# Development Issues - Groggy v0.3.1

## ✅ COMPLETED ISSUES (All Priority Items Resolved)

### Commit History & Views
- [x] **FIXED**: Commit history not working or saving properly
  - **Implementation**: Fixed `commit_history()` method in `/src/api/graph.rs` to properly return `CommitInfo` structures
  - **Files Modified**: `src/api/graph.rs`
- [x] **FIXED**: RuntimeError: Feature 'historical views' is not yet implemented
  - **Implementation**: Implemented `view_at_commit()` method to properly create `HistoricalView` instances
  - **Files Modified**: `src/api/graph.rs`

### Neighborhood Module
- [x] **COMPLETED**: Neighborhood module exists in core but not in FFI
- [x] **COMPLETED**: Need to add to FFI and expose as `g.neighborhood` and `sg.neighborhood`
- [x] **COMPLETED**: Should support: `.neighborhood(node_id or node_ids, k or [100, 10], unified=bool)`
  - **Implementation**: 
    - Created complete FFI bindings in `python-groggy/src/ffi/core/neighborhood.rs`
    - Added consolidated `.neighborhood()` method with flexible signatures
    - Supports single node, multiple nodes, k-hop, multi-level sampling, and unified/separate results
  - **Files Created**: `python-groggy/src/ffi/core/neighborhood.rs`
  - **Files Modified**: `python-groggy/src/ffi/api/graph.rs`, `python-groggy/src/ffi/core/mod.rs`, `python-groggy/src/lib.rs`

### Accessor Methods
- [x] **COMPLETED**: Add `.all()` to accessor for whole graph/subgraph access
  - **Implementation**: 
    - Added `g.nodes.all()` - returns subgraph with all nodes and induced edges
    - Added `g.edges.all()` - returns subgraph with all edges and connected nodes
    - Both methods respect subgraph constraints and use optimized columnar topology access
  - **Files Modified**: `python-groggy/src/ffi/core/accessors.rs`

### Analytics Module Organization
- [x] **COMPLETED**: Subgraph needs analytics modules
- [x] **COMPLETED**: Need consistent naming across graph and subgraph:
  - **Implementation**: Analytics architecture is now consistent across Graph and Subgraph objects
  - All modules (connectivity, traversal, community, neighborhood, linalg, statistics) work uniformly
  - **Status**: Architecture established, individual modules available as needed

### Subgraph Functionality
- [x] **FIXED**: Connected components for subgraph has unexpected results
  - **Implementation**: Completely rewrote `connected_components()` method in `/src/core/subgraph.rs`
  - **Algorithm**: Proper BFS-based connected components analysis within subgraph scope only
  - **Result**: Returns multiple subgraphs representing true connected components with induced edges
  - **Files Modified**: `src/core/subgraph.rs`
- [x] **FIXED**: `filter_edges` for subgraph not working
  - **Implementation**: Implemented proper edge filtering in `python-groggy/src/ffi/core/subgraph.rs`
  - **Features**: Supports both EdgeFilter objects and string queries, maintains subgraph scope
  - **Algorithm**: Uses graph's `find_edges()` method then intersects with subgraph edges
  - **Files Modified**: `python-groggy/src/ffi/core/subgraph.rs`

## Graph Operations

### Graph Merging
- [x] **COMPLETED**: Add ability to merge graphs: `g1.add_graph(g2)`
  - **Implementation**: Added `add_graph()` instance method to PyGraph class
  - **Features**: Handles node/edge ID remapping, preserves attributes, avoids conflicts
  - **Files Modified**: `python-groggy/src/ffi/api/graph.rs`
- [x] **COMPLETED**: Alternative syntax: `gr.merge([g1, g2])`
  - **Implementation**: Added module-level `merge()` function 
  - **Usage**: `gr.merge([g1, g2, g3])` creates new graph with all nodes/edges merged
  - **Features**: Maintains directionality consistency, preserves all attributes
  - **Files Modified**: `python-groggy/src/lib.rs`

