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FA-45451 / Data systems / Open access

Row-group pruning drops groups with missing minimum statistics · case 01

Row-group pruning drops groups with missing minimum statistics.

Verified by executionVariant 1 · 7 checks per implementationDownload source bundle ↓JSON ↗

ROOT CAUSE

row-group-pruning: Row-group pruning drops groups with missing minimum statistics.

VERIFIED REPAIR

Preserve the stated physical representation and operation order: Choose row groups that may satisfy an inclusive equality/range scan. Metadata [id,min,max,null_count,row_count] describes known integer values; unknown min/max cannot prove exclusion. Null-only and empty groups cannot satisfy a non-null range. Return candidate IDs without claiming every candidate contains a match.

Unsuccessful approach: Having an upper statistic does not make a missing lower statistic proof of exclusion.

Case contract

Choose row groups that may satisfy an inclusive equality/range scan. Metadata [id,min,max,null_count,row_count] describes known integer values; unknown min/max cannot prove exclusion. Null-only and empty groups cannot satisfy a non-null range. Return candidate IDs without claiming every candidate contains a match.

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:
        groups,low,high=d
        out=[]
        for ident,minimum,maximum,nulls,count in groups:
            if count==0: continue
            if nulls==count: continue
            if minimum is None or minimum>high: continue
            if maximum is not None and maximum<low: continue
            out.append(ident)
        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('inclusive low', solve([[[1, -1, 1, 0, 3]], 1, 3]), [1])
    check('inclusive high', solve([[[1, 3, 5, 0, 3]], 1, 3]), [1])
    check('missing minimum', solve([[[1, None, 3, 0, 3]], 1, 3]), [1])
    check('missing maximum', solve([[[1, 1, None, 0, 3]], 1, 3]), [1])
    check('null only', solve([[[1, None, None, 3, 3]], 1, 3]), [])
    check('empty group', solve([[[1, None, None, 0, 0]], 1, 3]), [])
    check('disjoint bounds', solve([[[1, -3, 0, 0, 3], [2, 4, 6, 0, 3]], 1, 3]), [])
elif N == 2:
    check('inclusive low', solve([[[1, 0, 2, 0, 3]], 2, 4]), [1])
    check('inclusive high', solve([[[1, 4, 6, 0, 3]], 2, 4]), [1])
    check('missing minimum', solve([[[1, None, 4, 0, 3]], 2, 4]), [1])
    check('missing maximum', solve([[[1, 2, None, 0, 3]], 2, 4]), [1])
    check('null only', solve([[[1, None, None, 3, 3]], 2, 4]), [])
    check('empty group', solve([[[1, None, None, 0, 0]], 2, 4]), [])
    check('disjoint bounds', solve([[[1, -2, 1, 0, 3], [2, 5, 7, 0, 3]], 2, 4]), [])
elif N == 3:
    check('inclusive low', solve([[[1, 1, 3, 0, 3]], 3, 5]), [1])
    check('inclusive high', solve([[[1, 5, 7, 0, 3]], 3, 5]), [1])
    check('missing minimum', solve([[[1, None, 5, 0, 3]], 3, 5]), [1])
    check('missing maximum', solve([[[1, 3, None, 0, 3]], 3, 5]), [1])
    check('null only', solve([[[1, None, None, 3, 3]], 3, 5]), [])
    check('empty group', solve([[[1, None, None, 0, 0]], 3, 5]), [])
    check('disjoint bounds', solve([[[1, -1, 2, 0, 3], [2, 6, 8, 0, 3]], 3, 5]), [])
elif N == 4:
    check('inclusive low', solve([[[1, 2, 4, 0, 3]], 4, 6]), [1])
    check('inclusive high', solve([[[1, 6, 8, 0, 3]], 4, 6]), [1])
    check('missing minimum', solve([[[1, None, 6, 0, 3]], 4, 6]), [1])
    check('missing maximum', solve([[[1, 4, None, 0, 3]], 4, 6]), [1])
    check('null only', solve([[[1, None, None, 3, 3]], 4, 6]), [])
    check('empty group', solve([[[1, None, None, 0, 0]], 4, 6]), [])
    check('disjoint bounds', solve([[[1, 0, 3, 0, 3], [2, 7, 9, 0, 3]], 4, 6]), [])
elif N == 5:
    check('inclusive low', solve([[[1, 3, 5, 0, 3]], 5, 7]), [1])
    check('inclusive high', solve([[[1, 7, 9, 0, 3]], 5, 7]), [1])
    check('missing minimum', solve([[[1, None, 7, 0, 3]], 5, 7]), [1])
    check('missing maximum', solve([[[1, 5, None, 0, 3]], 5, 7]), [1])
    check('null only', solve([[[1, None, None, 3, 3]], 5, 7]), [])
    check('empty group', solve([[[1, None, None, 0, 0]], 5, 7]), [])
    check('disjoint bounds', solve([[[1, 1, 4, 0, 3], [2, 8, 10, 0, 3]], 5, 7]), [])
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
inclusive low[1][1]Passed
inclusive high[1][1]Passed
missing minimum[][1]Failed
missing maximum[1][1]Passed
null only[][]Passed
empty group[][]Passed
disjoint bounds[][]Passed

