Advanced Graph Features

This guide covers advanced features of Reflow's graph system including history management, subgraph operations, optimization techniques, and performance tuning.

History Management

Basic History Operations

use reflow_network::graph::{Graph, GraphHistory};

// Create graph with history tracking
let (mut graph, mut history) = Graph::with_history();

// Make some changes
graph.add_node("input", "InputNode", None);
graph.add_node("output", "OutputNode", None);
graph.add_connection("input", "out", "output", "in", None);

// Undo last operation
if let Some(operation) = history.undo() {
    history.apply_inverse(&mut graph, operation)?;
    println!("Undid: {:?}", operation);
}

// Redo operation
if let Some(operation) = history.redo() {
    history.apply_operation(&mut graph, operation)?;
    println!("Redid: {:?}", operation);
}

Advanced History Configuration

use reflow_network::graph::{HistoryConfig, HistoryLimit};

// Create history with custom configuration
let history_config = HistoryConfig {
    limit: HistoryLimit::Operations(100),  // Limit to 100 operations
    compress_threshold: 50,                // Compress after 50 operations
    auto_cleanup: true,                    // Clean up old entries automatically
    track_metadata_changes: true,          // Track metadata changes
};

let (mut graph, mut history) = Graph::with_history_config(history_config);

// Alternative: Limit by memory usage
let memory_config = HistoryConfig {
    limit: HistoryLimit::Memory(10 * 1024 * 1024), // 10 MB limit
    ..Default::default()
};

History Compression

// Manually compress history
history.compress()?;

// Get compression statistics
let stats = history.compression_stats();
println!("Compressed {} operations into {} snapshots", 
    stats.original_operations, stats.compressed_snapshots);
println!("Memory saved: {:.1} MB", stats.memory_saved / 1024.0 / 1024.0);

// Force full compression
history.force_compress_all()?;

History Snapshots

use reflow_network::graph::Snapshot;

// Create named snapshot
let snapshot_id = history.create_snapshot("before_major_changes")?;

// Make changes...
graph.add_node("processor1", "DataProcessor", None);
graph.add_node("processor2", "DataProcessor", None);

// Restore to snapshot
history.restore_snapshot(&mut graph, &snapshot_id)?;

// List all snapshots
let snapshots = history.list_snapshots();
for snapshot in snapshots {
    println!("Snapshot: {} (created: {})", snapshot.name, snapshot.timestamp);
}

// Delete old snapshots
history.delete_snapshot("old_snapshot")?;

Branching History

use reflow_network::graph::HistoryBranch;

// Create branch from current state
let branch_id = history.create_branch("experimental_feature")?;

// Switch to branch
history.switch_branch(&mut graph, &branch_id)?;

// Make experimental changes
graph.add_node("experimental", "ExperimentalNode", None);

// Switch back to main branch
history.switch_branch(&mut graph, "main")?;

// Merge branch if satisfied with changes
history.merge_branch(&mut graph, &branch_id, "main")?;

History Events

use reflow_network::graph::HistoryEvents;

// Subscribe to history events
let history_receiver = history.event_channel().1.clone();

std::thread::spawn(move || {
    while let Ok(event) = history_receiver.recv() {
        match event {
            HistoryEvents::OperationAdded { operation, index } => {
                println!("Added operation {}: {:?}", index, operation);
            }
            HistoryEvents::Undo { operation } => {
                println!("Undid operation: {:?}", operation);
            }
            HistoryEvents::Redo { operation } => {
                println!("Redid operation: {:?}", operation);
            }
            HistoryEvents::SnapshotCreated { name, timestamp } => {
                println!("Created snapshot '{}' at {}", name, timestamp);
            }
            HistoryEvents::HistoryCompressed { before_size, after_size } => {
                println!("Compressed history: {} -> {} operations", before_size, after_size);
            }
        }
    }
});

Subgraph Operations

Creating Subgraphs

use reflow_network::graph::{Subgraph, SubgraphConfig};

