Multi-Representation Architecture for Resonance Storage

multi-rep-resonance-storage-arch.md

Multi-Representation Architecture for Resonance Storage

Overview

Just as traditional storage systems offer block, object, and file representations of data, resonance-based storage provides multiple complementary views of the same underlying information. Each representation optimizes for different access patterns while maintaining the core resonance addressing.

Core Representations

1. Resonance Object Storage (ROS)

Similar to: S3, Azure Blob Storage
Key: Resonance value (float)
Value: Arbitrary data + metadata

class ResonanceObject:
    """Object-like representation"""
    resonance: float          # Primary key
    data: bytes              # Actual content
    pattern: BinaryPattern   # Field activation
    metadata: Dict          # User metadata
    artifacts: List         # Transformation history
    
    def to_dict(self):
        return {
            'resonance': self.resonance,
            'data': self.data,
            'fields': self.pattern.active_fields,
            'metadata': self.metadata
        }

Use Cases:

• Document storage
• Media files
• API responses
• Unstructured data

Access Pattern:

# Store
obj = storage.put_object(data="Hello, World!")
# Returns: resonance value 2.847...

# Retrieve
obj = storage.get_object(2.847)
# Returns: ResonanceObject

2. Field Block Storage (FBS)

Similar to: EBS, Block devices
Structure: Fixed-size blocks organized by field patterns

class FieldBlock:
    """Block-like representation - fixed size chunks"""
    block_id: int            # Sequential block number
    field_index: int         # Which field (0-7)
    resonance_range: Tuple   # (min, max) resonance in block
    data: np.array          # Fixed-size data array
    
    # Each block contains items with same field pattern
    field_pattern: BinaryPattern
    items: List[ResonanceItem]

Storage Layout:

Field 0 (Identity): [Block 0][Block 1][Block 2]...
Field 1 (Growth):   [Block 0][Block 1][Block 2]...
Field 2 (φ):        [Block 0][Block 1][Block 2]...
...

Use Cases:

• Time-series data
• Numerical computations
• Fixed-record databases
• High-performance sequential access

Access Pattern:

# Write blocks
blocks = storage.write_blocks(
    field=2,  # φ field
    data=numpy_array,
    block_size=4096
)

# Read blocks
data = storage.read_blocks(
    field=2,
    start_block=0,
    count=10
)

3. Resonance Graph Storage (RGS)

Similar to: Neo4j, Graph databases
Structure: Nodes and edges with resonance-based addressing

class ResonanceNode:
    """Graph node representation"""
    resonance: float
    data: Any
    edges: List[ResonanceEdge]
    
class ResonanceEdge:
    """Edge between resonances"""
    source: float          # Source resonance
    target: float          # Target resonance
    weight: float          # Resonance similarity
    artifact: Artifact     # Transformation that created edge

Use Cases:

• Knowledge graphs
• Social networks
• Dependency tracking
• Semantic relationships

Access Pattern:

# Create nodes and edges
node1 = storage.create_node(data="Quantum")
node2 = storage.create_node(data="Computing")
edge = storage.create_edge(node1, node2, weight=0.85)

# Traverse
neighbors = storage.get_neighbors(node1.resonance, radius=0.2)
path = storage.shortest_path(node1.resonance, node2.resonance)

4. Resonance Column Storage (RCS)

Similar to: Parquet, Columnar databases
Structure: Columns organized by field activation

class ResonanceColumn:
    """Column-oriented representation"""
    field_mask: BinaryPattern   # Which fields define this column
    resonances: np.array       # Sorted resonance values
    data: pa.Table            # Arrow/Parquet table
    
    def query(self, predicate):
        """Efficient columnar query"""
        mask = self.evaluate_predicate(predicate)
        return self.data.filter(mask)

Storage Layout:

Column[0,1,2]:    # Items with fields 0,1,2 active
  resonances: [1.234, 1.456, 1.789, ...]
  data: [row1, row2, row3, ...]

Column[0,2,5]:    # Items with fields 0,2,5 active
  resonances: [2.345, 2.567, 2.890, ...]
  data: [row1, row2, row3, ...]

