# Prodigy Implementation Gaps Analysis
## Summary
After analyzing the current implementation against the whitepaper specifications, Prodigy has successfully implemented most core features but has several gaps and areas that need attention.
## ✅ Implemented Features
### Core MapReduce Pattern
- **MapReduce executor** (`src/cook/execution/mapreduce.rs`)
- **Setup phase** with command execution and output capture
- **Map phase** with parallel agent execution
- **Reduce phase** for result aggregation
- **JSON input support** with JSONPath extraction
- **Command input support** for file listing
### Parallel Execution & Isolation
- **WorktreePool** (`src/worktree/pool.rs`) for managing parallel worktrees
- **WorktreeManager** (`src/worktree/manager.rs`) for git worktree operations
- **Isolation strategies** with OnDemand, Pooled, Reuse, and Dedicated modes
- **Resource limits** and cleanup policies
- **Automatic worktree creation and cleanup**
### Dead Letter Queue (DLQ)
- **Full DLQ implementation** (`src/cook/execution/dlq.rs`)
- **Failure tracking** with detailed error history
- **Pattern analysis** for common failure types
- **DLQ storage** with persistence to disk
- **Worktree artifacts** preservation for debugging
### Retry Logic
- **Exponential backoff** retry strategy (`src/cook/retry.rs`)
- **Transient error detection** for network/rate limit issues
- **Configurable retry attempts** per task
- **Error classification** (Timeout, CommandFailed, WorktreeError, etc.)
### Progress & State Management
- **EnhancedProgressTracker** for real-time progress monitoring
- **JobStateManager** for checkpoint/resume capability
- **Event logging** with detailed execution history
- **Session state persistence** across restarts
## ✅ Recently Fixed Features
### 1. DLQ Reprocessing (Implemented)
**Whitepaper Spec**: "Later, reprocess failed items: `prodigy dlq retry workflow-id`"
**Current State**: ✅ IMPLEMENTED
- Full DLQ reprocessing with `prodigy dlq reprocess <job_id>`
- Streaming implementation to handle large queues
- Configurable parallelism with `--max-parallel`
- Dry run support with `--dry-run`
- Preserves correlation IDs and updates DLQ state
### 2. Job Resumption (Implemented)
**Whitepaper Spec**: Resume capability for interrupted workflows
**Current State**: ✅ IMPLEMENTED
- Full workflow resumption with `prodigy resume <workflow-id>`
- MapReduce job resumption with `prodigy resume-job <job-id>`
- Complete checkpoint-based recovery for all workflow types
- Variable state restoration and environment validation
- Cross-worktree coordination for parallel jobs
### 3. Resume Executor Full Implementation (Fixed)
**Previous Gap**: Partial implementation returning mock results
**Current State**: ✅ FIXED
- `resume()` method now delegates to `execute_from_checkpoint()` for full execution
- Added `resume_with_path()` for legacy checkpoints without stored workflow paths
- Proper workflow file loading and execution
- Complete test coverage with 3 new unit tests
## ❌ Remaining Gaps
### 3. Simplified MapReduce Syntax (Minor Gap)
**Whitepaper Spec**: Direct command arrays under `agent_template` and `reduce`
**Current Implementation**: Still uses nested `commands` structure in some places
```yaml
# Whitepaper syntax
agent_template:
- claude: "/process"
# Current implementation sometimes requires
agent_template:
commands:
- claude: "/process"
```
### 4. Filter & Sort Expressions (Partial Implementation)
**Whitepaper Spec**: Filter and sort work items with expressions like `item.score >= 5`
**Current State**:
- Fields exist in configuration
- No actual filtering/sorting logic implemented
- JSONPath extraction works but advanced filtering doesn't
### 5. Error Handling Directives (Incomplete)
**Whitepaper Spec**:
```yaml
on_item_failure: dlq # Save to DLQ
continue_on_failure: true # Don't stop entire job
```
**Current State**:
- `on_failure` handlers exist for individual commands
- No workflow-level failure handling configuration
- Missing `continue_on_failure` and `on_item_failure` options
### 6. Variable Interpolation (Limited)
**Whitepaper Spec**: Rich variable system with `${map.results}`, `${map.successful}`, etc.
**Current State**:
- Basic `${item}` interpolation works
- Missing aggregate variables like `${map.successful}`, `${map.total}`
- No cross-phase variable passing
### 7. Workflow Examples & Templates (Missing)
**Whitepaper Spec**: Pre-built templates for common patterns
**Current State**:
- No template system
- No example workflows included
- No `prodigy init` templates for MapReduce patterns
### 8. Performance Metrics (Limited)
**Whitepaper Spec**: Detailed performance tracking and reporting
**Current State**:
- Basic timing information collected
- No aggregated performance reports
- Missing throughput/efficiency metrics
## 🔧 Recommendations for Priority Fixes
### High Priority
1. **Implement DLQ Reprocessing** - Critical for production use
- Complete `DlqReprocessor::reprocess_items()` logic
- Add retry workflow generation
- Test with various failure scenarios
2. **Fix Job Resumption** - Essential for long-running workflows
- Implement checkpoint recovery logic
- Add state reconstruction from events
- Test interruption/recovery scenarios
### Medium Priority
3. **Complete Filter/Sort Logic** - Important for large-scale processing
- Implement expression evaluator for filters
- Add sorting mechanism for work items
- Support complex JSONPath queries
4. **Enhance Variable System** - Needed for complex workflows
- Add aggregate variables (`${map.total}`, etc.)
- Implement cross-phase variable passing
- Document available variables
### Low Priority
5. **Simplify YAML Syntax** - Quality of life improvement
- Remove nested `commands` requirement
- Update parser to handle both formats
- Migrate existing workflows
6. **Add Workflow Templates** - User experience enhancement
- Create common workflow templates
- Add to `prodigy init` command
- Include documentation
## Testing Gaps
1. **Integration tests for DLQ retry scenarios**
2. **Stress tests for parallel execution at scale**
3. **Recovery tests for interrupted MapReduce jobs**
4. **Performance benchmarks for large datasets**
5. **Cross-platform worktree management tests**
## Documentation Gaps
1. **MapReduce best practices guide**
2. **Performance tuning documentation**
3. **Troubleshooting guide for common failures**
4. **API documentation for extending Prodigy**
5. **Migration guide from sequential to MapReduce workflows**