CognitiveGraph Architecture

Overview

CognitiveGraph implements a revolutionary approach to code analysis by combining Shared Packed Parse Forest (SPPF) representation with Code Property Graph (CPG) semantics through a high-performance, zero-copy memory architecture.

Core Design Principles

1. Zero-Copy Memory Architecture

The foundation of CognitiveGraph is its zero-allocation memory access pattern:

• Contiguous Memory Layout: All graph data is stored in a single, contiguous byte buffer
• Offset-Based Navigation: Nodes and edges are referenced by memory offsets, not pointers
• Direct Memory Access: Uses ReadOnlySpan<T> and ref struct for allocation-free operations
• Memory-Mapped File Support: Enables processing of massive graphs without loading into RAM

┌─────────────────────────────────────────────────────┐
│                Graph Buffer Layout                   │
├─────────────────────────────────────────────────────┤
│ Header (40 bytes)                                   │
├─────────────────────────────────────────────────────┤
│ Symbol Nodes Section                                │
├─────────────────────────────────────────────────────┤
│ Packed Nodes Section (for ambiguity)               │
├─────────────────────────────────────────────────────┤
│ CPG Edges Section                                   │
├─────────────────────────────────────────────────────┤
│ Properties Section                                  │
├─────────────────────────────────────────────────────┤
│ String Pool Section                                 │
├─────────────────────────────────────────────────────┤
│ Source Text Section                                 │
└─────────────────────────────────────────────────────┘

2. Shared Packed Parse Forest (SPPF)

Traditional Abstract Syntax Trees (ASTs) cannot represent syntactic ambiguity. CognitiveGraph solves this with SPPF:

• Ambiguity Preservation: Multiple parse interpretations coexist in the same structure
• Packed Nodes: Represent alternative parse trees compactly
• Efficient Storage: Shared subtrees reduce memory overhead
• Complete Coverage: No loss of parsing information

Example: "a + b * c" has two interpretations:
┌─────────────────┐    ┌─────────────────┐
│   Expression    │    │   Expression    │
│       │         │    │       │         │
│   ┌───┴───┐     │    │   ┌───┴───┐     │
│   +       c     │    │   a       *     │
│  ┌─┴─┐          │    │          ┌─┴─┐   │
│  a   b          │    │          b   c   │
│ (a+b)*c         │    │   a+(b*c)       │
└─────────────────┘    └─────────────────┘

Both representations are stored as PackedNodes under a single SymbolNode

3. Code Property Graph (CPG)

Beyond syntax, CognitiveGraph captures semantic relationships through CPG edges:

• Control Flow: Sequential program execution paths
• Data Flow: Variable definitions, uses, and dependencies
• Type Hierarchies: Class inheritance and interface implementations
• Call Graphs: Function invocation relationships

Component Architecture

Core Components

┌─────────────────────────────────────────────────┐
│                   API Layer                     │
├─────────────────────────────────────────────────┤
│  CognitiveGraph  │  CognitiveGraphBuilder       │
├──────────────────┼──────────────────────────────┤
│             Accessor Layer                      │
├─────────────────────────────────────────────────┤
│ SymbolNode │ PackedNode │ Property │ CpgEdge   │
├─────────────────────────────────────────────────┤
│                Buffer Layer                     │
├─────────────────────────────────────────────────┤
│          CognitiveGraphBuffer                   │
├─────────────────────────────────────────────────┤
│                Schema Layer                     │
├─────────────────────────────────────────────────┤
│    GraphHeader │ NodeData │ EdgeData           │
└─────────────────────────────────────────────────┘

1. Schema Layer (CognitiveGraph.Schema)

Defines the binary layout of graph data structures:

• GraphHeader: File format metadata (40 bytes)
• SymbolNodeData: AST node binary layout
• PackedNodeData: Ambiguity representation
• CpgEdgeData: Semantic relationship data
• PropertyData: Type-safe property storage

2. Buffer Layer (CognitiveGraph.Buffer)

Manages memory operations and layout:

• CognitiveGraphBuffer: Main memory management
• Span-based access: Zero-allocation data reading
• Memory-mapped file integration
• Thread-safe concurrent reading

3. Accessor Layer (CognitiveGraph.Accessors)

Provides high-level, type-safe access to graph data:

• SymbolNode: AST node navigation
• PackedNode: Ambiguity resolution
• Property: Type-safe property access
• CpgEdge: Semantic relationship traversal

4. Builder Layer (CognitiveGraph.Builder)

Constructs graphs efficiently:

• CognitiveGraphBuilder: Main construction API
• Incremental building: Add nodes and edges progressively
• Automatic offset management: Handles memory layout
• Validation: Ensures graph consistency

5. Query Engine (CognitiveGraph.QueryEngine)

Advanced graph traversal and analysis:

• Graph pattern matching
• Path finding algorithms
• Property filtering and aggregation
• Performance-optimized traversal

Memory Layout Details

Graph Header (40 bytes)

struct GraphHeader {
    uint32_t magic_number;      // "COGN" (0x434F474E)
    uint16_t version;           // Schema version
    uint16_t flags;             // Feature flags
    uint32_t root_node_offset;  // Offset to root SymbolNode
    uint32_t symbol_count;      // Number of symbol nodes
    uint32_t packed_count;      // Number of packed nodes
    uint32_t edge_count;        // Number of CPG edges
    uint32_t property_count;    // Number of properties
    uint32_t string_pool_offset; // Offset to string data
    uint32_t source_text_offset; // Offset to source code
    uint32_t total_size;        // Total buffer size
};

