I'm currently building VanillaEDI , an open-source ANSI X12 EDI-to-JSON microservice. I got the idea seeing the struggles and cost of EDI tools in the supply chain/distribution world. Understandably so, it's an ancient standard for technology but is utilized by the largest companies doing business. If you've ever dealt with enterprise supply chains, you know legacy systems don't play nice. They don't send clean, paginated API requests. They just dump a single, massive multi-gigabyte text file containing a month's worth of invoices onto an FTP server and leave you to deal with it and also which is why the market exists.
If you try to load a 10GB file into server RAM using a standard .read() and .split(), fat chance your worker runs out of memory. The glaring fix is to write an asynchronous streaming parser that reads the file in small chunks, yielding segments as it finds the terminators.
As I'm writing the streaming parser, I'm thinking How do I even test this? Not a chance I'm trying to find or create a 10GB file, let alone commit it to the repo. Then the thought occurred to me! Remember back in college, that dreaded CS math class everyone was scared of? Turns out it has practical applications, specifically mathematical induction.
In discrete math, you don't prove a theory for an infinite set of numbers by testing every single number. You prove the base case (n = 1), and then you prove the transition logic (if it works for n, it works for n + 1). If the transition holds, the proof scales to infinity.
Instead of testing a large file with a normal chunk size, I used a standard 3-line, 150-byte EDI payload. Then, using Python's AsyncMock, I intercepted the file stream and intentionally choked the parser's read limit to a ridiculously small constraint of just 5 bytes.
I forced the engine to process the string ST*850*0001~ five bytes at a time. It had to pull ST*85, realize it hadn't hit a terminator, buffer it, pull 0*000, append it, pull 1~, find the terminator, yield the exact string, and keep the remainder. It forced the logic to cut the data at the absolute worst, most arbitrary boundaries possible.
pytest passed with flying colors and that was the "aha" moment. Because the algorithm correctly reassembles the string and splits on the terminator when the chunk size is 5 bytes, mathematical induction guarantees it will hold when the chunk size is 4 megabytes. The size of the actual file was completely irrelevant; only the boundary logic mattered.
We usually treat unit tests as simple sanity checks to make sure our individual pieces function correctly. But when you apply an inductive mindset, a unit test stops being a checklist and it becomes a mathematical proof of your architecture's scalability.
Turns out, you don't need 10 gigabytes of data to prove your code works. A simple concept we learned in college can do it for you!