docs(perf): fill benchmark tables with real BMI5 test data

[ls-ggg]

What

Replace all mock/placeholder data in the CubeSandbox performance benchmark article with real measurements from a Tencent Cloud BMI5 bare-metal node (96 cores, 375 GiB).

Changes

Benchmark articles (zh + en):

• §3.2 Startup latency: 4-tier concurrency data (1/10/20/50), including throughput and per-sandbox amortized time
• §3.3 Deployment density: 5-tier memory table (0/100/300/500/1000 sandboxes), per-VM overhead ~21–26 MB
• §3.3 Added TAP pool scaling instructions (tap_init_num config + network-agent restart)
• §4.1 Snapshot creation vs concurrency (1/5/10)
• §4.2 Snapshot vs dirty page size (0–1024 MB, 8 tiers)
• §4.3 Create-from-snapshot concurrency (1/10/20/50)
• §4.4 Rollback concurrency (1/5/10)
• §4.5 Clone concurrency (1/10/20/50, n=100)
• English version fully rewritten to align with Chinese version (data, descriptions, commands)

Benchmark scripts (examples/snapshot-rollback-clone/):

• Refactored to single-tier CLI style (-c, -n, -d, --no-header) for composable multi-tier sweeps
• Added warm-up, percentile reporting (min/p95/max), and JSON-compatible output

Test environment

• Tencent Cloud BMI5: Intel Xeon 8255C 96-core, 375 GiB DDR4, 3.84 TB NVMe SSD
• Sandbox spec: 2 vCPU / 2 GiB, CoW reflink on XFS
• All results measured with warm-up rounds discarded; 100% success rate across all tiers

[ls-ggg]

/cubebot

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Reviewing PR: docs(perf): fill benchmark tables with real BMI5 test data

Reviewing changes...

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✅ Overview

This PR does two things well:

1. Blog posts (zh + en): Replaces mock/placeholder benchmark data with real measurements from a Tencent Cloud BMI5 bare-metal node. Restructures the article with clear new sections (template-based creation, density, snapshot operations), adds metric definitions, environment verification steps, and complete reproduction commands.

2. Benchmark scripts (examples/snapshot-rollback-clone/): Refactors from hardcoded multi-tier scenarios to single-tier CLI tools with -c/-n/-d/--no-header flags, making them composable for sweep scripts. Adds warm-up rounds, p95 percentile reporting, and consistent output formatting.

✅ Data Consistency

I verified the following:

Section	Tables	Verified
3.2 Startup latency (concurrency)	4 tiers	Values match between zh/en articles
3.3 Density (memory overhead)	5 tiers	Values match between zh/en articles
4.1 Snapshot creation (concurrency)	3 tiers	Values match between zh/en articles
4.2 Snapshot vs dirty page size	8 tiers	Values match between zh/en articles
4.3 Create-from-snapshot	4 tiers	Values match between zh/en articles
4.4 Rollback	3 tiers	Values match between zh/en articles
4.5 Clone	4 scenarios	Values match between zh/en articles

All benchmark script invocations in the article match the CLI syntax of the refactored scripts.

✅ Blog Post Quality

• Article structure is logical: template creation → Snapshot operations
• Metric definitions (avg/min/p95/max/wall/per) are clearly listed upfront
• Environment setup with Hello World verification is a nice touch for reproducibility
• Safety warnings for density testing are appropriate
• TAP pool scaling instructions are clear
• English version is a faithful rewrite of the Chinese version (not a literal translation), with better technical phrasing

✅ Benchmark Script Quality

General:

• All 5 scripts follow the same clean pattern: parse_args() → run_round() → main() → __name__ == "__main__"
• percentile() implementation is correct (ceiling rank interpolation)
• --no-header flag enables sweep scripting
• Error handling in cleanup (finally with try/except) is consistent

Specific scripts:

