| 1 | import asyncio |
| 2 | import time |
| 3 | |
| 4 | import pytest |
| 5 | |
| 6 | from control.queue_manager import QueueManager |
| 7 | |
| 8 | |
| 9 | @pytest.mark.asyncio |
| 10 | async def test_process_tasks_returns_results_in_order(): |
| 11 | qm = QueueManager(max_workers=3) |
| 12 | |
| 13 | def make_task(value): |
| 14 | async def _task(): |
| 15 | return value |
| 16 | |
| 17 | return _task |
| 18 | |
| 19 | tasks = [make_task(i) for i in range(5)] |
| 20 | results = await qm.process_tasks(tasks) |
| 21 | assert results == [0, 1, 2, 3, 4] |
| 22 | |
| 23 | |
| 24 | @pytest.mark.asyncio |
| 25 | async def test_process_tasks_surfaces_exceptions_without_killing_others(): |
| 26 | # The previous implementation swallowed exceptions and returned None, |
| 27 | # making "task succeeded with None result" indistinguishable from |
| 28 | # "task threw". Failures must surface with the original exception attached. |
| 29 | qm = QueueManager(max_workers=3) |
| 30 | |
| 31 | async def succeed(): |
| 32 | return "ok" |
| 33 | |
| 34 | async def boom(): |
| 35 | raise RuntimeError("kapow") |
| 36 | |
| 37 | tasks = [succeed, boom, succeed] |
| 38 | results = await qm.process_tasks(tasks) |
| 39 | |
| 40 | assert results[0] == "ok" |
| 41 | assert isinstance(results[1], RuntimeError) |
| 42 | assert str(results[1]) == "kapow" |
| 43 | assert results[2] == "ok" |
| 44 | |
| 45 | |
| 46 | @pytest.mark.asyncio |
| 47 | async def test_process_tasks_respects_concurrency_cap(): |
| 48 | qm = QueueManager(max_workers=2) |
| 49 | in_flight = 0 |
| 50 | peak = 0 |
| 51 | |
| 52 | async def slow(): |
| 53 | nonlocal in_flight, peak |
| 54 | in_flight += 1 |
| 55 | peak = max(peak, in_flight) |
| 56 | await asyncio.sleep(0.05) |
| 57 | in_flight -= 1 |
| 58 | return "done" |
| 59 | |
| 60 | await qm.process_tasks([slow] * 8) |
| 61 | assert peak == 2, f"peak concurrency was {peak}, expected 2" |
| 62 | |
| 63 | |
| 64 | @pytest.mark.asyncio |
| 65 | async def test_download_batch_returns_results_in_order(): |
| 66 | qm = QueueManager(max_workers=3) |
| 67 | |
| 68 | async def echo(item): |
| 69 | return {"status": "success", "item": item} |
| 70 | |
| 71 | items = ["a", "b", "c"] |
| 72 | results = await qm.download_batch(echo, items) |
| 73 | assert [r["item"] for r in results] == ["a", "b", "c"] |
| 74 | assert all(r["status"] == "success" for r in results) |
| 75 | |
| 76 | |
| 77 | @pytest.mark.asyncio |
| 78 | async def test_download_batch_surfaces_exceptions_alongside_successes(): |
| 79 | # When one item raises, the other items must still complete and the |
| 80 | # exception must be observable in the results list. |
| 81 | qm = QueueManager(max_workers=3) |
| 82 | |
| 83 | async def maybe_fail(item): |
| 84 | if item == "bad": |
| 85 | raise ValueError(f"bad item: {item}") |
| 86 | return {"status": "success", "item": item} |
| 87 | |
| 88 | results = await qm.download_batch(maybe_fail, ["a", "bad", "c"]) |
| 89 | |
| 90 | assert isinstance(results[0], dict) and results[0]["status"] == "success" |
| 91 | assert isinstance(results[1], ValueError) |
| 92 | assert "bad item: bad" in str(results[1]) |
| 93 | assert isinstance(results[2], dict) and results[2]["status"] == "success" |
| 94 | |
| 95 | |
| 96 | @pytest.mark.asyncio |
| 97 | async def test_download_batch_respects_concurrency_cap(): |
| 98 | qm = QueueManager(max_workers=2) |
| 99 | in_flight = 0 |
| 100 | peak = 0 |
| 101 | |
| 102 | async def slow(_item): |
| 103 | nonlocal in_flight, peak |
| 104 | in_flight += 1 |
| 105 | peak = max(peak, in_flight) |
| 106 | await asyncio.sleep(0.05) |
| 107 | in_flight -= 1 |
| 108 | return {"status": "success"} |
| 109 | |
| 110 | start = time.time() |
| 111 | await qm.download_batch(slow, list(range(8))) |
| 112 | elapsed = time.time() - start |
| 113 | |
| 114 | assert peak == 2, f"peak concurrency was {peak}, expected 2" |
| 115 | # 8 tasks at concurrency 2, 0.05s each = 4 batches = 0.2s minimum |
| 116 | assert elapsed >= 0.18, f"elapsed {elapsed:.3f}s suggests concurrency cap not enforced" |
| 117 |