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FA-44841 / Data systems / Open access

An empty scalar subquery yields a fabricated zero · case 01

An empty scalar subquery yields a fabricated zero.

Verified by executionVariant 1 · 9 checks per implementationDownload source bundle ↓JSON ↗

ROOT CAUSE

scalar-subquery-cardinality: An empty scalar subquery yields a fabricated zero.

THE FAILURE

scalar-subquery-cardinality: An empty scalar subquery yields a fabricated zero.

Unsuccessful approach: An empty list is not the scalar null result.

Case contract

Evaluate a correlated scalar lookup per outer key. Zero rows yield None, exactly one row yields its value including None, and two or more matching rows yield the explicit value CARDINALITY. Count physical rows, not distinct or non-null values.

Why this case matters

A bounded deterministic data engine model makes representation and changelog faults reproducible.

1 / The failure

Exit 1
"""Failure Map reference implementation. Python standard library only."""
import json

N = 1
observations = []
def solve(d):
    try:
        keys,inner=d
        out=[]
        for key in keys:
            matches=[value for k,value in inner if key is not None and k==key]
            count=len(matches)
            if count==0: value=0
            elif count==1: value=matches[0]
            else: value='CARDINALITY'
            out.append(value)
        return out
    except (IndexError, KeyError, ValueError, StopIteration) as exc:
        return {"representation_error": type(exc).__name__}
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
if N == 1:
    check('duplicate physical rows', solve([['a'], [['a', 1], ['a', 1]]]), ['CARDINALITY'])
    check('one null and one value', solve([['a'], [['a', None], ['a', 1]]]), ['CARDINALITY'])
    check('unrelated keys', solve([['a', 'b'], [['a', 1], ['b', 2]]]), [1, 2])
    check('null correlation', solve([[None], [[None, 1]]]), [None])
    check('empty lookup', solve([['a'], []]), [None])
    check('single null', solve([['a'], [['a', None]]]), [None])
    check('two values', solve([['a'], [['a', 1], ['a', 2]]]), ['CARDINALITY'])
    check('null row cannot correlate', solve([['a'], [['a', 1], [None, 2]]]), [1])
    check('no outer rows', solve([[], [['a', 1]]]), [])
elif N == 2:
    check('duplicate physical rows', solve([['a'], [['a', 2], ['a', 2]]]), ['CARDINALITY'])
    check('one null and one value', solve([['a'], [['a', None], ['a', 2]]]), ['CARDINALITY'])
    check('unrelated keys', solve([['a', 'b'], [['a', 2], ['b', 3]]]), [2, 3])
    check('null correlation', solve([[None], [[None, 2]]]), [None])
    check('empty lookup', solve([['a'], []]), [None])
    check('single null', solve([['a'], [['a', None]]]), [None])
    check('two values', solve([['a'], [['a', 2], ['a', 3]]]), ['CARDINALITY'])
    check('null row cannot correlate', solve([['a'], [['a', 2], [None, 3]]]), [2])
    check('no outer rows', solve([[], [['a', 2]]]), [])
elif N == 3:
    check('duplicate physical rows', solve([['a'], [['a', 3], ['a', 3]]]), ['CARDINALITY'])
    check('one null and one value', solve([['a'], [['a', None], ['a', 3]]]), ['CARDINALITY'])
    check('unrelated keys', solve([['a', 'b'], [['a', 3], ['b', 4]]]), [3, 4])
    check('null correlation', solve([[None], [[None, 3]]]), [None])
    check('empty lookup', solve([['a'], []]), [None])
    check('single null', solve([['a'], [['a', None]]]), [None])
    check('two values', solve([['a'], [['a', 3], ['a', 4]]]), ['CARDINALITY'])
    check('null row cannot correlate', solve([['a'], [['a', 3], [None, 4]]]), [3])
    check('no outer rows', solve([[], [['a', 3]]]), [])
elif N == 4:
    check('duplicate physical rows', solve([['a'], [['a', 4], ['a', 4]]]), ['CARDINALITY'])
    check('one null and one value', solve([['a'], [['a', None], ['a', 4]]]), ['CARDINALITY'])
    check('unrelated keys', solve([['a', 'b'], [['a', 4], ['b', 5]]]), [4, 5])
    check('null correlation', solve([[None], [[None, 4]]]), [None])
    check('empty lookup', solve([['a'], []]), [None])
    check('single null', solve([['a'], [['a', None]]]), [None])
    check('two values', solve([['a'], [['a', 4], ['a', 5]]]), ['CARDINALITY'])
    check('null row cannot correlate', solve([['a'], [['a', 4], [None, 5]]]), [4])
    check('no outer rows', solve([[], [['a', 4]]]), [])
elif N == 5:
    check('duplicate physical rows', solve([['a'], [['a', 5], ['a', 5]]]), ['CARDINALITY'])
    check('one null and one value', solve([['a'], [['a', None], ['a', 5]]]), ['CARDINALITY'])
    check('unrelated keys', solve([['a', 'b'], [['a', 5], ['b', 6]]]), [5, 6])
    check('null correlation', solve([[None], [[None, 5]]]), [None])
    check('empty lookup', solve([['a'], []]), [None])
    check('single null', solve([['a'], [['a', None]]]), [None])
    check('two values', solve([['a'], [['a', 5], ['a', 6]]]), ['CARDINALITY'])
    check('null row cannot correlate', solve([['a'], [['a', 5], [None, 6]]]), [5])
    check('no outer rows', solve([[], [['a', 5]]]), [])
print(json.dumps({"observations": observations, "passed": all(x["passed"] for x in observations)}, ensure_ascii=False))
raise SystemExit(0 if all(x["passed"] for x in observations) else 1)
Boundary fixtureActualExpectedOutcome
duplicate physical rows['CARDINALITY']['CARDINALITY']Passed
one null and one value['CARDINALITY']['CARDINALITY']Passed
unrelated keys[1, 2][1, 2]Passed
null correlation[0][None]Failed
empty lookup[0][None]Failed
single null[None][None]Passed
two values['CARDINALITY']['CARDINALITY']Passed
null row cannot correlate[1][1]Passed
no outer rows[][]Passed

