# OCLAWMA-17: Job Dependency Graph (DAG Execution)

## Summary

Successfully implemented DAG (Directed Acyclic Graph) execution support for Oclawma's job queue system, enabling complex workflows with job dependencies and parallel execution.

## Files Created

### Core Implementation

1. **`src/oclawma/dag.py`** - Main DAG module (888 lines)
   - `DAGJob` - Job model with dependency tracking
   - `DAG` - Graph construction and validation
   - `DAGExecutor` - Parallel execution with topological sorting
   - `execute_dag()` and `execute_dag_async()` - Convenience functions
   - Progress tracking and status reporting

2. **`src/oclawma/queue/dag_integration.py`** - Queue integration (351 lines)
   - `DAGQueue` - Submit DAGs as queue jobs
   - `create_dag_from_jobs()` - Convert queue jobs to DAG
   - `dag_to_dict()` / `dict_to_dag()` - Serialization utilities

3. **`tests/test_dag.py`** - Comprehensive test suite (856 lines, 59 tests)
   - Tests for all DAG operations
   - Tests for parallel execution
   - Tests for error handling and retries
   - Tests for async execution

4. **`examples/dag_example.py`** - Usage examples (243 lines)
   - Simple linear workflows
   - Parallel job execution
   - Error handling and retries
   - Progress tracking
   - Topological sort visualization
   - Cycle detection

## Features Implemented

### Core DAG Features

1. **Graph Construction**
   - `add_job()` - Add jobs with dependencies
   - `add_dependency()` - Add dependencies between jobs
   - `remove_job()` - Remove jobs (with safety checks)
   - `get_job()` - Retrieve job by ID

2. **Validation**
   - `validate()` - Cycle detection using Kahn's algorithm
   - Missing dependency detection
   - Self-dependency prevention

3. **Execution**
   - Topological sorting for execution order
   - Parallel execution with configurable workers
   - Automatic retry with exponential backoff
   - Progress callbacks
   - Cancellation support

4. **Error Handling**
   - Continue on error option
   - Dependent job skipping on failure
   - Detailed error reporting

### Integration Features

1. **Queue Integration**
   - Submit DAGs as queue jobs
   - Execute DAGs through queue system
   - Convert existing queue jobs to DAG representation

2. **Serialization**
   - DAG to/from dictionary conversion
   - JSON-compatible representation

## Test Results

```
pytest tests/test_dag.py -v
========================= 59 passed =========================

pytest tests/test_queue.py tests/test_dag.py -v
========================= 115 passed ========================

ruff check src/oclawma/dag.py src/oclawma/queue/dag_integration.py tests/test_dag.py
All checks passed!

black --check src/oclawma/dag.py src/oclawma/queue/dag_integration.py tests/test_dag.py
All done! ✨ 🍰 ✨
```

## Usage Example

```python
from oclawma.dag import DAG, DAGJob, execute_dag

# Create a DAG
dag = DAG(dag_id="data-pipeline")

# Add jobs with dependencies
dag.add_job("fetch-data")
dag.add_job("clean-data", depends_on=["fetch-data"])
dag.add_job("analyze-data", depends_on=["clean-data"])

# Define job handler
def handler(job: DAGJob):
    print(f"Executing: {job.id}")
    return f"{job.id}-result"

# Execute with parallel workers
result = execute_dag(dag, handler, max_workers=4)

print(f"Status: {result.status.value}")
print(f"Completed: {result.progress.completed_jobs}/{result.progress.total_jobs}")
```

## API Reference

### Classes

- `DAG` - Main graph class
  - `add_job()`, `add_dependency()`, `remove_job()`
  - `validate()`, `topological_sort()`
  - `get_ready_jobs()`, `get_jobs_with_failed_deps()`

- `DAGJob` - Job definition
  - `id`, `payload`, `priority`, `depends_on`, `max_retries`

- `DAGExecutor` - Execution engine
  - `execute_dag()`, `execute_dag_async()`
  - `on_progress()`, `cancel()`, `get_progress()`

- `DAGResult` - Execution result
  - `status`, `job_results`, `progress`, `all_succeeded`

### Exceptions

- `DAGError` - Base exception
- `DAGCycleError` - Cycle detected
- `DAGNotFoundError` - Job not found

## Architecture

The DAG execution uses a level-by-level approach:

1. **Topological Sort** - Groups jobs by dependency level
2. **Ready Queue** - Tracks jobs whose dependencies are complete
3. **ThreadPoolExecutor** - Runs independent jobs in parallel
4. **Progress Tracking** - Callbacks for real-time updates

This ensures:
- Jobs execute in correct dependency order
- Maximum parallelism where possible
- Efficient resource utilization

## Future Enhancements (Optional)

- Web UI DAG visualization
- Persistent DAG storage
- DAG templates and reuse
- Conditional branching (if/then/else)
- Sub-DAG support
