Files
LEDMatrix/test/test_resource_monitor.py
T
ChuckBuildsandClaude Opus 5 0fd2bfae99 perf(plugins): stop rewriting a plugin's metrics file on every call
Plugin metrics were persisted to the cache inside monitor_call, so every
call by every plugin rewrote a small JSON file. Measured on a running rig:
one plugin's plugin_metrics file changed nine times a minute, with fourteen
such files active. Each is around 350 bytes, which on ext4 costs a 4KB block
plus a journal entry, so the cost is dominated by the write itself rather
than the payload. Cache writes accounted for essentially all of that device's
2.4 MB/min of SD traffic, on a card that wears out and has already failed
twice on the other rig.

Metrics cannot be de-duplicated the way health state can, because call_count
changes on every call and the timings usually do too. So they are rate-limited
instead: at most one write per plugin per 30 seconds.

The in-memory copy stays authoritative and exact -- a plugin's call_count is
still precise the instant after it runs. Only the cross-process snapshot the
web UI reads is delayed, and telemetry up to half a minute old is still a fair
description of a long-running plugin.

reset_metrics clears the throttle timestamp, so a reset is not left showing a
deleted key for the rest of the interval.

Extrapolating the sampled rate, this takes metric writes from roughly 126 a
minute to 28. Health persistence, the other half of the churn, is handled
separately in #475.

Verified by reverting the throttle: the churn test then reports 50 writes for
50 calls. 88 tests pass across resource monitor, plugin system and web API.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01STMbQE4YctTacQXfbYqKuW
2026-08-20 07:06:21 -04:00

