MongoDB Interview Questions & Answers

13. What is a shard in MongoDB?

A shard is a chunk of data that gets distributed across multiple servers for purposes of load balancing, replication, and partitioning/aggregation of data. Sharding is a process by which a large dataset can be split into smaller, more manageable chunks (shards) that can be distributed across various servers in different locations to improve the database's performance, stability, and scalability.

14. How does MongoDB ensure high availability and data durability?

MongoDB achieves high availability by using replica sets, which are groups of mongod processes that replicate data to one or more other members in the set. For durability, it relies on write acknowledgments (w) and WTPOption levels. The w value determines the number of replicas that must acknowledge an operation for it to be considered committed. The WTPOption level can be set to ensure data is written to durable storage before network operations are sent, providing stronger durability guarantees.

15. What is the purpose of the mongos process in MongoDB?

The mongos process acts as a query router that forwards all requests to secondary routers (shards) and aggregates the results. It provides application clients with a unified view of data distributed across multiple machines or locations, presenting a single, virtual collection or database, and abstracting away the underlying sharding details.

16. What is Chained Replication in MongoDB?

Chained replication extends the traditional replica set topology to support long-distance replication with members in different data centers. In this setup, primary nodes are connected to a set of secondaries through lower latency connections, and those secondaries then serve as sources for secondary members in remote data centers. This approach allows MongoDB to replicate data across continents with acceptable latencies.

17. What is the role of Chunk Sizes and Shard Key in Sharding?

The shard key is a document's key on which the data is to be partitioned, chosen based on access patterns and distribution characteristics to evenly distribute the data across the available shards. The chunk size determines how many documents are grouped together before being assigned to a particular shard. Properly selecting the shard key and configuring chunk sizes can lead to efficient data distribution, balanced load across shards, and ultimately improved performance of read and write operations.

18. What is the purpose of Sharding vs. Replication in MongoDB?

Sharding is used to scale out horizontally by distributing data across multiple servers (shards). Its primary goals are to improve read/write performance and to allow a dataset to grow beyond the limits that would cause a single machine to become a bottleneck. On the other hand, replication is used to provide redundancy and high availability of the data. Replication involves syncing data from one or more primary nodes to secondary nodes for fault tolerance and read scaling.

19. How does MongoDB support real-time analytics with its aggregation pipeline?

MongoDB supports real-time analytics using its powerful aggregation framework, which allows developers to process data as it is written to the database. This is achieved through a series of stages in the aggregation pipeline, where each stage can perform operations like filtering, grouping, sorting, and projecting results. The aggregation pipeline processes documents individually, allowing for a variety of complex queries on live operational data without needing to extract it into a separate analytics database.

20. What is the difference between a shard key and a minkey?

A shard key is the document field on which the data is partitioned across different servers (shards). It is carefully selected based on access patterns, cardinality, and evenness of distribution to ensure efficient data distribution across shards. A minkey, on the other hand, is a special kind of shard key used with TTL (Time-To-Live) indexes to periodically expire documents based on a query that matches certain criteria. If no minkey is set, the TTL key (__ttl) is automatically used as the default minkey.

21. How does MongoDB ensure data consistency during updates and writes?

MongoDB ensures data consistency through various write concern levels, which specify the acknowledgment required for a write operation to be considered successful. For example, a write concern of { w: "majority" } ensures that a majority of replicas have applied the transaction before it is acknowledged. Additionally, MongoDB supports strong consistency models for operations like gets and counts.

22. What are the different types of indexes available in MongoDB?

MongoDB supports several types of indexes, including:

• Single field indexes
• Compound indexes
• Hash indexes (used for range queries)
• Geospatial indexes
• Text indexes (for text search on string content)
• Partial filter expression indexes (for indexing based on query criteria)
• TTL indexes (to automatically expire data after a certain period)
• Background index build support

23. What is the difference between a read and write concern in MongoDB?

A read concern specifies how MongoDB retrieves data from the database, ensuring consistency, availability, and partition tolerance based on the application's needs. A write concern specifies the guarantees that MongoDB provides for write operations, such as durability (ensuring the data is stored safely) and acknowledgments (waiting for certain number of replicas to apply the operation). Write concerns also include options like journaling, which ensures data durability by writing to the transaction log before applying changes.

24. What are the considerations when choosing a shard key for MongoDB?

When choosing a shard key, one must consider:

• The access patterns of the database (read/write intensity, query shapes, and so on).
• The cardinality of the key to ensure an even distribution of data across shards.
• The write load and whether it is uniformly distributed.
• Whether the data needs to be ordered by the key.
• Future growth projections to avoid resharding later on.
• The potential for key changes, which can lead to data migration if the new key has a different distribution pattern.

25. How does MongoDB's Oplog support real-time replication and analytics?

The oplog (operations log) is a capped collection in MongoDB that records all operations applied to the databases on a replica set member. The oplog is used by tools like mongos and rsSecondary for real-time replication, allowing them to replicate operations as they happen. For analytics, applications can tail the oplog collection in real-time to process operations after they are written to the database but before they are committed, enabling real-time analytics scenarios such as monitoring, auditing, and triggering actions based on changes in the database.

These questions cover a broad range of MongoDB concepts and should be useful for both preparing for interviews or deepening your understanding of the database's capabilities and best practices.