expanded performance logging and switched to monotonic_ns for accurate measurements

cpu_governor_auto_adjust/helper.py

@@ -0,0 +1,10 @@
+def format_time_ns(elapsed_ns: float) -> str:
+    """Format elapsed time dynamically in an appropriate unit."""
+    if elapsed_ns < 1_000:  # Less than 1 µs
+        return f"{elapsed_ns} ns"  # Nanoseconds
+    elif elapsed_ns < 1_000_000:  # Less than 1 ms
+        return f"{elapsed_ns / 1_000:.2f} µs"  # Microseconds
+    elif elapsed_ns < 1_000_000_000:  # Less than 1 second
+        return f"{elapsed_ns / 1_000_000:.2f} ms"  # Milliseconds
+    else:
+        return f"{elapsed_ns / 1_000_000_000:.2f} s"  # Seconds

cpu_governor_auto_adjust/schedule.py

@@ -2,11 +2,12 @@ from cpu_governor_auto_adjust.app_class import AppClass
 from cpu_governor_auto_adjust.config import Config
 from cpu_governor_auto_adjust.trigger import Trigger
 from cpu_governor_auto_adjust.governor import GovernorControl, Governor, _governor_list
+from cpu_governor_auto_adjust.helper import format_time_ns
 import asyncio
 import signal
 from asyncio import Task
 from functools import cached_property, partial
-from time import monotonic
+from time import monotonic_ns as monotonic
 from typing import Callable, Any, Optional
 
 
@@ -60,8 +61,7 @@ class TriggerScheduler(AppClass):
         self.log.debug("scheduling task: %s", task.__name__)
         start = monotonic()
         await self.loop.create_task(task(*args))
-        end = monotonic()
+        self.log.debug("task %s finished in %s", task.__name__, format_time_ns(monotonic() - start))  
-        self.log.debug("task %s finished in %.3f ms", task.__name__, (end - start) * 1000)  
 
     async def run(self) -> None:
         """Run the scheduler and keep it alive until stopped."""

cpu_governor_auto_adjust/trigger.py

@@ -3,9 +3,10 @@ from cpu_governor_auto_adjust.config import Config, TriggerTuple
 from cpu_governor_auto_adjust.exceptions import MissingConfig, TriggerImportError
 from cpu_governor_auto_adjust.governor import Governor, _governor_list
 from functools import cached_property
-from time import monotonic
+from time import monotonic_ns as monotonic
 from datetime import datetime
 import asyncio
+from cpu_governor_auto_adjust.helper import format_time_ns
 
 
 class Trigger(AppClass):
@@ -15,16 +16,25 @@ class Trigger(AppClass):
         self.log.setLevel(self.config.loglevel.upper())
         self._timestamp_last_statistics = monotonic()
         self._total_consumed_time = 0
+        self._minimum_consumed_time = 10000000000
+        self._maximum_consumed_time = 0
         self.runs = 0
         
     def __hash__(self):
         return super().__hash__(hash(self.name))
     
     @property
-    def average_consumed_time(self) -> str:
+    def consumed_time(self) -> dict[str, str]:
+        _dict = {
+            "total": format_time_ns(self._total_consumed_time),
+            "minimum": format_time_ns(self._minimum_consumed_time),
+            "maximum": format_time_ns(self._maximum_consumed_time),
+            "average": 0,
+        }
         if self.runs == 0:
-            return 0
+            return _dict
-        return f"{(self._total_consumed_time / self.runs) * 1_000_000:.3f} µs"
+        _dict["average"] = format_time_ns(self._total_consumed_time / self.runs)
+        return _dict
     
     def log_statistics(self):
         class_vars = {}
@@ -50,8 +60,7 @@ class Trigger(AppClass):
                     value = str(value)
                 class_vars[key] = value
                 
-        end = monotonic()
+        self.log.info("info and statistics [time taken: %s]: %s", format_time_ns(monotonic() - start),  class_vars)
-        self.log.info("info and statistics [time taken: %.3fµs]: %s", (end - start) * 1_000_000,  class_vars)
     
     @property
     def trigger_state(self) -> str:
@@ -91,13 +100,15 @@ class Trigger(AppClass):
         while True:
             start = monotonic()
             self.log.debug("run trigger code of %s", self.__class__.__name__)
-            if self._timestamp_last_statistics + int(self._config.statistics_interval_in_seconds) < monotonic():
+            if self._timestamp_last_statistics + (int(self._config.statistics_interval_in_seconds) * 1_000_000_000) < start:
                 self.log_statistics()
-                self._timestamp_last_statistics = monotonic()
+                self._timestamp_last_statistics = start
             self.trigger_code()
             end = monotonic()
             self._total_consumed_time += end - start
+            self._minimum_consumed_time = min(self._minimum_consumed_time, end - start)
+            self._maximum_consumed_time = max(self._maximum_consumed_time, end - start)
             self.runs += 1
-            self.log.debug("trigger state: %s, finished in %.3f µs", self.trigger_state, (end - start) * 1_000_000)
+            self.log.debug("trigger state: %s, finished in %s", self.trigger_state, format_time_ns(end - start))
             await asyncio.sleep(self.config.interval_in_seconds)
     

cpu_governor_auto_adjust/triggers/cpu_load.py

@@ -51,13 +51,13 @@ class CpuLoadTrigger(Trigger):
         return any(load >= threshold for load, threshold in zip(self.current_load, self.high_threshold))
     
     @property
-    def current_load_average_over_under_threshold(self) -> bool:
+    def current_load_average_under_low_threshold(self) -> bool:
         return any(load <= threshold for load, threshold in zip(self.current_load, self.low_threshold))
     
     def trigger_code(self) -> None:
         current_active = self._active
         new_high_load_active = self.current_load_average_over_high_threshold
-        new_low_load_active = self.current_load_average_over_under_threshold
+        new_low_load_active = self.current_load_average_under_low_threshold
         
         if not current_active and new_high_load_active:
             self.log.info(

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "cpu_governor_auto_adjust"
-version = "0.5.12"
+version = "0.5.17"
 description = "This application has been developed to automatically change cpu governor based on certain triggers."
 authors = [
     { name = "Martin Reurekas", email = "martin@semrks.nl" }