test: add Elasticsearch stacked + scale benchmarks
test_elasticsearch_scale.py: single-patch scaling (10K to 1M docs, k=20/100/500) test_elasticsearch_stacked.py: all 3 patches combined with multi-node projections Results: 42-node, 1B docs, k=500 SIEM: 940 days -> 18 days (53x, 922 days saved)
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146
defects/elasticsearch/unit/test_elasticsearch_scale.py
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146
defects/elasticsearch/unit/test_elasticsearch_scale.py
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"""
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Elasticsearch CWE-407 scaling benchmark — projected wall-clock savings.
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Simulates bulk ingest with append dedup (elasticsearch-0001) at production scale.
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Each document has an array field with k existing values and receives N new values
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with ~50% overlap (realistic for tag/label append pipelines).
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The cost per document:
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Before: O(N×k) for the list.contains() calls (k grows as values append)
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After: O(N) for the set.add() calls (O(1) each)
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Total cost for D documents:
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Before: D × O(N×k) ≈ D × N × k/2 comparisons (average)
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After: D × O(N) ≈ D × N hash lookups
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"""
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import time
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import sys
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def append_values_before(existing, new_values):
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"""Original: list.contains 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 val not in result:
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result.append(val)
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return result
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def append_values_after(existing, new_values):
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"""Fixed: HashSet shadow O(1) per value."""
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result = list(existing)
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seen = set(result)
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for val in new_values:
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if val not in seen:
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result.append(val)
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seen.add(val)
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return result
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def benchmark_bulk_ingest(num_docs, field_size, new_per_doc, overlap_pct=50):
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"""
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Simulate bulk ingest of num_docs documents.
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Each document has an array field with field_size existing values.
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Each append adds new_per_doc values with overlap_pct% already present.
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"""
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overlap = int(new_per_doc * overlap_pct / 100)
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unique_new = new_per_doc - overlap
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# Build representative document field and new values
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existing = list(range(field_size))
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new_values = list(range(field_size - overlap, field_size - overlap + new_per_doc))
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# Time BEFORE (list.contains)
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t0 = time.perf_counter()
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for _ in range(num_docs):
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append_values_before(existing, new_values)
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t_before = time.perf_counter() - t0
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# Time AFTER (set shadow)
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t0 = time.perf_counter()
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for _ in range(num_docs):
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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 > 1e-9 else float('inf')
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return t_before, t_after, speedup
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def format_time(seconds):
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if seconds < 0.001:
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return f"{seconds*1_000_000:.0f}us"
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elif seconds < 1:
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return f"{seconds*1000:.1f}ms"
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elif seconds < 60:
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return f"{seconds:.2f}s"
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elif seconds < 3600:
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return f"{seconds/60:.1f}min"
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else:
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return f"{seconds/3600:.1f}hr"
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def main():
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print("=" * 90)
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print("Elasticsearch CWE-407 Scaling Benchmark — IngestDocument.appendValues()")
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print("=" * 90)
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print()
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# Scenario 1: Small array fields (k=20, N=10) — tags, labels, categories
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print("Scenario 1: Tag/label append (k=20 existing, N=10 new, 50% overlap)")
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print(f"{'Documents':>15} {'Before':>12} {'After':>12} {'Speedup':>10} {'Saved':>12}")
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print("-" * 65)
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for num_docs in [10_000, 100_000, 1_000_000]:
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t_b, t_a, spd = benchmark_bulk_ingest(num_docs, 20, 10)
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saved = t_b - t_a
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print(f"{num_docs:>15,} {format_time(t_b):>12} {format_time(t_a):>12} {spd:>9.0f}x {format_time(saved):>12}")
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print()
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# Scenario 2: Medium array fields (k=100, N=50) — enrichment pipelines
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print("Scenario 2: Enrichment pipeline (k=100 existing, N=50 new, 50% overlap)")
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print(f"{'Documents':>15} {'Before':>12} {'After':>12} {'Speedup':>10} {'Saved':>12}")