177 lines
6.9 KiB
Python

"""
Tests for src/plugin_system/resource_monitor.py
Focus areas:
- Execution-time metrics are captured regardless of psutil availability.
- CPU sampling is non-blocking (regression guard for the previous
``cpu_percent(interval=0.1)`` call that blocked 100 ms per monitored call).
- Resource limits are enforced.
"""
import time
import pytest
from unittest.mock import MagicMock
from src.plugin_system.resource_monitor import (
PluginResourceMonitor,
ResourceLimits,
ResourceLimitExceeded,
PSUTIL_AVAILABLE,
)
def _cache():
cache = MagicMock()
cache.get.return_value = None
return cache
class TestExecutionTimeMetrics:
def test_monitor_call_returns_value_and_records_call(self):
mon = PluginResourceMonitor(_cache(), enable_monitoring=False)
result = mon.monitor_call("p", lambda: 42)
assert result == 42
metrics = mon.get_metrics("p")
assert metrics.call_count == 1
assert metrics.total_execution_time >= 0.0
def test_avg_and_max_execution_time(self):
mon = PluginResourceMonitor(_cache(), enable_monitoring=False)
mon.monitor_call("p", lambda: time.sleep(0.01))
mon.monitor_call("p", lambda: None)
summary = mon.get_metrics_summary("p")
assert summary["call_count"] == 2
assert summary["max_execution_time"] >= summary["avg_execution_time"] >= 0.0
def test_exception_propagates_but_is_still_timed(self):
mon = PluginResourceMonitor(_cache(), enable_monitoring=False)
def boom():
raise ValueError("nope")
with pytest.raises(ValueError):
mon.monitor_call("p", boom)
# Execution time is still recorded even when the call raised.
assert mon.get_metrics("p").execution_time >= 0.0
class TestNonBlockingCpu:
def test_cpu_sampling_is_fast_when_disabled(self):
mon = PluginResourceMonitor(_cache(), enable_monitoring=False)
start = time.time()
for _ in range(50):
mon._get_process_cpu_percent()
# The old implementation blocked ~0.1s/call (~5s for 50). Non-blocking
# must complete near-instantly.
assert time.time() - start < 0.5
assert mon._get_process_cpu_percent() == 0.0
@pytest.mark.skipif(not PSUTIL_AVAILABLE, reason="psutil not installed")
def test_cpu_sampling_is_fast_with_psutil(self):
mon = PluginResourceMonitor(_cache(), enable_monitoring=True)
assert mon._process is not None
start = time.time()
for _ in range(30):
mon._get_process_cpu_percent()
# 30 blocking 0.1s samples would be ~3s; non-blocking must be well under.
assert time.time() - start < 0.5
def test_monitor_call_does_not_block_on_cpu_sampling(self):
mon = PluginResourceMonitor(_cache()) # enable depends on psutil
start = time.time()
for _ in range(25):
mon.monitor_call("p", lambda: None)
# 25 * 0.1s = 2.5s under the old blocking bug; must be far faster now.
assert time.time() - start < 1.0
class TestResourceLimits:
def test_execution_time_limit_raises(self):
mon = PluginResourceMonitor(_cache(), enable_monitoring=False)
mon.set_limits("p", ResourceLimits(max_execution_time=0.001))
with pytest.raises(ResourceLimitExceeded):
mon.monitor_call("p", lambda: time.sleep(0.02))
def test_reset_metrics_clears_counts(self):
cache = _cache()
mon = PluginResourceMonitor(cache, enable_monitoring=False)
mon.monitor_call("p", lambda: None)
assert mon.get_metrics("p").call_count == 1
mon.reset_metrics("p")
assert mon.get_metrics("p").call_count == 0
class TestForceReload:
def test_force_reload_refreshes_stale_snapshot(self):
"""A read-only consumer must see the writer process's latest persisted
metrics rather than a pinned first snapshot."""
cache = MagicMock()
persisted = {"value": None} # only the metrics key returns data
def cache_get(key, max_age=None, memory_ttl=None):
return persisted["value"] if key.startswith("plugin_metrics:") else None
cache.get.side_effect = cache_get
mon = PluginResourceMonitor(cache, enable_monitoring=False)
# First read snapshots empty metrics.
assert mon.get_metrics_summary("p")["call_count"] == 0
# The display service later persists real metrics.
persisted["value"] = {"call_count": 7, "total_execution_time": 1.4}
# Plain read stays stale...
assert mon.get_metrics_summary("p")["call_count"] == 0
# ...force_reload picks up the persisted values and bypasses memory.
fresh = mon.get_metrics_summary("p", force_reload=True)
assert fresh["call_count"] == 7
assert any(c.kwargs.get("memory_ttl") == 0 for c in cache.get.call_args_list)
class TestMetricsPersistenceChurn:
"""Metrics are telemetry; writing them on every call wore the SD card.
Each write is a ~350-byte file, which on ext4 costs a 4KB block plus a
journal entry. At roughly nine calls a minute per plugin across fourteen
plugins it dominated the device's write volume.
"""
def test_repeated_calls_persist_once_per_interval(self):
cache = _cache()
mon = PluginResourceMonitor(cache, enable_monitoring=False)
for _ in range(50):
mon.monitor_call("p", lambda: None)
writes = [c for c in cache.set.call_args_list
if c.args and str(c.args[0]).startswith("plugin_metrics:")]
assert len(writes) == 1, (
f"50 calls produced {len(writes)} metric writes; expected 1")
def test_the_interval_elapsing_allows_the_next_write(self, monkeypatch):
import src.plugin_system.resource_monitor as rm
cache = _cache()
mon = PluginResourceMonitor(cache, enable_monitoring=False)
mon.monitor_call("p", lambda: None)
# pretend the interval has passed
mon._metrics_persisted_at["p"] -= rm._METRICS_PERSIST_INTERVAL + 1
mon.monitor_call("p", lambda: None)
writes = [c for c in cache.set.call_args_list
if c.args and str(c.args[0]).startswith("plugin_metrics:")]
assert len(writes) == 2
def test_in_memory_metrics_stay_exact_while_writes_are_skipped(self):
mon = PluginResourceMonitor(_cache(), enable_monitoring=False)
for _ in range(20):
mon.monitor_call("p", lambda: None)
assert mon.get_metrics("p").call_count == 20
def test_reset_lets_the_next_call_persist_immediately(self):
cache = _cache()
mon = PluginResourceMonitor(cache, enable_monitoring=False)
mon.monitor_call("p", lambda: None)
mon.reset_metrics("p")
mon.monitor_call("p", lambda: None)
writes = [c for c in cache.set.call_args_list
if c.args and str(c.args[0]).startswith("plugin_metrics:")]
assert len(writes) == 2, "reset should clear the throttle timestamp"