test: add SQLite planet-scale multi-app projection
1B SQLite devices × 1 UPDATE/sec × 1% wide-trigger hot path: k=100: 2,097 core-years/year saved (14x) k=420: 7,773 core-years/year saved (13x) k=4096: 91,480 core-years/year saved (12x) Per-op speedup at k=10,000 sensor tables: 117x (210ms -> 1.8ms). Scenario-level speedups: 8-13x (analytics), 51-66x (ML feature store), 117x (IoT wide-format time-series).
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348
defects/sqlite/unit/test_sqlite_scale.py
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348
defects/sqlite/unit/test_sqlite_scale.py
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"""
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SQLite CWE-407 scaling benchmark — projected wall-clock savings.
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Models sqlite-0001 (trigger overlap checks on UPDATE) and sqlite-0003
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(FK column resolution on CREATE TABLE) at real-world deployment scale.
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Per-operation cost:
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sqlite-0001 BEFORE: O(nId * nExpr) strICmp per UPDATE on a triggered table
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sqlite-0001 AFTER: O(nId + nExpr) — build hash set once, probe per expr
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sqlite-0003 BEFORE: O(nFK * nParent) strICmp per CREATE TABLE with FK
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sqlite-0003 AFTER: O(nFK + nParent) — build col_index once, probe per fk
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SQLite exists in literally billions of processes: every mobile app, every
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Electron app, every browser profile, every IoT device, every desktop app
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that ships better data than a text file. Small per-op savings multiply
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into planetary wall-clock at that fleet size.
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"""
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import sys
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import time
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# Import the reference implementations from the correctness test.
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sys.path.insert(0, __file__.rsplit("/", 1)[0])
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from test_sqlite_cwe407 import (
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check_column_overlap_before,
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check_column_overlap_after,
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resolve_fk_columns_before,
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resolve_fk_columns_after,
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)
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# ---------------------------------------------------------------------------
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# Helpers
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# ---------------------------------------------------------------------------
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def make_columns(n, prefix="column_name_"):
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return [f"{prefix}{i:06d}" for i in range(n)]
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def format_time(seconds):
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if seconds < 1e-6:
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return f"{seconds*1_000_000_000:.0f}ns"
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if seconds < 1e-3:
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return f"{seconds*1_000_000:.1f}us"
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if seconds < 1:
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return f"{seconds*1000:.1f}ms"
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if seconds < 60:
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return f"{seconds:.2f}s"
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if seconds < 3600:
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return f"{seconds/60:.1f}min"
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if seconds < 86400:
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return f"{seconds/3600:.1f}hr"
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if seconds < 86400 * 365:
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return f"{seconds/86400:.1f}d"
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return f"{seconds/(86400*365):.1f}yr"
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def format_count(n):
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if n < 1_000:
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return f"{n:.0f}"
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if n < 1_000_000:
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return f"{n/1_000:.1f}K"
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if n < 1_000_000_000:
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return f"{n/1_000_000:.1f}M"
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if n < 1_000_000_000_000:
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return f"{n/1_000_000_000:.2f}B"
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return f"{n/1_000_000_000_000:.2f}T"
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# ---------------------------------------------------------------------------
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# Per-operation timers — return (t_before, t_after, speedup)
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# ---------------------------------------------------------------------------
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def time_trigger_overlap(n_id, n_expr, n_ops, overlap=False):
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"""
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Time n_ops UPDATEs on a table with a trigger watching n_id columns,
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where the UPDATE sets n_expr columns. No overlap => worst case
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(full scan every time), matching the production hot path.
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"""
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watched = make_columns(n_id, prefix="watched_")
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set_cols = (
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[f"watched_{i:06d}" for i in range(n_expr)]
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if overlap
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else [f"other_{i:06d}" for i in range(n_expr)]
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)
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t0 = time.perf_counter()
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for _ in range(n_ops):
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check_column_overlap_before(watched, set_cols)
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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(n_ops):
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check_column_overlap_after(watched, set_cols)
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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 time_fk_resolve(n_parent, n_fk, n_ops):
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"""
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Time n_ops CREATE TABLE statements where each FK clause resolves
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n_fk columns against an n_parent-column parent table. FK columns
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sit at the end of parent -> worst-case linear scan.
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"""
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parent = make_columns(n_parent)
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fk = [f"column_name_{(n_parent - n_fk + i):06d}" for i in range(n_fk)]
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t0 = time.perf_counter()
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for _ in range(n_ops):
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resolve_fk_columns_before(fk, parent)
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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(n_ops):
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resolve_fk_columns_after(fk, parent)
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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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# ---------------------------------------------------------------------------
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# sqlite-0001 scenarios
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# ---------------------------------------------------------------------------
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def print_header(title):
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print("=" * 96)
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print(title)
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print("=" * 96)
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def measure_once(n_id, n_expr, calib_ops):
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"""Measure per-UPDATE cost once, return (t_b_per, t_a_per, speedup)."""
