PostgreSQL vs MySQL: Which Database Wins in JSON, Indexing, and Concurrency?

Performance is a critical yet complex aspect of database management, influenced by configuration, hardware, and design. While PostgreSQL and MySQL share some similarities, each has unique features that can make one superior in specific scenarios.

How to Measure Performance

MySQL is renowned for fast reads of large workloads, often sacrificing concurrency when mixed with writes. PostgreSQL positions itself as a modern, standards‑compliant open‑source relational database with rich functionality. Historically, PostgreSQL offered more balanced performance—reads were slower than MySQL—but recent improvements have enhanced its write throughput and concurrency, narrowing the gap.

Older MyISAM engines in MySQL provide very fast reads but are no longer available in recent versions. InnoDB, which supports constraints and transactions, eliminates most differences for enterprise‑scale applications, and MySQL continues to evolve to reduce write‑heavy disparities.

JSON Queries Faster in Postgres

Steps Executed

Create a project (Java, Node, or Ruby) using both PostgreSQL and MySQL.

Generate a sample JSON object for WRITE and READ operations.

Assume the JSON object is ~14 MB, creating about 200–210 entries in each database.

Statistics

PostgreSQL : Average time (ms) – write: 2279.25 | read: 31.65 | update: 26.26

MySQL : Average time (ms) – write: 3501.05 | read: 49.99 | update: 62.45

Metrics

Indexes are a key factor in all databases. They speed up row retrieval but add overhead, so they should be used wisely. Without indexes, the server must scan the entire table, which becomes costly as tables grow. PostgreSQL and MySQL handle indexes differently.

Standard B‑tree index : PostgreSQL provides built‑in support for regular B‑tree and hash indexes, plus additional features.

Expression index : Allows indexing the result of an expression or function instead of a column value.

Partial index : Indexes only a subset of a table.

Assume a PostgreSQL table users defined as:

CREATE TABLE users ( id SERIAL PRIMARY KEY, email VARCHAR DEFAULT NULL, name VARCHAR);

Now consider creating the following indexes on that table:

“Partial indexes are built on a subset of rows defined by a predicate. They contain entries only for rows that satisfy the predicate, reducing index size and speeding up queries that would otherwise scan most of the table. They also lessen write overhead because fewer rows need index updates.” – PostgreSQL Documentation

MySQL’s indexes (PRIMARY KEY, UNIQUE, INDEX, FULLTEXT) are B‑tree based, with exceptions for spatial data (R‑tree). MySQL also supports hash indexes, and InnoDB uses inverted lists for FULLTEXT.

Database Replication

Replication—copying data from one server to another—differs between PostgreSQL and MySQL. Both provide various replication options beyond the classic primary‑standby model, each with its own consistency challenges.

Multi‑Version Concurrency Control

When multiple clients read and write concurrently, race conditions arise. Modern databases use transactions to mitigate these issues. PostgreSQL was the first DBMS to implement MVCC, allowing reads without blocking writes and vice‑versa, a key reason many enterprises prefer it over MySQL.

“Unlike most lock‑based systems, PostgreSQL maintains data consistency using a multiversion model. Each transaction sees a snapshot of the database at a point in time, protecting it from changes made by concurrent transactions.” – PostgreSQL Documentation

MySQL’s InnoDB engine also implements MVCC by storing old row versions in rollback segments, enabling concurrent reads and writes while supporting transaction rollback.

Conclusion

This article covered several performance differences between PostgreSQL and MySQL, emphasizing that overall database performance depends on hardware, OS, and deep understanding of the target system. Both databases have distinct strengths and trade‑offs; choosing the right features for a project and tuning them can significantly improve performance.