MongoDB Interview Questions and Answers

Sharding horizontally partitions a collection across mongos routers and shards. You choose a shard key, and MongoDB splits the key space into chunks distributed and balanced across shards.

Two strategies:

• Ranged sharding: contiguous key ranges per shard, good for range queries but can create hotspots on monotonic keys.
• Hashed sharding: hashes the key for even distribution, good for write scaling but poor for range queries.

The shard key must be present in every document and is immutable in older versions. A poor key causes jumbo chunks or an unbalanced cluster; a monotonically increasing timestamp sends all writes to one shard. Include a high-cardinality, frequently queried field, or use a compound key combining a coarse partition with a fine field. Queries including the shard key are targeted; others become scatter-gather and hit every shard.

Use find for simple filtering, projection, sorting and pagination on a single collection. It is lightweight and uses indexes efficiently.

Use aggregate when you must transform data: group, join, reshape, compute derived values, or run multi-stage logic. Aggregation can still use indexes for $match and $sort when they appear early.

db.sales.aggregate([
  { $match: { region: "EMEA", date: { $gte: ISODate("2024-01-01") } } },
  { $group: { _id: "$productId", total: { $sum: "$amount" } } },
  { $sort: { total: -1 } } ]);

Performance pitfalls: stages after $group or $unwind cannot use the original index, $lookup is costly, and a large $sort without an index may spill to disk. Put $match and $sort first, project early to shrink documents, and inspect explain output before shipping.