# MySQL vs MongoDB > Choose MySQL when relationships, database-enforced constraints, and transactions across related records drive the design. Choose MongoDB when bounded document aggregates match your main reads and writes, especially when records have varied structures. Both support transactions, replication, and flexible data, so justify the choice through access patterns and invariants rather than SQL versus NoSQL labels. Source: https://sysround.com/compare/mysql-vs-mongodb Updated: 2026-11-05 ## Side by side | | MySQL | MongoDB | |---|---|---| | Primary data model | Relational tables with explicit schemas | BSON documents with nested fields and arrays | | Related data | Foreign keys and joins support normalized models | Embed bounded aggregates or reference other documents; $lookup supports joins | | Transactions | InnoDB supports transactions across rows and tables | Single-document writes are atomic; multi-document transactions are supported | | Integrity rules | Foreign keys, unique constraints, and check constraints | Unique indexes and schema validation; no foreign-key enforcement | | Schema changes | Explicit migrations; JSON columns allow flexible fields | Mixed document shapes; validation and versioning still need planning | | Queries and indexes | SQL, joins, aggregations, and composite indexes | Document queries, aggregation pipelines, and compound and multikey indexes | | Horizontal scaling | Sharding usually adds application logic or middleware | Built-in sharding, with shard-key and routing trade-offs | | Read freshness and durability | Depend on transaction isolation, replication, and durability settings | Depend on read concern, write concern, and read preference | ## The differences that matter The useful modeling question is what must be read and changed together. MySQL lets you keep customers, orders, and products separate while joining them as needed. MongoDB can store an order and its bounded line items together, making that aggregate a single read and an atomic update boundary. Embedding an ever-growing comment history is different: document growth creates contention and eventually risks MongoDB's 16 MiB document limit. Both databases support transactions, but neither makes concurrency rules automatic. In MySQL, combine transactions with constraints, conditional updates, or row locks to protect invariants. In MongoDB, a conditional single-document update can protect an embedded aggregate; invariants spanning documents may require a transaction. MySQL foreign keys enforce references, while MongoDB applications must manage referenced-document integrity. MongoDB's flexible documents reduce the need to migrate every record before adding an optional field, but readers may still encounter several schema versions. MySQL provides explicit column types and can also store variable attributes in JSON. With either database, explain validation, backfills, compatibility during deployment, and how new fields will be indexed. Scaling depends on query routing and data distribution, not just database choice. MongoDB includes sharding, but a poor shard key can create hot shards or scatter-gather queries. MySQL sharding generally requires another routing layer and complicates cross-shard joins and transactions. For both, replica reads can be stale, and adding replicas does not remove a primary write bottleneck. ## Choose MySQL when - Payments, reservations, or inventory workflows have related records whose invariants benefit from transactions and database-enforced constraints. - The service needs several query paths across shared entities, and duplicating those entities would create substantial update fan-out. - Administrative and reporting queries frequently join operational tables in ways that are difficult to predict upfront. - The workload fits a primary with replicas, or you have a clear partitioning plan and established MySQL operational expertise. ## Choose MongoDB when - The main API reads and updates a bounded aggregate, such as a product listing with category-specific attributes. - Records vary substantially in structure, and document-oriented queries match how the application consumes them. - Most writes can preserve correctness within one document, without coordinating updates to many referenced records. - You need built-in sharding and can name a shard key that distributes load while keeping important queries targeted. ## Worked example: Marketplace listings versus checkout inventory Assume a marketplace has 10 million listings across 200 categories. Most listing reads need the title, seller ID, photos, and 20 to 80 category-specific attributes. MongoDB is a reasonable fit for that bounded listing document, provided common filters have suitable indexes and large media lives in object storage. Checkout has a different boundary: an order with three line items must reserve stock without overselling. MySQL can group inventory updates and order creation in one transaction when those records share a database. MongoDB can also use a multi-document transaction, but embedding listing attributes does not solve cross-item inventory coordination. Choose per workload; two databases also mean more operational work. ## In the interview - Start with two or three concrete access patterns and one correctness invariant. For example: fetch an order with its items, list a customer's recent orders, and prevent inventory from going negative. - Sketch the tables or documents before naming the winner. State which data is embedded, referenced, or duplicated, and explain how concurrent updates remain correct. - Separate atomicity, isolation, durability, and read freshness. Expect questions about stale replica reads, failover, transaction retries, and what an acknowledged write guarantees under your chosen configuration. - Explain the scaling boundary: dataset size, working set, write rate, and hottest tenant or entity. If you propose sharding, expect to defend the shard key, cross-shard operations, and resharding plan. ## Common mistakes - Claiming MongoDB has no transactions or that MySQL cannot store flexible JSON data. - Calling MySQL strongly consistent and MongoDB eventually consistent without specifying replication, read routing, isolation, and acknowledgment settings. - Choosing MongoDB because it 'scales horizontally' without showing a shard key or explaining hot entities and scatter-gather queries. - Embedding unbounded arrays or normalizing every document by habit, rather than choosing boundaries from access patterns and update ownership. ## Interview questions where it comes up - [Design Facebook Marketplace](https://sysround.com/questions/design-facebook-marketplace): Variable listing attributes suit a document-model discussion, while ownership, listing state, and messaging expose relationship and consistency requirements. - [Design a ticket booking system like Ticketmaster](https://sysround.com/questions/design-ticketmaster): Seat allocation forces you to explain conditional updates, transaction boundaries, and contention rather than relying on database labels. - [Design a payment system](https://sysround.com/questions/design-payment-system): Auditable balances, idempotent charges, and concurrent refunds make constraints and multi-record transaction design central to the storage choice. - [Design Wikipedia or Notion](https://sysround.com/questions/design-wikipedia): Page content, revision history, and page-tree changes illustrate the trade-off between document aggregates and relational integrity. - [Design a parts compatibility feature](https://sysround.com/questions/design-parts-compatibility): Bidirectional compatibility relationships and variable evidence fields require an explicit choice between normalized relations, references, and embedded data. ## FAQ ### Is MySQL faster than MongoDB? Neither is universally faster. A document read that replaces several joins may favor MongoDB, while indexed relational queries may favor MySQL for another workload. Compare representative queries with realistic indexes, data sizes, concurrency, and equivalent durability settings. ### Does MongoDB support ACID transactions like MySQL? MongoDB supports multi-document transactions on replica sets and sharded clusters, and individual document writes are atomic. MySQL's InnoDB engine supports transactions across rows and tables. Their isolation behavior, configuration options, and distributed transaction costs differ, so verify the guarantees your invariant requires. ### Should I choose MySQL or MongoDB for a system design interview? Choose MySQL when relationships and cross-record invariants are central, and MongoDB when bounded document aggregates closely match the workload. Neither earns credit by name alone. Show the data model, indexes, concurrency controls, and scaling plan that make your choice work. ## Related concepts - [SQL vs NoSQL](https://sysround.com/concepts/sql-vs-nosql) - [Database sharding](https://sysround.com/concepts/database-sharding) - [Database indexing](https://sysround.com/concepts/database-indexing) - [Denormalization](https://sysround.com/concepts/denormalization) - [Transaction isolation levels](https://sysround.com/concepts/isolation-levels) --- SysRound (https://sysround.com) is an AI practice tool for system design interviews: a voice or typed AI interviewer, a shared whiteboard, then feedback on every skill and a model answer. Its feedback is AI-generated and can be wrong.