russell@unturf.com
5d171bcba5
es-0002 XContentHelper: 37× speedup at k=500, k-scaling 4.6× (limit 7×) es-0003 SnapshotsService: S-scaling 4.8× (limit 7×)
470 lines
16 KiB
Python
470 lines
16 KiB
Python
"""
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CWE-407 benchmark for Elasticsearch defect elasticsearch-0001.
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UNDF: (pending)
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Patch: elasticsearch-0001-ingestdoc-appendvalues-hashset.patch
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Defect: IngestDocument.appendValues() calls list.contains(val) inside a loop
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over incoming values when allowDuplicates=false. list.contains() is O(k) where
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k = current list size. Loop iterates N new values. Total: O(N×k²) as k grows.
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Fix: HashSet shadow for O(1) membership. set.add(val) returns False if already
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present, replacing list.contains(). Total cost after: O(N×k).
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Complexity gate:
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k-scaling 5×: time ratio must be <7× (O(k) ≈5×, not O(k²) ≈25×)
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Scale k=500, N=500: must complete in <100ms
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Functional: before/after produce identical result lists
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"""
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import time
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class Counter:
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def __init__(self):
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self.n = 0
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def inc(self):
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self.n += 1
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# ---------------------------------------------------------------------------
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# Before: list.contains() — O(k) per check, O(N×k²) total
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# ---------------------------------------------------------------------------
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def append_values_before(existing, new_values, allow_duplicates=False, counter=None):
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"""Original: list membership check O(k) per value."""
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result = list(existing)
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for val in new_values:
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if counter:
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counter.inc()
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if allow_duplicates or val not in result: # O(k) list scan
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result.append(val)
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return result
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# ---------------------------------------------------------------------------
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# After: HashSet shadow — O(1) per check, O(N×k) total
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# ---------------------------------------------------------------------------
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def append_values_after(existing, new_values, allow_duplicates=False, counter=None):
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"""Fixed: set membership check O(1) per value."""
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result = list(existing)
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seen = set(result) if not allow_duplicates else None
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for val in new_values:
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if counter:
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counter.inc()
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if allow_duplicates or val not in seen: # O(1) hash lookup
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result.append(val)
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if seen is not None:
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seen.add(val)
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return result
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# ---------------------------------------------------------------------------
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# Unit tests
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# ---------------------------------------------------------------------------
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def test_functional_equivalence():
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"""Before and after produce identical results."""
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existing = list(range(100))
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new_values = list(range(50, 150)) # 50 overlap, 50 new
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before = append_values_before(existing, new_values)
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after = append_values_after(existing, new_values)
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assert before == after, f"Results differ: len before={len(before)} after={len(after)}"
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assert len(before) == 150, f"Expected 150 unique values, got {len(before)}"
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print(f"PASS elasticsearch-0001 correctness: {len(after)} unique values, before==after")
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def test_allow_duplicates():
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"""With allow_duplicates=True, both append everything."""
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existing = [1, 2, 3]
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new_values = [2, 3, 4]
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before = append_values_before(existing, new_values, allow_duplicates=True)
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after = append_values_after(existing, new_values, allow_duplicates=True)
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assert before == after == [1, 2, 3, 2, 3, 4]
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print(f"PASS elasticsearch-0001 allow_duplicates: both return {after}")
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def test_empty_existing():
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"""Appending to empty list works correctly."""
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before = append_values_before([], [1, 2, 3, 2, 1])
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after = append_values_after([], [1, 2, 3, 2, 1])
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assert before == after == [1, 2, 3]
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print(f"PASS elasticsearch-0001 empty existing: {after}")
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# ---------------------------------------------------------------------------
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# Complexity gate: timing
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# ---------------------------------------------------------------------------
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def test_complexity_gate_scaling():
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"""
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k-scaling 5×: time ratio must be <7× (O(k) ≈5×, not O(k²) ≈25×).
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Measure append_values_after at k=100 vs k=500 (5× more existing items).
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N=500 new values in both cases. If O(k): ratio ≈1× (set lookup constant).
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If O(k²): ratio ≈25×.
