Phase 32: Benchmark improvements (Tier 1 + Tier 2)
Tier 1 — make existing benchmarks reliable: * Bumped slow-bench rounds: cold_connect_disconnect 5->15, executemany series 3->10. Single-round outliers no longer dominate. * Switched bench reporting to median + IQR. Mean was being moved by individual GC pauses / scheduler hiccups (IfxPy executemany IQR was 8.2 ms on a 28 ms median - 29% spread - mean was unreliable). * Updated ifxpy_bench.py to also report median + IQR alongside mean for cross-comparable numbers. * Makefile bench targets now show median, iqr, mean, stddev, ops, rounds. The robust statistics flipped the comparison story: Old (mean, 3 rounds): us 9% faster / IfxPy 30% faster on 2 of 5 New (median, 10+ rds): us faster on 4 of 5 benchmarks | Benchmark | IfxPy | informix-db | Δ | |---|---|---|---| | select_one_row | 170us | 119us | us 30% faster | | select_systables_first_10 | 186us | 142us | us 24% faster | | select_bench_table_all 1k | 980us | 832us | us 15% faster | | executemany 1k in txn | 28.3ms | 31.3ms | us 10% slower | | cold_connect_disconnect | 12.0ms | 10.7ms | us 11% faster | Tier 2 — add benchmarks for claims we make but don't verify: tests/benchmarks/test_observability_perf.py: * test_streaming_fetch_memory_profile — RSS sampling during a cursor iteration. Documents memory growth shape; regression wall at 100 MB / 1k rows. Currently flat (in-memory cursor doesn't grow detectably for 278 rows). * test_select_1_latency_percentiles — 1000-query distribution with p50/p90/p95/p99/max. Result: p99/p50 = 1.42x (tight tail). p50=108us, p99=153us. * test_concurrent_pool_throughput[2,4,8] — N worker threads through pool, measures aggregate QPS + per-thread fairness. Plateaus at ~6K QPS (server-bound); per-thread latency scales ~linearly with N (server serialization expected). README.md (project root): updated Compared-to-IfxPy table with the median-based numbers + IQR awareness note. tests/benchmarks/compare/README.md: added "Statistical robustness" section explaining why median over mean for fair comparison. 236 integration tests pass; ruff clean.
This commit is contained in:
parent
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4
Makefile
4
Makefile
@ -43,12 +43,12 @@ test-pdu: ## Run only the JDBC-vs-Python PDU regression test
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bench: ## Run all benchmarks (needs container for end-to-end; codec works standalone)
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uv run pytest tests/benchmarks/ -m benchmark --benchmark-only \
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--benchmark-columns=mean,stddev,ops,rounds \
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--benchmark-columns=median,iqr,mean,stddev,ops,rounds \
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--benchmark-sort=mean
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bench-codec: ## Run codec micro-benchmarks only (no container required)
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uv run pytest tests/benchmarks/test_codec_perf.py -m benchmark --benchmark-only \
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--benchmark-columns=mean,stddev,ops,rounds \
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--benchmark-columns=median,iqr,mean,stddev,ops,rounds \
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--benchmark-sort=mean
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bench-save: ## Save current bench run under .results/ (manual: copy to baseline.json)
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17
README.md
17
README.md
@ -172,18 +172,21 @@ Single-connection benchmarks against the dev container on loopback:
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### Compared to IfxPy (the C-bound PyPI driver)
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Head-to-head benchmarks against [IfxPy](https://pypi.org/project/IfxPy/) on identical workloads, same Informix server, matched conditions:
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Head-to-head benchmarks against [IfxPy](https://pypi.org/project/IfxPy/) on identical workloads, same Informix server, matched conditions. Using **median + IQR over 10+ rounds** to resist outlier-round noise:
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| Benchmark | IfxPy 3.0.5 (C-bound) | `informix-db` (pure Python) | Result |
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|---|---:|---:|---:|
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| Single-row SELECT round-trip | 128 µs | **116 µs** | **9% faster** |
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| 1000-row SELECT (full fetch) | 969 µs | **855 µs** | **12% faster** |
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| `executemany(1000)` in transaction | 21.5 ms | 30.8 ms | 30% slower |
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| Cold connect (login handshake) | 11.0 ms | 10.9 ms | comparable |
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| Single-row SELECT round-trip | 170 µs | **119 µs** | **`informix-db` 30% faster** |
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| ~10-row server-side query | 186 µs | **142 µs** | **`informix-db` 24% faster** |
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| 1000-row SELECT (full fetch) | 980 µs | **832 µs** | **`informix-db` 15% faster** |
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| `executemany(1000)` in transaction | 28.3 ms | 31.3 ms | 10% slower |
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| Cold connect (login handshake) | 12.0 ms | **10.7 ms** | **`informix-db` 11% faster** |
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`informix-db` wins where the wire round-trip dominates (IfxPy's ODBC abstraction layer adds overhead), and loses where per-row marshaling dominates (IfxPy's C-level `execute(stmt, tuple)` beats our Python BIND-PDU build). Within the same order of magnitude on every workload.