Use Cases:

• Analytics queries
• Aggregations
• Field-based filtering
• OLAP workloads

5. Resonance Stream Storage (RSS)

Similar to: Kafka, Event streams
Structure: Append-only log organized by resonance ranges

class ResonanceStream:
    """Stream representation"""
    stream_id: str
    resonance_window: Tuple[float, float]
    partitions: List[StreamPartition]
    
class StreamPartition:
    """Ordered sequence of events"""
    partition_id: int
    resonance_range: Tuple
    events: List[ResonanceEvent]
    offset: int

Use Cases:

• Event sourcing
• Change data capture
• Real-time processing
• Audit logs

Access Pattern:

# Publish events
stream.publish(event_data)

# Subscribe to resonance range
subscription = stream.subscribe(
    min_resonance=2.0,
    max_resonance=3.0
)

for event in subscription:
    process(event)

6. Resonance Tensor Storage (RTS)

Similar to: TensorFlow TFRecords, HDF5
Structure: Multi-dimensional arrays with resonance indexing

class ResonanceTensor:
    """Tensor representation"""
    shape: Tuple[int, ...]
    dtype: np.dtype
    field_dimensions: Dict[int, int]  # Field → tensor dimension
    data: np.ndarray
    resonance_index: Dict[Tuple, float]

Use Cases:

• Machine learning datasets
• Scientific computing
• Image/video storage
• Multi-dimensional analysis

Unified Access Layer

All representations share a common access layer:

class UnifiedResonanceStorage:
    """Unified interface to all representations"""
    
    def store(self, data: Any, representation: str = 'auto'):
        """Store data in specified representation"""
        resonance = self.compute_resonance(data)
        
        if representation == 'auto':
            representation = self.detect_best_representation(data)
            
        if representation == 'object':
            return self.object_store.put(resonance, data)
        elif representation == 'block':
            return self.block_store.write(resonance, data)
        elif representation == 'graph':
            return self.graph_store.create_node(resonance, data)
        # ... etc
    
    def retrieve(self, resonance: float, representation: str = 'object'):
        """Retrieve in specified representation"""
        # Can convert between representations on the fly
        pass
    
    def transform_representation(self, resonance: float, 
                                from_repr: str, to_repr: str):
        """Transform between representations"""
        data = self.retrieve(resonance, from_repr)
        return self.store(data, to_repr)

Representation Selection Matrix

Use Case	Best Representation	Why
REST API data	Object	Variable size, self-contained
Time series	Block	Sequential access, fixed records
Knowledge base	Graph	Relationship traversal
Analytics	Column	Efficient aggregation
Event logs	Stream	Append-only, temporal
ML datasets	Tensor	Multi-dimensional operations

Cross-Representation Queries

The power comes from combining representations:

query HybridQuery {
  # Start with object
  document: resonance(address: 2.847) {
    data
    
    # Switch to graph view
    relatedDocuments: neighbors(radius: 0.2) {
      resonance
      
      # Use column view for analytics
      fieldStatistics: columnView {
        avgResonance
        fieldDistribution
      }
    }
    
    # Check stream for updates
    recentChanges: streamView(since: "1hour") {
      events {
        timestamp
        artifact
      }
    }
  }
}

Storage Hierarchy

┌─────────────────────────────────────────┐
│         Unified Resonance API           │
├─────────────────────────────────────────┤
│ Object │ Block │ Graph │ Column │Stream│
├─────────────────────────────────────────┤
│         Resonance Index Layer           │
├─────────────────────────────────────────┤
│      Physical Storage (S3, SSD, etc)    │
└─────────────────────────────────────────┘

Optimization Strategies

1. Representation Caching

# Frequently accessed objects get promoted to block storage
# for faster sequential access
if access_count > threshold:
    promote_to_block_storage(resonance)

2. Lazy Transformation

# Convert between representations only when needed
def get_as_graph(resonance):
    if not exists_in_graph_store(resonance):
        obj = get_from_object_store(resonance)
        convert_to_graph(obj)
    return get_from_graph_store(resonance)

3. Field-Aware Partitioning

# Partition data by active fields for better locality
def partition_by_fields(data):
    partitions = defaultdict(list)
    for item in data:
        key = tuple(item.pattern.active_fields)
        partitions[key].append(item)
    return partitions

Conclusion

The multi-representation architecture of resonance storage provides:

1. Flexibility: Choose the best representation for each use case
2. Performance: Optimized access patterns for different workloads
3. Interoperability: Transform between representations seamlessly
4. Completeness: Cover all traditional storage paradigms

Just as traditional systems evolved from "one-size-fits-all" to specialized storage types, resonance storage embraces multiple representations while maintaining the unified addressing scheme of mathematical resonance.