# Aperion Flow - Gap Analysis

## Executive Summary

This document identifies gaps and improvement areas for the Aperion Flow workflow engine, prioritized by security and reliability impact.

---

## Critical Priority

### 1. In-Memory Checkpoint Store (Production Risk)

**Current State:** `InMemoryCheckpointStore` loses all state on process restart.

**Risk:** Complete loss of workflow state in production, no crash recovery possible.

**Recommendation:**
```python
# Implement persistent checkpoint stores
class RedisCheckpointStore:
    """Redis-backed checkpoint persistence."""
    async def save(self, checkpoint: Checkpoint) -> None:
        key = f"flow:checkpoint:{checkpoint.execution_id}:{checkpoint.id}"
        await self.redis.setex(key, ttl=86400, value=checkpoint.to_json())

class PostgresCheckpointStore:
    """PostgreSQL-backed checkpoint persistence."""
    async def save(self, checkpoint: Checkpoint) -> None:
        await self.pool.execute(
            "INSERT INTO checkpoints (execution_id, checkpoint_id, data) VALUES ($1, $2, $3)",
            checkpoint.execution_id, checkpoint.id, checkpoint.to_json()
        )
```

**Priority:** CRITICAL - Required for production deployment

---

### 2. No Distributed Locking

**Current State:** Multiple executor instances can run the same pipeline concurrently.

**Risk:** Race conditions, duplicate execution, data corruption.

**Recommendation:**
```python
class DistributedLock(Protocol):
    async def acquire(self, resource: str, ttl: float) -> bool: ...
    async def release(self, resource: str) -> None: ...

# Use Redis SETNX or PostgreSQL advisory locks
async def execute_with_lock(self, pipeline: Pipeline, ...) -> ExecutionResult:
    lock_key = f"flow:lock:{pipeline.id}"
    if not await self.lock.acquire(lock_key, ttl=pipeline.timeout):
        raise ConcurrentExecutionError(f"Pipeline {pipeline.id} already running")
    try:
        return await self._execute_internal(pipeline, ...)
    finally:
        await self.lock.release(lock_key)
```

**Priority:** CRITICAL - Required for distributed deployment

---

### 3. No Execution Timeout Enforcement

**Current State:** Pipeline-level timeout exists in model but not enforced in executor.

**Risk:** Runaway pipelines consume resources indefinitely.

**Recommendation:**
```python
async def execute(self, pipeline: Pipeline, ...) -> ExecutionResult:
    try:
        return await asyncio.wait_for(
            self._run_pipeline(pipeline, context),
            timeout=pipeline.timeout
        )
    except asyncio.TimeoutError:
        context.mark_failed("Pipeline timeout exceeded", "TimeoutError")
        return self._build_result(context, pipeline, False)
```

**Priority:** CRITICAL - Resource protection

---

## High Priority

### 4. No Cancellation Support

**Current State:** No way to cancel a running pipeline execution.

**Risk:** Cannot stop runaway or unwanted executions.

**Recommendation:**
```python
class PipelineExecutor:
    def __init__(self):
        self._cancellation_tokens: dict[str, asyncio.Event] = {}

    async def cancel(self, execution_id: str) -> bool:
        if execution_id in self._cancellation_tokens:
            self._cancellation_tokens[execution_id].set()
            return True
        return False

    async def _run_stage(self, stage, context):
        if self._cancellation_tokens.get(context.execution_id, asyncio.Event()).is_set():
            raise CancellationError("Execution cancelled")
```

**Priority:** HIGH - Operational requirement

---

### 5. No Stage Compensation (Saga Pattern)

**Current State:** No rollback mechanism when pipeline fails mid-execution.

**Risk:** Partial state changes without cleanup in multi-step workflows.

**Recommendation:**
```python
class PipelineStage(BaseModel):
    # ... existing fields
    compensate_handler: str | None = None  # Rollback handler

async def _run_compensation(self, failed_stage_id: str, context: PipelineContext):
    """Run compensation for completed stages in reverse order."""
    for stage_id in reversed(context.completed_stage_ids):
        stage = self._get_stage(stage_id)
        if stage.compensate_handler:
            handler = await self._get_handler(stage.compensate_handler)
            await handler(context.data)
```

