Research: parse_number() as a SQLite extension: C vs Rust
Simon Willison’s Weblog
Newsletter
8th November 2026
Research
parse_number() as a SQLite extension: C vs Rust
— Four SQLite extensions implement the same parse_number() behavior in C and Rust, with tests covering type preservation, parsing rules, and floating-point accuracy. The optimized C implementation was fastest at 16–19 ns per row, about twice as fast as the Rust versions and far faster than SQL replace(). Plain C was slower on decimals because it relied on strtod(), while a compact fast path closed that gap; all versions matched across 8 million fuzz checks.
I was out walking the dog when I got curious about how easy/difficult it would be to write a SQLite C extension that adds a parse_number() function for turning strings like -$1,234.56 or '12.5%' into numeric values that could be summed and sorted.
I fired up regular Claude chat (Opus 5.5) and prompted:
I'm thinking about a SQLite custom function 'parse_number(value)' that strips ',', '$', '£' and '%', handles a leading '-', and returns 'NULL' for anything it can’t parse
Explore the difference between building this as a SQLite extension in C v.s. in Rust - I want to compare performance and code complexity and size of the resulting.so(or whatever) file
This gave me a solid starting point - the Rust extension was 321 KB (using Rusqlite) and 298 KB (using sqlite-loadable-rs), while the C extension was 14 KB. But the Rust extension was faster!
Turns out that's because the Rust equivalent of C's strtod is "twice as fast". So I prompted:
Try writing your own C function for parsing text into a decimal number that catches up to or beats the Rust implementation
And it did:
Net cost per row over 2 million rows, lower is better:
first C new C rusqlite sqlite-loadable
Mixed data 67 ns 19 ns 46 ns 41 ns
Integers only 25 ns 16 ns 36 ns 32 ns
Decimals only 106 ns 18 ns 55 ns 47 ns
Finally, I had it rewrite the prototype as a research report to match the others in simonw/research by prompting this:
Clone simonw/research from GitHub and turn your above investigation into a research report as seen in that repository - you can give me a zip file to download that contains your new folder
Here's the resulting report.
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This is a beat by Simon Willison, posted on 8th November 2026.
c 55
research 12
sqlite 490
rust 116
claude 324
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