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kevinveenbirkenbachandClaude Opus 4.8
d2f5efa7fe
Add plugins/lookup/playwright_workers.py computing a conservative parallel-worker count (CPU/RAM-derived, app-under-test concurrency-capped, reduced on CI) and wire test_e2e_playwright_workers + test_e2e_playwright_fully_parallel + test_e2e_playwright_test_timeout into playwright.config.js (read from env) and 02_run_one.yml (passed via -e). fullyParallel stays false so parallelism is file-level only and intra-file ordering assumptions stay safe. Raise run_timeout to 3600 for addon-heavy roles (nextcloud ~50 specs). Split the role's variables by Ansible precedence and intent: derived/fixed constants live in vars/main.yml (UPPERCASE, high precedence, not operator-tuned), operator-tunable knobs live in defaults/main.yml (lowercase, overridable). VARIANT_INDEX stays UPPERCASE with a single definition. Update references across tasks/templates/inventory/docs accordingly. Includes a unit test for playwright_workers and an autoformat pass. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
90 lines
3.1 KiB
Python
90 lines
3.1 KiB
Python
from __future__ import annotations
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import os
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from pathlib import Path
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from typing import Any
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from ansible.plugins.lookup import LookupBase
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# Conservative sizing for browser e2e that runs against the live, co-resident
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# application stack. The binding constraint is rarely raw CPU: it is the
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# application-under-test concurrency (Keycloak/Synapse rate limits, DB
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# connections), RAM per Chromium worker, and the deployed stack sharing the
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# host. So divide CPUs aggressively, reserve RAM for the stack, and hard-cap.
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_PER_WORKER_GB = 1.5 # Chromium + node + headroom per worker
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_RAM_FRACTION = 0.5 # leave half of RAM for the deployed stack under test
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_CPU_DIVISOR = 4 # browser workers are heavy and the stack is co-resident
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_HARD_CAP = 6 # app-under-test concurrency ceiling (login/rate limits)
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_CI_CAP = 2 # CI runners: stay minimal and stable
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_CI_ENV = ("CI", "GITHUB_ACTIONS", "GITLAB_CI", "BUILDKITE", "JENKINS_URL")
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def _cpu_count() -> int:
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try:
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return max(1, len(os.sched_getaffinity(0)))
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except (AttributeError, OSError):
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return max(1, os.cpu_count() or 1)
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def _ram_gb() -> float:
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try:
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with Path("/proc/meminfo").open(encoding="utf-8") as fh:
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for line in fh:
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if line.startswith("MemTotal:"):
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return int(line.split()[1]) / (1024.0 * 1024.0)
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except (OSError, ValueError, IndexError):
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pass
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return 4.0 # conservative fallback when /proc/meminfo is unreadable
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def _is_ci() -> bool:
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return any(os.environ.get(k) for k in _CI_ENV)
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def compute_workers(
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cpus: int,
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ram_gb: float,
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ci: bool,
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*,
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per_worker_gb: float = _PER_WORKER_GB,
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ram_fraction: float = _RAM_FRACTION,
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cpu_divisor: int = _CPU_DIVISOR,
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hard_cap: int = _HARD_CAP,
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ci_cap: int = _CI_CAP,
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) -> int:
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cpu_workers = max(1, cpus // max(1, cpu_divisor))
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ram_workers = max(1, int((ram_gb * ram_fraction) // per_worker_gb))
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workers = min(cpu_workers, ram_workers, hard_cap)
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if ci:
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workers = min(workers, ci_cap)
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return max(1, workers)
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class LookupModule(LookupBase):
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"""
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lookup('playwright_workers')
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Conservative, stable parallel-worker count for the Playwright e2e suite.
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Derived from the control host's effective CPU and RAM, divided down because
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browser workers are heavy and run against the co-resident application stack,
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reserved against RAM, hard-capped for the application-under-test's
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concurrency limits, and reduced further on CI. Always returns an int >= 1.
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Tunable via kwargs: cpu_divisor, hard_cap, ci_cap, per_worker_gb,
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ram_fraction.
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"""
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def run(self, terms, variables: dict[str, Any] | None = None, **kwargs):
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return [
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compute_workers(
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_cpu_count(),
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_ram_gb(),
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_is_ci(),
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per_worker_gb=float(kwargs.get("per_worker_gb", _PER_WORKER_GB)),
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ram_fraction=float(kwargs.get("ram_fraction", _RAM_FRACTION)),
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cpu_divisor=int(kwargs.get("cpu_divisor", _CPU_DIVISOR)),
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hard_cap=int(kwargs.get("hard_cap", _HARD_CAP)),
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ci_cap=int(kwargs.get("ci_cap", _CI_CAP)),
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)
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]
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