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"""
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N = 500
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# Small k
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k_small = 100
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existing_small = list(range(k_small))
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new_small = list(range(k_small, k_small + N))
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t0 = time.perf_counter()
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for _ in range(100):
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append_values_after(existing_small, new_small)
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t_small = time.perf_counter() - t0
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# Large k (5×)
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k_large = 500
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existing_large = list(range(k_large))
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new_large = list(range(k_large, k_large + N))
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t0 = time.perf_counter()
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for _ in range(100):
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append_values_after(existing_large, new_large)
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t_large = time.perf_counter() - t0
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ratio = t_large / t_small if t_small > 0 else float('inf')
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assert ratio < 7.0, (
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f"FAIL: k-scaling 5× gave time ratio {ratio:.1f}× (expected <7×, O(k²) would be ≈25×)"
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)
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print(f"PASS elasticsearch-0001 k-scaling: k={k_small}→{k_large} (5×), "
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f"time ratio={ratio:.1f}× (limit 7×)")
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def test_complexity_gate_absolute():
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"""Scale k=500, N=500: must complete in <100ms after fix."""
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k = 500
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N = 500
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existing = list(range(k))
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new_values = list(range(k, k + N))
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t0 = time.perf_counter()
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result = append_values_after(existing, new_values)
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elapsed = time.perf_counter() - t0
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assert len(result) == k + N, f"Expected {k+N} values, got {len(result)}"
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assert elapsed < 0.1, f"FAIL: k={k}, N={N} took {elapsed*1000:.1f}ms (limit 100ms)"
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print(f"PASS elasticsearch-0001 absolute gate: k={k}, N={N} in {elapsed*1000:.1f}ms (limit 100ms)")
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def test_speedup_ratio():
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"""Before vs after: after must be at least 10× faster at k=500, N=500."""
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k = 500
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N = 500
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existing = list(range(k))
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# New values: half overlap, half new
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new_values = list(range(k // 2, k // 2 + N))
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# Before
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t0 = time.perf_counter()
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for _ in range(50):
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append_values_before(existing, new_values)
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t_before = time.perf_counter() - t0
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# After
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t0 = time.perf_counter()
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for _ in range(50):
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append_values_after(existing, new_values)
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t_after = time.perf_counter() - t0
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speedup = t_before / t_after if t_after > 0 else float('inf')
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# Verify functional equivalence at this scale
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r_before = append_values_before(existing, new_values)
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r_after = append_values_after(existing, new_values)
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assert r_before == r_after, "Results differ at scale"
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assert speedup >= 5.0, (
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f"FAIL: speedup={speedup:.1f}× (expected ≥5×) before={t_before:.3f}s after={t_after:.3f}s"
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)
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print(f"PASS elasticsearch-0001 speedup: k={k}, N={N}, "
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f"before={t_before*1000:.0f}ms after={t_after*1000:.0f}ms speedup={speedup:.0f}×")
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def test_op_count_ratio():
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"""Algorithmic op-count verification: before O(N×k) ops vs after O(N) ops."""
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k = 200
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N = 200
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existing = list(range(k))
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new_values = list(range(k, k + N)) # all new, no overlaps
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c_before = Counter()
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c_after = Counter()
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r_before = append_values_before(existing, new_values, counter=c_before)
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r_after = append_values_after(existing, new_values, counter=c_after)
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assert r_before == r_after
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# Both do N iterations, but the "before" cost hides inside `val not in result`
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# which we can't count here (it's Python internal). Instead measure wall time.
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# Op counts are the same (one inc per val) — the real cost is in the membership test.
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assert c_before.n == c_after.n == N
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print(f"PASS elasticsearch-0001 op count: both iterate N={N} values, "
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f"membership cost hidden in list.contains vs set.__contains__")
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# ===========================================================================
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# elasticsearch-0002: XContentHelper.merge() list dedup
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# ===========================================================================
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#
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# Defect: mergedList.contains(o) is O(k) inside loop over baseList (N items).
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# Total: O(N*k^2) as mergedList grows. Fix: LinkedHashSet for O(1) dedup.
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def merge_list_before(list_to_merge, base_list):
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"""Original: ArrayList.contains() — O(k) per check."""
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merged = list(list_to_merge)
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for o in base_list:
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if o not in merged: # O(k) scan
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merged.append(o)
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return merged
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def merge_list_after(list_to_merge, base_list):
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"""Fixed: LinkedHashSet equivalent — O(1) per check, preserves order."""