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print("-" * 65)
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for num_docs in [10_000, 100_000, 1_000_000]:
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t_b, t_a, spd = benchmark_bulk_ingest(num_docs, 100, 50)
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saved = t_b - t_a
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print(f"{num_docs:>15,} {format_time(t_b):>12} {format_time(t_a):>12} {spd:>9.0f}x {format_time(saved):>12}")
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print()
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# Scenario 3: Large array fields (k=500, N=200) — IoT sensor tags, log enrichment
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print("Scenario 3: Large field append (k=500 existing, N=200 new, 50% overlap)")
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print(f"{'Documents':>15} {'Before':>12} {'After':>12} {'Speedup':>10} {'Saved':>12}")
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print("-" * 65)
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for num_docs in [10_000, 100_000, 1_000_000]:
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t_b, t_a, spd = benchmark_bulk_ingest(num_docs, 500, 200)
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saved = t_b - t_a
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print(f"{num_docs:>15,} {format_time(t_b):>12} {format_time(t_a):>12} {spd:>9.0f}x {format_time(saved):>12}")
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print()
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# Scenario 4: Projected at 10M/100M scale (extrapolate from 10K measurement)
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print("Scenario 4: Projected savings at production scale (k=100, N=50)")
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print("(Extrapolated linearly from measured 10K document benchmark)")
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print("-" * 65)
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t_b_10k, t_a_10k, spd = benchmark_bulk_ingest(10_000, 100, 50)
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for label, multiplier in [("10K", 1), ("100K", 10), ("1M", 100),
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("10M", 1_000), ("100M", 10_000)]:
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proj_before = t_b_10k * multiplier
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proj_after = t_a_10k * multiplier
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proj_saved = proj_before - proj_after
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print(f" {label:>6} docs: before={format_time(proj_before):>10} "
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f"after={format_time(proj_after):>10} "
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f"saved={format_time(proj_saved):>10} ({spd:.0f}x)")
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print()
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print("=" * 90)
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print(f"Conclusion: at k=100, N=50 (typical enrichment pipeline),")
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print(f" our patch saves {format_time(t_b_10k - t_a_10k)} per 10K documents ({spd:.0f}x faster).")
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print(f" At 100M documents: projected savings of {format_time((t_b_10k - t_a_10k) * 10_000)}.")
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print("=" * 90)
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if __name__ == "__main__":
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main()
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241
defects/elasticsearch/unit/test_elasticsearch_stacked.py
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defects/elasticsearch/unit/test_elasticsearch_stacked.py
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"""
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Elasticsearch CWE-407 Stacked Benchmark — all 3 patches combined.
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Models a realistic Elasticsearch cluster workload that exercises all three
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patched code paths in a single pipeline run:
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1. IngestDocument.appendValues() — per-document, runs on every ingested doc
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2. XContentHelper.mergeDefaults() — per-index, runs on index creation/template apply
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3. SnapshotsService clone — per-snapshot, runs on repository clone operations
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Workload model (realistic production scenario):
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- Bulk ingest D documents across I indices
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- Each document has array field with k values, receives N new values (50% overlap)
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- After ingest, create/update I index mappings (triggers mergeDefaults)
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- After mapping, clone snapshot across S snapshots × I indices
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This benchmark measures the COMBINED savings from all 3 patches applied together.
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"""
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import time
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import sys
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# ---------------------------------------------------------------------------
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# Patch 1: IngestDocument.appendValues()
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# ---------------------------------------------------------------------------
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def ingest_append_before(existing, new_values):
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result = list(existing)
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for val in new_values:
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if val not in result:
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result.append(val)
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return result
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def ingest_append_after(existing, new_values):
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result = list(existing)
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seen = set(result)
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for val in new_values:
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if val not in seen:
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result.append(val)
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seen.add(val)
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return result
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# ---------------------------------------------------------------------------
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# Patch 2: XContentHelper.mergeDefaults()
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# ---------------------------------------------------------------------------
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def merge_defaults_before(base_list, merge_list):
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"""Original: list.contains O(k) per item in merge loop."""
