json11.4 KB

Cedar Street Tacos: Numeric expected results JSON

Numeric expected results JSON for Cedar Street Tacos. The exact numbers a test asserts: document counts, per-order and per-invoice totals, both tax conventions for every invoice, declared and true ageing for both statements, the remittance reconciliation identity, the 5 three-way match outcomes and all 6 deliberate defects with the assertion that detects each one. The correct purchase order net at list price is 488.57 CAD.

Data import JSON parsing Schema validation

json

application/json

11.4 KB

Kit: restaurant-food-truck
Schema Version: 1
Synthetic: true
As Of: 2026-09-08
Currency: CAD
Tax Name: GST

Binary json: no in-browser preview. Download it above to open in a compatible application.

Specifications

Kit: restaurant-food-truck
Schema Version: 1
Synthetic: true
As Of: 2026-09-08
Currency: CAD
Tax Name: GST
Tax Rate: 0.05
Deliberate Defects: 6
Three Way Match Rows: 14
Matched Rows: 9

Testing contract

Reference control

Scenario: Use as the answer key for the other files in this folder: parse them, compute the same figures and compare.
Expected result: The exact numbers a test asserts: document counts, per-order and per-invoice totals, both tax conventions for every invoice, declared and true ageing for both statements, the remittance reconciliation identity, the 5 three-way match outcomes and all 6 deliberate defects with the assertion that detects each one. The correct purchase order net at list price is 488.57 CAD.

What is a .json file?

JSON (JavaScript Object Notation) is a lightweight, text-based data-interchange format representing objects, arrays, strings, numbers, booleans, and null. It is language-independent, human-readable, and the dominant format for web APIs and configuration. It requires a single well-formed root value.

How to use this file

Use an example JSON file to test parsers and serializers, schema validation, Unicode and number-precision handling, and API request or response processing.

How to use this file for testing

“Cedar Street Tacos: Numeric expected results JSON” is a deterministic Novus Examples fixture for Data import, JSON parsing, Schema validation. Realistic faker-generated datasets with documented schemas for testing import and ETL flows.

Documented properties for this file: JSON · 11,642 bytes. Compare results against paired or grouped companions on this page when present (clean↔damaged, searchable↔scanned, or format twins) so scores stay reproducible across runs.

Download the file once, keep the path stable in CI or local scripts, and treat the spec table as the contract: dimensions, seeds, field lists, and roles are intentional. Corrupt or invalid samples are labelled as such, expect parsers to fail loudly rather than silently accept them.

Data fixtures document their exact quirks (delimiters, encodings, null handling, schema, and row counts) in the spec table. Point your parser or importer at the file and assert it handles the documented edge cases; clean and deliberately-messy siblings make before/after diffs straightforward.

Code examples

import json

with open("expected-results.json") as f:
    data = json.load(f)
print(type(data), len(data))

Generated by generation/restaurant_procurement.py. Free for any use, no attribution required, license.