Generating One Billion SQLite Rows in Under a Minute: Python, PyPy, and Rust Performance Comparison

A developer attempted to generate a 1 billion‑row SQLite database on a 2019 MacBook Pro (2.4 GHz quad‑core i5) within one minute; the initial Python script using a simple for‑loop took about 15 minutes for 10 million rows and was deemed far too slow.

Switching to batch inserts of 100 k rows reduced the runtime to roughly 10 minutes, revealing that SQLite’s per‑transaction overhead was the main bottleneck.

Further SQLite optimizations were applied: disabling journal_mode , turning off synchronous , increasing cache_size , setting locking_mode=EXCLUSIVE , and using temp_store=MEMORY . The author warned that these settings are unsuitable for production environments.

Running the batch version under PyPy (which claims a 4× speedup over CPython) required only 2.5 minutes, about 3.5× faster than the CPython run.

A Rust rewrite was then explored. The naïve Rust loop still took about 3 minutes, but using prepared statements and inserting in batches of 50 rows cut the time to 34.3 seconds; a multithreaded version with one consumer thread and four producer threads achieved the best result of 32.37 seconds.

Testing with an in‑memory SQLite database ( :memory: ) showed the Rust version completing in 29 seconds, indicating that disk I/O contributed only ~2 seconds to the overall runtime and that most time is spent generating and inserting data.

Benchmark results were summarized in a table: Rust – 33 seconds, PyPy – 126 seconds, CPython – 210 seconds.

The final recommendations emphasized using SQLite PRAGMA statements, prepared statements, and batch inserts; acknowledging that PyPy can be significantly faster than CPython, while async approaches do not always yield speed gains.