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execute_tasks_h5() wait for future to complete #712

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Merged
jan-janssen merged 1 commit into from
Jul 12, 2025
Merged

execute_tasks_h5() wait for future to complete #712

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10 changes: 10 additions & 0 deletions executorlib/task_scheduler/file/shared.py
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Original file line number Diff line number Diff line change
Expand Up @@ -86,6 +86,16 @@ def execute_tasks_h5(
with contextlib.suppress(queue.Empty):
task_dict = future_queue.get_nowait()
if task_dict is not None and "shutdown" in task_dict and task_dict["shutdown"]:
while len(memory_dict) > 0:
memory_dict = {
key: _check_task_output(
task_key=key,
future_obj=value,
cache_directory=cache_dir_dict[key],
)
for key, value in memory_dict.items()
if not value.done()
}
Comment on lines +89 to +98
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@coderabbitai coderabbitai bot Jul 11, 2025

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🛠️ Refactor suggestion

Add timeout and sleep to prevent busy waiting and infinite loops.

The implementation correctly ensures all pending futures complete before shutdown, but the busy-wait loop could consume unnecessary CPU resources and potentially run indefinitely if futures never complete.

Consider adding a timeout mechanism and sleep interval to improve performance and reliability:

+import time
+
 while len(memory_dict) > 0:
+    time.sleep(0.1)  # Add small delay to prevent busy waiting
     memory_dict = {
         key: _check_task_output(
             task_key=key,
             future_obj=value,
             cache_directory=cache_dir_dict[key],
         )
         for key, value in memory_dict.items()
         if not value.done()
     }
+    # Optional: Add timeout to prevent infinite loops
+    # if time.time() - start_time > max_wait_time:
+    #     break

This prevents excessive CPU usage while waiting for futures to complete and provides an escape mechanism for stuck futures.

📝 Committable suggestion

‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.

Suggested change
while len(memory_dict) > 0:
memory_dict = {
key: _check_task_output(
task_key=key,
future_obj=value,
cache_directory=cache_dir_dict[key],
)
for key, value in memory_dict.items()
if not value.done()
}
import time
while len(memory_dict) > 0:
time.sleep(0.1) # Add small delay to prevent busy waiting
memory_dict = {
key: _check_task_output(
task_key=key,
future_obj=value,
cache_directory=cache_dir_dict[key],
)
for key, value in memory_dict.items()
if not value.done()
}
# Optional: Add timeout to prevent infinite loops
# if time.time() - start_time > max_wait_time:
# break
🤖 Prompt for AI Agents 
In executorlib/task_scheduler/file/shared.py around lines 89 to 98, the current
loop busy-waits while checking for incomplete futures, which can cause high CPU
usage and potential infinite loops. To fix this, add a sleep interval inside the
loop to pause briefly between checks, reducing CPU consumption. Additionally,
implement a timeout mechanism that breaks the loop if futures do not complete
within a reasonable time, preventing infinite waiting. Use time tracking to
enforce the timeout and ensure the loop exits gracefully.

if terminate_function is not None:
for task in process_dict.values():
terminate_function(task=task)
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