// Extract subgraph by node selection
let selected_nodes = vec!["processor1", "processor2", "connector"];
let subgraph = graph.extract_subgraph(&selected_nodes)?;

println!("Extracted subgraph:");
println!("  Nodes: {:?}", subgraph.nodes);
println!("  Internal connections: {}", subgraph.internal_connections.len());
println!("  External connections: {}", subgraph.external_connections.len());

// Create subgraph with configuration
let config = SubgraphConfig {
    include_metadata: true,
    preserve_external_connections: true,
    auto_add_ports: true,
};

let configured_subgraph = graph.extract_subgraph_with_config(&selected_nodes, &config)?;

Subgraph Analysis

use reflow_network::graph::SubgraphAnalysis;

let analysis = graph.analyze_subgraph(&subgraph);

println!("Subgraph Analysis:");
println!("  Node count: {}", analysis.node_count);
println!("  Connection count: {}", analysis.connection_count);
println!("  Max depth: {}", analysis.max_depth);
println!("  Is cyclic: {}", analysis.is_cyclic);
println!("  Branching factor: {:.2}", analysis.branching_factor);
println!("  Complexity score: {:.2}", analysis.complexity_score);

// Detailed connectivity analysis
println!("  Entry points: {:?}", analysis.entry_points);
println!("  Exit points: {:?}", analysis.exit_points);
println!("  Internal clusters: {}", analysis.internal_clusters);

Subgraph Operations

// Clone subgraph
let cloned_subgraph = subgraph.clone();

// Merge subgraphs
let merged = Subgraph::merge(vec![subgraph1, subgraph2, subgraph3])?;

// Subtract subgraph (remove nodes)
let remainder = graph.subtract_subgraph(&subgraph)?;

// Replace subgraph with optimized version
let optimized = optimize_subgraph(&subgraph)?;
graph.replace_subgraph(&subgraph, &optimized)?;

Subgraph Templates

use reflow_network::graph::{SubgraphTemplate, TemplateParameter};

// Create reusable subgraph template
let template = SubgraphTemplate {
    name: "data_processing_pipeline".to_string(),
    description: "Standard data processing pipeline".to_string(),
    nodes: subgraph.nodes.clone(),
    connections: subgraph.internal_connections.clone(),
    parameters: vec![
        TemplateParameter {
            name: "buffer_size".to_string(),
            param_type: "integer".to_string(),
            default_value: Some(json!(1024)),
            description: "Buffer size for data processing".to_string(),
        }
    ],
};

// Instantiate template with parameters
let instance_params = HashMap::from([
    ("buffer_size".to_string(), json!(2048))
]);

let instance = template.instantiate("pipeline_1", instance_params)?;
graph.add_subgraph_instance(instance)?;

Graph Optimization

Automatic Optimization

use reflow_network::graph::{OptimizationConfig, OptimizationLevel};

// Basic optimization
let optimized_graph = graph.optimize()?;

// Advanced optimization with configuration
let optimization_config = OptimizationConfig {
    level: OptimizationLevel::Aggressive,
    remove_redundant_nodes: true,
    merge_compatible_nodes: true,
    optimize_connection_paths: true,
    reorder_for_cache_locality: true,
    minimize_communication_cost: true,
};

let optimized = graph.optimize_with_config(&optimization_config)?;

// Apply optimizations in-place
graph.apply_optimizations(&optimization_config)?;

Redundancy Elimination

use reflow_network::graph::RedundancyAnalysis;

// Find redundant nodes
let redundancy = graph.analyze_redundancy();

println!("Redundancy Analysis:");
for redundant in redundancy.redundant_nodes {
    println!("  Node '{}': {}", redundant.node, redundant.reason);
    
    match redundant.redundancy_type {
        RedundancyType::DuplicateFunction => {
            println!("    Can be merged with: {:?}", redundant.merge_candidates);
        }
        RedundancyType::NoOperation => {
            println!("    Performs no operation - can be removed");
        }
        RedundancyType::BypassableTransform => {
            println!("    Transform can be bypassed");
        }
    }
}

