Plot Fio Results

Plot FIO Benchmark Results

Generates a 4-panel PNG chart from fio benchmark markdown result files produced by CIChurn.fio_distributed.

When to Use

• After running one or more fio_distributed scenarios
• When results files exist in results/ (or a custom directory)
• To compare throughput, IOPS, and latency across different client counts

Output

• <results_dir>/fio_benchmark_results.png — 4-panel chart saved in the same directory as the input files
• Panel 1: Aggregate (sum) bandwidth per run, sorted by client count
• Panel 2: Aggregate IOPS per run
• Panel 3: Avg vs P99 latency per run (grouped bar)
• Panel 4: Actual bandwidth vs ideal linear scaling (saturation gap shaded)
• All panels: x-axis shows client count numbers only (e.g. 3, 4, 9, 20), labelled "FIO Client Count" — no filenames, no rotation

Procedure

1. Locate result files — accepts an optional directory argument (default: results/ relative to workspace root); files may be in dated subdirectories (e.g. results/apr7/); each file is a markdown document with a ## Summary Table section

2. Parse each file — extract the summary row using this regex pattern:

   ^\| (\d+)\s+\| (\d+)\s+\| (\d+)\s+\| (\S+)\s+\| (\S+)\s+\| (\S+)\s+\| (\d+)\s+\| (\d+)\s+
   \| ([\d.]+ \S+/s)\s+\| ([\d.]+ \S+/s)\s+\| ([\d.]+ \S+/s)\s+\| ([\d.]+ \S+/s)\s+
   \| ([\d.k]+)\s+\| ([\d.k]+)\s+\| ([\d.k]+)\s+\| ([\d.k]+)\s+\| ([\d.]+)\s+\| ([\d.]+)
   

   Fields: clients, vols_per_client, total_vols, profile, rw, bs, numjobs, iodepth, bw_sum, bw_avg, bw_max, bw_min, iops_sum, iops_avg, iops_max, iops_min, avg_lat_ms, p99_lat_ms

3. Normalise bandwidth — convert all BW values to MiB/s:

   • GiB/s × 1024, MiB/s × 1, KiB/s ÷ 1024

4. Normalise IOPS — strip trailing k and multiply by 1000

5. Sort rows by clients ascending

6. Run the plot script — use [./scripts/plot_fio_results.py](./scripts/plot_fio_results.py) (already present in scripts/), passing the results directory as an argument:

   uv run --with matplotlib scripts/plot_fio_results.py <results_dir>
   

   e.g. uv run --with matplotlib scripts/plot_fio_results.py results/apr7

   The script uses matplotlib.use("Agg") — no display required.

7. Confirm output — verify <results_dir>/fio_benchmark_results.png was saved in the same folder as the input files

Key Observations to Report

After generating the chart, summarise:

• At what client count throughput/IOPS plateaued (storage saturation point)
• Latency degradation factor from lowest to highest client count
• Whether results are from a single profile or mixed (flag mixed profiles)

Result File Format Reference

## Summary Table

| Client Nodes | Volumes/Client | Total Volumes | Profile | RW | Block Size | NumJobs | IoDepth | BW Sum | BW Avg | BW Max | BW Min | IOPS Sum | IOPS Avg | IOPS Max | IOPS Min | Avg Latency (ms) | 99th Percentile Latency (ms) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 9 | 1 | 9 | db-workload | randrw | 4k | 1 | 64 | 260 MiB/s | 29 MiB/s | ... | ... | 66.6k | ... | ... | ... | 9.74 | 36.96 |

Notes

• bw_min/bw_max are per-client values, not bounds on the sum — do not use them as error bars on the aggregate
• Multiple rows per file are supported (e.g. a sweep over iodepth levels in the same run)
• The uv run --with matplotlib flag adds matplotlib without modifying pyproject.toml
• The script reads files with suffix "" (no extension) or .md — both formats are supported
• The .png output is skipped automatically because .png is neither "" nor .md
• The PNG is always written to the same directory as the input files (the directory argument), not hardcoded to results/
• Result files from scp are typically named <short-uuid>-summary.md (e.g. d72df443-summary.md)