From fecb50f22101f3bb12826d238f8f11ac424a6474 Mon Sep 17 00:00:00 2001
From: Vittoria <lanzo.vittoria@gmail.com>
Date: Sun, 3 May 2026 14:18:28 +0200
Subject: [PATCH] add benchmark script for jsonable_encoder lazy allocation fix

---
 scripts/bench_jsonable_encoder.py | 69 +++++++++++++++++++++++++++++++
 1 file changed, 69 insertions(+)
 create mode 100644 scripts/bench_jsonable_encoder.py

diff --git a/scripts/bench_jsonable_encoder.py b/scripts/bench_jsonable_encoder.py
new file mode 100644
index 0000000000..f4eadc931d
--- /dev/null
+++ b/scripts/bench_jsonable_encoder.py
@@ -0,0 +1,69 @@
+"""
+Benchmark: lazy allowed_keys allocation in jsonable_encoder.
+
+Usage:
+    uv run python scripts/bench_jsonable_encoder.py
+
+Run against both branches to compare:
+    git stash      # unpatched
+    uv run python scripts/bench_jsonable_encoder.py
+    git stash pop  # patched
+    uv run python scripts/bench_jsonable_encoder.py
+
+Reference results (20 rounds x 300 iters, mean, Python 3.12, FastAPI 0.136.1):
+
+    Payload                               mean µs/call    stdev
+    ------------------------------------------------------------
+    small dict (3 keys)          before:      5.37µs      0.95
+                                  after:      4.93µs      0.65   -8.2%
+    large dict (300 items, nested)
+                                 before:  12,158.80µs    557.40
+                                  after:  11,431.07µs    506.29  -6.0%
+"""
+
+import statistics
+import timeit
+from typing import Any
+
+from fastapi.encoders import jsonable_encoder
+
+LARGE_ITEMS: list[dict[str, Any]] = [
+    {
+        "id": i,
+        "name": f"item-{i}",
+        "values": list(range(25)),
+        "meta": {"active": True, "group": i % 10, "tag": f"t{i % 5}"},
+    }
+    for i in range(300)
+]
+LARGE_METADATA: dict[str, Any] = {
+    "source": "benchmark",
+    "version": 1,
+    "flags": {"a": True, "b": False, "c": True},
+    "notes": ["x" * 50, "y" * 50, "z" * 50],
+}
+LARGE_PAYLOAD: dict[str, Any] = {"items": LARGE_ITEMS, "metadata": LARGE_METADATA}
+SMALL_PAYLOAD: dict[str, Any] = {"name": "foo", "value": 123}
+
+ROUNDS = 20
+ITERS = 300
+
+
+def bench(payload: dict[str, Any]) -> tuple[float, float]:
+    times = []
+    for _ in range(ROUNDS):
+        t = timeit.timeit(lambda: jsonable_encoder(payload), number=ITERS)
+        times.append(t / ITERS * 1e6)
+    return statistics.mean(times), statistics.stdev(times)
+
+
+if __name__ == "__main__":
+    print(f"{'Payload':<35} {'mean µs/call':>14} {'stdev':>8}")
+    print("-" * 60)
+    for label, payload in [
+        ("small dict (3 keys)", SMALL_PAYLOAD),
+        ("large dict (300 items, nested)", LARGE_PAYLOAD),
+    ]:
+        mean, sd = bench(payload)
+        print(f"{label:<35} {mean:>12.2f}µs {sd:>6.2f}")
+    print(f"\n({ROUNDS} rounds x {ITERS} iters each)")