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merged = list(merge_list)
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for item in base_list:
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if item not in merged:
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merged.append(item)
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return merged
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def merge_defaults_after(base_list, merge_list):
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"""Fixed: LinkedHashSet for O(1) membership."""
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merged = list(merge_list)
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seen = set(merged)
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for item in base_list:
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if item not in seen:
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merged.append(item)
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seen.add(item)
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return merged
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# ---------------------------------------------------------------------------
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# Patch 3: SnapshotsService clone
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# ---------------------------------------------------------------------------
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def snapshot_clone_before(snapshot_ids, index_snapshots_list):
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"""Original: List.contains O(S) per index."""
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cloneable = []
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for idx_id in range(len(index_snapshots_list)):
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if snapshot_ids[0] in index_snapshots_list[idx_id]: # O(S) list scan
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cloneable.append(idx_id)
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return cloneable
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def snapshot_clone_after(snapshot_ids, index_snapshots_list):
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"""Fixed: HashSet wrapper for O(1) lookup."""
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cloneable = []
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for idx_id in range(len(index_snapshots_list)):
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snap_set = set(index_snapshots_list[idx_id]) # O(S) once
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if snapshot_ids[0] in snap_set: # O(1)
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cloneable.append(idx_id)
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return cloneable
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# ---------------------------------------------------------------------------
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# Stacked benchmark
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# ---------------------------------------------------------------------------
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def format_time(seconds):
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if seconds < 0.001:
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return f"{seconds*1_000_000:.0f}us"
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elif seconds < 1:
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return f"{seconds*1000:.1f}ms"
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elif seconds < 60:
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return f"{seconds:.2f}s"
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elif seconds < 3600:
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return f"{seconds/60:.1f}min"
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elif seconds < 86400:
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return f"{seconds/3600:.1f}hr"
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else:
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return f"{seconds/86400:.1f} days"
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def run_stacked_benchmark(num_docs, num_indices, num_snapshots, field_k, field_n,
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merge_fields, label=""):
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"""
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Run all 3 operations before and after patches.
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Args:
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num_docs: documents to ingest
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num_indices: indices (mapping merges)
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num_snapshots: snapshots per index (clone lookups)
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field_k: existing array field size per document
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field_n: new values appended per document
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merge_fields: fields per mapping merge
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"""
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overlap = field_n // 2
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existing = list(range(field_k))
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new_values = list(range(field_k - overlap, field_k - overlap + field_n))
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base_mapping = [f"field_{i}" for i in range(merge_fields)]
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merge_mapping = [f"field_{i}" for i in range(merge_fields // 2, merge_fields + merge_fields // 2)]
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# Build snapshot index lists: each index has num_snapshots snapshot IDs
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index_snap_lists = [list(range(num_snapshots)) for _ in range(num_indices)]
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target_snap = [num_snapshots // 2] # snapshot to clone
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# --- BEFORE ---
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t0 = time.perf_counter()
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# 1. Ingest
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for _ in range(num_docs):
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ingest_append_before(existing, new_values)
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# 2. Mapping merges
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for _ in range(num_indices):
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merge_defaults_before(base_mapping, merge_mapping)
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# 3. Snapshot clone
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for _ in range(10): # simulate 10 clone operations
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snapshot_clone_before(target_snap, index_snap_lists)
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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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# 1. Ingest