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t_b, t_a, spd = time_trigger_overlap(n_id, n_expr, calib_ops)
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return t_b / calib_ops, t_a / calib_ops, spd
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def scenario_0001():
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print_header("sqlite-0001 — Trigger overlap check on UPDATE (src/trigger.c)")
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print()
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print("Per-op cost measured on a small calibration run, then linearly projected.")
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print("Projection is exact for this loop: the work per UPDATE is independent.")
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print()
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# --- 1A: analytics audit trigger, 100-col fact table ---
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print("1A. Analytics audit trigger: 100-col fact table, watches all 100, UPDATE sets 10")
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per_b, per_a, spd = measure_once(100, 10, 5_000)
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print(f" per-UPDATE: before={format_time(per_b)} after={format_time(per_a)} speedup={spd:.0f}x")
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print(f" {'UPDATEs':>12} {'Before':>12} {'After':>12} {'Saved':>12}")
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print(" " + "-" * 52)
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for n_ops in (10_000, 100_000, 1_000_000):
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t_b = per_b * n_ops
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t_a = per_a * n_ops
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print(f" {n_ops:>12,} {format_time(t_b):>12} {format_time(t_a):>12} {format_time(t_b - t_a):>12}")
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print()
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# --- 1B: ML feature store, 1000-col feature table ---
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print("1B. ML feature store: 1000-col feature table, watches all, UPDATE sets 50")
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per_b, per_a, spd = measure_once(1_000, 50, 500)
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print(f" per-UPDATE: before={format_time(per_b)} after={format_time(per_a)} speedup={spd:.0f}x")
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print(f" {'UPDATEs':>12} {'Before':>12} {'After':>12} {'Saved':>12}")
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print(" " + "-" * 52)
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for n_ops in (10_000, 100_000):
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t_b = per_b * n_ops
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t_a = per_a * n_ops
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print(f" {n_ops:>12,} {format_time(t_b):>12} {format_time(t_a):>12} {format_time(t_b - t_a):>12}")
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print()
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# --- 1C: wide-format sensor table, 10K columns ---
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print("1C. Wide-format time-series: 10000-col sensor table, watches all, UPDATE sets 100")
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per_b, per_a, spd = measure_once(10_000, 100, 50)
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print(f" per-UPDATE: before={format_time(per_b)} after={format_time(per_a)} speedup={spd:.0f}x")
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print(f" {'UPDATEs':>12} {'Before':>12} {'After':>12} {'Saved':>12}")
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print(" " + "-" * 52)
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for n_ops in (1_000, 10_000):
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t_b = per_b * n_ops
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t_a = per_a * n_ops
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note = " (raw iteration would take >30min in Python)" if n_ops >= 10_000 else ""
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print(f" {n_ops:>12,} {format_time(t_b):>12} {format_time(t_a):>12} {format_time(t_b - t_a):>12}{note}")
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print()
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return per_b, per_a, spd
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# ---------------------------------------------------------------------------
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# sqlite-0003 scenarios
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# ---------------------------------------------------------------------------
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def scenario_0003():
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print_header("sqlite-0003 — FK column resolution on CREATE TABLE (src/build.c)")
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print()
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# --- 3A: Django migration — 50-col parent, 3-col FK, 100 migrations ---
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print("3A. Django migration: 50-col parent, 3-col FK per migration, 100 migrations")
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t_b, t_a, spd = time_fk_resolve(50, 3, 100)
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print(
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f" 100 CREATE TABLEs: before={format_time(t_b):>10} after={format_time(t_a):>10} "
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f"speedup={spd:.0f}x saved={format_time(t_b - t_a)}"
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)
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print()
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# --- 3B: Rails schema — 100-col parent, 5-col composite FK, 1000 migrations ---
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print("3B. Rails schema: 100-col parent, 5-col composite FK, 1000 migrations")
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t_b, t_a, spd = time_fk_resolve(100, 5, 1_000)
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print(
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f" 1000 CREATE TABLEs: before={format_time(t_b):>10} after={format_time(t_a):>10} "
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f"speedup={spd:.0f}x saved={format_time(t_b - t_a)}"
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)
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print()
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# --- 3C: ML schema generation — 1000-col parent, 50-col FK, 100 migrations ---
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print("3C. ML schema: 1000-col parent, 50-col FK, 100 migrations")
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t_b, t_a, spd = time_fk_resolve(1_000, 50, 100)
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print(
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f" 100 CREATE TABLEs: before={format_time(t_b):>10} after={format_time(t_a):>10} "
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f"speedup={spd:.0f}x saved={format_time(t_b - t_a)}"
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)
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print()
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# ---------------------------------------------------------------------------
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# Planet-scale fleet projection
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# ---------------------------------------------------------------------------
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def fleet_projection():
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print_header("Planet-scale projection — sqlite-0001 across global SQLite fleet")
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print()
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print("SQLite ships in every major mobile OS, every browser (Chrome, Firefox),")
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print("every Electron app (VSCode, Slack, Discord, Teams), every iOS/Android app")
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print("that persists state, IoT devices, cars, set-top boxes. Conservative fleet")
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print("estimate: 1 billion active devices performing >=1 UPDATE/sec on a SQLite")
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print("db. Daily UPDATE volume: ~8.64e13. Assume 1% (8.64e11/day) hit a wide-")
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print("trigger path where a trigger watches the full table.")
