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FA-45476 / Data systems / Open access

Row-group pruning reports physically empty groups as scan candidates · case 01

Row-group pruning reports physically empty groups as scan candidates.

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

ROOT CAUSE

row-group-pruning: Row-group pruning reports physically empty groups as scan candidates.

THE FAILURE

row-group-pruning: Row-group pruning reports physically empty groups as scan candidates.

Unsuccessful approach: Rejecting negative counts leaves empty metadata candidates intact.

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 and 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[1][]Failed
disjoint bounds[][]Passed

SHA-256 / c5a177b227c4781239a1d3d86df3d98b5cddd0690ac6d5135ba248d165f8df69

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 count>0 and 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[1][]Failed
disjoint bounds[][]Passed

SHA-256 / 7628976fa611b282f6ed4093005331764fd4d49352e7c09f43d5d28bf7c91e07

HELD IN THE MEMBER ARCHIVE

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

This mechanism has 7 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 2026-09-29T14:44:22.629815+00:00.

Case digest / 321ce0e2a28b2712d7201c8a0e4f1f260c10f688af20fcccab5d1b2dc034370b