Creating a Graph Implementation

This chapter provides detailed guidance for implementing a new Graph API backend. It expands on the concepts introduced in the Implementation Guide for Graph Backends.

Design Considerations

Before starting implementation, consider these key design decisions:

Storage Strategy: How will you store vertices and edges? Options include:
- Adjacency lists (good for sparse graphs)
- Adjacency matrices (good for dense graphs)
- Edge lists
- Custom hybrid approaches
Memory vs. Performance Trade-offs: Will your implementation prioritize:
- Low memory usage
- Fast traversal operations
- Fast mutation operations
- Fast index lookups
Feature Support: Which Graph API features will you support?
- Basic graph operations (required)
- Label indexes
- Hash indexes
- Range indexes
- Full-text indexes
- Other specialized indexes
Usage Context: Is your implementation designed for:
- General-purpose use
- Specific application domains
- Particular data patterns

Core Implementation Approach

A typical implementation follows these steps:

1. Define ID Types

#![allow(unused)]
fn main() {
pub struct MyVertexId {
    // Implementation-specific fields
    label: u16,
    index: u32,
}

pub struct MyEdgeId {
    // Implementation-specific fields
    label: u16,
    index: u32,
    from: MyVertexId,
    to: MyVertexId,
}
}

Make sure your ID types:

Implement Copy, Clone, Debug, Eq, PartialEq, and Hash
Are small and efficient (IDs are used extensively)
Can be converted to ElementId

2. Create Core Graph Structure

#![allow(unused)]
fn main() {
pub struct MyGraph<Vertex, Edge>
where
    Vertex: Element,
    Edge: Element,
{
    // Your internal storage
    vertices: Vec<VertexCollection>,
    edges: Vec<EdgeCollection>,
    // Optional index structures
    // ...
}
}

3. Implement Reference Types

Reference types provide safe access to graph elements:

#![allow(unused)]
fn main() {
pub struct MyVertexReference<'graph, Graph>
where
    Graph: graph_api_lib::Graph,
{
    id: Graph::VertexId,
    weight: &'graph Graph::Vertex,
}

pub struct MyVertexReferenceMut<'graph, Graph>
where
    Graph: graph_api_lib::Graph,
{
    id: Graph::VertexId,
    weight: &'graph mut Graph::Vertex,
    // Reference to indexes for updating
    indexes: &'graph mut IndexCollection,
}
}

Implement similar types for EdgeReference and EdgeReferenceMut.

4. Implement Iterator Types

Create iterator types for traversing vertices and edges:

#![allow(unused)]
fn main() {
pub struct MyVertexIter<'search, 'graph, Graph>
where
    Graph: graph_api_lib::Graph + 'graph,
{
    // Internal state
    graph: &'graph Graph,
    current_position: usize,
    // ...
}

impl<'graph, Graph> Iterator for MyVertexIter<'_, 'graph, Graph>
where
    Graph: graph_api_lib::Graph<VertexId=MyVertexId> + 'graph,
{
    type Item = MyVertexReference<'graph, Graph>;

    fn next(&mut self) -> Option<Self::Item> {
        // Implementation
    }
}
}

5. Mutation Listener for Indexes

If your graph supports indexes, implement a mutation listener:

#![allow(unused)]
fn main() {
pub struct MyMutationListener<'reference, Element> {
    // References to index structures
    indexes: &'reference mut IndexCollection,
    id: VertexInternalId,
}

impl<'reference, Element> graph_api_lib::MutationListener<'reference, Element>
for MyMutationListener<'reference, Element>
where
    Element: graph_api_lib::Element,
{
    fn update(&mut self, index: <Element::Label as Label>::Index, before: Value, after: Value) {
        // Update indexes as needed
    }
}
}

Implementation Tips

Efficiency Considerations

Memory Layout: Keep related data together to improve cache locality.
Data Structures: Choose the right data structures for your use case:
- Vec for dense storage with stable IDs
- HashMap for sparse storage with integer IDs
- BTreeMap for ordered collections
- Consider specialized structures like smallbox for tiny collections
Avoid Cloning: Pass references whenever possible instead of cloning data.
Index Carefully: Indexes improve query performance but add overhead to mutations.

Supporting Advanced Features

For implementing advanced features like range and full-text indexing:

Range Indexes: Consider using BTreeMap or similar ordered collections.
Full-text Indexes: Implement tokenization, stemming, and inverted indexes.
Custom Properties: Support for user-defined property types may require generics.

Example Implementation Structure

A typical implementation might be organized like this:

my_graph/
├── src/
│   ├── lib.rs          # Main exports and Graph trait implementation
│   ├── id.rs           # ID type definitions
│   ├── graph/
│   │   ├── mod.rs      # Core graph implementation
│   │   ├── iter.rs     # Iterator implementations
│   │   └── debug.rs    # Debug helpers
│   └── index/
│       ├── mod.rs      # Index type definitions
│       ├── hash.rs     # Hash index implementation
│       ├── range.rs    # Range index implementation
│       └── full_text.rs # Full-text index implementation
└── tests/
    └── integration.rs  # Integration tests using test_suite!

Next Steps

After implementing the basic graph functionality:

Review the Testing Your Implementation chapter for testing strategies.
Check the Implementing Indexes chapter for adding index support.

Remember that implementing a graph backend is an iterative process. Start with a minimal working implementation, test it thoroughly, and then add more features incrementally.

graph-api