Benchmarks¶

All benchmarks run on Apple M-series, PostgreSQL 18, Python 3.14t (free-threaded), Zig ReleaseFast. Methodology: wrk -t4 -c20 -d8s, 3 runs, median selected, jitter reported.

REST API Throughput (Bookstore API)¶

Endpoint	rps	p50	p99	Jitter
GET /health (no DB)	19,890	0.98ms	2.79ms	1.1%
GET /api/v1/books/stats (aggregate)	11,420	1.73ms	12.21ms	7.6%
GET /api/v1/reviews/ (cursor pagination)	7,500	2.51ms	6.58ms	0.7%
GET /api/v1/books/1 (detail + select_related)	6,349	2.85ms	14.19ms	1.1%
GET /api/v1/books/ (list + serializer)	5,337	3.37ms	20.92ms	7.7%
GET /api/v1/books/?search=python (FTS)	4,340	4.14ms	15.99ms	0.4%

Template Rendering (HyperNews)¶

Endpoint	rps	p50	p99
GET /login (template-only)	40,459	0.38ms	2.38ms
GET /forums (directory)	40,328	0.35ms	2.67ms
GET / (cached homepage)	40,000+	0.36ms	3.35ms
GET /user/alice (multi-query)	40,608	0.35ms	5.38ms

Database (pg.zig vs psycopg3)¶

Operation	pg.zig	psycopg3	Speedup
SELECT by PK	21K ops/s	10K ops/s	2.06x
SELECT range	—	—	4.18x
UPDATE	—	—	1.52x
COPY bulk import	536K rows/s	12K rows/s	42.8x

Guard System Overhead¶

Guard Type	Overhead
Single guard (Require.role)	0.21 us
3-guard chain	0.40 us
GuardSpec creation	0.85 us

JSON (SIMD Zig vs Python stdlib)¶

Operation	Native	stdlib	Speedup
json_loads (tiny object)	94ns	576ns	6.1x
json_loads (integer)	48ns	467ns	9.8x
json_loads (float)	80ns	518ns	6.5x
json_loads (boolean)	49ns	441ns	9.0x
json_dumps (dict)	196ns	—	—

String Operations (SIMD Zig vs Python stdlib)¶

Operation	Native	stdlib	Speedup
html_escape (with chars)	111ns	376ns	3.4x
url_encode (long path)	113ns	1390ns	12.3x
url_decode (percent)	88ns	1505ns	17.1x
parse_query_string (10p)	1574ns	5596ns	3.6x

Validation (Native Zig)¶

Operation	Throughput
Model creation (init_model_full)	1.6M/sec
Per-field validation	6.7M fields/sec
Batch int validation (SIMD)	51.5M ints/sec
Batch model validation	13.1M models/sec
SIMD email validation	63ns/email

Template Compilation¶

Operation	Native	Jinja2	Speedup
Compile	7.1us	1.66ms	234x
Render (cached)	36us	61us	1.7x

WhereNode Compile (Zig vs Python)¶

Scenario	Python	Zig	Speedup
Simple leaf	442ns	169ns	2.6x
3-filter AND	1737ns	464ns	3.7x
Complex nested (4 children)	3364ns	868ns	3.9x

Native Metric Primitives¶

Operation	Latency	Target
counter_inc	78ns	50ns
gauge_set	76ns	50ns
histogram_observe	81ns	100ns
counter_vec_inc	132ns	250ns
histogram_vec_observe	124ns	300ns

Reproduce¶

uv run hyper-build --release
uv run python scripts/bench_bookstore_wrk.py
uv run python scripts/bench_hypernews_wrk.py
uv run python scripts/bench_guard_overhead.py

Results saved to logs/bench_*.json and logs/bench_*.txt.