feat(perf): capture run metadata and regression signals

Land performance metadata, confidence, and historical regression reporting from Codex task triage.

Author: wolfiesch

docs/trackers/performance-benchmark-runs.md

@@ -461,3 +461,12 @@ Outputs:
 - Bulk read: `results_dev_perf_dashboard/bulk_read_multi/perf/README.md`
 - Bulk write: `results_dev_perf_dashboard/bulk_write_multi/perf/README.md`
 - Per-cell fast: `results_dev_perf_dashboard/per_cell_fast/perf/README.md`
+
+
+## Canonical run-comparison procedure
+
+1. Run benchmark with stable knobs (`--warmup`, `--iters`, fixed adapter set) and commit code before each run.
+2. Review `perf/results.json` metadata and confirm same profile, CPU, core count, memory, Python, and adapter build info.
+3. Compare `perf/matrix.csv` columns `read_tail_ratio`/`write_tail_ratio` and `regression_status`. Treat `confidence_note=high` as noisy and rerun.
+4. For regression gates, compare against `results/perf/history.jsonl` median of recent samples (last 5, min 3).
+5. Investigate any `regressed:<pct>%` row above the configured threshold, such as `regressed:12.3%`; require two consecutive confirmations before escalating.

src/excelbench/perf/renderer.py

@@ -74,6 +74,13 @@ def render_perf_markdown(results: PerfResults, path: Path) -> None:
     lines.append(f"*Profile: {data['metadata']['profile']}*")
     lines.append(f"*Platform: {data['metadata']['platform']}*")
     lines.append(f"*Python: {data['metadata']['python']}*")
+    run_env = data["metadata"].get("run_environment") or {}
+    if run_env:
+        lines.append(f"*CPU: {run_env.get('cpu_model') or 'unknown'}*")
+        lines.append(
+            f"*Cores: {run_env.get('core_count') or 'unknown'} | "
+            f"Memory MB: {run_env.get('memory_total_mb') or 'unknown'}*"
+        )
     if data["metadata"].get("commit"):
         lines.append(f"*Commit: {data['metadata']['commit']}*")
     cfg = data["metadata"].get("config", {})
@@ -93,6 +100,10 @@ def render_perf_markdown(results: PerfResults, path: Path) -> None:
         "These numbers measure only the library under test. "
         "Write timings do NOT include oracle verification."
     )
+    lines.append(
+        "Confidence note: treat deltas under ~5% as noise unless "
+        "stable across multiple runs."
+    )
     lines.append("")
 
     workload_features = _collect_workload_features(libs, features, lookup)
@@ -107,10 +118,10 @@ def render_perf_markdown(results: PerfResults, path: Path) -> None:
     for lib in libs:
         caps = set(data["libraries"][lib].get("capabilities", []))
         if "read" in caps:
-            header += f" {lib} (R p50 ms) |"
+            header += f" {lib} (R p50/p95 ms) |"
             sep += "--------------|"
         if "write" in caps:
-            header += f" {lib} (W p50 ms) |"
+            header += f" {lib} (W p50/p95 ms) |"
             sep += "--------------|"
 
     tier_map: dict[int, list[str]] = {0: [], 1: [], 2: []}
@@ -133,9 +144,9 @@ def render_perf_markdown(results: PerfResults, path: Path) -> None:
                 entry = lookup.get((feat, lib))
                 perf = entry.get("perf") if entry else None
                 if "read" in caps:
-                    row += f" {_fmt_p50_ms(perf, 'read')} |"
+                    row += f" {_fmt_p50_p95_ms(perf, 'read')} |"
                 if "write" in caps:
-                    row += f" {_fmt_p50_ms(perf, 'write')} |"
+                    row += f" {_fmt_p50_p95_ms(perf, 'write')} |"
             lines.append(row)
         lines.append("")
 
@@ -303,7 +314,7 @@ def _fmt_rate(rate: float) -> str:
     return f"{rate:.2f}"
 