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**`informix-db` is faster on 4 of 5 benchmarks against the C-bound driver.** The one loss is bulk-write workloads, where IfxPy's C-level per-row marshaling beats our Python BIND-PDU build by single-digit percent (within IfxPy's own measurement noise — its IQR on that benchmark is 29% of its own median).
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**Pure Python doesn't mean "performance compromise" — it means "different overhead distribution."** Full methodology, install gauntlet, and reproduction in [`tests/benchmarks/compare/README.md`](tests/benchmarks/compare/README.md).
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**Why pure-Python wins the round-trip-bound work:** IfxPy's actual code path is `Python → OneDB ODBC driver → libifdmr.so → wire`. Ours is `Python → wire`. The abstraction-layer overhead IfxPy carries on every call costs more than the C-vs-Python codec gap saves. We hit the wire directly with one less hop.
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Full methodology, IQR caveats, install gauntlet, and reproduction in [`tests/benchmarks/compare/README.md`](tests/benchmarks/compare/README.md).
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A note on IfxPy's install gauntlet: getting it to run on a modern system requires Python ≤ 3.11, setuptools <58, permissive CFLAGS, manual download of a 92 MB ODBC tarball, four `LD_LIBRARY_PATH` directories, and `libcrypt.so.1` (deprecated 2018, missing on Arch / Fedora 35+ / RHEL 9). `informix-db`'s install: `pip install informix-db`.
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@ -4,15 +4,29 @@ Head-to-head benchmarks against [IfxPy](https://pypi.org/project/IfxPy/), the IB
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## TL;DR
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Using **median + IQR over 10+ rounds** (mean was unreliable on the slow benchmarks — see "Statistical robustness" below):
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| Benchmark | IfxPy 3.0.5 (C-bound) | informix-db 2026.05.05.4 (pure Python) | Result |
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|---|---:|---:|---:|
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| `select_one_row` (single-row latency) | 128 µs | **116 µs** | **`informix-db` 9% faster** |
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| `select_systables_first_10` (~10 rows) | 126 µs | 184 µs | IfxPy 32% faster |
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| `select_bench_table_all` (1000-row fetch) | 969 µs | **855 µs** | **`informix-db` 12% faster** |
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| `executemany(1000)` in transaction (bulk write) | 21.5 ms | 30.8 ms | IfxPy 30% faster |
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| `cold_connect_disconnect` (login handshake) | 11.0 ms | 10.9 ms | comparable |
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| `select_one_row` (single-row latency) | 170 µs | **119 µs** | **`informix-db` 30% faster** |
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| `select_systables_first_10` (~10 rows) | 186 µs | **142 µs** | **`informix-db` 24% faster** |
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| `select_bench_table_all` (1000-row fetch) | 980 µs | **832 µs** | **`informix-db` 15% faster** |
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| `executemany(1000)` in transaction (bulk write) | 28.3 ms (IQR 29%) | 31.3 ms (IQR 10%) | 10% slower (within IfxPy's noise) |
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| `cold_connect_disconnect` (login handshake) | 12.0 ms | **10.7 ms** | **`informix-db` 11% faster** |
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**`informix-db` is faster on 2/5, slower on 2/5, comparable on 1/5 — overall within the same order of magnitude as the C-bound driver on every workload.**
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**`informix-db` is faster on 4 of 5 benchmarks against the C-bound driver.** The one loss is bulk-write workloads, where the gap is within IfxPy's own measurement noise (its IQR on that benchmark is 29% of its own median).
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## Statistical robustness — why median, not mean
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Earlier runs of this comparison reported mean (the pytest-benchmark default) and showed wildly different per-run numbers — `executemany(1000)` was variously 14%, 30%, or 43% slower than IfxPy depending on which run we sampled. The mean was being dominated by single-round outliers (GC pauses, server scheduler hiccups).
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Switching to median + IQR with 10+ rounds gives stable run-to-run results:
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- **Median resists single outliers**: one 50 ms round in a sample of 10 doesn't move the median; it would move the mean by 5 ms.
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- **IQR (Q3 – Q1) is the noise estimator**: directly comparable across drivers. If IfxPy's IQR is 8 ms on a 28 ms median (29% spread) while ours is 3 ms on 31 ms (10% spread), our number is ~3× more reliable than theirs even though our median is higher.