// Automatically remove redundant nodes
graph.remove_redundant_nodes()?;

Node Fusion

use reflow_network::graph::FusionCandidate;

// Find nodes that can be fused together
let fusion_candidates = graph.find_fusion_candidates();

for candidate in fusion_candidates {
    println!("Fusion opportunity: {:?}", candidate.nodes);
    println!("  Estimated speedup: {:.1}x", candidate.estimated_speedup);
    println!("  Memory savings: {:.1} MB", candidate.memory_savings);
    
    // Apply fusion if beneficial
    if candidate.estimated_speedup > 1.5 {
        graph.fuse_nodes(&candidate.nodes, &candidate.fusion_strategy)?;
    }
}

Connection Optimization

use reflow_network::graph::ConnectionOptimization;

// Optimize connection routing
let connection_opt = ConnectionOptimization {
    minimize_wire_length: true,
    reduce_crossings: true,
    bundle_parallel_connections: true,
    use_hierarchical_routing: true,
};

graph.optimize_connections(&connection_opt)?;

// Find and eliminate unnecessary intermediate nodes
let bypass_candidates = graph.find_bypass_candidates();
for candidate in bypass_candidates {
    if candidate.is_safe_to_bypass() {
        graph.bypass_node(&candidate.node)?;
    }
}

Performance Tuning

Memory Optimization

use reflow_network::graph::{MemoryConfig, MemoryOptimization};

// Configure memory usage
let memory_config = MemoryConfig {
    node_pool_size: 1000,
    connection_pool_size: 5000,
    metadata_cache_size: 10 * 1024 * 1024, // 10 MB
    enable_lazy_loading: true,
    compress_metadata: true,
};

graph.configure_memory(&memory_config)?;

// Apply memory optimizations
let memory_opt = MemoryOptimization {
    compact_node_storage: true,
    use_interned_strings: true,
    enable_copy_on_write: true,
    garbage_collect_threshold: 0.8,
};

graph.apply_memory_optimization(&memory_opt)?;

Index Optimization

use reflow_network::graph::IndexConfig;

// Optimize internal indices for better performance
let index_config = IndexConfig {
    connection_index_type: IndexType::HashMap, // Fast lookups
    node_index_type: IndexType::BTreeMap,      // Ordered iteration
    spatial_index_enabled: true,               // For layout operations
    cache_frequently_accessed: true,
};

graph.rebuild_indices(&index_config)?;

// Enable adaptive indexing
graph.enable_adaptive_indexing(true);

Parallel Processing Setup

use reflow_network::graph::{ParallelConfig, ThreadingModel};

// Configure parallel processing
let parallel_config = ParallelConfig {
    max_threads: num_cpus::get(),
    threading_model: ThreadingModel::WorkStealing,
    enable_parallel_analysis: true,
    parallel_layout_threshold: 100, // Use parallel layout for >100 nodes
    chunk_size: 50,
};

graph.configure_parallel_processing(&parallel_config)?;

// Enable parallel operations
graph.enable_parallel_operations(true);

Benchmarking and Profiling

use reflow_network::graph::{Benchmark, ProfileConfig};
use std::time::Instant;

// Benchmark graph operations
let benchmark = Benchmark::new(&graph);

let results = benchmark.run_full_suite()?;
println!("Benchmark Results:");
println!("  Node addition: {:.2}μs", results.node_addition_time.as_micros());
println!("  Connection creation: {:.2}μs", results.connection_time.as_micros());
println!("  Cycle detection: {:.2}ms", results.cycle_detection_time.as_millis());
println!("  Layout calculation: {:.2}ms", results.layout_time.as_millis());
println!("  Validation: {:.2}ms", results.validation_time.as_millis());

// Profile specific operations
let profile_config = ProfileConfig {
    sample_rate: 1000, // Sample every 1000 operations
    track_memory: true,
    track_time: true,
    output_format: OutputFormat::Json,
};

let profiler = graph.create_profiler(&profile_config)?;
profiler.start();