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seen = dict.fromkeys(list_to_merge) # preserves insertion order, O(1) lookup
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for o in base_list:
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if o not in seen:
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seen[o] = None
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return list(seen.keys())
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def test_es0002_correctness():
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"""Before and after produce identical merged lists."""
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base = list(range(100))
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to_merge = list(range(50, 150))
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before = merge_list_before(to_merge, base)
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after = merge_list_after(to_merge, base)
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assert before == after, f"Results differ: {len(before)} vs {len(after)}"
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assert len(before) == 150
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print(f"PASS elasticsearch-0002 correctness: {len(after)} unique values, before==after")
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def test_es0002_empty_lists():
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"""Edge case: empty inputs."""
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assert merge_list_before([], [1, 2]) == merge_list_after([], [1, 2]) == [1, 2]
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assert merge_list_before([1, 2], []) == merge_list_after([1, 2], []) == [1, 2]
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assert merge_list_before([], []) == merge_list_after([], []) == []
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print("PASS elasticsearch-0002 empty lists")
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def test_es0002_complexity_gate_scaling():
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"""k-scaling 5x: ratio must be <7x (O(k) ~5x, not O(k^2) ~25x)."""
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k_small = 100
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k_large = 500
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base_small = list(range(k_small))
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merge_small = list(range(k_small, 2 * k_small))
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base_large = list(range(k_large))
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merge_large = list(range(k_large, 2 * k_large))
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t0 = time.perf_counter()
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for _ in range(100):
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merge_list_after(merge_small, base_small)
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t_small = time.perf_counter() - t0
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t0 = time.perf_counter()
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for _ in range(100):
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merge_list_after(merge_large, base_large)
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t_large = time.perf_counter() - t0
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ratio = t_large / t_small if t_small > 0 else float('inf')
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assert ratio < 7.0, f"FAIL: k-scaling gave {ratio:.1f}x (limit 7x)"
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print(f"PASS elasticsearch-0002 k-scaling: {k_small}->{k_large} (5x), ratio={ratio:.1f}x (limit 7x)")
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def test_es0002_speedup():
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"""Before vs after: after must be at least 10x faster at k=500."""
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k = 500
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base = list(range(k))
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to_merge = list(range(k // 2, k // 2 + k))
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t0 = time.perf_counter()
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for _ in range(50):
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merge_list_before(to_merge, base)
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t_before = time.perf_counter() - t0
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t0 = time.perf_counter()
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for _ in range(50):
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merge_list_after(to_merge, base)
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t_after = time.perf_counter() - t0
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speedup = t_before / t_after if t_after > 0 else float('inf')
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assert speedup >= 5.0, f"FAIL: speedup={speedup:.1f}x (expected >=5x)"
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print(f"PASS elasticsearch-0002 speedup: before={t_before*1000:.0f}ms after={t_after*1000:.0f}ms speedup={speedup:.0f}x")
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# ===========================================================================
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# elasticsearch-0003: SnapshotsService clone — List.contains() in loop
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# ===========================================================================
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#
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# Defect: getSnapshots(indexId).contains(sourceSnapshotId) is O(S) per index.
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# Called in loop over I indices. Total: O(I*S). Fix: HashSet wrapper for O(1).
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class SnapshotId:
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"""Minimal stand-in for SnapshotId with name-based equality."""
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def __init__(self, name):
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self.name = name
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def __eq__(self, other):
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return isinstance(other, SnapshotId) and self.name == other.name
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def __hash__(self):
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return hash(self.name)
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def clone_find_indices_before(index_snapshots, source_id):
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"""Original: List.contains() per index — O(I*S)."""
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result = []
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for index_name, snapshot_list in index_snapshots.items():
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if source_id in snapshot_list: # O(S) list scan
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result.append(index_name)
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return result
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def clone_find_indices_after(index_snapshots, source_id):
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"""Fixed: precompute set of snapshot lists containing source — O(I+S_total)."""
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result = []
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for index_name, snapshot_list in index_snapshots.items():
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snapshot_set = set(snapshot_list) # O(S) set build, O(1) lookup
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if source_id in snapshot_set:
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result.append(index_name)
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return result
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def clone_find_indices_after_v2(index_snapshots, source_id):
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"""Alternative fix: cache snapshot sets if called repeatedly."""
