0d5b9730e3
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.
81 lines
2.6 KiB
Python
81 lines
2.6 KiB
Python
"""Sample container CPU and memory during a sizing run.
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WHY: the sizing table claims specific vCPU and RAM figures. Those can only be
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confirmed by watching the container under load, not by reasoning about greenlet
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size. This records peak RSS and CPU so a claimed "4 GB is enough" is either
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demonstrated or falsified.
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Runs on the HOST (needs the docker CLI), unlike the generators.
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Usage:
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python sample_resources.py --container shopdb-sizing-api --seconds 120
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"""
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import argparse
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import json
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import re
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import statistics
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import subprocess
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import time
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def parse_size(text):
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"""Docker stats prints like '1.234GiB' / '512MiB'. Return megabytes."""
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match = re.match(r'^([0-9.]+)\s*([KMGT]?i?B)$', text.strip())
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if not match:
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return 0.0
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value = float(match.group(1))
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unit = match.group(2).upper().replace('I', '')
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factors = {'B': 1.0 / (1024 * 1024), 'KB': 1.0 / 1024, 'MB': 1.0,
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'GB': 1024.0, 'TB': 1024.0 * 1024.0}
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return value * factors.get(unit, 1.0)
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument('--container', required=True)
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parser.add_argument('--seconds', type=int, default=120)
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parser.add_argument('--interval', type=float, default=2.0)
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arguments = parser.parse_args()
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cpu_samples = []
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memory_samples = []
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deadline = time.monotonic() + arguments.seconds
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while time.monotonic() < deadline:
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try:
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output = subprocess.check_output(
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['docker', 'stats', '--no-stream', '--format',
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'{{.CPUPerc}}|{{.MemUsage}}', arguments.container],
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stderr=subprocess.DEVNULL, text=True).strip()
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except subprocess.CalledProcessError:
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break
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if not output:
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break
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cpu_text, memory_text = output.split('|', 1)
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cpu_samples.append(float(cpu_text.replace('%', '').strip()))
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used = memory_text.split('/')[0]
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memory_samples.append(parse_size(used))
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time.sleep(arguments.interval)
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def summarize(values):
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if not values:
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return {'samples': 0}
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ordered = sorted(values)
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index = min(int(len(ordered) * 0.95), len(ordered) - 1)
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return {
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'samples': len(values),
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'median': round(statistics.median(values), 1),
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'p95': round(ordered[index], 1),
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'max': round(max(values), 1),
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}
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print(json.dumps({
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'container': arguments.container,
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'cpu_percent': summarize(cpu_samples),
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'memory_mb': summarize(memory_samples),
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}, indent=2))
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if __name__ == '__main__':
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main()
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