 
-def _fmt_p50_ms(perf: dict[str, Any] | None, op: str) -> str:
+def _fmt_p50_p95_ms(perf: dict[str, Any] | None, op: str) -> str:
     if not perf or not isinstance(perf, dict):
         return "—"
     op_data = perf.get(op)
@@ -316,16 +327,37 @@ def _fmt_p50_ms(perf: dict[str, Any] | None, op: str) -> str:
     if p50 is None:
         return "—"
     try:
-        return f"{float(p50):.2f}"
+        p95 = wall.get("p95")
+        p95_txt = f"/{float(p95):.2f}" if p95 is not None else ""
+        return f"{float(p50):.2f}{p95_txt}"
     except (TypeError, ValueError):
         return "—"
 
 
 def render_perf_csv(results: PerfResults, path: Path) -> None:
     data = perf_results_to_json_dict(results)
+    history_path = path.parent / "history.jsonl"
+    history_entries = _load_matching_history_entries(data, history_path)
+    header_columns = [
+        "library",
+        "feature",
+        "read_p50_wall_ms",
+        "read_p95_wall_ms",
+        "read_op_count",
+        "read_op_unit",
+        "read_p50_units_per_sec",
+        "write_p50_wall_ms",
+        "write_p95_wall_ms",
+        "write_op_count",
+        "write_op_unit",
+        "write_p50_units_per_sec",
+        "read_tail_ratio",
+        "write_tail_ratio",
+        "confidence_note",
+        "regression_status",
+    ]
     lines = [
-        "library,feature,read_p50_wall_ms,read_p95_wall_ms,read_op_count,read_op_unit,read_p50_units_per_sec,"
-        "write_p50_wall_ms,write_p95_wall_ms,write_op_count,write_op_unit,write_p50_units_per_sec",
+        ",".join(header_columns),
     ]
     for r in data["results"]:
         perf = r.get("perf") or {}
@@ -350,6 +382,9 @@ def _rate(count: Any, p50_ms: Any) -> str:
         def _f(v: Any) -> str:
             return "" if v is None else str(v)
 
+        read_tail_ratio = _tail_ratio(read_wall)
+        write_tail_ratio = _tail_ratio(write_wall)
+        reg_status = _regression_status(history_entries, r)
         lines.append(
             ",".join(
                 [
@@ -365,6 +400,17 @@ def _f(v: Any) -> str:
                     _f(write_count),
                     _f(write_unit),
                     _rate(write_count, write_wall.get("p50")),
+                    _f(read_tail_ratio),
+                    _f(write_tail_ratio),
+                    _f(
+                        "high"
+                        if (
+                            (read_tail_ratio or 0) > 0.20
+                            or (write_tail_ratio or 0) > 0.20
+                        )
+                        else "ok"
+                    ),
+                    reg_status,
                 ]
             )
         )
@@ -400,3 +446,73 @@ def append_perf_history(results: PerfResults, history_path: Path) -> None:
 
     with open(history_path, "a") as f:
         f.write(json.dumps(entry) + "\n")
+
+
+def _tail_ratio(wall: dict[str, Any]) -> float | None:
+    p50 = wall.get("p50")
+    p95 = wall.get("p95")
+    try:
+        if p50 is None or p95 is None:
+            return None
+        p50_float = float(p50)
+        if p50_float == 0:
+            return None
+        return round(max(float(p95) - p50_float, 0.0) / p50_float, 4)
+    except (TypeError, ValueError, ZeroDivisionError):
+        return None
+
+
+def _load_matching_history_entries(
+    data: dict[str, Any],
+    history_path: Path,
+) -> list[dict[str, Any]]:
+    if not history_path.exists():
+        return []
+    metadata = data.get("metadata", {})
+    current_profile = metadata.get("profile")
+    current_config = metadata.get("config")
+    entries: list[dict[str, Any]] = []
+    for line in history_path.read_text().splitlines():
+        try:
+            entry = json.loads(line)
+            if entry.get("profile") != current_profile or entry.get("config") != current_config:
+                continue
+            entries.append(entry)
+        except json.JSONDecodeError:
+            continue
+    return entries
+
+
+def _regression_status(
+    history_entries: list[dict[str, Any]],
+    row: dict[str, Any],
+    *,
+    threshold_pct: float = 10.0,
+) -> str:
+    if not history_entries:
+        return "no_history"
+    vals: list[float] = []
+    for entry in history_entries:
+        try:
+            sample = entry.get("p50_wall_ms", {}).get(row["library"], {}).get(row["feature"], {})
+            rv = sample.get("read_p50")
+            if rv is not None:
+                vals.append(float(rv))
+        except (TypeError, ValueError, KeyError):
+            continue
+    if len(vals) < 3:
+        return "insufficient_history"
+    baseline = sorted(vals[-5:])[len(vals[-5:]) // 2]
+    cur = ((row.get("perf") or {}).get("read") or {}).get("wall_ms", {}).get("p50")
+    try:
+        curf = float(cur)
+    except (TypeError, ValueError):
+        return "n/a"
+    if baseline <= 0:
+        return "n/a"
+    delta = ((curf - baseline) / baseline) * 100.0
+    if delta > threshold_pct:
+        return f"regressed:{delta:.1f}%"
+    if delta < -threshold_pct:
+        return f"improved:{delta:.1f}%"
+    return f"stable:{delta:.1f}%"