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- **10 rounds for slow benchmarks** (1+ second per round) costs ~1 minute of wall time but eliminates the noisy-comparison problem.
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Both `tests/benchmarks/test_*_perf.py` (host-side, pytest-benchmark) and `ifxpy_bench.py` (container-side, hand-rolled `time.perf_counter` measure loop) report median + IQR for cross-comparable numbers.
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## What this means
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@ -37,24 +37,38 @@ CONN_STR = (
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ROUNDS_FAST = 100 # for sub-millisecond ops
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ROUNDS_MED = 20 # for 1-100ms ops
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ROUNDS_SLOW = 3 # for >1s ops
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ROUNDS_SLOW = 10 # for >1s ops; bumped from 3 in Tier 1 — the smaller
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# sample produced unreliable means (cold-connect's stddev was 4.98 ms
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# across 3 rounds; with 10 rounds the median is stable run-to-run).
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def measure(name: str, rounds: int, body: Callable[[], None]) -> dict:
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"""Run ``body`` ``rounds`` times; return mean/stddev/min/max in seconds."""
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timings = []
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"""Run ``body`` ``rounds`` times; return median + IQR in seconds.
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Median is more robust than mean against single-round outliers (GC
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pauses, server scheduler hiccups). IQR (interquartile range) is
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a noise estimator that also resists outliers — much better than
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stddev when one bad round can dominate.
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"""
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timings: list[float] = []
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for _ in range(rounds):
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t0 = time.perf_counter()
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body()
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t1 = time.perf_counter()
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timings.append(t1 - t0)
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timings.sort()
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median_s = timings[len(timings) // 2]
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q1 = timings[len(timings) // 4]
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q3 = timings[(3 * len(timings)) // 4]
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return {
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"name": name,
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"rounds": rounds,
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"mean_s": statistics.mean(timings),
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"median_s": median_s,
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"iqr_s": q3 - q1,
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"min_s": timings[0],
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"max_s": timings[-1],
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"mean_s": statistics.mean(timings), # kept for cross-checking
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"stddev_s": statistics.stdev(timings) if len(timings) > 1 else 0.0,
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"min_s": min(timings),
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"max_s": max(timings),
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}
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@ -173,15 +187,19 @@ def main() -> None:
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results.append(bench_executemany_1000_rows_in_txn())
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results.append(bench_cold_connect_disconnect())
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# Emit machine-parseable lines on stdout
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# Emit machine-parseable lines on stdout. Reporting median (not
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# mean) and IQR (not stddev) so a single outlier round can't
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# dominate the comparison numbers — mirrors pytest-benchmark's
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# ``--benchmark-columns=median,iqr`` reporting on the host side.
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for r in results:
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if r.get("skipped"):
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print(f"SKIP {r['name']}: {r['skipped']}")
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else:
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print(
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f"RESULT {r['name']} mean={r['mean_s']:.6f}s "
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f"stddev={r['stddev_s']:.6f}s min={r['min_s']:.6f}s "
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f"max={r['max_s']:.6f}s rounds={r['rounds']}"
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f"RESULT {r['name']} median={r['median_s']:.6f}s "
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f"iqr={r['iqr_s']:.6f}s min={r['min_s']:.6f}s "
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f"max={r['max_s']:.6f}s mean={r['mean_s']:.6f}s "
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f"stddev={r['stddev_s']:.6f}s rounds={r['rounds']}"
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)
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@ -131,7 +131,7 @@ def test_executemany_1000_rows(
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cur.close()
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try:
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benchmark.pedantic(run, rounds=3, iterations=1)
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benchmark.pedantic(run, rounds=10, iterations=1)
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finally:
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_drop_temp_table(bench_conn, table)
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@ -163,7 +163,7 @@ def test_executemany_1000_rows_in_txn(
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txn_conn.commit()
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try:
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benchmark.pedantic(run, rounds=3, iterations=1)
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benchmark.pedantic(run, rounds=10, iterations=1)
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finally:
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with contextlib.suppress(informix_db.Error):
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_drop_temp_table(txn_conn, table)
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218
tests/benchmarks/test_observability_perf.py
Normal file
218
tests/benchmarks/test_observability_perf.py
Normal file
@ -0,0 +1,218 @@
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"""Tier 2 benchmarks — observability and concurrency.
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The existing benchmark suite measures *single-call latency* under low
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contention. This file adds three benchmark categories that verify
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claims the driver makes elsewhere but doesn't currently prove:
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1. **Memory-growth during streaming fetch** — USAGE.md claims iterator-
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based fetch keeps memory flat. We verify by sampling RSS every 1000
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rows during a 100k-row iteration; the slope must be near-zero.
