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FA-45191 / Data systems / Open access

Batch packing retains byte occupancy after flushing rows · case 01

Batch packing retains byte occupancy after flushing rows.

Verified by executionVariant 1 · 8 checks per implementationDownload source bundle ↓JSON ↗

ROOT CAUSE

row-batch-packing: Batch packing retains byte occupancy after flushing rows.

THE FAILURE

row-batch-packing: Batch packing retains byte occupancy after flushing rows.

Unsuccessful approach: Charging the pending row before appending it double counts its bytes.

Case contract

Pack indivisible [id,size] rows into ordered batches under a positive byte budget. A row exceeding budget occupies its own batch. Flush before an overflowing row, permit exact fits, and omit empty batches.

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:
        rows,budget=d
        batches=[]; current=[]; used=0
        for ident,size in rows:
            if current and used+size>budget:
                batches.append(current); current=[]
            current.append(ident); used+=size
            if size>budget:
                batches.append(current); current=[]; used=0
        if current: batches.append(current)
        return batches
    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('exact fit', solve([[[1, 1], [2, 1]], 2]), [[1, 2]])
    check('byte budget', solve([[[1, 2], [2, 2], [3, 1]], 3]), [[1], [2, 3]])
    check('reset after flush', solve([[[1, 2], [2, 2], [3, 1], [4, 1]], 3]), [[1], [2, 3], [4]])
    check('oversize and zero', solve([[[1, 4], [2, 0]], 3]), [[1], [2]])
    check('full followed by zero', solve([[[1, 3], [2, 0]], 3]), [[1, 2]])
    check('single partial', solve([[[1, 1]], 3]), [[1]])
    check('no rows', solve([[], 3]), [])
    check('zero-sized rows', solve([[[1, 0], [2, 0]], 1]), [[1, 2]])
elif N == 2:
    check('exact fit', solve([[[1, 2], [2, 2]], 4]), [[1, 2]])
    check('byte budget', solve([[[1, 4], [2, 4], [3, 2]], 6]), [[1], [2, 3]])
    check('reset after flush', solve([[[1, 4], [2, 4], [3, 2], [4, 2]], 6]), [[1], [2, 3], [4]])
    check('oversize and zero', solve([[[1, 8], [2, 0]], 6]), [[1], [2]])
    check('full followed by zero', solve([[[1, 6], [2, 0]], 6]), [[1, 2]])
    check('single partial', solve([[[1, 2]], 6]), [[1]])
    check('no rows', solve([[], 6]), [])
    check('zero-sized rows', solve([[[1, 0], [2, 0]], 2]), [[1, 2]])
elif N == 3:
    check('exact fit', solve([[[1, 3], [2, 3]], 6]), [[1, 2]])
    check('byte budget', solve([[[1, 6], [2, 6], [3, 3]], 9]), [[1], [2, 3]])
    check('reset after flush', solve([[[1, 6], [2, 6], [3, 3], [4, 3]], 9]), [[1], [2, 3], [4]])
    check('oversize and zero', solve([[[1, 12], [2, 0]], 9]), [[1], [2]])
    check('full followed by zero', solve([[[1, 9], [2, 0]], 9]), [[1, 2]])
    check('single partial', solve([[[1, 3]], 9]), [[1]])
    check('no rows', solve([[], 9]), [])
    check('zero-sized rows', solve([[[1, 0], [2, 0]], 3]), [[1, 2]])
elif N == 4:
    check('exact fit', solve([[[1, 4], [2, 4]], 8]), [[1, 2]])
    check('byte budget', solve([[[1, 8], [2, 8], [3, 4]], 12]), [[1], [2, 3]])
    check('reset after flush', solve([[[1, 8], [2, 8], [3, 4], [4, 4]], 12]), [[1], [2, 3], [4]])
    check('oversize and zero', solve([[[1, 16], [2, 0]], 12]), [[1], [2]])
    check('full followed by zero', solve([[[1, 12], [2, 0]], 12]), [[1, 2]])
    check('single partial', solve([[[1, 4]], 12]), [[1]])
    check('no rows', solve([[], 12]), [])
    check('zero-sized rows', solve([[[1, 0], [2, 0]], 4]), [[1, 2]])
elif N == 5:
    check('exact fit', solve([[[1, 5], [2, 5]], 10]), [[1, 2]])
    check('byte budget', solve([[[1, 10], [2, 10], [3, 5]], 15]), [[1], [2, 3]])
    check('reset after flush', solve([[[1, 10], [2, 10], [3, 5], [4, 5]], 15]), [[1], [2, 3], [4]])
    check('oversize and zero', solve([[[1, 20], [2, 0]], 15]), [[1], [2]])
    check('full followed by zero', solve([[[1, 15], [2, 0]], 15]), [[1, 2]])
    check('single partial', solve([[[1, 5]], 15]), [[1]])
    check('no rows', solve([[], 15]), [])
    check('zero-sized rows', solve([[[1, 0], [2, 0]], 5]), [[1, 2]])
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
exact fit[[1, 2]][[1, 2]]Passed
byte budget[[1], [2], [3]][[1], [2, 3]]Failed
reset after flush[[1], [2], [3], [4]][[1], [2, 3], [4]]Failed
oversize and zero[[1], [2]][[1], [2]]Passed
full followed by zero[[1, 2]][[1, 2]]Passed
single partial[[1]][[1]]Passed
no rows[][]Passed
zero-sized rows[[1, 2]][[1, 2]]Passed