// Perform operations...
graph.add_node("test", "TestNode", None);
// ... more operations

let profile_results = profiler.stop_and_collect();
profile_results.save_to_file("graph_profile.json")?;

Large Graph Handling

Streaming Operations

use reflow_network::graph::{StreamingConfig, GraphStream};

// Handle very large graphs with streaming
let streaming_config = StreamingConfig {
    chunk_size: 1000,
    memory_limit: 100 * 1024 * 1024, // 100 MB
    enable_disk_spillover: true,
    compression_level: 6,
};

let graph_stream = GraphStream::new(streaming_config);

// Process graph in chunks
for chunk in graph_stream.process_in_chunks(&large_graph) {
    let chunk_result = process_graph_chunk(chunk)?;
    graph_stream.accumulate_result(chunk_result);
}

let final_result = graph_stream.finalize()?;

Lazy Loading

use reflow_network::graph::LazyGraph;

// Create lazy-loading graph for very large datasets
let lazy_graph = LazyGraph::from_file("massive_graph.json")?;

// Nodes and connections are loaded on demand
if let Some(node) = lazy_graph.get_node("some_node")? {
    // Node is loaded into memory only when accessed
    println!("Node component: {}", node.component);
}

// Preload specific subgraphs for better performance
lazy_graph.preload_subgraph(&["critical_node_1", "critical_node_2"])?;

Distributed Graph Processing

use reflow_network::graph::{DistributedGraph, NodePartition};

// Partition graph across multiple nodes
let partitions = graph.create_partitions(4)?; // 4 partitions

for (i, partition) in partitions.iter().enumerate() {
    println!("Partition {}: {} nodes", i, partition.nodes.len());
    
    // Deploy partition to worker node
    let worker_id = format!("worker_{}", i);
    deploy_partition_to_worker(&worker_id, partition)?;
}

// Coordinate distributed operations
let distributed_graph = DistributedGraph::new(partitions);
let distributed_result = distributed_graph.execute_distributed_analysis().await?;

Advanced Analysis

Machine Learning Integration

use reflow_network::graph::{MLFeatures, GraphEmbedding};

// Extract features for machine learning
let features = graph.extract_ml_features();

println!("Graph ML Features:");
println!("  Node features: {} dimensions", features.node_features.len());
println!("  Edge features: {} dimensions", features.edge_features.len());
println!("  Global features: {} dimensions", features.global_features.len());

// Generate graph embeddings
let embedding_config = EmbeddingConfig {
    embedding_size: 128,
    walk_length: 10,
    num_walks: 100,
    context_size: 5,
};

let embeddings = graph.generate_embeddings(&embedding_config)?;

// Use embeddings for similarity analysis
let similar_nodes = embeddings.find_similar_nodes("reference_node", 5)?;
for (node, similarity) in similar_nodes {
    println!("Similar node: {} (similarity: {:.3})", node, similarity);
}

Pattern Mining

use reflow_network::graph::{PatternMiner, FrequentPattern};

// Mine frequent subgraph patterns
let miner = PatternMiner::new();
let patterns = miner.mine_frequent_patterns(&graph, 0.1)?; // 10% minimum support

for pattern in patterns {
    println!("Frequent pattern (support: {:.1}%):", pattern.support * 100.0);
    println!("  Nodes: {:?}", pattern.nodes);
    println!("  Connections: {:?}", pattern.connections);
    
    // Find all instances of this pattern
    let instances = graph.find_pattern_instances(&pattern)?;
    println!("  Found in {} locations", instances.len());
}

Anomaly Detection

use reflow_network::graph::{AnomalyDetector, AnomalyType};

// Detect structural anomalies
let detector = AnomalyDetector::new();
let anomalies = detector.detect_anomalies(&graph)?;

for anomaly in anomalies {
    match anomaly.anomaly_type {
        AnomalyType::UnusualDegree => {
            println!("Node '{}' has unusual connectivity: {} connections", 
                anomaly.node, anomaly.score);
        }
        AnomalyType::IsolatedCluster => {
            println!("Isolated cluster detected around node '{}'", anomaly.node);
        }
        AnomalyType::UnexpectedPattern => {
            println!("Unexpected pattern at node '{}' (novelty: {:.2})", 
                anomaly.node, anomaly.score);
        }
    }
}

