MongoDB Interview Questions and Answers for 10 years experience

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100 MongoDB Interview Questions & Answers (10+ Years Experience)

  1. What are the key differences between MongoDB and relational databases?

    • Answer: MongoDB is a NoSQL, document-oriented database, while relational databases (like MySQL, PostgreSQL) are structured with tables and rows. Key differences include data modeling (documents vs. tables), schema flexibility (schemaless vs. fixed schema), scalability (horizontal vs. vertical), query language (JSON-like queries vs. SQL), and ACID properties (relaxed consistency vs. strict ACID compliance). MongoDB excels in handling unstructured data and offers high scalability and flexibility, while relational databases prioritize data integrity and transactional consistency.

  2. Explain the concept of sharding in MongoDB.

    • Answer: Sharding is a horizontal partitioning technique that distributes data across multiple servers (shard servers). It improves scalability and performance by dividing a large dataset into smaller, manageable chunks. A config server manages the metadata of the sharded cluster, while a router (mongos) acts as a proxy, directing queries to the appropriate shard.

  3. Describe different types of indexes in MongoDB and when to use them.

    • Answer: MongoDB offers various indexes, including single-field, compound, geospatial, hashed, and text indexes. Single-field indexes speed up queries on a single field. Compound indexes optimize queries involving multiple fields. Geospatial indexes are for location-based queries. Hashed indexes are suitable for equality matches. Text indexes facilitate full-text searches. The choice depends on the most frequent query patterns.

  4. How does replica set ensure high availability in MongoDB?

    • Answer: A replica set consists of one primary and multiple secondary nodes. The primary handles write operations, while secondaries replicate data from the primary. If the primary fails, a secondary automatically promotes to become the new primary, ensuring continuous availability. This setup also provides read scaling by distributing read operations across secondary nodes.

  5. Explain the concept of aggregation framework in MongoDB.

    • Answer: The aggregation framework provides a powerful way to process data and produce customized results. It uses a pipeline of stages (like $match, $group, $project, $sort, $limit) to filter, group, transform, and reshape data. It's analogous to SQL's SELECT statements but offers more flexibility for complex data manipulations.

  6. What are the different types of MongoDB drivers? Which one have you used extensively and why?

    • Answer: MongoDB offers drivers for various programming languages (e.g., Node.js, Python, Java, C#, PHP). [Answer should specify a driver, e.g., "I've extensively used the Python driver (pymongo) because of its mature ecosystem, ease of use, and strong community support. It seamlessly integrates with other Python libraries and tools."]

  7. How do you handle transactions in MongoDB?

    • Answer: MongoDB's transaction support depends on the version. Earlier versions relied on application-level transactions. With multi-document transactions (available in newer versions), you can ensure atomicity across multiple documents within a single collection or across multiple collections. This leverages the capabilities of replica sets.

  8. Explain the difference between `find()` and `aggregate()` methods.

    • Answer: `find()` retrieves documents based on specified criteria, essentially performing a query. `aggregate()` processes data through a pipeline of stages to transform and reshape it, performing more complex operations beyond simple querying.

  9. What is the purpose of the `$lookup` operator in the aggregation framework?

    • Answer: The `$lookup` operator performs a join operation, similar to a relational database join. It allows you to join documents from different collections based on specified fields.

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