# MongoDB vs PostgreSQL

URL: https://softwaredictionary.org/compare/mongodb-vs-postgresql
Last updated: 2026-10-05

In short: PostgreSQL keeps data in fixed-schema tables and joins them with SQL, while MongoDB stores flexible JSON-like documents and was built to shard across servers.

## What is the difference between MongoDB and PostgreSQL?

PostgreSQL keeps data in tables of rows and columns, with types and constraints checked by the database, and relates tables to each other with foreign keys and SQL joins. MongoDB keeps data as documents in collections: each document is a JSON-like object, stored as BSON, that can nest arrays and sub-documents, and documents in one collection don't have to share the same fields.

The way you model data follows from that. In PostgreSQL you normalize: an order, its lines and its customer live in separate tables and are joined when read. In MongoDB you usually embed what is read together, so an order document carries its lines and one read returns everything. That makes simple reads fast and the schema easy to change, at the cost of duplicated data and harder queries across documents.

The gap has narrowed. PostgreSQL stores and indexes JSON in its `jsonb` type, so a relational database can hold flexible documents too, and MongoDB has offered multi-document ACID transactions since version 4.0, as well as schema validation. Scaling still differs: MongoDB was designed to shard data across servers, while PostgreSQL usually grows on one primary with read replicas, with extensions or managed services for sharding.

For most applications with related data, reports and transactions, PostgreSQL is a sound default. MongoDB fits best when the data is naturally shaped like documents, varies from record to record, or has to spread across many servers from the start. Either can run a typical web app well, and the team's experience matters as much as the engine.

| Aspect | MongoDB | PostgreSQL |
| --- | --- | --- |
| Data model | Collections of JSON-like documents | Tables with rows and columns |
| Schema | Flexible; validation is optional | Defined up front and enforced |
| Relationships | Embedding, or references joined with `$lookup` | Foreign keys and joins |
| Query language | The MongoDB Query API and aggregation pipelines | SQL |
| Transactions | ACID, across documents since 4.0 | Full ACID, across any tables |
| Scaling | Sharding across servers, built in | One primary with read replicas; sharding through extensions |
| JSON | The native format | `jsonb` columns, with indexes |

## Choose MongoDB when

- Records vary a lot in shape, or the schema changes often.
- Data is read and written as whole documents, such as profiles or catalogs.
- You expect to spread data across many servers and want sharding built in.

## Choose PostgreSQL when

- Your data is relational: customers, orders and invoices that refer to each other.
- You need complex queries, reports or strict consistency across many tables.
- You want one database that also handles JSON, full-text search and geodata through extensions.

## Frequently asked questions

**Is MongoDB faster than PostgreSQL?**

It depends on the workload. Reading one whole document can be very fast in MongoDB, while joins, aggregations and complex filters are often faster in PostgreSQL. Indexes and the data model matter far more than the engine.

**Can PostgreSQL replace MongoDB?**

Often, for apps that need some flexible data: `jsonb` columns store and index documents inside a relational database. MongoDB keeps the edge for very document-centric data and for built-in sharding.

**Does MongoDB support transactions?**

Yes. Since version 4.0 it supports multi-document ACID transactions, though its data model aims to keep most writes to a single document, which is atomic on its own.

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