Compare Models, Not Brands
The useful question is not which database is faster but which model fits the access pattern. Relational storage rewards stable entities with many relationships and ad-hoc reporting: orders, invoices, permissions, inventory. Document storage rewards aggregates that are read and written as a unit and whose shape varies by tenant: product catalogs with per-category attributes, event payloads, user-generated drafts. Both support JSON, so the decision is about the centre of gravity rather than a hard boundary.
Sketch the five most frequent queries and the five most feared. If the feared queries involve joining four tables and aggregating by month, a relational engine with a query planner will serve them. If they involve fetching one aggregate by key and returning it whole, a document store avoids the object-relational mapping entirely. Write the list down before benchmarking anything.
Transactions and Constraints
PostgreSQL enforces foreign keys, unique constraints, check constraints, and serialisable isolation inside a mature transaction engine. MongoDB supports multi-document transactions within a replica set or sharded cluster, but the modelling philosophy expects you to keep related data in one document so transactions are rarely needed. That difference is cultural and practical: relational schemas invite cross-entity integrity, while document schemas invite embedding and duplication.
Duplication is not automatically wrong; it is a trade-off between write cost and read cost. If an order embeds its line items, changing a line requires rewriting the order but reading one is a single lookup. If line items are separate rows, reads join but updates are surgical. Choose based on which happens more often and whether the aggregate has an independent lifecycle outside its parent.
Query Patterns and Indexes
MongoDB indexes are B-tree based and developers choose them explicitly, which makes query shape visible in code review. PostgreSQL indexes come in several kinds: B-tree for equality and ranges, GIN for arrays and full-text search, GiST for geometric data, and BRIN for large append-only tables. Both databases fail the same way when an index does not match the query: a collection or table scan that passes in staging and collapses under production volume.
Compose index keys in the order the query filters and sorts, and confirm with EXPLAIN ANALYZE rather than intuition. A compound index on (tenant_id, status, created_at) serves a tenant-scoped list ordered by date; a separate index on status alone rarely earns its write cost. Watch for low-selectivity prefixes and for sorting after filtering, which forces an in-memory sort once the working set exceeds what fits in memory.
-- Confirm the index is used and the sort is not in memory.
EXPLAIN (ANALYZE, BUFFERS)
SELECT id, status, created_at
FROM orders
WHERE tenant_id = $1
AND status = 'open'
ORDER BY created_at DESC
LIMIT 50;Schema Evolution and Migrations
PostgreSQL migrations change structure and data under a known schema, which is explicit but requires care on large tables. Adding a column with a default is cheap on modern versions, but adding a NOT NULL constraint without a validated path locks the table. Use the expand-and-contract pattern: add the nullable column, backfill in batches, add the constraint as NOT VALID, validate it, then remove the old column in a later release. Run the backfill outside peak hours and watch replication lag while it proceeds.
Document stores absorb additive fields without migration, which is genuinely convenient early and genuinely risky later. After two years, a collection holds six generations of documents and every query must handle all of them. Version each document with a schemaVersion field, validate writes against the current shape, and run a lazy migration that rewrites old documents when they are next touched.
Operate What You Can Observe
Both systems are operationally demanding at scale. PostgreSQL asks for connection pooling, autovacuum tuning, replication lag monitoring, and attention to bloat. MongoDB asks for replica set health, shard key selection, and awareness of index build impact on primary throughput. A shard key with low cardinality creates a hot shard; a poorly chosen pooling layer exhausts connections under load. Size the pool from the database connection limit, not from the application instance count.
Choose the store your team can run on a Saturday. Tools, backups, and institutional knowledge matter more than benchmark percentages that assume ideal hardware. Write down the operational risks for each candidate, name who is on call, and rehearse a restore. A database you understand deeply under pressure beats one that won a synthetic throughput test on a blog post.