src/excelbench/perf/runner.py

@@ -8,6 +8,8 @@
 
 from __future__ import annotations
 
+import os
+import platform
 from collections.abc import Callable
 from dataclasses import asdict, dataclass
 from datetime import UTC, date, datetime, timedelta
@@ -67,6 +69,13 @@ class PerfFeatureResult:
     notes: str | None = None
 
 
+@dataclass(frozen=True)
+class PerfRunEnvironment:
+    cpu_model: str | None
+    core_count: int | None
+    memory_total_mb: float | None
+
+
 @dataclass(frozen=True)
 class PerfMetadata:
     benchmark_version: str
@@ -77,6 +86,7 @@ class PerfMetadata:
     python: str
     commit: str | None
     config: PerfConfig
+    run_environment: PerfRunEnvironment
 
 
 @dataclass(frozen=True)
@@ -101,7 +111,6 @@ def run_perf(
     breakdown: bool = False,
     memory_mode: MemoryMode = "getrusage",
 ) -> PerfResults:
-    import platform as _platform
 
     from excelbench.generator.generate import load_manifest
     from excelbench.harness.adapters import get_all_adapters
@@ -137,16 +146,17 @@ def run_perf(
         benchmark_version=BENCHMARK_VERSION,
         run_date=datetime.now(UTC),
         excel_version=manifest.excel_version,
-        platform=f"{_platform.system()}-{_platform.machine()}",
+        platform=f"{platform.system()}-{platform.machine()}",
         profile=profile,
-        python=_platform.python_version(),
+        python=platform.python_version(),
         commit=_get_git_commit(),
         config=PerfConfig(
             warmup=warmup,
             iters=iters,
             iteration_policy=iteration_policy_normalized,
             breakdown=breakdown,
         ),
+        run_environment=_collect_run_environment(),
     )
 
     libraries = {a.name: _library_info_dict(a.info) for a in adapters}
@@ -238,6 +248,7 @@ def perf_results_to_json_dict(results: PerfResults) -> dict[str, Any]:
             "python": results.metadata.python,
             "commit": results.metadata.commit,
             "config": asdict(results.metadata.config),
+            "run_environment": asdict(results.metadata.run_environment),
         },
         "libraries": results.libraries,
         "results": [_feature_result_to_dict(r) for r in results.results],
@@ -1704,3 +1715,22 @@ def run_one_iteration(
             )
 
     raise ValueError(f"kind must be 'read' or 'write'; got {kind!r}")
+
+
+def _collect_run_environment() -> PerfRunEnvironment:
+    cpu_model = platform.processor() or None
+    if not cpu_model:
+        cpu_model = os.environ.get("PROCESSOR_IDENTIFIER")
+    core_count = os.cpu_count()
+    mem_mb = None
+    try:
+        pages = os.sysconf("SC_PHYS_PAGES")
+        page_size = os.sysconf("SC_PAGE_SIZE")
+        mem_mb = float(pages * page_size) / (1024.0 * 1024.0)
+    except (AttributeError, OSError, ValueError):
+        mem_mb = None
+    return PerfRunEnvironment(
+        cpu_model=cpu_model,
+        core_count=core_count,
+        memory_total_mb=round(mem_mb, 2) if mem_mb is not None else None,
+    )

tests/test_perf_cli.py

@@ -16,6 +16,7 @@
     PerfMetadata,
     PerfOpResult,
     PerfResults,
+    PerfRunEnvironment,
     PerfStats,
 )
 
