wifi-densepose/examples/data/framework/docs/STREAMING.md

RuVector Streaming Data Ingestion

Real-time streaming data ingestion with windowed analysis, pattern detection, and backpressure handling.

Features

• Async Stream Processing: Non-blocking ingestion of continuous data streams
• Windowed Analysis: Support for tumbling and sliding time windows
• Real-time Pattern Detection: Automatic pattern detection with customizable callbacks
• Backpressure Handling: Automatic flow control to prevent memory overflow
• Comprehensive Metrics: Throughput, latency, and pattern detection statistics
• SIMD Acceleration: Leverages optimized vector operations for high performance
• Parallel Processing: Configurable concurrency for batch operations

Quick Start

use ruvector_data_framework::{
    StreamingEngine, StreamingEngineBuilder,
    ruvector_native::{Domain, SemanticVector},
};
use futures::stream;
use std::time::Duration;

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Create streaming engine with builder pattern
    let mut engine = StreamingEngineBuilder::new()
        .window_size(Duration::from_secs(60))
        .slide_interval(Duration::from_secs(30))
        .batch_size(100)
        .max_buffer_size(10000)
        .build();

    // Set pattern detection callback
    engine.set_pattern_callback(|pattern| {
        println!("Pattern detected: {:?}", pattern.pattern.pattern_type);
        println!("Confidence: {:.2}", pattern.pattern.confidence);
    }).await;

    // Create a stream of vectors
    let vectors = vec![/* your SemanticVector instances */];
    let vector_stream = stream::iter(vectors);

    // Ingest the stream
    engine.ingest_stream(vector_stream).await?;

    // Get metrics
    let metrics = engine.metrics().await;
    println!("Processed: {} vectors", metrics.vectors_processed);
    println!("Patterns detected: {}", metrics.patterns_detected);
    println!("Throughput: {:.1} vectors/sec", metrics.throughput_per_sec);

    Ok(())
}

Window Types

Sliding Windows

Overlapping time windows that provide continuous analysis:

let engine = StreamingEngineBuilder::new()
    .window_size(Duration::from_secs(60))      // 60-second windows
    .slide_interval(Duration::from_secs(30))   // Slide every 30 seconds
    .build();

Use case: Continuous monitoring with overlapping context

Tumbling Windows

Non-overlapping time windows for discrete analysis:

let engine = StreamingEngineBuilder::new()
    .window_size(Duration::from_secs(60))
    .tumbling_windows()                        // No overlap
    .build();

Use case: Batch processing with clear boundaries

Configuration

StreamingConfig

Field	Type	Default	Description
window_size	Duration	60s	Time window size
slide_interval	Option<Duration>	Some(30s)	Sliding window interval (None = tumbling)
max_buffer_size	usize	10,000	Max vectors before backpressure
batch_size	usize	100	Vectors per batch
max_concurrency	usize	4	Max parallel processing tasks
auto_detect_patterns	bool	true	Enable automatic pattern detection
detection_interval	usize	100	Detect patterns every N vectors

OptimizedConfig (Discovery)

Field	Type	Default	Description
similarity_threshold	f64	0.65	Min cosine similarity for edges
mincut_sensitivity	f64	0.12	Min-cut change threshold
cross_domain	bool	true	Enable cross-domain pattern detection
use_simd	bool	true	Use SIMD acceleration
significance_threshold	f64	0.05	P-value threshold for significance

Pattern Detection

The streaming engine automatically detects patterns using statistical significance testing:

engine.set_pattern_callback(|pattern| {
    match pattern.pattern.pattern_type {
        PatternType::CoherenceBreak => {
            println!("Network fragmentation detected!");
        },
        PatternType::Consolidation => {
            println!("Network strengthening detected!");
        },
        PatternType::BridgeFormation => {
            println!("Cross-domain connection detected!");
        },
        PatternType::Cascade => {
            println!("Temporal causality detected!");
        },
        _ => {}
    }

    // Check statistical significance
    if pattern.is_significant {
        println!("P-value: {:.4}", pattern.p_value);
        println!("Effect size: {:.2}", pattern.effect_size);
    }
}).await;

Pattern Types

• CoherenceBreak: Network is fragmenting (min-cut decreased)
• Consolidation: Network is strengthening (min-cut increased)
• EmergingCluster: New dense subgraph forming
• DissolvingCluster: Existing cluster dissolving
• BridgeFormation: Cross-domain connections forming
• Cascade: Changes propagating through network
• TemporalShift: Temporal pattern change detected
• AnomalousNode: Outlier vector detected

Metrics

StreamingMetrics

pub struct StreamingMetrics {
    pub vectors_processed: u64,        // Total vectors ingested
    pub patterns_detected: u64,        // Total patterns found
    pub avg_latency_ms: f64,          // Average processing latency
    pub throughput_per_sec: f64,      // Vectors per second
    pub windows_processed: u64,        // Time windows analyzed
    pub backpressure_events: u64,     // Times buffer was full
    pub errors: u64,                  // Processing errors
    pub peak_buffer_size: usize,      // Max buffer usage
}

Access metrics:

let metrics = engine.metrics().await;
println!("Throughput: {:.1} vectors/sec", metrics.throughput_per_sec);
println!("Avg latency: {:.2}ms", metrics.avg_latency_ms);
println!("Uptime: {:.1}s", metrics.uptime_secs());

