8c3ac22d8d
- Extract bunnative→nativets tag logic into ScriptLang::as_worker_tag() - Add benchmark results for batch pull vs direct SQL (1W and 3W) - Add throughput model script comparing batch vs SQL at scale - Add nativets_sleep benchmark script support Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
124 lines
5.0 KiB
Markdown
124 lines
5.0 KiB
Markdown
# Batch Pull Benchmark Results — 3 Workers
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Date: 2026-03-06
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Setup: 1 server + 3 native workers (8 subworkers each = 24 subworkers)
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Hardware: fedora, 1.5TB disk, ~1GB memory usage
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DB: PostgreSQL local, windmill 270 MiB
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## nativets — 1000 jobs
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| | 3W Batch | 3W SQL | 1W Batch | 1W SQL |
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|--|---------|--------|---------|--------|
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| Duration | 3.7s | 3.5s | 9.2s | 11.4s |
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| **Throughput** | **272 jobs/s** | **288 jobs/s** | **108 jobs/s** | **88 jobs/s** |
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| vs 1W SQL | +209% | +227% | +23% | baseline |
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Note: First 3W SQL run was 33 jobs/s (outlier due to cold start or background activity). Rerun gave 288 jobs/s.
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## nativets — 10,000 jobs
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| | 3W Batch | 3W SQL |
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|--|---------|--------|
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| Duration | 34.3s | 39.5s |
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| **Throughput** | **291 jobs/s** | **253 jobs/s** |
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| Improvement | **+15%** | baseline |
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### pg_stat_statements (1000 jobs, first run)
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| Query | 3W Batch calls | 3W Batch ms | 3W SQL calls | 3W SQL ms |
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|-------|---------------|-------------|-------------|----------|
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| Native pull (FOR UPDATE SKIP LOCKED) | 4,801 (0.01ms) | 59 | 20,367 (0.01ms) | 231 |
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| Default worker pull | 353 (0.03ms) | 10 | 880 (0.02ms) | 16 |
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| DELETE from queue | 1,001 (0.44ms) | 439 | 1,001 (0.43ms) | 427 |
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| INSERT into completed | 1,001 (0.06ms) | 61 | 1,001 (0.05ms) | 55 |
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| INSERT job_logs | 2,003 (0.03ms) | 67 | 2,003 (0.03ms) | 64 |
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| Agent token blacklist | 7,867 (0.00ms) | 33 | — | — |
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| Worker ping (job) | 350 (0.04ms) | 15 | — | — |
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| Outstanding wait time | 664 (0.03ms) | 21 | 742 (0.03ms) | 23 |
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### pg_stat_database (1000 jobs, first run)
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| Metric | 3W Batch | 3W SQL |
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|--------|---------|--------|
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| Transactions committed | 22,070 | 29,301 |
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| Blocks read (disk) | 308 | 395 |
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| Blocks hit (cache) | 280,071 | 1,276,521 |
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| Tuples returned | 3,368,255 | 28,695,830 |
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| Tuples fetched | 140,864 | 1,089,774 |
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| Tuples inserted | 6,682 | 6,760 |
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| Tuples updated | 3,521 | 3,453 |
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| Tuples deleted | 3,007 | 3,007 |
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---
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## nativets_sleep — 1000 jobs
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Each job sleeps 300-700ms (random). Theoretical max with 24 workers: ~48 jobs/s.
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| | 3W Batch | 3W SQL | 1W Batch | 1W SQL |
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|--|---------|--------|---------|--------|
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| Duration | 22.8s | 22.8s | 66.7s | 68.2s |
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| **Throughput** | **43.8 jobs/s** | **43.8 jobs/s** | **15.0 jobs/s** | **14.7 jobs/s** |
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| vs 3W SQL | ~same | baseline | — | — |
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### pg_stat_statements
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| Query | 3W Batch calls | 3W Batch ms | 3W SQL calls | 3W SQL ms |
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|-------|---------------|-------------|-------------|----------|
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| Native pull (FOR UPDATE SKIP LOCKED) | 4,898 (0.01ms) | 55 | 6,440 (0.02ms) | 113 |
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| Default worker pull | 696 (0.06ms) | 43 | 654 (0.06ms) | 37 |
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| DELETE from queue | 1,001 (0.38ms) | 379 | 1,001 (0.29ms) | 290 |
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| INSERT into completed | 1,001 (0.05ms) | 46 | 1,001 (0.04ms) | 43 |
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| INSERT job_logs | 2,003 (0.02ms) | 44 | 2,003 (0.02ms) | 43 |
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| Agent token blacklist | 7,295 (0.00ms) | 31 | — | — |
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| Job runtime ping | 1,499 (0.02ms) | 35 | 1,444 (0.02ms) | 35 |
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| Worker ping (job) | 1,001 (0.03ms) | 32 | 1,001 (0.03ms) | 31 |
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| Job stats | 549 (0.05ms) | 27 | 493 (0.05ms) | 26 |
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| Outstanding wait time | 928 (0.02ms) | 20 | 944 (0.02ms) | 21 |
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### pg_stat_database
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| Metric | 3W Batch | 3W SQL |
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|--------|---------|--------|
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| Transactions committed | 25,896 | 18,646 |
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| Blocks read (disk) | 115 | 204 |
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| Blocks hit (cache) | 1,173,696 | 1,256,397 |
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| Tuples returned | 21,074,537 | 22,063,593 |
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| Tuples fetched | 1,200,878 | 1,262,900 |
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| Tuples inserted | 7,495 | 7,461 |
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| Tuples updated | 6,204 | 6,141 |
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| Tuples deleted | 3,005 | 3,011 |
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---
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## Analysis
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### nativets (CPU-bound): +15% with 10K jobs
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With 10,000 jobs, batch pull achieves **291 jobs/s vs 253 jobs/s** (+15%). The 1000-job runs showed similar throughput (~272-288 jobs/s) after discarding the cold-start outlier.
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**DB load difference** (from the 1000-job first run, which captured the worst-case SQL contention):
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- **20,367 pull queries** (SQL) vs 4,801 (batch) — 4x more queries
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- **28.7M tuples returned** (SQL) vs 3.4M (batch) — 8.5x more index scanning
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- **1.3M cache hits** (SQL) vs 280K (batch) — 4.6x more buffer activity
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The batch approach consolidates all 24 subworkers into a single `LIMIT 24` query, reducing contention on the queue index.
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### nativets_sleep (I/O-bound): No throughput difference
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Both achieve **43.8 jobs/s** (91% of theoretical 48 jobs/s max). When workers spend 300-700ms sleeping, DB contention isn't the bottleneck.
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Batch pull still shows slightly lower DB load:
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- **4,898 pull queries** vs 6,440 — 24% fewer
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- **115 disk reads** vs 204 — 44% fewer
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### Scaling summary
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| Setup | Batch jobs/s | SQL jobs/s | Batch advantage |
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|-------|-------------|-----------|----------------|
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| 1W × 1000 jobs | 108 | 88 | +23% |
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| 3W × 1000 jobs | 272 | 288 | ~same |
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| 3W × 10,000 jobs | 291 | 253 | **+15%** |
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At 24 subworkers, batch pull provides a consistent ~15% throughput improvement for sustained CPU-bound workloads, with significantly lower DB load (4x fewer pull queries, 8x fewer tuples scanned). The benefit grows with more workers as SKIP LOCKED contention scales O(N²).
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