Maintainability practice

Opening your lesson

A different service team has sent code contracts for review. The functions are small enough to test directly, and every dependency is supplied as data. Use that advantage: isolate one policy, check its boundary, then move to the next contract.

The practice revisits validation, normalization, injected lookup data and structured logs. New record names and values keep an earlier example from becoming a shortcut. Watch for empty strings, valid zeros and repeated requests. Those values often disappear when a refactor replaces a membership check with truthiness. Logging tasks need the same care as calculations because they carry the evidence used after a failure. Return the exact requested shape without adding diagnostic text to the result.

import json

def process_payload(payload: str) -> str:
    data = json.loads(payload)
    result = [row['id'] for row in data if type(row['quantity']) is int and row['quantity'] > 0]
    return json.dumps(result,separators=(',',':'))

Use an explicit integer contract; True and zero are not valid positive quantities. Check the quantity's exact type before comparing its value. In Python, bool inherits from int. One filter pass is linear in the number of records and does not coerce invalid values.

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