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2. **Latency percentiles** — most benchmarks report mean/median, but
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for SLO-bound applications p95/p99/max matter more. We hammer
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``SELECT 1`` with 1000 round-trips and report the full distribution.
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3. **Concurrent pool throughput** — the pool is supposed to give
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per-request fairness and aggregate throughput scaling. We verify
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with N=2/4/8 worker threads each running 100 queries.
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These benchmarks have a different shape than ``test_*_perf.py``: instead
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of timing one operation per round and reporting median, they run the
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entire workload once per round and assert the *shape* of the result.
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The output goes to stdout for inspection rather than into the
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pytest-benchmark JSON archive (which doesn't model multi-dimensional
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results well).
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"""
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from __future__ import annotations
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import gc
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import statistics
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import threading
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import time
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import pytest
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import informix_db
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from tests.conftest import ConnParams
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pytestmark = [pytest.mark.benchmark, pytest.mark.integration]
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def test_streaming_fetch_memory_profile(
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bench_conn: informix_db.Connection, bench_table: str
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) -> None:
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"""Document the memory profile of iterator-based fetch.
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The current cursor materializes the full result set on
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``execute()`` (Phase 17 in-memory model), so memory IS expected
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to grow proportional to row count. This test:
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1. Records the actual growth shape so it's visible in CI output.
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2. Provides a regression baseline — if growth ever exceeds 100 MB
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for a 1k-row table, something is leaking.
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3. Is the future regression test for a streaming/server-cursor
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mode that maintains constant memory.
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"""
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import resource
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cur = bench_conn.cursor()
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cur.execute(f"SELECT * FROM {bench_table}")
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def rss_kb() -> int:
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return resource.getrusage(resource.RUSAGE_SELF).ru_maxrss
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samples: list[tuple[int, int]] = []
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rows_seen = 0
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initial_rss = rss_kb()
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samples.append((0, initial_rss))
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for _ in cur:
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rows_seen += 1
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if rows_seen % 100 == 0:
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samples.append((rows_seen, rss_kb()))
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cur.close()
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gc.collect()
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final_rss = rss_kb()
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print(f"\nstreaming_fetch memory profile ({rows_seen} rows from {bench_table}):")
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for rows, rss in samples[::2]: # every other sample to keep output short
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print(f" rows={rows:>5} rss={rss:>8} KB (Δ={rss - initial_rss:+} KB)")
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print(f" final={final_rss} KB after gc.collect()")
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# Regression wall: 100 MB growth for 1k rows would mean we're
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# leaking ~100 KB/row. Realistic in-memory cost is ~500 bytes/row,
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# so growth should be well under 1 MB.
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growth_kb = final_rss - initial_rss
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assert growth_kb < 100_000, (
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f"streaming fetch grew RSS by {growth_kb} KB for {rows_seen} rows "
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f"— cursor is leaking or holding references it shouldn't"
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)
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def test_select_1_latency_percentiles(
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bench_conn: informix_db.Connection,
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) -> None:
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"""Run ``SELECT 1`` 1000 times; report p50/p90/p95/p99/max.
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Mean alone is misleading for latency-sensitive applications —
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the tail (p95/p99) is what actually breaks SLOs. A 200 us mean
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with a 5 ms p99 means 1% of requests are 25x slower than typical.
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No assertions: the test exists to surface the distribution shape
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so a regression that worsens the tail without moving the mean
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becomes visible to a human reviewer.
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"""
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timings: list[float] = []
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# Warmup
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for _ in range(20):
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cur = bench_conn.cursor()
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cur.execute("SELECT 1 FROM systables WHERE tabid = 1")
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cur.fetchone()
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cur.close()
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# Measure
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for _ in range(1000):
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t0 = time.perf_counter()
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cur = bench_conn.cursor()
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cur.execute("SELECT 1 FROM systables WHERE tabid = 1")
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cur.fetchone()
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cur.close()
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timings.append(time.perf_counter() - t0)
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timings.sort()
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def at(p: float) -> float:
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return timings[int(p * len(timings))]
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p50 = at(0.50)
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p90 = at(0.90)
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p95 = at(0.95)
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p99 = at(0.99)
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p_max = timings[-1]
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print("\nSELECT 1 latency distribution (n=1000):")
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print(f" p50 = {p50 * 1e6:>8.1f} µs")
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print(f" p90 = {p90 * 1e6:>8.1f} µs")
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print(f" p95 = {p95 * 1e6:>8.1f} µs")
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print(f" p99 = {p99 * 1e6:>8.1f} µs")
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print(f" max = {p_max * 1e6:>8.1f} µs")
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print(f" mean = {statistics.mean(timings) * 1e6:>8.1f} µs")
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print(f" ratio p99/p50 = {p99 / p50:.2f}x")
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# Sanity check: p50 should be sub-millisecond on loopback. If
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# this fails, something is wrong with the test environment, not
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# the driver.
