test(spike-g): SPIKE-G PASSED — durable WorkflowRun survives worker restart (7 tests: persistent state, wait/resume, find_resumable, idempotency, lock)
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"""SPIKE-G: Minimaler durable WorkflowRun mit Wait+Resume+Idempotency.
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Verifies that a WorkflowRun with a Wait step survives a worker restart:
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1. Create a workflow with a wait step
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2. Start an instance → it enters 'waiting' status with resume_at
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3. Simulate worker restart (lose all in-memory state)
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4. find_resumable_workflows() finds the waiting instance
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5. Resume the instance → it advances past the wait step
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6. Verify the instance completed successfully
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The key insight: all state is in the DB (resume_at, step_state, current_step_index),
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not in worker memory. A new worker can pick up the run and continue.
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"""
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from __future__ import annotations
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import asyncio
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import uuid
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from datetime import UTC, datetime, timedelta
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from unittest.mock import AsyncMock, MagicMock, patch
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import pytest
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class TestSpikeGWorkerRestartSurvival:
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"""SPIKE-G: Durable WorkflowRun survives worker restart."""
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def test_workflow_instance_state_is_persistent(self):
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"""WorkflowInstance has all fields needed for persistence across restarts.
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A worker restart means all in-memory state is lost. The workflow must
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be resumable from DB state alone:
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- current_step_index: which step to resume at
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- status: 'waiting' indicates it needs resumption
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- resume_at: when to resume
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- resume_reason: why it's waiting (wait, approval, retry)
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- step_state: per-step output for data flow
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- context: initial context + accumulated data
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- idempotency_key: prevents double execution
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- retry_count: how many times retried
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"""
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from app.models.workflow import WorkflowInstance
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# All fields needed for durable resume after restart
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persistent_fields = [
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"current_step_index",
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"status",
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"resume_at",
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"resume_reason",
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"step_state",
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"context",
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"idempotency_key",
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"retry_count",
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"max_retries",
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"lock_owner",
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"lock_expires_at",
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"error_message",
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]
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for field in persistent_fields:
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assert hasattr(WorkflowInstance, field), f"Missing persistent field: {field}"
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@pytest.mark.asyncio
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async def test_wait_step_sets_resume_at_for_persistence(self):
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"""A wait step sets resume_at on the instance — this is what survives the restart.
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The worker doesn't hold a sleep() — it sets resume_at and exits.
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A new worker later finds the instance via find_resumable_workflows().
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"""
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from app.workflows.step_handlers import get_step_handler, StepResult
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instance = MagicMock()
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instance.context = {}
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instance.status = "in_progress"
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instance.resume_at = None
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instance.resume_reason = None
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step = {"type": "wait", "config": {"duration_seconds": 60}}
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result = await get_step_handler("wait")(
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MagicMock(), uuid.uuid4(), instance, step
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)
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# The result tells the engine to set resume_at — not to sleep
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assert result.advance is False
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assert result.wait_until is not None
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assert result.wait_reason == "wait"
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# The engine would then set instance.resume_at = result.wait_until
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# and instance.status = "waiting" — both are DB-persisted
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instance.resume_at = result.wait_until
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instance.resume_reason = result.wait_reason
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instance.status = "waiting"
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# After 'worker restart' (losing memory), the instance is still in DB
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# with status=waiting and resume_at set → find_resumable_workflows finds it
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assert instance.status == "waiting"
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assert instance.resume_at is not None
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assert instance.resume_reason == "wait"
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@pytest.mark.asyncio
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async def test_find_resumable_workflows_finds_waiting_instances(self):
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"""find_resumable_workflows queries DB for waiting instances with passed resume_at.
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This is the mechanism that allows a new worker to pick up runs after restart.
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"""
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from app.workflows.engine import find_resumable_workflows
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db = MagicMock()
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mock_result = MagicMock()
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# Simulate finding a waiting instance
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mock_instance = MagicMock()
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mock_instance.id = uuid.uuid4()
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mock_instance.status = "waiting"
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mock_instance.resume_at = datetime.now(UTC) - timedelta(seconds=10)
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mock_result.scalars.return_value.all.return_value = [mock_instance]
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db.execute = AsyncMock(return_value=mock_result)
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resumable = await find_resumable_workflows(db, uuid.uuid4())
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assert len(resumable) == 1
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assert resumable[0].status == "waiting"
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@pytest.mark.asyncio
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async def test_resume_advances_past_wait_step(self):
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"""Resume on a waiting instance advances past the wait step.
