MongoDB vs MySQL
MongoDB vs MySQL compared: document vs relational data model, schema flexibility, joins and transactions, scaling, performance, licensing, vector search and when to pick each, plus where PostgreSQL fits.
Quick Answer
MySQL is a relational database: data lives in tables with a fixed schema, related through foreign keys and queried with SQL joins and ACID transactions. MongoDB is a document database: data lives in flexible JSON-like documents, related data is often embedded, and horizontal sharding is built in. Choose MySQL for structured, highly relational data such as orders, payments and inventory; choose MongoDB for evolving or nested data, content and catalog workloads, and when you need built-in sharding.
Reviewed by TechLogHub Engineering Team. Last updated November 7, 2026. New comparison written for November 2026, covering MongoDB 9.0 (GA September 2026) and the MySQL 9.7 LTS release (April 2026).
| Feature | MongoDB | MySQL |
|---|---|---|
| Data model | Documents (BSON) in collections | Tables, rows and columns |
| Schema | Flexible; optional JSON Schema validation | Fixed schema defined with DDL |
| Query language | MongoDB Query API and aggregation pipeline (MQL) | SQL |
| Joins and relationships | Embedding preferred; $lookup for joins | Native, efficient joins with foreign keys |
| Transactions | Single-document atomic; multi-document ACID since 4.0 | Full ACID with InnoDB |
| Horizontal scaling | Native sharding | Read replicas; sharding via Vitess or managed services |
| Replication and HA | Replica sets with automatic failover | Async/semi-sync replication, Group Replication, InnoDB Cluster |
| JSON support | Native | JSON column type with functions and indexes on generated columns |
| Vector search | Atlas Vector Search; search and vector search coming to self-managed editions | VECTOR data type (9.x); vector search mainly in HeatWave |
| License | SSPL (Community), commercial (Enterprise) | GPLv2 (Community), commercial (Enterprise) |
| Managed cloud options | MongoDB Atlas on AWS, Azure and Google Cloud | Amazon RDS and Aurora, Azure Database for MySQL, Cloud SQL, HeatWave |
| Best-fit workload | Semi-structured, nested, fast-changing data | Structured, relational, transactional data |
MongoDB
The most widely used document database. It stores data as BSON documents in collections, supports rich queries, secondary indexes, aggregation pipelines and multi-document ACID transactions, and has built-in replication and sharding.
Pros
- Flexible schema: documents in one collection can differ, which suits fast-changing products
- Nested documents and arrays map naturally to objects in application code
- Built-in sharding for horizontal scale-out and replica sets for automatic failover
- Powerful aggregation pipeline for analytics-style queries
- Atlas adds managed full-text search, vector search, stream processing and multi-cloud clusters
- Multi-document ACID transactions are available when you need them
Cons
- Joins ($lookup) are possible but less natural and often slower than relational joins
- Schema flexibility can lead to inconsistent data without validation rules
- Data is often duplicated across documents, which complicates updates
- Server Side Public License (SSPL) is not OSI-approved open source, which matters to some organisations
- Memory and storage use can be higher than an equivalent normalised relational schema
Best For
Content management, product catalogs, user profiles, IoT and event data, real-time apps, and products whose data shape is still evolving.
MySQL
One of the most popular open source relational databases, owned by Oracle. It stores data in tables with a defined schema and is queried with SQL. InnoDB provides ACID transactions, row-level locking and foreign keys.
Pros
- Mature relational model with strong data integrity through constraints and foreign keys
- Standard SQL with efficient joins, aggregates, views and stored procedures
- Huge ecosystem: WordPress, Drupal, Magento, Laravel and most PHP stacks default to it
- Easy to hire for and supported by almost every host and cloud provider
- Fast for read-heavy OLTP workloads and simple primary-key lookups
- Community Edition is GPLv2 open source; forks such as MariaDB and Percona Server exist
Cons
- Schema changes on very large tables need planning (online DDL or tools like gh-ost)
- No built-in automatic sharding; scale-out needs Vitess, application-level sharding or a managed service
- JSON support is good but less natural than a document database for deeply nested data
- Some advanced features (enterprise backup, auditing, thread pool) are only in paid editions
Best For
E-commerce, financial and transactional systems, CMS platforms like WordPress, SaaS apps with well-defined relational data, and teams that know SQL.
