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

#![allow(unused)]
fn main() {
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

#![allow(unused)]
fn main() {
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

#![allow(unused)]
fn main() {
// 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

#![allow(unused)]
fn main() {
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

#![allow(unused)]
fn main() {
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

#![allow(unused)]
fn main() {
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

#![allow(unused)]
fn main() {
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

#![allow(unused)]
fn main() {
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

#![allow(unused)]
fn main() {
// 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

#![allow(unused)]
fn main() {
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

#![allow(unused)]
fn main() {
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

#![allow(unused)]
fn main() {
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

#![allow(unused)]
fn main() {
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

#![allow(unused)]
fn main() {
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

#![allow(unused)]
fn main() {
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

#![allow(unused)]
fn main() {
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

#![allow(unused)]
fn main() {
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

#![allow(unused)]
fn main() {
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

#![allow(unused)]
fn main() {
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

#![allow(unused)]
fn main() {
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

#![allow(unused)]
fn main() {
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

#![allow(unused)]
fn main() {
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

#![allow(unused)]
fn main() {
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

#![allow(unused)]
fn main() {
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

#![allow(unused)]
fn main() {
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

#![allow(unused)]
fn main() {
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

#![allow(unused)]
fn main() {
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

#![allow(unused)]
fn main() {
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

#![allow(unused)]
fn main() {
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

#![allow(unused)]
fn main() {
// 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

#![allow(unused)]
fn main() {
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

#![allow(unused)]
fn main() {
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

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

Scalability Best Practices

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

Maintainability Best Practices

1. Use version control: Track graph schema changes
2. Implement proper error handling: Handle edge cases gracefully
3. Document custom extensions: Maintain clear plugin documentation
4. Use consistent naming: Follow naming conventions
5. 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