SHA-256 / 07f0591512213b65d7d8371c4db97d9b1409ca9a3545a60d82766da61da1e0da

2 / The unsuccessful fix

Exit 1
"""Failure Map reference implementation. Python standard library only."""
import json

N = 1
observations = []
def solve(d):
    try:
        keys,inner=d
        out=[]
        for key in keys:
            matches=[value for k,value in inner if key is not None and k==key]
            count=len(matches)
            if count==0: value=[]
            elif count==1: value=matches[0]
            else: value='CARDINALITY'
            out.append(value)
        return out
    except (IndexError, KeyError, ValueError, StopIteration) as exc:
        return {"representation_error": type(exc).__name__}
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
if N == 1:
    check('duplicate physical rows', solve([['a'], [['a', 1], ['a', 1]]]), ['CARDINALITY'])
    check('one null and one value', solve([['a'], [['a', None], ['a', 1]]]), ['CARDINALITY'])
    check('unrelated keys', solve([['a', 'b'], [['a', 1], ['b', 2]]]), [1, 2])
    check('null correlation', solve([[None], [[None, 1]]]), [None])
    check('empty lookup', solve([['a'], []]), [None])
    check('single null', solve([['a'], [['a', None]]]), [None])
    check('two values', solve([['a'], [['a', 1], ['a', 2]]]), ['CARDINALITY'])
    check('null row cannot correlate', solve([['a'], [['a', 1], [None, 2]]]), [1])
    check('no outer rows', solve([[], [['a', 1]]]), [])
elif N == 2:
    check('duplicate physical rows', solve([['a'], [['a', 2], ['a', 2]]]), ['CARDINALITY'])
    check('one null and one value', solve([['a'], [['a', None], ['a', 2]]]), ['CARDINALITY'])
    check('unrelated keys', solve([['a', 'b'], [['a', 2], ['b', 3]]]), [2, 3])
    check('null correlation', solve([[None], [[None, 2]]]), [None])
    check('empty lookup', solve([['a'], []]), [None])
    check('single null', solve([['a'], [['a', None]]]), [None])
    check('two values', solve([['a'], [['a', 2], ['a', 3]]]), ['CARDINALITY'])
    check('null row cannot correlate', solve([['a'], [['a', 2], [None, 3]]]), [2])
    check('no outer rows', solve([[], [['a', 2]]]), [])
elif N == 3:
    check('duplicate physical rows', solve([['a'], [['a', 3], ['a', 3]]]), ['CARDINALITY'])
    check('one null and one value', solve([['a'], [['a', None], ['a', 3]]]), ['CARDINALITY'])
    check('unrelated keys', solve([['a', 'b'], [['a', 3], ['b', 4]]]), [3, 4])
    check('null correlation', solve([[None], [[None, 3]]]), [None])
    check('empty lookup', solve([['a'], []]), [None])
    check('single null', solve([['a'], [['a', None]]]), [None])
    check('two values', solve([['a'], [['a', 3], ['a', 4]]]), ['CARDINALITY'])
    check('null row cannot correlate', solve([['a'], [['a', 3], [None, 4]]]), [3])
    check('no outer rows', solve([[], [['a', 3]]]), [])
elif N == 4:
    check('duplicate physical rows', solve([['a'], [['a', 4], ['a', 4]]]), ['CARDINALITY'])
    check('one null and one value', solve([['a'], [['a', None], ['a', 4]]]), ['CARDINALITY'])