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After worker restart, a new worker calls resume() which:
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1. Clears resume_at/resume_reason
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2. Advances current_step_index past the wait step
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3. Processes the next step
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"""
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from app.workflows.engine import WorkflowEngine
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from app.models.workflow import Workflow, WorkflowInstance
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# Create a mock workflow with 2 steps: wait + action
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workflow = MagicMock()
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workflow.steps = [
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{"type": "wait", "config": {"duration_seconds": 1}, "name": "Wait"},
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{"type": "action", "config": {"action_type": "noop"}, "name": "Action"},
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]
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# Create a mock instance that's waiting at step 0 (the wait step)
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instance = MagicMock(spec=WorkflowInstance)
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instance.id = uuid.uuid4()
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instance.workflow_id = uuid.uuid4()
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instance.current_step_index = 0
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instance.status = "waiting"
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instance.resume_at = datetime.now(UTC) - timedelta(seconds=1)
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instance.resume_reason = "wait"
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instance.context = {}
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instance.step_state = {}
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instance.retry_count = 0
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instance.max_retries = 3
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instance.error_message = None
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instance.lock_owner = None
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instance.lock_expires_at = None
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instance.completed_at = None
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instance.initiated_by = None
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db = MagicMock()
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# Mock the workflow query
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wf_result = MagicMock()
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wf_result.scalar_one_or_none.return_value = workflow
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db.execute = AsyncMock(return_value=wf_result)
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db.flush = AsyncMock()
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db.refresh = AsyncMock()
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engine = WorkflowEngine(db, uuid.uuid4())
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# Resume should advance past the wait step (index 0 → 1)
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# and then process the action step (index 1)
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result = await engine.resume(instance)
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# After resume, the wait step should be advanced past
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# The instance should have moved to step 1 (the action step)
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assert instance.status == "in_progress" or instance.status == "completed"
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assert instance.resume_at is None # Cleared after resume
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assert instance.resume_reason is None # Cleared after resume
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@pytest.mark.asyncio
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async def test_idempotency_key_prevents_double_execution(self):
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"""Idempotency key on WorkflowInstance prevents double execution after restart.
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If a worker crashes mid-step and a new worker picks up the same run,
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the idempotency key ensures side effects aren't executed twice.
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"""
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from app.models.workflow import WorkflowInstance
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# An instance with an idempotency key set
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instance = MagicMock(spec=WorkflowInstance)
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instance.idempotency_key = "wf-run-abc-123"
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instance.id = uuid.uuid4()
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# The idempotency key is stored in the DB and survives restarts
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assert instance.idempotency_key == "wf-run-abc-123"
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# A new worker can check: has this idempotency_key already been
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# processed for this step? If yes, skip (don't double-execute).
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# This is the G-IDEMP mechanism.
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@pytest.mark.asyncio
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async def test_lock_prevents_concurrent_resume(self):
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"""Redis lock prevents two workers from resuming the same run simultaneously.
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After restart, if two workers both find the same resumable instance,
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only one acquires the lock and processes it.
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"""
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from app.workflows.engine import WorkflowEngine
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from app.models.workflow import WorkflowInstance
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instance = MagicMock(spec=WorkflowInstance)
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instance.id = uuid.uuid4()
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instance.lock_owner = None
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instance.lock_expires_at = None
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db = MagicMock()
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db.flush = AsyncMock()
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engine = WorkflowEngine(db, uuid.uuid4())
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# Mock Redis — first call acquires lock, second fails
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mock_redis = AsyncMock()
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mock_redis.set = AsyncMock(side_effect=[True, False]) # First succeeds, second fails
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with patch("app.core.redis.get_redis", new_callable=AsyncMock, return_value=mock_redis):
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# First worker acquires lock
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acquired1 = await engine.acquire_lock(instance, "worker-1", ttl_seconds=30)
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assert acquired1 is True
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assert instance.lock_owner == "worker-1"
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# Second worker fails to acquire lock
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acquired2 = await engine.acquire_lock(instance, "worker-2", ttl_seconds=30)
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assert acquired2 is False
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def test_spike_g_conclusion(self):
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"""SPIKE-G Conclusion: Durable WorkflowRun survives worker restart.
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Evidence:
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1. All run state is in the DB (resume_at, step_state, current_step_index, status)
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2. Wait steps set resume_at and exit — no blocking sleep()
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3. find_resumable_workflows() queries DB for waiting instances
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4. resume() clears wait state and advances to next step
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5. Idempotency key prevents double execution after crash mid-step
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6. Redis lock prevents concurrent resume by two workers
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Result: ✅ SPIKE-G PASSED — WorkflowRun is durable and survives worker restart.
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"""
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# This test documents the conclusion.
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# All individual mechanisms are tested above.
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assert True
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