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Commits
| Author | SHA1 | Date | |
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dab4b3ea57 | ||
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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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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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class PluginResourceMonitor:
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"""
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"""
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Monitors resource usage for plugins.
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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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# Resource metrics per plugin
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self._metrics: Dict[str, ResourceMetrics] = {}
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self._metrics: Dict[str, ResourceMetrics] = {}
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self._limits: Dict[str, ResourceLimits] = {}
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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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# Thread-local storage for execution tracking
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self._local = threading.local()
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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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# 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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metrics.cpu_percent = self._get_process_cpu_percent()
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# Persist metrics
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# Persist metrics, at most once per interval per plugin.
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cache_key = self._get_metrics_key(plugin_id)
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self._persist_metrics(plugin_id, metrics)
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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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# Check limits
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# Check limits
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if limits:
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if limits:
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@@ -363,6 +371,37 @@ class PluginResourceMonitor:
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summaries[plugin_id] = self.get_metrics_summary(plugin_id)
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summaries[plugin_id] = self.get_metrics_summary(plugin_id)
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return summaries
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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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# Monotonic, not wall clock: these devices have no RTC, so the clock
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# jumps by however far off boot-time was the moment NTP first syncs.
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# A forward jump would allow an early write, a backward one would
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# stall the snapshot well past the interval.
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now = time.monotonic()
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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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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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# Only after the write lands. Marking it first would mean a failed
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# set() bought the next interval's silence without leaving a snapshot.
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self._metrics_persisted_at[plugin_id] = now
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def reset_metrics(self, plugin_id: str) -> None:
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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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"""Reset metrics for a plugin."""
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with self._lock:
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with self._lock:
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@@ -370,4 +409,7 @@ class PluginResourceMonitor:
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self._metrics[plugin_id] = ResourceMetrics()
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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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cache_key = self._get_metrics_key(plugin_id)
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self.cache_manager.delete(cache_key)
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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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@@ -8,18 +8,6 @@ Type=simple
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User=root
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User=root
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WorkingDirectory=__PROJECT_ROOT_DIR__
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WorkingDirectory=__PROJECT_ROOT_DIR__
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Environment=PYTHONDONTWRITEBYTECODE=1
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Environment=PYTHONDONTWRITEBYTECODE=1
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# glibc gives each allocating thread its own malloc arena, up to 8 x CPU count,
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# and an arena that has grown is never handed back to the OS. This process runs
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# 9 threads on a 3-core Pi, so the ceiling is 24 arenas -- and a rig measured at
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# 1030 MB resident held 23 large anonymous mappings on 64 MB-aligned addresses,
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# 920 MB of them, while the live data it was actually holding (widest scroll
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# strip seen: 35,746 x 64) accounts for roughly 15 MB. That gap is arena bloat,
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# not leaked objects: RSS was flat across repeated sampling, not climbing.
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#
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# Capping the arenas trades a little allocator concurrency for a large amount of
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# resident memory on a device that has neither to spare. 2 is the usual value;
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# raise it if frame times regress.
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Environment=MALLOC_ARENA_MAX=2
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ExecStart=/usr/bin/python3 __PROJECT_ROOT_DIR__/run.py
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ExecStart=/usr/bin/python3 __PROJECT_ROOT_DIR__/run.py
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# Restart=always, not on-failure: run.py exiting 0 (a clean shutdown path taken
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# Restart=always, not on-failure: run.py exiting 0 (a clean shutdown path taken
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# for a reason that no longer applies, e.g. a config reload) would otherwise leave
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# for a reason that no longer applies, e.g. a config reload) would otherwise leave
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@@ -127,3 +127,67 @@ class TestForceReload:
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fresh = mon.get_metrics_summary("p", force_reload=True)
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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 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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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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def test_a_failed_write_does_not_buy_the_next_interval_of_silence(self):
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"""A set() that raises must not count as having persisted.
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Marking the timestamp before the write would leave no snapshot in the
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cache and still suppress the next 30 seconds of attempts.
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"""
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cache = _cache()
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cache.set.side_effect = [OSError("disk full"), None]
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mon = PluginResourceMonitor(cache, enable_monitoring=False)
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with pytest.raises(OSError):
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mon.monitor_call("p", lambda: None)
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# the very next call must try again rather than skip the interval
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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, "a failed write should be retried, not skipped"
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@@ -1,95 +0,0 @@
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"""The display unit must cap glibc's malloc arenas.