    check('unrelated keys', solve([['a', 'b'], [['a', 4], ['b', 5]]]), [4, 5])
    check('null correlation', solve([[None], [[None, 4]]]), [None])
    check('empty lookup', solve([['a'], []]), [None])
    check('single null', solve([['a'], [['a', None]]]), [None])
    check('two values', solve([['a'], [['a', 4], ['a', 5]]]), ['CARDINALITY'])
    check('null row cannot correlate', solve([['a'], [['a', 4], [None, 5]]]), [4])
    check('no outer rows', solve([[], [['a', 4]]]), [])
elif N == 5:
    check('duplicate physical rows', solve([['a'], [['a', 5], ['a', 5]]]), ['CARDINALITY'])
    check('one null and one value', solve([['a'], [['a', None], ['a', 5]]]), ['CARDINALITY'])
    check('unrelated keys', solve([['a', 'b'], [['a', 5], ['b', 6]]]), [5, 6])
    check('null correlation', solve([[None], [[None, 5]]]), [None])
    check('empty lookup', solve([['a'], []]), [None])
    check('single null', solve([['a'], [['a', None]]]), [None])
    check('two values', solve([['a'], [['a', 5], ['a', 6]]]), ['CARDINALITY'])
    check('null row cannot correlate', solve([['a'], [['a', 5], [None, 6]]]), [5])
    check('no outer rows', solve([[], [['a', 5]]]), [])
print(json.dumps({"observations": observations, "passed": all(x["passed"] for x in observations)}, ensure_ascii=False))
raise SystemExit(0 if all(x["passed"] for x in observations) else 1)
Boundary fixtureActualExpectedOutcome
duplicate physical rows['CARDINALITY']['CARDINALITY']Passed
one null and one value['CARDINALITY']['CARDINALITY']Passed
unrelated keys[1, 2][1, 2]Passed
null correlation[[]][None]Failed
empty lookup[[]][None]Failed
single null[None][None]Passed
two values['CARDINALITY']['CARDINALITY']Passed
null row cannot correlate[1][1]Passed
no outer rows[][]Passed

SHA-256 / 355c6de61a90527ede42861864568415abb7281020aec4258be316840934eeea

HELD IN THE MEMBER ARCHIVE

The verified repair and its recorded checks are member-only.

This mechanism has 9 recorded checks per implementation. The open-access tier publishes the failure and the unsuccessful fix; the repaired source that passes every check, and the observations that prove it, are available to members.

Every case sharing this mechanism uses the same contract and the same repair, so this one record is held back for all of them.

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Verification & scope

Offline stipulated semantics over valid small inputs; no performance, concurrency, or production-engine conformance claim. This reproducer isolates one failure mechanism. Results cover the supplied fixtures. Variants within a family share a test contract and should remain grouped when constructing evaluation splits. Related mechanisms with a shared evaluation_group must also remain together; these controlled models are not independent production incidents.

Observations recorded using Python 3.12.14 at 2026-09-29T14:44:16.263733+00:00.

Case digest / 2e2ffd10ce057e310a8bdb8c0086bdc9282bedaba418551000e3f5d17f9970f7