Why Is MongoDB Faster Than Postgres for Certain Workloads?

Q: Why is MongoDB faster than Postgres in some cases?

A: Key points to cover (with the caveat that "faster" is workload-dependent, not universal):

• Document model locality: MongoDB stores a whole logical entity (e.g., an order with its line items) as a single BSON document, avoiding joins that a normalized relational schema in Postgres would require across multiple tables - fewer joins means fewer random I/O lookups for read-heavy, denormalized access patterns.
• Schema flexibility: no need for schema migrations/ALTER TABLE locks when fields change, which can matter for write-heavy, evolving-schema workloads.
• Horizontal scaling: MongoDB has built-in sharding designed for horizontal write scaling out of the box, whereas Postgres horizontal write-scaling (sharding/partitioning across nodes) typically needs extensions or manual architecture (e.g., Citus).
• Where Postgres wins instead: complex multi-table joins, strong ACID transactional guarantees across multiple documents/rows, and workloads needing rich SQL analytics - MongoDB's flexibility comes at the cost of weaker cross-document transactional guarantees (though multi-document ACID transactions do exist since MongoDB 4.0, with a performance cost).