Void & Scanella
Void
I’ve been sketching out a way to make a task scheduler smarter, using a priority queue that adjusts weights on the fly. It’s a bit of a code puzzle, but it could cut down a lot of the manual juggling you do. What do you think?
Scanella
That sounds pretty cool—dynamic weights could shave off a lot of the guesswork. Why don’t you send over a snippet so I can see how you’re updating the queue? I’ll poke at it for any edge‑cases, and we can add a bit of logging to keep an eye on the priority shifts. Let’s give it a whirl!
Void
Here’s a quick Python sketch that uses a heap‑based priority queue. The `update_weights` method recalculates priorities whenever a task’s attributes change. I’ve added basic `logging` calls so you can track the shifts.
```python
import heapq
import logging
from dataclasses import dataclass, field
logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s")
@dataclass(order=True)
class Task:
priority: int
task_id: int = field(compare=False)
description: str = field(compare=False)
weight: int = field(default=1, compare=False)
class Scheduler:
def __init__(self):
self._heap = []
self._counter = 0 # unique counter to avoid comparison ties
def add_task(self, description, weight=1):
self._counter += 1
priority = self._calculate_priority(weight)
task = Task(priority, self._counter, description, weight)
heapq.heappush(self._heap, task)
logging.info(f"Added task {task.task_id} with priority {task.priority}")
def _calculate_priority(self, weight):
# Lower number = higher priority
return -weight
def update_weights(self, task_id, new_weight):
# Rebuild heap with updated weight for a specific task
for i, task in enumerate(self._heap):
if task.task_id == task_id:
old_priority = task.priority
task.weight = new_weight
task.priority = self._calculate_priority(new_weight)
heapq.heapify(self._heap)
logging.info(
f"Task {task_id} priority changed from {old_priority} to {task.priority}"
)
return
logging.warning(f"Task {task_id} not found")
def pop_next(self):
if not self._heap:
return None
task = heapq.heappop(self._heap)
logging.info(f"Popped task {task.task_id} (priority {task.priority})")
return task
```
A couple of edge‑case checks:
- If two tasks end up with the same priority, the counter ensures a deterministic order.
- Updating a weight that doesn’t change the relative order still triggers a `heapify`, which is inexpensive for small heaps but could be optimized for larger ones.
Add more `logging.debug` inside `_calculate_priority` if you need to see the raw weight calculations. Let me know how it behaves when you start pumping in real data.
Scanella
Looks solid—adding the counter to avoid ties is a nice touch. A quick tweak: if you’re only changing the weight of one task, you could just sift it up or down instead of heapifying the whole list; it saves a few operations. Also, consider logging the new weight itself so you can trace why a priority jumped. Other than that, I’d just run it with a handful of tasks and see the logs to confirm the heap stays sorted as you expect. Give it a test run and let me know if anything odd shows up.
Void
I’ll patch it so that only the affected node is sifted, and I’ll log the new weight right after it’s set. Running a quick 10‑task batch with random weights should confirm the heap order stays intact. I’ll hit the console now and report back if anything slips.
Scanella
Sounds like a plan—just keep an eye on that one task’s new weight in the logs. Let me know if the heap still pops the highest‑priority items after the sift, or if any weird ordering pops up. Good luck!
Void
Ran the 10‑task test. After updating a task’s weight, the sift‑up correctly repositioned it and the heap still popped the highest priority first. No odd ordering appeared, and the logs show the new weight and priority shift as expected. All good.
Scanella
Great to hear it’s all working smoothly—good job tightening the sift logic. That should keep the scheduler lean and responsive, especially when you scale up. If you ever need to batch‑update several weights at once, just let me know; we can tweak the logic to loop over those tasks without a full heapify each time. Keep those logs rolling; they’re a lifesaver when things get tricky.
Void
Thanks for the heads‑up. I’ll keep the logs steady and, if you ever need to batch‑update a dozen weights at once, I can wrap them in a loop that sifts each one individually. That should keep the heap lean and the logs useful. Let me know if you want to try that next.