• bench_snapshot_dirty.py — Clean separation of concerns; run_round() handles complete lifecycle (create → dirty → snapshot → warm-up → restore → cleanup)
• bench_rollback_concurrency.py — Correctly documents the snapshot ownership constraint; each sandbox creates its own checkpoint
• bench_clone_concurrency.py — Uses SDK's native Sandbox.clone(n=n, concurrency=concurrency) for clean concurrent cloning

🔍 Suggestions

1. Duplicated percentile() across 5 scripts

The percentile() function is identical in every script. Consider extracting it to a shared module like bench_util.py to reduce duplication. (Minor — self-contained scripts are a valid trade-off for usability.)

2. bench_snapshot_dirty.py — L146 dirty data validity check

dirty_mb_avg = statistics.mean(dirty_list) / (1024 * 1024) if dirty_list[0] >= 0 else -1

This only checks the first element. Using if any(d >= 0 for d in dirty_list) would be more robust, though in practice if the first call returns -1 (log unavailable), all subsequent calls will too. This is a readability nitpick.

3. bench_rollback_concurrency.py — no -d flag in run commands

The article's command examples for rollback don't show -d, but the script defaults to dirty_mb=0:

python bench_rollback_concurrency.py -c 1  -n 5
python bench_rollback_concurrency.py -c 5  -n 5 --no-header
python bench_rollback_concurrency.py -c 10 -n 5 --no-header

This is technically correct (default is 0, matching "pure rollback" in the description), so the article accurately reflects what was tested. No change needed, but noting it for completeness.

✅ Verdict

LGTM. The PR is well-structured, the data is consistent across both languages, and the benchmark scripts are cleanly refactored. The English article is a proper rewrite aligned with the Chinese version.

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| Branch: ls-ggg/perf-benchmark-v2

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✅ PR Review: docs(perf): fill benchmark tables with real BMI5 test data

Progress

• Gather context and understand the PR
• Review code quality and documentation accuracy
• Post review feedback

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Overview

This PR replaces mock/placeholder benchmark data with real measurements from a Tencent Cloud BMI5 bare-metal node. The changes span documentation (English + Chinese) and 5 Python benchmark scripts. Overall, the quality is high — the refactoring to a single-tier CLI pattern is well-motivated, the data is presented clearly, and the scripts are robust.

Below are my findings organized by severity and category.

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🔴 Potential Issues

1. bench_snapshot_dirty.py:146 — Partial dirty_list validation

dirty_mb_avg = statistics.mean(dirty_list) / (1024 * 1024) if dirty_list[0] >= 0 else -1

This only validates the first element. If the vmm.log grep succeeds in round 0 (returns a valid byte count > 0) but fails in a later round (returns -1), the result silently averages -1 bytes into dirty_mb_avg. Consider:

if any(d < 0 for d in dirty_list):
    dirty_mb_avg = -1
else:
    dirty_mb_avg = statistics.mean(dirty_list) / (1024 * 1024)

Fix this →

2. §3.2 Data — Per-sandbox amortized (55.8 ms) vs avg (47.8 ms) at c=1

For concurrency=1:

avg	Per-sandbox amortized
47.8 ms	55.8 ms

The ~8 ms gap between "avg individual latency" and "wall/N" at serial concurrency is non-trivial. If avg is the mean of individual request latencies and amortized = wall_time / N, the gap suggests ~8 ms of per-request serial overhead (connection setup, dispatch, etc.) that individual timing doesn't capture. This is common with benchmarking frameworks, but the discrepancy could confuse readers. Consider adding a brief footnote explaining why they differ at c=1.

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🟡 Minor Issues

3. grep_snapshot_bytes — fragile log matching

bench_snapshot_dirty.py greps the full vmm.log for sandbox_id with -i (case-insensitive). Sandbox IDs are hex — the -i flag is unnecessary (# hex chars don't have letter-case ambiguity before a-f). Additionally, short hex substrings (e.g., "1a", "0f") could match stale entries from previous runs. Consider anchoring the match or adding a timestamp filter.

