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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
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co-authored by
Claude Opus 5
parent
cf0a551f7b
commit
0fd2bfae99
@@ -49,6 +49,20 @@ class ResourceMetrics:
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self.total_execution_time = self.total_execution_time / self.call_count
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#: How often a plugin's metrics are written to the cache, in seconds.
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#:
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#: Persisting on every call meant a small file rewritten roughly nine times a
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#: minute per plugin. On a rig with fourteen active plugins that was ~126
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#: writes a minute for metrics alone, and since each ~350-byte file costs a
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#: 4KB block plus an ext4 journal entry, it dominated the device's write
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#: volume -- on an SD card, which wears out.
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#:
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#: The in-memory copy stays authoritative and exact; only the cross-process
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#: snapshot the web UI reads is delayed, and telemetry up to half a minute old
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#: is still a fair description of a long-running plugin.
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_METRICS_PERSIST_INTERVAL = 30.0
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class PluginResourceMonitor:
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"""
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Monitors resource usage for plugins.
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@@ -75,6 +89,10 @@ class PluginResourceMonitor:
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# Resource metrics per plugin
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self._metrics: Dict[str, ResourceMetrics] = {}
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self._limits: Dict[str, ResourceLimits] = {}
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# When each plugin's metrics last reached the cache. Metrics change on
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# every call, so they cannot be de-duplicated the way health state can;
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# they are rate-limited instead. See _METRICS_PERSIST_INTERVAL.
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self._metrics_persisted_at: Dict[str, float] = {}
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# Thread-local storage for execution tracking
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self._local = threading.local()
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@@ -232,18 +250,8 @@ class PluginResourceMonitor:
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# CPU is harder to measure per-call, so we track it separately
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metrics.cpu_percent = self._get_process_cpu_percent()
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# Persist metrics
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cache_key = self._get_metrics_key(plugin_id)
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self.cache_manager.set(cache_key, {
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'memory_mb': metrics.memory_mb,
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'cpu_percent': metrics.cpu_percent,
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'execution_time': metrics.execution_time,
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'call_count': metrics.call_count,
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'total_execution_time': metrics.total_execution_time,
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'max_execution_time': metrics.max_execution_time,
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'min_execution_time': metrics.min_execution_time if metrics.min_execution_time != float('inf') else 0.0,
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'last_update_time': metrics.last_update_time
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})
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# Persist metrics, at most once per interval per plugin.
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self._persist_metrics(plugin_id, metrics)
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# Check limits
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if limits:
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@@ -363,6 +371,31 @@ class PluginResourceMonitor:
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summaries[plugin_id] = self.get_metrics_summary(plugin_id)
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return summaries
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def _persist_metrics(self, plugin_id: str, metrics: ResourceMetrics,
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force: bool = False) -> None:
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"""Write a plugin's metrics to the cache, at most once per interval.
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Caller must hold ``self._lock``.
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"""
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now = time.time()
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if not force and now - self._metrics_persisted_at.get(plugin_id, 0.0) \
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< _METRICS_PERSIST_INTERVAL:
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return
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self._metrics_persisted_at[plugin_id] = now
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cache_key = self._get_metrics_key(plugin_id)
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self.cache_manager.set(cache_key, {
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'memory_mb': metrics.memory_mb,
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'cpu_percent': metrics.cpu_percent,
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'execution_time': metrics.execution_time,
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'call_count': metrics.call_count,
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'total_execution_time': metrics.total_execution_time,
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'max_execution_time': metrics.max_execution_time,
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'min_execution_time': (metrics.min_execution_time
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if metrics.min_execution_time != float('inf')
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else 0.0),
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'last_update_time': metrics.last_update_time,
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})
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def reset_metrics(self, plugin_id: str) -> None:
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"""Reset metrics for a plugin."""
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with self._lock:
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@@ -370,4 +403,7 @@ class PluginResourceMonitor:
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self._metrics[plugin_id] = ResourceMetrics()
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cache_key = self._get_metrics_key(plugin_id)
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self.cache_manager.delete(cache_key)
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# Let the next call persist immediately rather than leaving the
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# deleted key absent for the rest of the interval.
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self._metrics_persisted_at.pop(plugin_id, None)
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@@ -127,3 +127,50 @@ class TestForceReload:
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fresh = mon.get_metrics_summary("p", force_reload=True)
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assert fresh["call_count"] == 7
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assert any(c.kwargs.get("memory_ttl") == 0 for c in cache.get.call_args_list)
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class TestMetricsPersistenceChurn:
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"""Metrics are telemetry; writing them on every call wore the SD card.
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Each write is a ~350-byte file, which on ext4 costs a 4KB block plus a
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journal entry. At roughly nine calls a minute per plugin across fourteen
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plugins it dominated the device's write volume.
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"""
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def test_repeated_calls_persist_once_per_interval(self):
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cache = _cache()
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mon = PluginResourceMonitor(cache, enable_monitoring=False)
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for _ in range(50):
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mon.monitor_call("p", lambda: None)
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writes = [c for c in cache.set.call_args_list
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if c.args and str(c.args[0]).startswith("plugin_metrics:")]
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assert len(writes) == 1, (
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f"50 calls produced {len(writes)} metric writes; expected 1")
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def test_the_interval_elapsing_allows_the_next_write(self, monkeypatch):
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import src.plugin_system.resource_monitor as rm
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cache = _cache()
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mon = PluginResourceMonitor(cache, enable_monitoring=False)
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mon.monitor_call("p", lambda: None)
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# pretend the interval has passed
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mon._metrics_persisted_at["p"] -= rm._METRICS_PERSIST_INTERVAL + 1
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mon.monitor_call("p", lambda: None)
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writes = [c for c in cache.set.call_args_list
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if c.args and str(c.args[0]).startswith("plugin_metrics:")]
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assert len(writes) == 2
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def test_in_memory_metrics_stay_exact_while_writes_are_skipped(self):
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mon = PluginResourceMonitor(_cache(), enable_monitoring=False)
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for _ in range(20):
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mon.monitor_call("p", lambda: None)
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assert mon.get_metrics("p").call_count == 20
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def test_reset_lets_the_next_call_persist_immediately(self):
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cache = _cache()
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mon = PluginResourceMonitor(cache, enable_monitoring=False)
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mon.monitor_call("p", lambda: None)
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mon.reset_metrics("p")
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mon.monitor_call("p", lambda: None)
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writes = [c for c in cache.set.call_args_list
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if c.args and str(c.args[0]).startswith("plugin_metrics:")]
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assert len(writes) == 2, "reset should clear the throttle timestamp"
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