Graph Transformation

Rule-Based Transformations

use reflow_network::graph::{TransformationRule, RuleEngine};

// Define transformation rules
let rule = TransformationRule {
    name: "optimize_serial_processors".to_string(),
    pattern: GraphPattern::parse("A -> B -> C where A.type == B.type == 'Processor'")?,
    replacement: GraphReplacement::parse("A+B+C -> OptimizedProcessor")?,
    condition: |nodes| {
        // Custom condition logic
        nodes.iter().all(|n| n.metadata.get("parallelizable") == Some(&json!(true)))
    },
};

// Apply transformation rules
let rule_engine = RuleEngine::new();
rule_engine.add_rule(rule);

let transformed_graph = rule_engine.apply_rules(&graph)?;

Graph Morphing

use reflow_network::graph::{MorphingConfig, MorphingStrategy};

// Gradually transform graph structure
let morphing_config = MorphingConfig {
    strategy: MorphingStrategy::Gradual,
    steps: 10,
    preserve_semantics: true,
    target_layout: Some(target_positions),
};

let morphing_steps = graph.create_morphing_sequence(&target_graph, &morphing_config)?;

for (step, intermediate_graph) in morphing_steps.enumerate() {
    println!("Morphing step {}/{}", step + 1, morphing_config.steps);
    
    // Apply intermediate graph state
    apply_graph_state(&intermediate_graph);
    
    // Optional: pause for animation
    std::thread::sleep(std::time::Duration::from_millis(100));
}

Custom Extensions

Plugin System

use reflow_network::graph::{GraphPlugin, PluginConfig};

// Create custom graph plugin
struct MyCustomPlugin {
    config: PluginConfig,
}

impl GraphPlugin for MyCustomPlugin {
    fn initialize(&mut self, graph: &mut Graph) -> Result<(), GraphError> {
        // Plugin initialization logic
        println!("Initializing custom plugin for graph: {}", graph.name);
        Ok(())
    }
    
    fn on_node_added(&mut self, graph: &Graph, node: &GraphNode) {
        // Custom logic when nodes are added
        println!("Plugin: Node added: {}", node.id);
    }
    
    fn on_connection_added(&mut self, graph: &Graph, connection: &GraphConnection) {
        // Custom logic when connections are added
        println!("Plugin: Connection added");
    }
    
    fn custom_analysis(&self, graph: &Graph) -> CustomAnalysisResult {
        // Custom analysis implementation
        CustomAnalysisResult::new()
    }
}

// Register and use plugin
graph.register_plugin("my_plugin", Box::new(MyCustomPlugin::new()))?;
graph.enable_plugin("my_plugin")?;

// Call custom analysis
let custom_result = graph.call_plugin_analysis("my_plugin")?;

Event Hooks

use reflow_network::graph::{EventHook, HookPriority};

// Create custom event hook
let custom_hook = EventHook::new()
    .on_node_added(|graph, node| {
        println!("Custom hook: Node {} added to graph {}", node.id, graph.name);
    })
    .on_connection_added(|graph, connection| {
        println!("Custom hook: Connection added");
    })
    .with_priority(HookPriority::High);

// Register hook
graph.register_hook("custom_logger", custom_hook)?;

// Temporary hooks for specific operations
graph.with_temporary_hook("validation_hook", |graph| {
    // This hook only applies during this operation
    let validation = graph.validate_flow()?;
    Ok(validation)
})?;

Error Recovery

Automatic Error Recovery

use reflow_network::graph::{ErrorRecovery, RecoveryStrategy};

// Configure automatic error recovery
let recovery_config = ErrorRecovery {
    strategy: RecoveryStrategy::Rollback,
    max_retries: 3,
    backup_frequency: 10, // Create backup every 10 operations
    auto_fix_common_issues: true,
};

graph.configure_error_recovery(&recovery_config)?;

