MongoDB Interview Questions and Answers

MongoDB is a document database. Data is stored as flexible BSON documents inside collections instead of rows in tables with a fixed schema. Each document can have its own fields, and nested objects and arrays are native.

db.users.insertOne({
  name: "Ada",
  roles: ["admin", "author"],
  address: { city: "London" }
});

Key differences:

• Relationships are modelled by embedding or by references, and joins are optional via $lookup.
• Schema is enforced by the application, optionally validated with JSON Schema validators.
• Scaling out uses replica sets for availability and sharding for horizontal scale.
• Transactions exist but are not the primary consistency unit; single-document writes are atomic.

Use MongoDB when the access pattern is document-oriented and the schema evolves quickly. Prefer a relational database when you need complex multi-table joins and strong relational integrity.

An index is a B-tree structure storing a sorted subset of a collection's fields, letting the planner find documents without scanning everything. Without an index a query does a COLLSCAN; with one it can do an IXSCAN.

A compound index covers multiple fields in a defined order:

db.orders.createIndex({ customerId: 1, createdAt: -1 });

Order matters. This index supports queries on customerId alone and on customerId plus createdAt, but not on createdAt alone, because of the leftmost-prefix rule. Direction matters for sorts: sorting customerId ascending and createdAt descending uses this index directly.

Every index speeds reads but slows writes and consumes memory, so create only what your queries need. Use explain("executionStats") to confirm an index is used and to see how many documents were examined versus returned.