# PostgreSQL vs MySQL > Choose PostgreSQL when richer constraints, specialized indexes, or extensions simplify your data model and queries. Choose MySQL when its transactional feature set meets your needs and your team already operates it well. Both support reliable transactional applications; neither automatically solves sharding, failover, or high-contention writes. Source: https://sysround.com/compare/postgresql-vs-mysql Updated: 2026-11-05 ## Side by side | | PostgreSQL | MySQL | |---|---|---| | Transactional baseline | PostgreSQL: ACID transactions, foreign keys, MVCC | MySQL with InnoDB: ACID transactions, foreign keys, MVCC | | Default isolation | Read Committed; Serializable uses serialization failure detection | InnoDB defaults to Repeatable Read; range writes can involve gap locks | | Constraints | Partial unique indexes, exclusion constraints, deferrable constraints | Unique, foreign key, and check constraints; fewer advanced constraint options | | Index options | B-tree, GIN, GiST, BRIN, expression and partial indexes | InnoDB B-tree, functional, spatial, and full-text indexes; no general partial indexes | | Row storage | Heap tables; indexes point to heap tuples | InnoDB clusters rows by primary key; secondary indexes include it | | JSON and extensions | JSONB with GIN indexing; extensions such as PostGIS | Native JSON; targeted indexing through generated columns or expressions | | Replication | Physical streaming and logical replication; synchronous options | Binary-log replication, semisynchronous options, and Group Replication | | Materialized views | Native materialized views with explicit refresh | Typically maintained through tables, jobs, or application logic | | Operational concerns | Vacuum, dead tuples, WAL retention, connection management | Undo purge, locking, binary-log retention, connection management | ## The differences that matter Constraints can change where correctness lives. PostgreSQL can enforce uniqueness only among active records with a partial unique index, or reject overlapping reservations with an exclusion constraint. MySQL can enforce many equivalent business rules, but some require generated-column techniques, a different schema, or explicit transaction locking. The comparison here assumes modern MySQL using InnoDB. Isolation behavior matters more than the ACID label. PostgreSQL defaults to Read Committed; InnoDB defaults to Repeatable Read. InnoDB locking range queries can take next-key locks that affect neighboring inserts, depending on the query, index, and isolation level. PostgreSQL Serializable can abort transactions that would create serialization anomalies. With either engine, explain the exact transaction, locks, and retry behavior rather than relying on defaults. Storage layout changes index costs. InnoDB stores row data in the primary-key B-tree, and secondary indexes carry the primary key, so a wide primary key increases secondary-index storage. PostgreSQL separates heap storage from indexes; updates create tuple versions that vacuum must eventually reclaim. These differences affect key selection, update-heavy workloads, and maintenance, not a universal performance ranking. Query requirements can make PostgreSQL's feature set decisive. GIN indexes over JSONB, PostGIS spatial operations, and partial indexes can avoid custom application logic or a separate serving structure. MySQL supports JSON, spatial queries, and sophisticated SQL too, but capabilities and usable index paths differ. Name the actual query and inspect its execution plan before choosing. Neither engine name establishes your durability or scaling guarantees. Replica reads can be stale, failover can lose acknowledged writes under some replication configurations, and replication does not automatically distribute writes across independent shards. State the acknowledgment policy, replica-routing rules, failover fencing, and recovery objectives separately. ## Choose PostgreSQL when - Your schema needs database-enforced rules such as nonoverlapping date ranges, conditional uniqueness, or deferred constraint checks. - Your core queries benefit directly from PostgreSQL-specific indexes, JSONB indexing, or PostGIS. - You need native materialized views and can define acceptable refresh costs and staleness. - Your team already operates PostgreSQL reliably, and switching would add migration risk without a demonstrated benefit. ## Choose MySQL when - Your workload is conventional transactional CRUD, and your team has stronger MySQL operational experience. - Your existing services, migration tools, backup procedures, and on-call practices are standardized on MySQL. - Primary-key access dominates, and InnoDB's clustered layout fits a deliberately chosen compact primary key. - You have validated a MySQL-based replication or sharding stack against your workload and do not need PostgreSQL-specific features. ## Worked example: Preventing overlapping hotel reservations Suppose a hotel