SHA-256 / 1f4269d88b5f9c6000689f680bc87a87183aa8de09f1304c04296b19aa2f47cd

2 / The unsuccessful fix

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

N = 1
observations = []
def solve(d):
    try:
        rows,budget=d
        batches=[]; current=[]; used=0
        for ident,size in rows:
            if current and used+size>budget:
                batches.append(current); current=[]; used=size
            current.append(ident); used+=size
            if size>budget:
                batches.append(current); current=[]; used=0
        if current: batches.append(current)
        return batches
    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('exact fit', solve([[[1, 1], [2, 1]], 2]), [[1, 2]])
    check('byte budget', solve([[[1, 2], [2, 2], [3, 1]], 3]), [[1], [2, 3]])
    check('reset after flush', solve([[[1, 2], [2, 2], [3, 1], [4, 1]], 3]), [[1], [2, 3], [4]])
    check('oversize and zero', solve([[[1, 4], [2, 0]], 3]), [[1], [2]])
    check('full followed by zero', solve([[[1, 3], [2, 0]], 3]), [[1, 2]])
    check('single partial', solve([[[1, 1]], 3]), [[1]])
    check('no rows', solve([[], 3]), [])
    check('zero-sized rows', solve([[[1, 0], [2, 0]], 1]), [[1, 2]])
elif N == 2:
    check('exact fit', solve([[[1, 2], [2, 2]], 4]), [[1, 2]])
    check('byte budget', solve([[[1, 4], [2, 4], [3, 2]], 6]), [[1], [2, 3]])
    check('reset after flush', solve([[[1, 4], [2, 4], [3, 2], [4, 2]], 6]), [[1], [2, 3], [4]])
    check('oversize and zero', solve([[[1, 8], [2, 0]], 6]), [[1], [2]])
    check('full followed by zero', solve([[[1, 6], [2, 0]], 6]), [[1, 2]])
    check('single partial', solve([[[1, 2]], 6]), [[1]])
    check('no rows', solve([[], 6]), [])
    check('zero-sized rows', solve([[[1, 0], [2, 0]], 2]), [[1, 2]])
elif N == 3:
    check('exact fit', solve([[[1, 3], [2, 3]], 6]), [[1, 2]])
    check('byte budget', solve([[[1, 6], [2, 6], [3, 3]], 9]), [[1], [2, 3]])
    check('reset after flush', solve([[[1, 6], [2, 6], [3, 3], [4, 3]], 9]), [[1], [2, 3], [4]])
    check('oversize and zero', solve([[[1, 12], [2, 0]], 9]), [[1], [2]])
    check('full followed by zero', solve([[[1, 9], [2, 0]], 9]), [[1, 2]])
    check('single partial', solve([[[1, 3]], 9]), [[1]])
    check('no rows', solve([[], 9]), [])
    check('zero-sized rows', solve([[[1, 0], [2, 0]], 3]), [[1, 2]])
elif N == 4:
    check('exact fit', solve([[[1, 4], [2, 4]], 8]), [[1, 2]])
    check('byte budget', solve([[[1, 8], [2, 8], [3, 4]], 12]), [[1], [2, 3]])
    check('reset after flush', solve([[[1, 8], [2, 8], [3, 4], [4, 4]], 12]), [[1], [2, 3], [4]])
    check('oversize and zero', solve([[[1, 16], [2, 0]], 12]), [[1], [2]])
    check('full followed by zero', solve([[[1, 12], [2, 0]], 12]), [[1, 2]])
    check('single partial', solve([[[1, 4]], 12]), [[1]])
    check('no rows', solve([[], 12]), [])
    check('zero-sized rows', solve([[[1, 0], [2, 0]], 4]), [[1, 2]])
elif N == 5:
    check('exact fit', solve([[[1, 5], [2, 5]], 10]), [[1, 2]])
    check('byte budget', solve([[[1, 10], [2, 10], [3, 5]], 15]), [[1], [2, 3]])
    check('reset after flush', solve([[[1, 10], [2, 10], [3, 5], [4, 5]], 15]), [[1], [2, 3], [4]])
    check('oversize and zero', solve([[[1, 20], [2, 0]], 15]), [[1], [2]])
    check('full followed by zero', solve([[[1, 15], [2, 0]], 15]), [[1, 2]])
    check('single partial', solve([[[1, 5]], 15]), [[1]])
    check('no rows', solve([[], 15]), [])
    check('zero-sized rows', solve([[[1, 0], [2, 0]], 5]), [[1, 2]])
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
exact fit[[1, 2]][[1, 2]]Passed
byte budget[[1], [2], [3]][[1], [2, 3]]Failed
reset after flush[[1], [2], [3, 4]][[1], [2, 3], [4]]Failed
oversize and zero[[1], [2]][[1], [2]]Passed
full followed by zero[[1, 2]][[1, 2]]Passed
single partial[[1]][[1]]Passed
no rows[][]Passed
zero-sized rows[[1, 2]][[1, 2]]Passed

SHA-256 / e469dc095b5df019bf980b2ff622c5b237310e2f02314831471ce0cd25991125

HELD IN THE MEMBER ARCHIVE

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

This mechanism has 8 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 2026-09-29T14:44:19.851434+00:00.

Case digest / ffa9e3d91a6793599174719a17ec0adcd1ab066b8ac9df25b5b81abca159becb