FA-45466 / Data systems / Open access
Row-group pruning excludes a minimum equal to the range upper bound · case 01
Row-group pruning excludes a minimum equal to the range upper bound.
ROOT CAUSE
row-group-pruning: Row-group pruning excludes a minimum equal to the range upper bound.
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: Decrementing the upper bound still rejects an equal minimum.
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 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 fixture | Actual | Expected | Outcome |
|---|---|---|---|
| inclusive low | [1] | [1] | Passed |
| inclusive high | [] | [1] | Failed |
| missing minimum | [1] | [1] | Passed |
| missing maximum | [1] | [1] | Passed |
| null only | [] | [] | Passed |
| empty group | [] | [] | Passed |
| disjoint bounds | [] | [] | Passed |
SHA-256 / 3a5f7e757f1c14c138bcc47157d3d3ece384e7864a5323aa0c8b78dbd4880dfc
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 not None and minimum>high-1: 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 fixture | Actual | Expected | Outcome |
|---|---|---|---|
| inclusive low | [1] | [1] | Passed |
| inclusive high | [] | [1] | Failed |
| missing minimum | [1] | [1] | Passed |
| missing maximum | [1] | [1] | Passed |
| null only | [] | [] | Passed |
| empty group | [] | [] | Passed |
| disjoint bounds | [] | [] | Passed |
SHA-256 / 275feeba34604fa7cd822f1028f31f80b81c95036646b210a8411490d5a32aba
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 fixture | Actual | Expected | Outcome |
|---|---|---|---|
| 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 2026-09-29T14:44:22.529169+00:00.
Case digest / 6917c9e9a1d8fe096fa905c7eb8b844d86a00980060508220e50afa97b32cf10