Add load test harness for pool and fleet sizing

Adds the scripts used to size the connection pool and validate fleet polling under load: burst arrival and mixed workload drivers, pool and resource samplers, a UI probe, host seed/drop SQL, and a gevent Dockerfile. Hosts are addressed by compose service name (api:5001) rather than a local path, so the harness runs against any stack rather than one machine.

loadtest_token.py mints a scoped collector token named LOADTEST and prints the secret once, then revokes only that token by name so a real collector token is never touched. envkeyfallback is false, so the harness needs a genuine managed token rather than an env key.

scripts/loadtest/sample_resources.py

@@ -0,0 +1,80 @@
+"""Sample container CPU and memory during a sizing run.
+
+WHY: the sizing table claims specific vCPU and RAM figures. Those can only be
+confirmed by watching the container under load, not by reasoning about greenlet
+size. This records peak RSS and CPU so a claimed "4 GB is enough" is either
+demonstrated or falsified.
+
+Runs on the HOST (needs the docker CLI), unlike the generators.
+
+Usage:
+    python sample_resources.py --container shopdb-sizing-api --seconds 120
+"""
+import argparse
+import json
+import re
+import statistics
+import subprocess
+import time
+
+
+def parse_size(text):
+    """Docker stats prints like '1.234GiB' / '512MiB'. Return megabytes."""
+    match = re.match(r'^([0-9.]+)\s*([KMGT]?i?B)$', text.strip())
+    if not match:
+        return 0.0
+    value = float(match.group(1))
+    unit = match.group(2).upper().replace('I', '')
+    factors = {'B': 1.0 / (1024 * 1024), 'KB': 1.0 / 1024, 'MB': 1.0,
+               'GB': 1024.0, 'TB': 1024.0 * 1024.0}
+    return value * factors.get(unit, 1.0)
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--container', required=True)
+    parser.add_argument('--seconds', type=int, default=120)
+    parser.add_argument('--interval', type=float, default=2.0)
+    arguments = parser.parse_args()
+
+    cpu_samples = []
+    memory_samples = []
+    deadline = time.monotonic() + arguments.seconds
+
+    while time.monotonic() < deadline:
+        try:
+            output = subprocess.check_output(
+                ['docker', 'stats', '--no-stream', '--format',
+                 '{{.CPUPerc}}|{{.MemUsage}}', arguments.container],
+                stderr=subprocess.DEVNULL, text=True).strip()
+        except subprocess.CalledProcessError:
+            break
+        if not output:
+            break
+        cpu_text, memory_text = output.split('|', 1)
+        cpu_samples.append(float(cpu_text.replace('%', '').strip()))
+        used = memory_text.split('/')[0]
+        memory_samples.append(parse_size(used))
+        time.sleep(arguments.interval)
+
+    def summarize(values):
+        if not values:
+            return {'samples': 0}
+        ordered = sorted(values)
+        index = min(int(len(ordered) * 0.95), len(ordered) - 1)
+        return {
+            'samples': len(values),
+            'median': round(statistics.median(values), 1),
+            'p95': round(ordered[index], 1),
+            'max': round(max(values), 1),
+        }
+
+    print(json.dumps({
+        'container': arguments.container,
+        'cpu_percent': summarize(cpu_samples),
+        'memory_mb': summarize(memory_samples),
+    }, indent=2))
+
+
+if __name__ == '__main__':
+    main()