// Operations are automatically protected
match graph.add_connection("nonexistent", "out", "target", "in", None) {
    Err(e) => {
        // Graph automatically attempts recovery
        println!("Error occurred but graph recovered: {}", e);
    }
    Ok(_) => println!("Operation succeeded"),
}

Manual Recovery Operations

// Create manual checkpoint
let checkpoint = graph.create_checkpoint("before_risky_operation")?;

// Perform risky operations
match risky_graph_operation(&mut graph) {
    Ok(result) => {
        // Success - commit changes
        graph.commit_checkpoint(&checkpoint)?;
        Ok(result)
    }
    Err(e) => {
        // Failure - rollback to checkpoint
        graph.rollback_to_checkpoint(&checkpoint)?;
        Err(e)
    }
}

Integration Patterns

Event Sourcing

use reflow_network::graph::{EventStore, GraphEvent};

// Set up event sourcing
let event_store = EventStore::new("graph_events.log")?;
graph.enable_event_sourcing(&event_store)?;

// All graph changes are automatically logged
graph.add_node("event_sourced", "EventNode", None);
// Event is automatically persisted

// Replay events to reconstruct graph state
let events = event_store.read_events_from(timestamp)?;
let reconstructed_graph = Graph::replay_events(events)?;

CQRS Pattern

use reflow_network::graph::{CommandHandler, QueryHandler};

// Separate command and query responsibilities
let command_handler = CommandHandler::new(&mut graph);
let query_handler = QueryHandler::new(&graph);

// Commands modify state
command_handler.execute(AddNodeCommand {
    id: "cmd_node".to_string(),
    component: "CommandNode".to_string(),
    metadata: None,
})?;

// Queries read state (potentially from optimized read models)
let node_info = query_handler.get_node_info("cmd_node")?;
let analysis = query_handler.analyze_connectivity("cmd_node")?;

Best Practices Summary

Performance Best Practices

Use appropriate data structures: Choose indices based on access patterns
Enable lazy loading: For large graphs, load data on demand
Configure memory limits: Prevent memory exhaustion
Use parallel processing: Enable for CPU-intensive operations
Cache analysis results: Store expensive computations

Scalability Best Practices

Partition large graphs: Distribute across multiple nodes
Stream large operations: Process data in chunks
Use compression: Reduce memory and storage requirements
Implement backpressure: Control data flow rates
Monitor resource usage: Track memory and CPU consumption

Maintainability Best Practices

Use version control: Track graph schema changes
Implement proper error handling: Handle edge cases gracefully
Document custom extensions: Maintain clear plugin documentation
Use consistent naming: Follow naming conventions
Implement comprehensive testing: Test all graph operations

Next Steps

Building Visual Editors - Complete tutorial
Performance Optimization - Advanced optimization techniques
Creating Graph - Basic operations
Graph Analysis - Validation and analysis
Layout System - Positioning and visualization

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Advanced Graph Features

History Management

Basic History Operations

Advanced History Configuration

History Compression

History Snapshots

Branching History

History Events

Subgraph Operations

Creating Subgraphs

Subgraph Analysis

Subgraph Operations

Subgraph Templates

Graph Optimization

Automatic Optimization

Redundancy Elimination

Node Fusion

Connection Optimization

Performance Tuning

Memory Optimization

Index Optimization

Parallel Processing Setup

Benchmarking and Profiling

Large Graph Handling

Streaming Operations

Lazy Loading

Distributed Graph Processing

Advanced Analysis

Machine Learning Integration

Pattern Mining

Anomaly Detection

Graph Transformation

Rule-Based Transformations

Graph Morphing

Custom Extensions

Plugin System

Event Hooks

Error Recovery

Automatic Error Recovery

Manual Recovery Operations

Integration Patterns

Event Sourcing

CQRS Pattern

Best Practices Summary

Performance Best Practices

Scalability Best Practices

Maintainability Best Practices

Next Steps