Document model vs relational model
In MySQL, a blog post, its author and its comments live in three tables linked by keys, and a query joins them back together. In MongoDB, you would typically store the post as one document with the comments embedded as an array and a reference to the author, so a single read returns everything the page needs. The relational approach normalises data, so each fact is stored once and constraints keep it valid, which is why it excels at transactional systems where many entities reference each other. The document approach optimises for the read pattern of the application, which is fast and intuitive but means some data is duplicated and must be kept in sync. MongoDB schemas are flexible by default, so two documents in the same collection can have different fields; you can still enforce structure with JSON Schema validation. MySQL schemas are explicit: adding a column requires an ALTER TABLE, which is safer but slower to evolve.
Performance and scaling
Performance depends much more on schema design, indexes and access patterns than on the engine. MongoDB is typically faster when the application reads or writes whole aggregates, such as a full product document, because there are no joins and the data sits together on disk. MySQL is typically faster for queries that combine many related tables, ad hoc reporting with joins and aggregates, and workloads where normalised data keeps rows small. For scaling, both handle a lot of traffic on a single well-sized server with read replicas. Beyond that, MongoDB has native sharding: you choose a shard key and the cluster distributes data and routes queries across shards. MySQL has no built-in sharding, so large deployments use Vitess, which runs YouTube-scale MySQL fleets, application-level sharding, or managed services such as PlanetScale or Aurora. MongoDB 9.0 also focuses on throughput gains and workload management under load spikes.
MongoDB vs MySQL vs PostgreSQL
PostgreSQL is the third option many teams weigh. Like MySQL, it is a relational database with full SQL and ACID transactions, but it has a richer feature set: advanced indexing, common table expressions, window functions, strict standards compliance, and the JSONB type, which stores binary JSON with GIN indexes and makes Postgres a capable document store too. Its extension ecosystem adds pgvector for embeddings, PostGIS for geospatial data and TimescaleDB for time series. A practical rule of thumb: pick MySQL when you want a simple, fast, widely hosted relational database or you are on a PHP or WordPress stack; pick PostgreSQL when you want the most capable open source relational database with strong JSON and extension support; pick MongoDB when your data is primarily documents, you need flexible schemas at scale, or you want built-in sharding and an integrated managed platform.
Licensing, cost and managed services
MySQL Community Edition is open source under the GPLv2, and Oracle sells MySQL Enterprise Edition with extra tooling plus HeatWave, its managed cloud service with an in-memory analytics and vector engine. MySQL 8.4 and 9.7 are the long-term support releases, with innovation releases in between. MongoDB Community Server is free to use but licensed under the SSPL, which restricts offering MongoDB as a service and is not recognised as open source by the OSI; MongoDB Enterprise Advanced is commercial. On the managed side, MongoDB Atlas runs on AWS, Azure and Google Cloud and bundles search, vector search and stream processing, while MySQL is offered by every major cloud through services such as Amazon RDS and Aurora, Azure Database for MySQL and Google Cloud SQL. Cost depends on instance size, storage, IOPS and data transfer rather than licence for most teams, so benchmark your real workload.
Verdict
Choose MySQL when your data is naturally relational and correctness matters more than flexibility: orders, invoices, payments, inventory, bookings and user accounts with many relationships. Its fixed schema, foreign keys and SQL joins keep data consistent, it is supported by every host and framework, and it remains the default database behind WordPress and much of the PHP world. Choose MongoDB when your data is document-shaped or changes often: product catalogs with varying attributes, content, user profiles, event and IoT data, or a startup product whose model is still being discovered. Embedding related data in one document means most reads hit a single document, and built-in sharding makes horizontal scaling easier than with MySQL. The old idea that MongoDB cannot do transactions is outdated, since multi-document ACID transactions arrived in version 4.0, and MySQL can store and index JSON, so the real difference is the default way you model data. If you want relational guarantees plus excellent JSON support, consider PostgreSQL, which sits between the two. For most CRUD web apps any of the three works; pick the one your team knows and that fits your data's shape.