**Priority:** HIGH - Data consistency for complex workflows

---

### 6. No Metrics/Observability

**Current State:** No metrics collection for monitoring.

**Risk:** No visibility into execution performance, failure rates.

**Recommendation:**
```python
from prometheus_client import Counter, Histogram, Gauge

PIPELINE_EXECUTIONS = Counter('flow_pipeline_executions_total', 'Total pipeline executions', ['pipeline', 'status'])
PIPELINE_DURATION = Histogram('flow_pipeline_duration_seconds', 'Pipeline execution duration', ['pipeline'])
STAGE_DURATION = Histogram('flow_stage_duration_seconds', 'Stage execution duration', ['pipeline', 'stage'])
ACTIVE_EXECUTIONS = Gauge('flow_active_executions', 'Currently running executions')
```

**Priority:** HIGH - Production observability

---

## Medium Priority

### 7. No Rate Limiting

**Current State:** Unlimited concurrent executions allowed.

**Risk:** Resource exhaustion under load.

**Recommendation:**
```python
class PipelineExecutor:
    def __init__(self, max_concurrent: int = 100):
        self._semaphore = asyncio.Semaphore(max_concurrent)

    async def execute(self, pipeline, ...):
        async with self._semaphore:
            return await self._execute_internal(pipeline, ...)
```

**Priority:** MEDIUM - Load protection

---

### 8. No Execution History Cleanup

**Current State:** In-memory storage grows unbounded.

**Risk:** Memory exhaustion over time.

**Recommendation:**
```python
class CheckpointStore(Protocol):
    async def cleanup_old(self, older_than: datetime) -> int:
        """Delete checkpoints older than timestamp."""
        ...

# Schedule periodic cleanup
async def cleanup_task():
    while True:
        await asyncio.sleep(3600)  # Hourly
        cutoff = datetime.now(UTC) - timedelta(days=7)
        await store.cleanup_old(cutoff)
```

**Priority:** MEDIUM - Long-term stability

---

### 9. No Handler Validation at Pipeline Creation

**Current State:** Handler validity only checked at runtime.

**Risk:** Late failure discovery, poor developer experience.

**Recommendation:**
```python
def validate_pipeline(pipeline: Pipeline, registry: dict[str, Callable]) -> list[str]:
    """Validate pipeline before execution."""
    errors = []
    for stage in pipeline.stages:
        if stage.handler not in registry:
            try:
                importlib.import_module(stage.handler.rsplit('.', 1)[0])
            except ImportError:
                errors.append(f"Handler not found: {stage.handler}")
    return errors
```

**Priority:** MEDIUM - Developer experience

---

### 10. No Event Replay for Debugging

**Current State:** Events emitted but not stored for replay.

**Risk:** Difficult to debug failed executions.

**Recommendation:**
```python
class EventStore:
    """Persistent event store for replay and debugging."""
    async def append(self, execution_id: str, event: dict) -> None: ...
    async def get_events(self, execution_id: str) -> list[dict]: ...
    async def replay(self, execution_id: str) -> AsyncIterator[dict]: ...
```

**Priority:** MEDIUM - Debugging support

---

## Low Priority

### 11. No Stage Dependencies by Name

**Current State:** Dependencies use stage IDs (UUIDs).

**Improvement:** Allow dependencies by stage name for readability.

```python
@model_validator(mode="after")
def resolve_dependencies(self) -> "Pipeline":
    name_to_id = {s.name: s.id for s in self.stages}
    for stage in self.stages:
        stage.depends_on = [
            name_to_id.get(dep, dep) for dep in stage.depends_on
        ]
```

---

### 12. No Webhook/Callback Support

**Current State:** No way to notify external systems on completion.

**Improvement:** Add webhook callbacks.

```python
class Pipeline(BaseModel):
    on_complete_webhook: str | None = None
    on_failure_webhook: str | None = None
```

---

## Implementation Roadmap

| Phase | Items | Effort |
|-------|-------|--------|
| 1 - Production Ready | #1, #2, #3, #6 | 2 weeks |
| 2 - Operations | #4, #7, #8 | 1 week |
| 3 - Reliability | #5, #9, #10 | 2 weeks |
| 4 - Polish | #11, #12 | 1 week |

---

## References

- [Temporal Workflow Patterns](https://temporal.io/blog/how-modern-workflow-orchestration-solves-scalability-challenges)
- [Circuit Breaker Best Practices](https://python.elitedev.in/python/python-circuit-breaker-pattern-complete-guide-with-redis-async-support-and-implementation-examples-24514c38/)
- [Checkpoint Recovery Patterns](https://www.chriswirz.com/distributed-systems/rollback-recovery)
- [DAG Best Practices](https://www.astronomer.io/docs/learn/dag-best-practices/)