4. percentile() — duplicated across 5 scripts

Each script defines its own percentile() helper. While the scripts are designed to be standalone (copyable), extracting this into a shared utility module in examples/snapshot-rollback-clone/ would reduce duplication and risk of divergence. Low priority — the standalone design is deliberate.

5. bench_rollback_concurrency.py:52 — exception path in rollback_one

def rollback_one(sb, checkpoint_id: str, dirty_mb: int):
    if dirty_mb > 0:
        sb.run_code(f"...")
    sb.rollback(checkpoint_id)

If sb.run_code() succeeds but sb.rollback() throws, the sandbox isn't killed at this level (it's killed in run_round's cleanup loop). The bench_clone_concurrency.py pattern with try/finally handles this better.

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🟢 Observations / Style Notes

6. Excellent warm-up pattern in all scripts — the first-round discard eliminates page-cache cold-read noise, and the single-tier design makes multi-tier sweeping composable with shell scripts.

7. Cleanup discipline is generally good — all scripts kill sandboxes and delete snapshots in finally blocks or explicit cleanup loops. The bench_clone_concurrency.py and bench_rollback_concurrency.py scripts use try/finally in run_round which is the recommended pattern.

8. Documentation accuracy — command blocks are tested, tables render correctly in both English and Chinese versions, metric definitions are clear, and the Chinese article is well-aligned with the English one. The TAP pool scaling instructions (§3.3) are a nice addition for readers who need to push beyond default limits.

9. Inconsistent SSL_CERT_FILE mention — SSL_CERT_FILE is exported in §3.1 Setup but omitted from subsequent command blocks in §3.2 and §3.3 (which reference "same as §3.2"). For HTTPS deployments, users might miss this when jumping directly to a later section. Consider adding a brief mention in §3.2's environment setup block.

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Summary

This is a solid PR. The data is well-presented, the scripts are professionally structured, and the documentation is thorough. I found one correctness bug (item 1 — partial validation), one data presentation question (item 2), and several minor quality suggestions.

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📋 Status Update — PR Synchronize Event

Branch: ls-ggg/perf-benchmark-v2 @ 619eb0d
Commit: docs(perf): fill benchmark tables with real BMI5 test data

This PR was synchronized (e.g., rebased on master), but no code changes were detected since the previous reviews. The single commit 619eb0d is unchanged.

Previous Review Findings

The two earlier reviews (from two separate action runs) identified the following items:

🔴 Potential Issues (Unaddressed)

#	File	Issue	Severity
1	bench_snapshot_dirty.py:146	dirty_list[0] >= 0 only validates first element; if a later grep_snapshot_bytes() call returns -1, it silently corrupts the average	Bug
2	§3.2 table (en + zh)	Per-sandbox amortized (55.8 ms) vs avg (47.8 ms) at c=1 has an ~8 ms gap that could confuse readers; a footnote would help	Data clarity

🟡 Minor Issues (Unaddressed)

#	File	Issue
3	bench_snapshot_dirty.py:65	grep -i sandbox_id — case-insensitive flag unnecessary for hex IDs
4	All 5 scripts	percentile() is duplicated across every script; extract to shared module
5	bench_rollback_concurrency.py:51-55	Exception in sb.rollback() bypasses sandbox cleanup at that scope level (caught by run_round's loop though)
6	Blog posts (en + zh)	SSL_CERT_FILE exported in §3.1 but omitted from subsequent section command blocks

✅ Verified OK

• All table values match between zh/en articles
• Benchmark script CLI syntax matches article command examples
• Article structure, metric definitions, and environment verification steps
• Warm-up / cleanup patterns in all scripts

Summary

The branch is unchanged — previous review feedback still applies. Let me know if you'd like me to implement any of the fixes identified above.

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| Branch: ls-ggg/perf-benchmark-v2 |