has 200 individually assigned rooms and receives 30,000 reservation attempts during a promotion. A reservation covers a date range, and active holds must block overlapping bookings. PostgreSQL can use a GiST exclusion constraint on room ID and a half-open date range, typically with the btree_gist extension, to reject overlaps among active records. Expired holds must be explicitly released; a time-dependent index predicate is not the solution. In MySQL, one alternative is a room-night table with a unique key on room ID and date, inserting every night in one transaction. Both designs can be correct, but they have different storage, contention, and expiration workflows. ## In the interview - Start with the invariant and access patterns: prevent overselling, enforce idempotency, fetch an account's recent transactions, or query nearby listings. Then identify which engine feature materially helps. - Describe one critical transaction in detail, including predicates, indexes, lock scope, and retries after deadlocks or serialization failures. Expect the interviewer to introduce two concurrent requests. - Separate local transaction correctness from replication guarantees. Explain whether a read after a write must reach the primary and what happens to acknowledged writes during failover. - Give a capacity plan based on row size, index size, read/write mix, and hot records. Say what you would measure before adding replicas or sharding rather than claiming either engine is inherently faster. - If either engine fits, say so and choose based on operational familiarity. Expect follow-ups about schema migrations, connection pooling, backups, and restore testing. ## Common mistakes - Claiming PostgreSQL is for complex applications while MySQL is only for simple ones. Both support joins, transactions, window functions, and demanding production workloads. - Treating ACID or Repeatable Read as proof that a read-then-write business operation is race-free. Correctness depends on the statements, constraints, and concurrency control. - Assuming replicas provide fresh reads, zero-loss failover, or write scaling without specifying replication and routing behavior. - Choosing from generic speed claims without checking the schema, execution plans, indexes, engine versions, and durability settings. ## Interview questions where it comes up - [Design a hotel booking system](https://sysround.com/questions/design-hotel-booking-system): Date-range inventory exposes the difference between PostgreSQL exclusion constraints and alternative transactional inventory schemas in MySQL. - [Design a ticket booking system like Ticketmaster](https://sysround.com/questions/design-ticketmaster): Seat holds require explicit locking or conditional updates, making isolation and contention behavior central to either database choice. - [Design a payment system](https://sysround.com/questions/design-payment-system): Idempotency keys, ledger transactions, and replica-read policies require concrete constraints and durability guarantees in either engine. - [Design a restaurant review app like Yelp](https://sysround.com/questions/design-yelp): Location-based discovery makes spatial query capabilities and index support meaningful parts of the database decision. - [Design a large data migration to the cloud](https://sysround.com/questions/design-cloud-data-migration): Moving between engines requires accounting for schema semantics, change capture, replication lag, and a fenced cutover. ## FAQ ### Is PostgreSQL faster than MySQL? There is no workload-independent winner. Query shape, indexes, data distribution, memory, concurrency, and durability settings can dominate the result. Compare execution plans and test representative traffic, including tail latency and contention. ### Is PostgreSQL more ACID-compliant than MySQL? PostgreSQL and MySQL with InnoDB both support ACID transactions. Their isolation behavior and available constraints differ, and durability depends on configuration and failure assumptions. Neither makes arbitrary application logic race-free. ### Should I choose PostgreSQL or MySQL in a system design interview? Choose the engine whose concrete capabilities or operational fit support your design, then explain the critical transaction and scaling path. PostgreSQL is a useful choice when advanced constraints or indexing matter; MySQL is equally defensible when its features fit and the operational ecosystem favors it. The interviewer will usually learn more from your correctness and failure analysis than from the product name. ## Related concepts - [Database indexing](https://sysround.com/concepts/database-indexing) - [MVCC](https://sysround.com/concepts/mvcc) - [Transaction isolation levels](https://sysround.com/concepts/isolation-levels) - [Database replication](https://sysround.com/concepts/replication) - [Database sharding](https://sysround.com/concepts/database-sharding) --- 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.