Performance Optimization

Batch Size

Larger batches improve throughput but increase latency:

.batch_size(500)  // High throughput, higher latency
.batch_size(50)   // Lower throughput, lower latency

Concurrency

Increase parallel processing for CPU-bound workloads:

.max_concurrency(8)  // 8 concurrent batch processors

Buffer Size

Control memory usage and backpressure:

.max_buffer_size(50000)  // Larger buffer, less backpressure
.max_buffer_size(1000)   // Smaller buffer, more backpressure

SIMD Acceleration

Enable SIMD for 4-8x speedup on vector operations:

use ruvector_data_framework::optimized::OptimizedConfig;

let discovery_config = OptimizedConfig {
    use_simd: true,  // Enable SIMD (default)
    ..Default::default()
};

Examples

Climate Data Streaming

use futures::stream;
use std::time::Duration;

// Configure for climate data analysis
let engine = StreamingEngineBuilder::new()
    .window_size(Duration::from_secs(3600))    // 1-hour windows
    .slide_interval(Duration::from_secs(900))  // Slide every 15 minutes
    .batch_size(200)
    .max_concurrency(4)
    .build();

// Stream climate observations
let climate_stream = get_climate_data_stream().await?;
engine.ingest_stream(climate_stream).await?;

Financial Market Data

// Configure for high-frequency financial data
let engine = StreamingEngineBuilder::new()
    .window_size(Duration::from_secs(60))      // 1-minute windows
    .slide_interval(Duration::from_secs(10))   // Slide every 10 seconds
    .batch_size(1000)                          // Large batches
    .max_concurrency(8)                        // High parallelism
    .detection_interval(500)                   // Check patterns frequently
    .build();

let market_stream = get_market_data_stream().await?;
engine.ingest_stream(market_stream).await?;

Backpressure Handling

The streaming engine automatically applies backpressure when the buffer fills:

let engine = StreamingEngineBuilder::new()
    .max_buffer_size(5000)  // Limit to 5000 vectors
    .build();

// Engine will slow down ingestion if processing can't keep up
engine.ingest_stream(fast_stream).await?;

let metrics = engine.metrics().await;
println!("Backpressure events: {}", metrics.backpressure_events);

Error Handling

use ruvector_data_framework::Result;

async fn ingest_with_error_handling() -> Result<()> {
    let mut engine = StreamingEngineBuilder::new().build();

    match engine.ingest_stream(vector_stream).await {
        Ok(_) => println!("Ingestion complete"),
        Err(e) => {
            eprintln!("Ingestion error: {}", e);
            let metrics = engine.metrics().await;
            eprintln!("Processed {} vectors before error", metrics.vectors_processed);
        }
    }

    Ok(())
}

Running the Examples

# Basic streaming demo
cargo run --example streaming_demo --features parallel

# Specific examples
cargo run --example streaming_demo --features parallel -- sliding
cargo run --example streaming_demo --features parallel -- tumbling
cargo run --example streaming_demo --features parallel -- patterns
cargo run --example streaming_demo --features parallel -- throughput

Best Practices

1. Choose appropriate window sizes: Too small = noise, too large = delayed detection
2. Tune batch size: Balance throughput vs. latency for your use case
3. Monitor backpressure: High backpressure indicates processing bottleneck
4. Use SIMD: Enable SIMD for significant performance gains on x86_64
5. Set significance thresholds: Adjust p-value threshold to reduce false positives
6. Profile your workload: Use metrics to identify optimization opportunities

Troubleshooting

High Latency

• Reduce batch size
• Increase concurrency
• Enable SIMD acceleration
• Check for slow pattern callbacks

High Memory Usage

• Reduce max_buffer_size
• Reduce window size
• Increase processing speed

Missed Patterns

• Increase detection_interval frequency
• Lower similarity_threshold
• Lower significance_threshold
• Increase window overlap (sliding windows)

Architecture

                    ┌─────────────────────┐
                    │  Input Stream       │
                    └──────────┬──────────┘
                               │
                    ┌──────────▼──────────┐
                    │  Backpressure       │
                    │  Semaphore          │
                    └──────────┬──────────┘
                               │
            ┌──────────────────┼──────────────────┐
            │                  │                  │
    ┌───────▼────────┐ ┌──────▼─────────┐ ┌─────▼──────┐
    │  Window 1      │ │  Window 2      │ │  Window N  │
    │  (Sliding)     │ │  (Sliding)     │ │  (Sliding) │
    └───────┬────────┘ └──────┬─────────┘ └─────┬──────┘
            │                  │                  │
            └──────────────────┼──────────────────┘
                               │
                    ┌──────────▼──────────┐
                    │  Batch Processor    │
                    │  (Parallel)         │
                    └──────────┬──────────┘
                               │
                    ┌──────────▼──────────┐
                    │  Discovery Engine   │
                    │  (SIMD + Min-Cut)   │
                    └──────────┬──────────┘
                               │
                    ┌──────────▼──────────┐
                    │  Pattern Detection  │
                    │  (Statistical)      │
                    └──────────┬──────────┘
                               │
                    ┌──────────▼──────────┐
                    │  Callbacks          │
                    └─────────────────────┘

License

Same as RuVector project.