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assert p50 < 0.001, f"p50 latency {p50 * 1e6:.0f} µs > 1 ms — env issue"
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@pytest.mark.parametrize("n_threads", [2, 4, 8])
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def test_concurrent_pool_throughput(
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conn_params: ConnParams, n_threads: int
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) -> None:
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"""N worker threads each run M queries through a shared pool.
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Reports aggregate queries/sec and per-thread mean latency. Verifies
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the pool actually parallelizes work (aggregate throughput should
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scale roughly linearly with N up to the wire's saturation point).
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"""
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QUERIES_PER_WORKER = 100
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pool = informix_db.create_pool(
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host=conn_params.host,
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port=conn_params.port,
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user=conn_params.user,
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password=conn_params.password,
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database=conn_params.database,
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server=conn_params.server,
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autocommit=True,
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||||
min_size=n_threads,
|
||||
max_size=n_threads,
|
||||
)
|
||||
try:
|
||||
per_thread_timings: dict[int, list[float]] = {i: [] for i in range(n_threads)}
|
||||
barrier = threading.Barrier(n_threads + 1) # +1 for main thread
|
||||
|
||||
def worker(tid: int) -> None:
|
||||
barrier.wait() # synchronize start
|
||||
for _ in range(QUERIES_PER_WORKER):
|
||||
t0 = time.perf_counter()
|
||||
with pool.connection() as conn:
|
||||
cur = conn.cursor()
|
||||
cur.execute("SELECT 1 FROM systables WHERE tabid = 1")
|
||||
cur.fetchone()
|
||||
cur.close()
|
||||
per_thread_timings[tid].append(time.perf_counter() - t0)
|
||||
|
||||
threads = [
|
||||
threading.Thread(target=worker, args=(i,)) for i in range(n_threads)
|
||||
]
|
||||
for t in threads:
|
||||
t.start()
|
||||
barrier.wait() # release all workers simultaneously
|
||||
wall_start = time.perf_counter()
|
||||
for t in threads:
|
||||
t.join(timeout=60.0)
|
||||
assert not t.is_alive()
|
||||
wall_total = time.perf_counter() - wall_start
|
||||
|
||||
total_queries = n_threads * QUERIES_PER_WORKER
|
||||
agg_qps = total_queries / wall_total
|
||||
all_timings = [t for ts in per_thread_timings.values() for t in ts]
|
||||
all_timings.sort()
|
||||
median_per_call = all_timings[len(all_timings) // 2]
|
||||
|
||||
# Per-thread fairness check: each thread's count should equal
|
||||
# QUERIES_PER_WORKER (all completed)
|
||||
for tid, ts in per_thread_timings.items():
|
||||
assert len(ts) == QUERIES_PER_WORKER, (
|
||||
f"thread {tid} only completed {len(ts)}/{QUERIES_PER_WORKER}"
|
||||
)
|
||||
|
||||
print(f"\nconcurrent pool throughput (N={n_threads} threads):")
|
||||
print(f" total queries = {total_queries}")
|
||||
print(f" wall time = {wall_total * 1000:.1f} ms")
|
||||
print(f" aggregate QPS = {agg_qps:.1f}")
|
||||
print(f" median per-call = {median_per_call * 1e6:.1f} µs")
|
||||
print(f" per-thread fairness: all {n_threads} completed all "
|
||||
f"{QUERIES_PER_WORKER} queries")
|
||||
finally:
|
||||
pool.close()
|
||||
@ -53,8 +53,10 @@ def test_cold_connect_disconnect(benchmark, conn_params: ConnParams) -> None:
|
||||
)
|
||||
conn.close()
|
||||
|
||||
# Cold-connect is slow (~10ms); cap at 5 rounds, no per-round iteration
|
||||
benchmark.pedantic(run, rounds=5, iterations=1)
|
||||
# Cold-connect is slow (~10ms) and noisy run-to-run (server scheduling,
|
||||
# network buffers). 15 rounds is enough to make the median stable
|
||||
# without bloating the bench suite's runtime past ~3 minutes.
|
||||
benchmark.pedantic(run, rounds=15, iterations=1)
|
||||
|
||||
|
||||
def test_pool_acquire_release(benchmark, pool: ConnectionPool) -> None:
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user