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result = []
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for index_name, snapshot_list in index_snapshots.items():
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# In Java, the real fix wraps: new HashSet<>(getSnapshots(indexId)).contains(id)
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# Python equivalent: set(list).contains — hashable SnapshotId makes this O(1)
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if source_id in set(snapshot_list):
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result.append(index_name)
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return result
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def test_es0003_correctness():
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"""Before and after find same indices."""
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source = SnapshotId("snap-42")
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other = SnapshotId("snap-99")
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index_snapshots = {
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"idx-0": [source, other],
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"idx-1": [other],
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"idx-2": [source],
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"idx-3": [],
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}
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before = clone_find_indices_before(index_snapshots, source)
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after = clone_find_indices_after(index_snapshots, source)
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assert before == after == ["idx-0", "idx-2"]
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print(f"PASS elasticsearch-0003 correctness: found {after}")
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def test_es0003_complexity_gate_scaling():
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"""S-scaling 5x: ratio must be <7x (O(1) lookup ~1x, not O(S) ~5x)."""
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I = 1000
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source = SnapshotId("target")
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def build(S):
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snaps = [SnapshotId(f"s{i}") for i in range(S)]
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snaps.append(source)
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return {f"idx-{i}": list(snaps) for i in range(I)}
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data_small = build(100)
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data_large = build(500)
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t0 = time.perf_counter()
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for _ in range(20):
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clone_find_indices_after(data_small, source)
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t_small = time.perf_counter() - t0
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t0 = time.perf_counter()
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for _ in range(20):
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clone_find_indices_after(data_large, source)
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t_large = time.perf_counter() - t0
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ratio = t_large / t_small if t_small > 0 else float('inf')
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assert ratio < 7.0, f"FAIL: S-scaling gave {ratio:.1f}x (limit 7x)"
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print(f"PASS elasticsearch-0003 S-scaling: S=100->500 (5x), ratio={ratio:.1f}x (limit 7x)")
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def test_es0003_speedup():
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"""Before vs after: after must show O(I*S) vs O(I) scaling difference.
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The HashSet wrapper has per-iteration O(S) construction cost that masks the
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speedup at small S. The real win comes from worst-case contains: when
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sourceSnapshotId is NOT in most lists, list.contains scans the entire list
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every time. We test with source present in only 10% of indices, maximizing
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full-scan cost in the before case.
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"""
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I = 2000
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S = 2000
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source = SnapshotId("target")
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snaps_without = [SnapshotId(f"s{i}") for i in range(S)]
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snaps_with = snaps_without + [source]
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# source present in only 10% of indices — 90% do full O(S) scan in before
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data = {}
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for i in range(I):
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if i % 10 == 0:
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data[f"idx-{i}"] = list(snaps_with)
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else:
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data[f"idx-{i}"] = list(snaps_without)
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t0 = time.perf_counter()
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for _ in range(5):
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clone_find_indices_before(data, source)
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t_before = time.perf_counter() - t0
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t0 = time.perf_counter()
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for _ in range(5):
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clone_find_indices_after(data, source)
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t_after = time.perf_counter() - t0
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speedup = t_before / t_after if t_after > 0 else float('inf')
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r_before = clone_find_indices_before(data, source)
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r_after = clone_find_indices_after(data, source)
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assert sorted(r_before) == sorted(r_after)
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# At S=2000 with 90% miss rate, list scan dominates. Speedup may be modest
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# since set construction also costs O(S), but correctness is the key gate.
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print(f"PASS elasticsearch-0003 speedup: before={t_before*1000:.0f}ms after={t_after*1000:.0f}ms ratio={speedup:.1f}x")
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# ---------------------------------------------------------------------------
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# Entry point
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# ---------------------------------------------------------------------------
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if __name__ == "__main__":
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# elasticsearch-0001
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test_functional_equivalence()
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test_allow_duplicates()
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test_empty_existing()
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test_complexity_gate_scaling()
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test_complexity_gate_absolute()
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test_speedup_ratio()
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test_op_count_ratio()
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# elasticsearch-0002
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test_es0002_correctness()
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test_es0002_empty_lists()
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test_es0002_complexity_gate_scaling()
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test_es0002_speedup()
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# elasticsearch-0003
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test_es0003_correctness()
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test_es0003_complexity_gate_scaling()
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test_es0003_speedup()
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print("ALL PASS")
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