test(e2e): adaptive frequency escalation and recovery round trip (SIENTIAPDE-2193)
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@@ -50,6 +50,12 @@ First run creates TTL index on notification collection used by scouter pipelines
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Two `simple_metrics` pipelines for the same `model_id` (60 min and 1440 min windows) → one
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Temporal schedule per window, each with its own `frequency` as interval.
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### 1.7.2 Adaptive frequency escalation/recovery round trip
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Laborious-style write (`adaptive_state.escalated=true` + BSON-datetime `updated_at`) on one
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window → next run switches only that schedule to `frequency_alert`; the recovery write switches it
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back to `frequency`.
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---
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## 2. Alerts workflow
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@@ -1,6 +1,6 @@
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"""E2E tests for the Orchestrator main workflow."""
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from datetime import timedelta
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from datetime import UTC, datetime, timedelta
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import pytest
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from pymongo import MongoClient
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@@ -259,3 +259,57 @@ async def test_scenario_1_7_1_simple_metrics_parallel_windows(
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short, long = 'model-sm-simple_metrics-60m', 'model-sm-simple_metrics-1440m'
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assert await _schedule_every(temporal_host, short) == timedelta(hours=1)
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assert await _schedule_every(temporal_host, long) == timedelta(days=1)
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def _laborious_adaptive_write(mongo_uri: str, schedule_name: str, escalated: bool) -> None:
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"""Replicates laborious `update_simple_metrics_adaptive_state` on an escalated flip."""
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mongo = MongoClient(mongo_uri)
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try:
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mongo[E2E_DATABASE]['pipelines'].update_one(
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{'schedule_name': schedule_name},
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{
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'$set': {
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'adaptive_state': {'escalated': escalated, 'healthy_streak': 0},
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# BSON datetime, as laborious writes it; a string would break the tick.
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'updated_at': datetime.now(UTC),
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}
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},
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)
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finally:
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mongo.close()
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@pytest.mark.e2e
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@pytest.mark.asyncio
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async def test_scenario_1_7_2_simple_metrics_adaptive_frequency_round_trip(
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temporal_env: WorkflowEnvironment,
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orchestrator_worker,
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stub_workers,
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mongo_uri: str,
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temporal_host: str,
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redis_client: Redis,
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):
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"""1.7.2: Escalation written by laborious switches only that window to frequency_alert; recovery switches it back."""
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scenario = load_scenario_input('orchestrator_simple_metrics_windows')
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_apply_orchestrator_seeds(scenario, mongo_uri, redis_client)
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short, long = 'model-sm-simple_metrics-60m', 'model-sm-simple_metrics-1440m'
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async def run(tag: str) -> None:
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await start_and_await_workflow(
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temporal_env.client,
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Orchestrator.run,
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scenario['workflow_input'],
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make_workflow_id(f'orchestrator-sm-adaptive-{tag}'),
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)
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await run('create')
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assert await _schedule_every(temporal_host, short) == timedelta(hours=1)
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_laborious_adaptive_write(mongo_uri, short, escalated=True)
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await run('escalate')
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assert await _schedule_every(temporal_host, short) == timedelta(minutes=5)
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assert await _schedule_every(temporal_host, long) == timedelta(days=1)
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_laborious_adaptive_write(mongo_uri, short, escalated=False)
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await run('recover')
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assert await _schedule_every(temporal_host, short) == timedelta(hours=1)
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