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<\/figure>\nMake the fetch dependency replaceable<\/h2>\n
The caller should depend on an access result, not on a particular HTTP library or guessed solver method. Give that result a content classification and a diagnostic reference. Keep storage outside the adapter so an unfamiliar response cannot become a successful database update by accident.<\/p>\n
from dataclasses import dataclass\nfrom typing import Literal\n\n@dataclass\nclass AccessResult:\n kind: Literal[\"content\", \"unresolved\", \"denied\", \"error\"]\n body: str = \"\"\n\ndef process(url, fetch, accept, parse):\n result = fetch(url)\n if result.kind != \"content\" or not accept(result.body):\n return {\"ready_to_save\": False}\n return {\"ready_to_save\": True, \"record\": parse(result.body)}<\/code><\/pre>\nThis Python 3.8+ example defines only an internal contract. A production caller must validate the extracted record before writing it. None of the field names is presented as an actual API response schema.<\/p>\n
Turn edge cases into fixtures<\/h2>\n
Inject a fake fetch function that returns expected content, an unresolved verification response, a denial or a transport failure. Use your own synthetic page structure. Testing the control flow does not require downloading or retaining third-party personal records.<\/p>\n
Check that only expected, accepted content reaches parsing. Add a fixture that has a plausible page structure but the wrong test identity. Then exercise a parser failure separately. These cases reveal whether a failure is being incorrectly converted into an empty but apparently successful result.<\/p>\n
Normalize at one boundary<\/h2>\n
The real adapter owns authentication configuration, time limits and documented response interpretation. It should map provider output into the internal contract in one place. Do not spread response-envelope handling or brand-name checks across every business parser.<\/p>\n