Postgres vs MySQL vs SQLite: Comparing SQL Performance Across Engines

2026-03-10 · Source: KDnuggets · Field: Technology & Digital — Data Science & Analytics, Software Development & Engineering · Depth: Intermediate, long

Summary

A benchmark study compared PostgreSQL, MySQL, and SQLite across four analytical SQL interview questions, two at medium and two at hard difficulty, to evaluate their performance in handling joins, window functions, date arithmetic, and complex aggregations. The study found that SQLite was the fastest across all questions due to its serverless, in-memory architecture, making it ideal for small to medium datasets and embedded applications. PostgreSQL demonstrated superior performance on complex analytical queries, such as 3-month rolling averages and common friends-of-friends, leveraging its advanced query planner and extensive indexing. MySQL outperformed PostgreSQL on simpler queries, like identifying risky projects and repeat purchases, balancing robust performance with general-purpose reliability, especially in modern versions (8.0+).

Key takeaway

For Data Scientists and Software Engineers selecting a database, understand that your choice should align with workload complexity. If your application requires complex analytical queries and advanced SQL features, PostgreSQL is your optimal choice. For lightweight, embedded applications or smaller datasets, SQLite offers unmatched speed. If you need a balance of performance for transactional and simpler analytical queries, MySQL remains a strong contender, especially with version 8.0+.

Key insights

Database choice significantly impacts application performance, with each SQL engine excelling in different workloads.

Principles

• SQLite is fastest for small, embedded datasets.
• PostgreSQL excels in complex analytical queries.
• MySQL balances performance for general-purpose and transactional loads.

Method

Benchmarking involved solving four analytical SQL problems (two medium, two hard) across PostgreSQL, MySQL, and SQLite, focusing on execution time and syntax variations for joins, window functions, date arithmetic, and aggregations.

In practice

• Use `DATEDIFF()` in MySQL for date arithmetic.
• Employ `julianday()` in SQLite for date calculations.
• Create composite indexes on join columns for performance.

Topics

• SQL Database Benchmarking
• PostgreSQL
• MySQL
• SQLite
• Analytical SQL

Best for: Data Scientist, Data Engineer, Software Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by KDnuggets.