Top 40 MongoDB Interview Questions & Answers 2025

Use MongoDB for: flexible/evolving schemas, hierarchical data, high write throughput, horizontal scaling needs, rapid development. Use relational for: complex transactions, strict data integrity, complex joins, established schemas. MongoDB excels at: content management, real-time analytics, IoT data, catalogs. Consider hybrid approaches for complex applications.

Key patterns: Embedded documents (denormalization for read performance), References (normalization for data integrity), Bucket pattern (time-series), Computed pattern (pre-compute aggregations), Subset pattern (frequently accessed data), Extended reference (cache related data). Design based on access patterns, not just data relationships.

Aggregation processes documents through stages: $match (filter), $group (aggregate), $project (reshape), $sort, $limit, $lookup (join), $unwind (flatten arrays). Stages execute sequentially; output of one is input to next. Optimize by: placing $match early, using indexes, limiting fields with $project. For complex analytics, consider Atlas Data Lake.

Sharding distributes data across multiple servers (shards) for horizontal scaling. Components: mongos (router), config servers (metadata), shards (data). Shard key determines distribution - choose carefully (cardinality, write distribution, query isolation). Use when: data exceeds single server, need write scaling, geographic distribution. Start without sharding; add when needed.

Write concern: acknowledgment level (w:1, w:majority, w:all). Read concern: isolation level (local, majority, linearizable). Read preference: which nodes to read from. Transactions (4.0+): multi-document ACID across replica sets. Causal consistency: ensures read-your-writes. For critical data, use w:majority + readConcern:majority.