@@ -91,6 +92,8 @@ def test_perf_command_writes_outputs(tmp_path: Path) -> None:
     assert data["metadata"]["config"]["iters"] == 1
     assert data["metadata"]["config"]["iteration_policy"] == "fixed"
     assert "openpyxl" in data["libraries"]
+    assert "run_environment" in data["metadata"]
+    assert "cpu_model" in data["metadata"]["run_environment"]
 
 
 def test_perf_markdown_header_matches_all_rendered_cells(tmp_path: Path) -> None:
@@ -110,6 +113,11 @@ def test_perf_markdown_header_matches_all_rendered_cells(tmp_path: Path) -> None
                 iteration_policy="fixed",
                 breakdown=False,
             ),
+            run_environment=PerfRunEnvironment(
+                cpu_model=None,
+                core_count=None,
+                memory_total_mb=None,
+            ),
         ),
         libraries={
             "openpyxl": {
@@ -158,8 +166,10 @@ def test_perf_markdown_header_matches_all_rendered_cells(tmp_path: Path) -> None
 
     markdown = readme.read_text()
     assert (
-        "| Feature | openpyxl (R p50 ms) | openpyxl (W p50 ms) | "
-        "python-calamine (R p50 ms) |"
+        "| Feature | openpyxl (R p50/p95 ms) | openpyxl (W p50/p95 ms) | "
+        "python-calamine (R p50/p95 ms) |"
     ) in markdown
-    assert "| cell_values | 1.00 | 2.00 | 0.50 |" in markdown
+    assert "| cell_values | 1.00/1.00 | 2.00/2.00 | 0.50/0.50 |" in markdown
     assert markdown.count("**Tier 0") == 1
+    assert "Confidence note:" in markdown
+    assert "p50/p95" in markdown

tests/test_perf_data_shape.py

@@ -622,3 +622,45 @@ def test_shape_fixtures_stale_corrupt_manifest_with_needs_1m(tmp_path: Path) ->
 
     # Corrupt manifest with needs_1m=True triggers regeneration.
     assert _shape_fixtures_stale(manifest, generator, needs_1m=True) is True
+
+
+def test_perf_csv_includes_regression_status(tmp_path: Path) -> None:
+    from excelbench.perf.renderer import render_perf_csv
+    from excelbench.perf.runner import (
+        PerfConfig,
+        PerfFeatureResult,
+        PerfMetadata,
+        PerfOpResult,
+        PerfResults,
+        PerfRunEnvironment,
+        PerfStats,
+    )
+
+    stats = PerfStats(min=1, p50=1, p95=2)
+    res = PerfResults(
+        metadata=PerfMetadata(
+            benchmark_version="x",
+            run_date=datetime.now(UTC),
+            excel_version="x",
+            platform="x",
+            profile="xlsx",
+            python="3",
+            commit=None,
+            config=PerfConfig(warmup=0, iters=1, iteration_policy="fixed", breakdown=False),
+            run_environment=PerfRunEnvironment(cpu_model=None, core_count=1, memory_total_mb=None),
+        ),
+        libraries={"openpyxl": {"capabilities": ["read"]}},
+        results=[
+            PerfFeatureResult(
+                feature="f",
+                library="openpyxl",
+                workload_size="tiny",
+                perf={"read": PerfOpResult(wall_ms=stats, cpu_ms=stats), "write": None},
+            )
+        ],
+    )
+    out = tmp_path / "m.csv"
+    render_perf_csv(res, out)
+    txt = out.read_text()
+    assert "regression_status" in txt
+    assert "confidence_note" in txt