SHA-256 / 5904cf8098b6f5db675853af2f697b68911ed058512bb3a059a705f5355b5297

2 / The unsuccessful fix

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

N = 1
observations = []
def solve(d):
    try:
        groups,low,high=d
        out=[]
        for ident,minimum,maximum,nulls,count in groups:
            if count==0: continue
            if nulls==count: continue
            if minimum is None and maximum is not None: continue
            if minimum is not None and minimum>high: continue
            if maximum is not None and maximum<low: continue
            out.append(ident)
        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('inclusive low', solve([[[1, -1, 1, 0, 3]], 1, 3]), [1])
    check('inclusive high', solve([[[1, 3, 5, 0, 3]], 1, 3]), [1])
    check('missing minimum', solve([[[1, None, 3, 0, 3]], 1, 3]), [1])
    check('missing maximum', solve([[[1, 1, None, 0, 3]], 1, 3]), [1])
    check('null only', solve([[[1, None, None, 3, 3]], 1, 3]), [])
    check('empty group', solve([[[1, None, None, 0, 0]], 1, 3]), [])
    check('disjoint bounds', solve([[[1, -3, 0, 0, 3], [2, 4, 6, 0, 3]], 1, 3]), [])
elif N == 2:
    check('inclusive low', solve([[[1, 0, 2, 0, 3]], 2, 4]), [1])
    check('inclusive high', solve([[[1, 4, 6, 0, 3]], 2, 4]), [1])
    check('missing minimum', solve([[[1, None, 4, 0, 3]], 2, 4]), [1])
    check('missing maximum', solve([[[1, 2, None, 0, 3]], 2, 4]), [1])
    check('null only', solve([[[1, None, None, 3, 3]], 2, 4]), [])
    check('empty group', solve([[[1, None, None, 0, 0]], 2, 4]), [])
    check('disjoint bounds', solve([[[1, -2, 1, 0, 3], [2, 5, 7, 0, 3]], 2, 4]), [])
elif N == 3:
    check('inclusive low', solve([[[1, 1, 3, 0, 3]], 3, 5]), [1])
    check('inclusive high', solve([[[1, 5, 7, 0, 3]], 3, 5]), [1])
    check('missing minimum', solve([[[1, None, 5, 0, 3]], 3, 5]), [1])
    check('missing maximum', solve([[[1, 3, None, 0, 3]], 3, 5]), [1])
    check('null only', solve([[[1, None, None, 3, 3]], 3, 5]), [])
    check('empty group', solve([[[1, None, None, 0, 0]], 3, 5]), [])
    check('disjoint bounds', solve([[[1, -1, 2, 0, 3], [2, 6, 8, 0, 3]], 3, 5]), [])
elif N == 4:
    check('inclusive low', solve([[[1, 2, 4, 0, 3]], 4, 6]), [1])
    check('inclusive high', solve([[[1, 6, 8, 0, 3]], 4, 6]), [1])
    check('missing minimum', solve([[[1, None, 6, 0, 3]], 4, 6]), [1])
    check('missing maximum', solve([[[1, 4, None, 0, 3]], 4, 6]), [1])
    check('null only', solve([[[1, None, None, 3, 3]], 4, 6]), [])
    check('empty group', solve([[[1, None, None, 0, 0]], 4, 6]), [])
    check('disjoint bounds', solve([[[1, 0, 3, 0, 3], [2, 7, 9, 0, 3]], 4, 6]), [])
elif N == 5:
    check('inclusive low', solve([[[1, 3, 5, 0, 3]], 5, 7]), [1])
    check('inclusive high', solve([[[1, 7, 9, 0, 3]], 5, 7]), [1])
    check('missing minimum', solve([[[1, None, 7, 0, 3]], 5, 7]), [1])
    check('missing maximum', solve([[[1, 5, None, 0, 3]], 5, 7]), [1])
    check('null only', solve([[[1, None, None, 3, 3]], 5, 7]), [])
    check('empty group', solve([[[1, None, None, 0, 0]], 5, 7]), [])
    check('disjoint bounds', solve([[[1, 1, 4, 0, 3], [2, 8, 10, 0, 3]], 5, 7]), [])
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
inclusive low[1][1]Passed
inclusive high[1][1]Passed
missing minimum[][1]Failed
missing maximum[1][1]Passed
null only[][]Passed
empty group[][]Passed
disjoint bounds[][]Passed