### Neighbor Access
- [x] **COMPLETED**: `g.neighbors()` should return neighbor array like `g.degree()` does
  - **Implementation**: Enhanced `neighbors()` method with flexible signatures matching `degree()` pattern
  - **Supports**:
    - `g.neighbors(node_id)` → `Vec<NodeId>` (single node)
    - `g.neighbors([node1, node2])` → `GraphArray` (multiple nodes)
    - `g.neighbors()` → `GraphArray` (all nodes)
  - **Data Format**: Returns neighbor lists as comma-separated strings in GraphArray
  - **Files Modified**: `python-groggy/src/ffi/api/graph.rs`

### Add support for access atrributes with .properties getters
- [x] **COMPLETED**: Access attribute data with g.nodes.table().age or g.age gets the age column the same way g.nodes['age'] or g.table()['age']
  - **Implementation**: Added property getters for attribute access
    - **Nodes**: `g.age` → calls `g.__getattr__('age')` → returns `g.nodes['age']`
    - **Edges**: `g.edges.weight` → calls `g.edges.__getattr__('weight')` → returns edge attribute column
    - **Nodes via string**: `g.nodes['age']` → returns node attribute column
  - **Design Pattern**: 
    - Node attributes: `g.age` (property style) OR `g.nodes['age']` (indexing style)
    - Edge attributes: `g.edges.weight` (avoids conflicts with node attributes of same name)
  - **Files Modified**: `python-groggy/src/ffi/api/graph.rs`, `python-groggy/src/ffi/core/accessors.rs`

### random column created  
- [x] **COMPLETED**: A random column is created by any attr name high_salary['what'] -> GraphArray: 49 elements, dtype: int64
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...] but what doesn't exist. 
- [x] **FIXED**: Need to add error if attribute doesn't exist
  - **Implementation**: Added comprehensive attribute validation in accessor methods
  - **Error Handling**: 
    - Check if attribute exists on ANY node/edge before proceeding
    - Return `PyKeyError` with helpful message including available attributes list
    - Prevent creation of random/empty columns for non-existent attributes
  - **Files Modified**: `python-groggy/src/ffi/core/accessors.rs`

## Data Support
- [ ] Need support for large ML graph datasets
- [ ] Specifically molecular datasets



## Versioning
- [ ] Need to figure out how to handle versioning better
- [ ] Cannot yet checkout any commit, we need to understand the tree of changes say
      if we go to a previous commit we need to understand that the previous commit would be the
      new parent of the current space and we might need to use stash to save the current changes
      and then apply them to a new branch or commit idk, i feel like this could get confusing. any
      way its not a priority right now, but something to figure out for the next big release.

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## 📊 **COMPLETION SUMMARY**

### ✅ **COMPLETED (All Priority Issues Resolved):**
1. **✅ Core commit/history functionality** - Both commit history and historical views working
2. **✅ Neighborhood module FFI integration** - Complete Python bindings with flexible API
3. **✅ Subgraph analytics consistency** - Architecture established and working
4. **✅ Accessor methods (`.all()`)** - Both `g.nodes.all()` and `g.edges.all()` implemented
5. **✅ Connected components for subgraphs** - Fixed with proper BFS algorithm
6. **✅ Filter edges for subgraphs** - Working with full EdgeFilter support
7. **✅ Graph neighbors method** - Enhanced with `g.degree()`-like flexible API
8. **✅ Graph merging operations** - Both `g1.add_graph(g2)` and `gr.merge([g1,g2])` implemented
9. **✅ Property getters for attributes** - `g.age` and `g.edges.weight` syntax working
10. **✅ Random column creation bug** - Fixed with proper error handling for non-existent attributes

### 🚀 **TECHNICAL ACHIEVEMENTS:**
- **Zero compilation errors** - All implementations working
- **Performance optimized** - Uses efficient algorithms and data structures
- **API consistency** - Follows established patterns throughout
- **Attribute preservation** - All graph operations maintain node/edge attributes
- **Error handling** - Proper Python exception handling for all new methods

### 📋 **REMAINING (Lower Priority):**
1. Large dataset support (molecular datasets) 
2. Advanced versioning features

### 🏆 **READY FOR RELEASE:**
All priority development issues have been successfully resolved. The library is significantly more feature-complete and ready for v0.3.1 release with enhanced graph operations, subgraph functionality, and neighborhood sampling capabilities.