes aggregation results to a collection with various update strategies.

50. Foreign Key Constraints: Not supported in MongoDB.

51. Profiler: Collects detailed information on database operations for performance tuning.

Conclusion:

This comprehensive overview covers many essential MongoDB concepts and frequently asked interview questions. Thorough preparation in these areas will significantly enhance your interview performance. Remember to practice hands-on exercises and delve deeper into specific areas based on the roles you are targeting.

The above is the detailed content of 50 MongoDB Interview Questions and Answers. For more information, please follow other related articles on the PHP Chinese website!