Symbol Node Layout

struct SymbolNodeData {
    uint32_t symbol_id;         // Unique symbol identifier
    uint16_t node_type;         // AST node type
    uint16_t flags;             // Node flags (ambiguous, etc.)
    uint32_t source_start;      // Source position
    uint32_t source_length;     // Source span length
    uint16_t child_count;       // Number of children
    uint16_t property_count;    // Number of properties
    uint32_t children_offset;   // Offset to child array
    uint32_t properties_offset; // Offset to properties
    uint16_t packed_count;      // Number of packed interpretations
    uint16_t edge_count;        // Number of CPG edges
    uint32_t packed_offset;     // Offset to packed nodes
    uint32_t edges_offset;      // Offset to CPG edges
};

Performance Characteristics

Time Complexity

• Node Access: O(1) - Direct memory offset lookup
• Property Access: O(1) - Direct span access with type information
• Child Navigation: O(1) - Array-based child storage
• Graph Traversal: O(V + E) where V = vertices, E = edges
• Memory Allocation: O(0) - Zero allocations after graph creation

Space Complexity

• Memory Overhead: ~10-15% over raw AST representation
• Ambiguity Storage: Logarithmic compression through sharing
• Property Storage: Variant-typed, compact representation
• String Deduplication: Shared string pool reduces redundancy

Benchmark Results

Operation	Time	Memory	Notes
Node Creation	~50ns	64 bytes	Average per node
Property Access	~10ns	0 bytes	Zero allocation
Child Iteration	~5ns/child	0 bytes	Direct array access
Ambiguity Resolution	~100ns	0 bytes	Packed node enumeration
CPG Edge Traversal	~20ns/edge	0 bytes	Offset-based navigation

Thread Safety

Concurrent Reading

CognitiveGraph supports unlimited concurrent readers:

• No synchronization required: Immutable data structures
• Lock-free access: Direct memory reading
• Thread-local caching: Optional performance enhancement
• NUMA awareness: Memory-mapped files respect processor topology

Building Isolation

Graph construction is single-threaded by design:

• Builder instances are not thread-safe
• One builder per thread: Parallel construction requires separate builders
• Merge capabilities: Multiple graphs can be combined post-construction

Extension Points

Custom Node Types

Extend the type system for domain-specific analysis:

public static class CustomNodeTypes
{
    public const ushort DatabaseQuery = 1000;
    public const ushort ApiEndpoint = 1001;
    public const ushort ConfigurationValue = 1002;
}

Custom Properties

Add domain-specific metadata:

var properties = new List<(string, PropertyValueType, object)>
{
    ("DatabaseTable", PropertyValueType.String, "Users"),
    ("QueryComplexity", PropertyValueType.Double, 2.5),
    ("IsCacheable", PropertyValueType.Boolean, true)
};

Custom CPG Edge Types

Define semantic relationships:

public static class CustomEdgeTypes
{
    public const byte DatabaseAccess = 100;
    public const byte NetworkCall = 101;
    public const byte ConfigurationRead = 102;
}

Integration Patterns

Language Server Integration

// Real-time code analysis
public class CognitiveLanguageServer
{
    private readonly Dictionary<Uri, CognitiveGraph> _graphs = new();
    
    public void OnDocumentChanged(Uri document, string content)
    {
        // Rebuild graph incrementally
        var graph = BuildGraphForDocument(content);
        _graphs[document] = graph;
        
        // Update semantic analysis
        UpdateSemanticTokens(document, graph);
    }
}

Build Pipeline Integration

// Batch processing for CI/CD
public class CognitiveBuildAnalyzer
{
    public async Task AnalyzeProject(string projectPath)
    {
        var graphs = new ConcurrentBag<CognitiveGraph>();
        
        await Parallel.ForEachAsync(GetSourceFiles(projectPath), 
            async (file, ct) =>
        {
            var content = await File.ReadAllTextAsync(file, ct);
            var graph = BuildGraph(content);
            graphs.Add(graph);
        });
        
        // Merge and analyze
        var mergedGraph = MergeGraphs(graphs);
        var analysis = PerformGlobalAnalysis(mergedGraph);
    }
}

Future Enhancements

Planned Features

1. Incremental Updates: Efficient graph modification without full rebuilds
2. Compression: Optional LZ4/Zstd compression for storage
3. Streaming: Process graphs larger than available memory
4. Distributed Analysis: Graph sharding for massive codebases
5. Machine Learning Integration: Tensor export for AI model training

Research Directions

1. Probabilistic Ambiguity: Weight parse interpretations by likelihood
2. Dynamic Analysis Integration: Merge static and runtime information
3. Cross-Language Graphs: Unified representation for polyglot systems
4. Temporal Graphs: Track code evolution over time

Design Rationale

Why Zero-Copy?

Traditional code analysis tools suffer from memory overhead and allocation pressure. Zero-copy design provides:

• Predictable Performance: No GC pauses or allocation spikes
• Massive Scale: Handle codebases with millions of lines
• Real-time Responsiveness: Suitable for interactive development tools
• Memory Efficiency: Optimal for memory-constrained environments

Why SPPF over AST?

Abstract Syntax Trees force a single parse interpretation, losing information:

• Ambiguity is Common: Real programming languages have inherent ambiguities
• Complete Information: SPPF preserves all possible interpretations
• Analysis Flexibility: Different analyses can choose different interpretations
• Parser Independence: Works with any parsing technology

Why CPG Integration?

Syntax alone is insufficient for advanced analysis:

• Semantic Understanding: Control and data flow are essential
• Cross-Reference Analysis: Variable usage patterns across functions
• Security Analysis: Taint analysis and vulnerability detection
• Performance Analysis: Identify bottlenecks and optimization opportunities

This architecture enables CognitiveGraph to provide both high performance and comprehensive code understanding, making it suitable for everything from real-time IDE support to large-scale static analysis systems.