# 09 - Benchmark Execution & Resource Measurement

## Context / Goal
Execute all 35 (Q1-Q7 × 5) benchmark cells with rigorous resource metering; produce the raw measurement table that feeds extrapolation and reporting.

## Dependencies
08.

## Processing instructions
Write `bench_runner.py` (tabs):
- Each cell runs as a **separate subprocess** so RSS/CPU are attributable. For PostgreSQL, also sample the backend PID via `pg_stat_activity` + psutil.
- Metrics per run: wall time (s), peak RSS (MB, psutil `memory_info().rss` sampled at 50 ms), CPU time user+sys (s), CPU utilization % (cpu_time / wall / cores), read bytes (psutil `io_counters`), result row count, checksum-match flag.
- Protocol: 1 warm-up run (discarded) + 3 measured runs per cell; report median and min/max. Randomize cell order to spread cache effects; record run order.
- Cold-cache variant: repeat the full matrix once after dropping caches if permitted (`sync; echo 3 > /proc/sys/vm/drop_caches` requires root - if not available, skip and note it).
- Failures/timeouts: hard timeout 30 min per run; record `TIMEOUT` and continue (expected possibility: RDF Q5).
- Storage footprint: consolidate `storage_sizes.csv` (all formats, incl. gzip-compressed archival sizes of each: `tar.gz` of CSV tree, gzip JSON, sqlite db gz, pg dump, nt.gz).

## Output
- `./out/bench/results_raw.csv`: format, query, run, wall_s, peak_rss_mb, cpu_s, cpu_util_pct, read_mb, rows, checksum_ok.
- `./out/bench/results_median.csv` - aggregated.
- `./out/bench/storage_sizes.csv` - final consolidated.
- Marker `./out/.done/09.ok`.