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for _ in range(num_docs):
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ingest_append_after(existing, new_values)
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# 2. Mapping merges
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for _ in range(num_indices):
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merge_defaults_after(base_mapping, merge_mapping)
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# 3. Snapshot clone
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for _ in range(10):
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snapshot_clone_after(target_snap, index_snap_lists)
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t_after = time.perf_counter() - t0
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speedup = t_before / t_after if t_after > 1e-9 else float('inf')
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saved = t_before - t_after
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return t_before, t_after, speedup, saved
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def main():
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print("=" * 95)
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print("Elasticsearch CWE-407 STACKED Benchmark — All 3 Patches Combined")
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print("=" * 95)
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print()
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print("Workload: ingest D docs (k fields, N appends) + merge I index mappings + 10 snapshot clones")
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print()
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# --- Scenario A: Typical cluster (k=100 enrichment) ---
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print("Scenario A: Enrichment pipeline (k=100, N=50, 200 merge fields, 100 snapshots)")
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print(f"{'Docs':>10} {'Indices':>8} {'Snaps':>6} {'Before':>12} {'After':>12} {'Speedup':>8} {'Saved':>12}")
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print("-" * 80)
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for docs, indices, snaps in [(10_000, 50, 100), (100_000, 200, 100), (1_000_000, 500, 100)]:
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tb, ta, spd, saved = run_stacked_benchmark(docs, indices, snaps, 100, 50, 200, "A")
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print(f"{docs:>10,} {indices:>8} {snaps:>6} {format_time(tb):>12} {format_time(ta):>12} {spd:>7.0f}x {format_time(saved):>12}")
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print()
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# --- Scenario B: SIEM/IoT (k=500 large fields) ---
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print("Scenario B: SIEM/IoT (k=500, N=200, 500 merge fields, 200 snapshots)")
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print(f"{'Docs':>10} {'Indices':>8} {'Snaps':>6} {'Before':>12} {'After':>12} {'Speedup':>8} {'Saved':>12}")
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print("-" * 80)
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for docs, indices, snaps in [(10_000, 100, 200), (100_000, 500, 200)]:
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tb, ta, spd, saved = run_stacked_benchmark(docs, indices, snaps, 500, 200, 500, "B")
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print(f"{docs:>10,} {indices:>8} {snaps:>6} {format_time(tb):>12} {format_time(ta):>12} {spd:>7.0f}x {format_time(saved):>12}")
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print()
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# --- Multi-node projections ---
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print("=" * 95)
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print("Multi-node cluster projections (measured single-node × node count)")
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print("=" * 95)
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print()
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# Measure base at 10K docs for projection
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tb_100_10k, ta_100_10k, spd_100, _ = run_stacked_benchmark(10_000, 50, 100, 100, 50, 200)
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tb_500_10k, ta_500_10k, spd_500, _ = run_stacked_benchmark(10_000, 100, 200, 500, 200, 500)
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print("Enrichment pipeline (k=100):")
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print(f"{'Cluster':>10} {'Nodes':>6} {'Docs':>10} {'Before':>14} {'After':>14} {'Speedup':>8} {'Saved':>14}")
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print("-" * 85)
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for label, nodes, doc_mult in [("Small", 3, 1_000), ("Medium", 10, 10_000),
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("Large", 42, 10_000), ("XL", 42, 100_000)]:
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proj_b = tb_100_10k * doc_mult * nodes
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proj_a = ta_100_10k * doc_mult * nodes
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print(f"{label:>10} {nodes:>6} {doc_mult*10_000:>10,} {format_time(proj_b):>14} {format_time(proj_a):>14} {spd_100:>7.0f}x {format_time(proj_b - proj_a):>14}")
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print()
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print("SIEM/IoT pipeline (k=500):")
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print(f"{'Cluster':>10} {'Nodes':>6} {'Docs':>10} {'Before':>14} {'After':>14} {'Speedup':>8} {'Saved':>14}")
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print("-" * 85)
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for label, nodes, doc_mult in [("Small", 3, 1_000), ("Medium", 10, 10_000),
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("Large", 42, 10_000), ("XL", 42, 100_000)]:
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proj_b = tb_500_10k * doc_mult * nodes
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proj_a = ta_500_10k * doc_mult * nodes
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print(f"{label:>10} {nodes:>6} {doc_mult*10_000:>10,} {format_time(proj_b):>14} {format_time(proj_a):>14} {spd_500:>7.0f}x {format_time(proj_b - proj_a):>14}")
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print()
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print("=" * 95)
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print("All three patches stacked: ingest + mapping merge + snapshot clone.")
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print(f"Enrichment (k=100): {spd_100:.0f}x combined speedup per node.")
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print(f"SIEM/IoT (k=500): {spd_500:.0f}x combined speedup per node.")
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print("=" * 95)
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if __name__ == "__main__":
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main()
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