SHA-256 / e36eb35dbd1f070d6fa3443473fa6e11a5b94b8fb3aa020ef135249db6dd36dd

3 / The verified repair

Exit 0
"""Failure Map reference implementation. Python standard library only."""
import json

N = 1
observations = []
def solve(d):
    try:
        groups,low,high=d
        out=[]
        for ident,minimum,maximum,nulls,count in groups:
            if count==0: continue
            if nulls==count: continue
            if minimum is not None and minimum>high: continue
            if maximum is not None and maximum<low: continue
            out.append(ident)
        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('inclusive low', solve([[[1, -1, 1, 0, 3]], 1, 3]), [1])
    check('inclusive high', solve([[[1, 3, 5, 0, 3]], 1, 3]), [1])
    check('missing minimum', solve([[[1, None, 3, 0, 3]], 1, 3]), [1])
    check('missing maximum', solve([[[1, 1, None, 0, 3]], 1, 3]), [1])
    check('null only', solve([[[1, None, None, 3, 3]], 1, 3]), [])
    check('empty group', solve([[[1, None, None, 0, 0]], 1, 3]), [])
    check('disjoint bounds', solve([[[1, -3, 0, 0, 3], [2, 4, 6, 0, 3]], 1, 3]), [])
elif N == 2:
    check('inclusive low', solve([[[1, 0, 2, 0, 3]], 2, 4]), [1])
    check('inclusive high', solve([[[1, 4, 6, 0, 3]], 2, 4]), [1])
    check('missing minimum', solve([[[1, None, 4, 0, 3]], 2, 4]), [1])
    check('missing maximum', solve([[[1, 2, None, 0, 3]], 2, 4]), [1])
    check('null only', solve([[[1, None, None, 3, 3]], 2, 4]), [])
    check('empty group', solve([[[1, None, None, 0, 0]], 2, 4]), [])
    check('disjoint bounds', solve([[[1, -2, 1, 0, 3], [2, 5, 7, 0, 3]], 2, 4]), [])
elif N == 3:
    check('inclusive low', solve([[[1, 1, 3, 0, 3]], 3, 5]), [1])
    check('inclusive high', solve([[[1, 5, 7, 0, 3]], 3, 5]), [1])
    check('missing minimum', solve([[[1, None, 5, 0, 3]], 3, 5]), [1])
    check('missing maximum', solve([[[1, 3, None, 0, 3]], 3, 5]), [1])
    check('null only', solve([[[1, None, None, 3, 3]], 3, 5]), [])
    check('empty group', solve([[[1, None, None, 0, 0]], 3, 5]), [])
    check('disjoint bounds', solve([[[1, -1, 2, 0, 3], [2, 6, 8, 0, 3]], 3, 5]), [])
elif N == 4:
    check('inclusive low', solve([[[1, 2, 4, 0, 3]], 4, 6]), [1])
    check('inclusive high', solve([[[1, 6, 8, 0, 3]], 4, 6]), [1])
    check('missing minimum', solve([[[1, None, 6, 0, 3]], 4, 6]), [1])
    check('missing maximum', solve([[[1, 4, None, 0, 3]], 4, 6]), [1])
    check('null only', solve([[[1, None, None, 3, 3]], 4, 6]), [])
    check('empty group', solve([[[1, None, None, 0, 0]], 4, 6]), [])
    check('disjoint bounds', solve([[[1, 0, 3, 0, 3], [2, 7, 9, 0, 3]], 4, 6]), [])
elif N == 5:
    check('inclusive low', solve([[[1, 3, 5, 0, 3]], 5, 7]), [1])
    check('inclusive high', solve([[[1, 7, 9, 0, 3]], 5, 7]), [1])
    check('missing minimum', solve([[[1, None, 7, 0, 3]], 5, 7]), [1])
    check('missing maximum', solve([[[1, 5, None, 0, 3]], 5, 7]), [1])
    check('null only', solve([[[1, None, None, 3, 3]], 5, 7]), [])
    check('empty group', solve([[[1, None, None, 0, 0]], 5, 7]), [])
    check('disjoint bounds', solve([[[1, 1, 4, 0, 3], [2, 8, 10, 0, 3]], 5, 7]), [])
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
inclusive low[1][1]Passed
inclusive high[1][1]Passed
missing minimum[1][1]Passed
missing maximum[1][1]Passed
null only[][]Passed
empty group[][]Passed
disjoint bounds[][]Passed

SHA-256 / 2f34a83777543a2e3abf037782ed3f5dfa135cb4cb2fe88a0d882dad5df05d06

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 2026-09-29T14:44:22.313576+00:00.

Case digest / 106aa5e714695905369dec2066cdfee5a084f5ac9f600059f1932cec85d15786