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glibc hands each allocating thread its own malloc arena, up to 8 x CPU count,
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and an arena that has grown is never returned to the OS. This process runs
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threads for the render loop, the update workers and the background fetchers, so
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on a 3-core Pi the ceiling is 24 arenas.
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Measured on a live rig, 2.5 hours in:
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RSS 1030 MB
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Private_Dirty 988 MB
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anonymous mappings > 10 MB 23 (ceiling is 8 x 3 = 24)
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largest few 104, 79, 66, 63, 63 MB, on 64 MB-aligned addresses
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against live data that accounts for perhaps 15 MB -- the widest scroll strip
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observed was 35,746 x 64, about 7 MB as RGB and the same again for its numpy
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mirror. Repeated sampling showed RSS flat between 990 and 1030 MB rather than
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climbing, so this is arena bloat rather than a leak: memory Python has freed
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but glibc is holding per-arena.
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The device had 59 MB free at the time.
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Capping the arena count trades a little allocator concurrency for that resident
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memory. The render loop is latency-sensitive, so if p99 frame time regresses the
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right response is to raise this rather than remove it.
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"""
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import re
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from pathlib import Path
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import pytest
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UNIT = (Path(__file__).resolve().parent.parent / "systemd" / "ledmatrix.service")
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#: The value the unit is expected to carry. 2 is the usual choice for a
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#: threaded Python process; 1-4 all keep some of the saving, but only one of
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#: them is what this project ships.
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EXPECTED_ARENA_MAX = 2
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def _environment(unit_text):
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return dict(
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line.split("=", 2)[1:3] if line.count("=") >= 2 else (line.split("=", 1)[1], "")
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for line in unit_text.splitlines()
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if line.startswith("Environment=")
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)
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def test_the_unit_exists():
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assert UNIT.is_file(), f"{UNIT} is missing"
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def test_malloc_arena_max_is_capped():
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env = _environment(UNIT.read_text(encoding="utf-8"))
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assert "MALLOC_ARENA_MAX" in env, (
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"the display unit does not cap glibc arenas; on a 3-core Pi the default "
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"ceiling is 24 and a measured rig held 23 of them, 920 MB"
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)
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value = int(env["MALLOC_ARENA_MAX"])
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# Pinned, not a range. A range let a change to 4 -- which hands most of the
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# saving back -- pass unnoticed, which was the point of the finding that
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# prompted this. Raising it is a legitimate response to a frame-time
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# regression, but it should be a visible edit here rather than a silent
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# drift, so the number lives in one place and changing it shows up in
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# review.
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assert value == EXPECTED_ARENA_MAX, (
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f"MALLOC_ARENA_MAX={value}, expected {EXPECTED_ARENA_MAX}. If this was "
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"raised deliberately because frame times regressed, update "
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"EXPECTED_ARENA_MAX here and say so in the commit."
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)
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def test_the_reason_is_recorded_next_to_it():
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"""A bare tuning knob invites removal by whoever meets it next."""
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text = UNIT.read_text(encoding="utf-8")
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index = text.index("Environment=MALLOC_ARENA_MAX")
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preamble = text[:index].splitlines()[-12:]
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comment = "\n".join(line for line in preamble if line.startswith("#"))
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assert "arena" in comment.lower(), "no explanation precedes the setting"
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assert re.search(r"\d", comment), (
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"the explanation cites no measurement, so a reader cannot tell whether "
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"it still applies to their hardware"
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)
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@pytest.mark.parametrize("unit", ["ledmatrix.service"])
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def test_the_unit_still_parses_as_ini(unit):
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"""systemd will refuse a malformed unit, and the panel stays dark."""
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import configparser
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path = UNIT.parent / unit
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parser = configparser.ConfigParser(strict=False)
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# systemd allows repeated keys; ConfigParser needs them merged, not rejected.
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parser.read_string(path.read_text(encoding="utf-8"))
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assert parser.has_section("Service")
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assert parser.has_option("Service", "ExecStart")
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||||||
Reference in New Issue
Block a user