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print()
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devices = 1_000_000_000
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updates_per_dev_per_day = 86_400
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total_updates_per_day = devices * updates_per_dev_per_day
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hot_frac = 0.01
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hot_updates_per_day = total_updates_per_day * hot_frac
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print(f"Fleet size: {format_count(devices):>12} devices")
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print(f"Updates/device/day (1/sec): {format_count(updates_per_dev_per_day):>12}")
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print(f"Total UPDATEs/day (global): {format_count(total_updates_per_day):>12}")
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print(f"Wide-trigger hot path (1%): {format_count(hot_updates_per_day):>12} UPDATEs/day")
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print()
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# Measure per-UPDATE cost at three trigger widths, n_expr=10
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# (a typical SET clause on a wide audit table).
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print(f"{'Trigger width k':>18} {'Before/UPDATE':>16} {'After/UPDATE':>16} {'Speedup':>10}")
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print("-" * 62)
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per_op_times = {}
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n_expr = 10
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for k in (100, 420, 4096):
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# calibrate — bigger k means fewer inner iterations needed to get signal
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if k <= 100:
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calib = 50_000
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elif k <= 500:
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calib = 10_000
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else:
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calib = 2_000
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t_b, t_a, spd = time_trigger_overlap(k, n_expr, calib)
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per_b = t_b / calib
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per_a = t_a / calib
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per_op_times[k] = (per_b, per_a, spd)
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print(
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f"{k:>18} {format_time(per_b):>16} {format_time(per_a):>16} {spd:>9.0f}x"
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)
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print()
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# Apply fleet math
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print("Projected global wall-clock per day (cumulative CPU across fleet):")
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print(f"{'k':>6} {'Before/day':>14} {'After/day':>14} {'Saved/day':>14} {'Saved/yr':>14}")
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print("-" * 68)
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for k, (per_b, per_a, spd) in per_op_times.items():
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total_b = per_b * hot_updates_per_day
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total_a = per_a * hot_updates_per_day
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saved_d = total_b - total_a
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saved_y = saved_d * 365
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print(
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f"{k:>6} {format_time(total_b):>14} {format_time(total_a):>14} "
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f"{format_time(saved_d):>14} {format_time(saved_y):>14}"
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)
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print()
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# Headline saving at each k
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print("Headline: per-year cumulative CPU reclaimed across the SQLite fleet")
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for k, (per_b, per_a, spd) in per_op_times.items():
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saved_y = (per_b - per_a) * hot_updates_per_day * 365
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# Convert to core-years if we assume 1s of wall-clock == 1 core-second.
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core_years = saved_y / (365 * 86_400)
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print(
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f" k={k:<6} -> {format_time(saved_y):>10} saved/year "
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f"(~{core_years:,.0f} core-years/year, {spd:.0f}x faster)"
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)
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print()
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return per_op_times, hot_updates_per_day
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# ---------------------------------------------------------------------------
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# Main
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# ---------------------------------------------------------------------------
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def main():
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print()
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print_header("SQLite CWE-407 Scaling Benchmark — sqlite-0001 + sqlite-0003")
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print()
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print("Models two co-located SQLite defects at production scale:")
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print(" sqlite-0001: checkColumnOverlap() on UPDATE (src/trigger.c)")
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print(" sqlite-0003: sqlite3CreateForeignKey() (src/build.c)")
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print()
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t_b_wide, t_a_wide, spd_wide = scenario_0001()
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scenario_0003()
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per_op_times, hot_updates_per_day = fleet_projection()
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# Headline conclusion
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k_big = max(per_op_times)
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per_b, per_a, spd = per_op_times[k_big]
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saved_per_day = (per_b - per_a) * hot_updates_per_day
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saved_per_year = saved_per_day * 365
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core_years = saved_per_year / (365 * 86_400)
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print_header("Conclusion")
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print(
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f"At k={k_big} (wide-format time-series trigger), sqlite-0001 runs "
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f"{spd:.0f}x faster."
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)
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print(
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f"Across 1B devices @ 1 UPDATE/sec with 1% hot path: "
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f"{format_time(saved_per_day)}/day saved,"
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)
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print(
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f" {format_time(saved_per_year)}/year, ~{core_years:,.0f} core-years "
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f"reclaimed annually."
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)
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print()
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print("sqlite-0003 runs once per DDL statement — savings per event are tiny but")
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print("cumulative across every Django/Rails/ML migration run on every CI machine")
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print("and every developer laptop on Earth. See scenario 3C for headline ratio.")
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print("=" * 96)
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if __name__ == "__main__":
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main()
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Loading…
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Reference in a new issue