Advanced Analytics¶

Navigation¶

• Advanced Analytics
• Navigation
• Overview
• Features
  • Transaction Analytics
  • Market Intelligence
  • Performance Metrics
• Implementation
  • Data Collection
  • Data Processing
• Real-Time Analytics
  • Stream Processing
  • Event Processing
• Data Visualization
  • Chart Generation
  • Dashboard Configuration
• Machine Learning
  • Model Training
  • Prediction
• Performance Optimization
  • Caching Strategy
  • Data Aggregation
• API Integration
  • REST API
  • WebSocket Streaming
• Security
  • Access Control
  • Data Protection
• Monitoring
  • System Metrics
  • Health Checks
• Configuration Examples
  • Development
  • Production
• Best Practices
• Support

- [WebSocket Streaming](#websocket-streaming)

- [Health Checks](#health-checks)

Overview¶

Anya Enterprise's Advanced Analytics module provides comprehensive data analysis and visualization capabilities for blockchain transactions, market trends, and system performance. This enterprise-grade solution offers real-time insights and predictive analytics.

Features¶

Transaction Analytics¶

• Real-time transaction monitoring (Guide)

• Pattern recognition (Details)
• Anomaly detection (Guide)
• Volume analysis (Details)

• Fee estimation (Guide)

Market Intelligence¶

• Price analysis (Guide)

• Market trends (Details)
• Liquidity metrics (Guide)

• Volatility indicators (Details)
• Correlation analysis (Guide)

Performance Metrics¶

• System health monitoring (Guide)
• Resource utilization (Details)
• Network performance (Guide)

• API response times (Details)
• Error rates (Guide)

Implementation¶

Data Collection¶

pub struct AnalyticsCollector {
    pub config: CollectorConfig,
<!-- Broken link removed: [System Metrics Guide](../monitoring/system-metrics.md) -->
    pub metrics: MetricsRegistry,
    pub storage: TimeSeriesDB,
<!-- Broken link removed: [Health Checks Guide](../monitoring/health-checks.md) -->
}

impl AnalyticsCollector {
<!-- Broken link removed: [Development Configuration Guide](./development-config.md) -->
    pub async fn collect_metrics(&self) -> Result<(), CollectorError> {
        // Implementation details
<!-- Broken link removed: [Production Configuration Guide](./production-config.md) -->
    }
}

For collection details, see Data Collection Guide.

Data Processing¶

pub async fn process_transaction_data(
    transactions: Vec<Transaction>,
    config: ProcessingConfig,
) -> Result<AnalyticsResult, ProcessingError> {
    // Implementation details
}

For processing details, see Data Processing Guide.

Real-Time Analytics¶

Stream Processing¶

pub struct AnalyticsStream {
    pub input: mpsc::Receiver<AnalyticsEvent>,
    pub processor: StreamProcessor,
    pub output: mpsc::Sender<AnalyticsResult>,
}

impl AnalyticsStream {
    pub async fn process_events(&mut self) -> Result<(), StreamError> {
        while let Some(event) = self.input.recv().await {
            let result = self.processor.process_event(event).await?;
            self.output.send(result).await?;
        }
        Ok(())
    }
}

For stream processing details, see Stream Processing Guide.

Event Processing¶

#[derive(Debug)]
pub enum AnalyticsEvent {
    Transaction(TransactionData),
    Block(BlockData),
    Market(MarketData),
    System(SystemMetrics),
}

For event processing details, see Event Processing Guide.

Data Visualization¶

Chart Generation¶

pub struct ChartGenerator {
    pub config: ChartConfig,
    pub renderer: ChartRenderer,
}

impl ChartGenerator {
    pub fn generate_chart(
        &self,
        data: &AnalyticsData,
        options: ChartOptions,
    ) -> Result<Chart, ChartError> {
        // Implementation details
    }
}

For chart generation details, see Chart Generation Guide.

Dashboard Configuration¶

[dashboard]
refresh_rate = 5000  # milliseconds
default_timespan = "24h"
max_data_points = 1000

[dashboard.charts]
transaction_volume = true
price_trends = true
system_metrics = true

For dashboard configuration details, see Dashboard Configuration Guide.

Machine Learning¶

Model Training¶

pub struct MLModel {
    pub config: ModelConfig,
    pub trainer: ModelTrainer,
    pub validator: ModelValidator,
}

impl MLModel {
    pub async fn train(
        &mut self,
        training_data: TrainingData,
    ) -> Result<(), TrainingError> {
        // Implementation details
    }
}

For model training details, see Model Training Guide.

Prediction¶

pub async fn predict_metrics(
    model: &MLModel,
    input_data: InputData,
) -> Result<Prediction, PredictionError> {
    // Implementation details
}

For prediction details, see Prediction Guide.

Performance Optimization¶

Caching Strategy¶

pub struct AnalyticsCache {
    pub config: CacheConfig,
    pub storage: CacheStorage,
}

impl AnalyticsCache {
    pub async fn get_or_compute<T>(
        &self,
        key: CacheKey,
        computer: impl FnOnce() -> Future<Output = T>,
    ) -> Result<T, CacheError> {
        // Implementation details
    }
}

Data Aggregation¶

pub struct Aggregator {
    pub config: AggregationConfig,
    pub storage: TimeSeriesDB,
}

impl Aggregator {
    pub async fn aggregate_data(
        &self,
        timespan: Duration,
    ) -> Result<AggregatedData, AggregationError> {
        // Implementation details
    }
}

For data aggregation details, see Data Aggregation Guide.

API Integration¶

REST API¶

#[get("/analytics/transactions")]
pub async fn get_transaction_analytics(
    Query(params): Query<AnalyticsParams>,
    State(state): State<AppState>,
) -> Result<Json<AnalyticsResponse>, Error> {
    // Implementation details
}

For REST API details, see REST API Guide.

WebSocket Streaming¶

pub struct AnalyticsWebSocket {
    pub config: WebSocketConfig,
    pub stream: WebSocketStream,
}

impl AnalyticsWebSocket {
    pub async fn stream_analytics(
        &mut self,
        filters: StreamFilters,
    ) -> Result<(), WebSocketError> {
        // Implementation details
    }
}

For WebSocket streaming details, see WebSocket Streaming Guide.

Security¶

Access Control¶

#[derive(Debug)]
pub struct AnalyticsPermissions {
    pub read: Vec<Permission>,
    pub write: Vec<Permission>,
    pub admin: Vec<Permission>,
}

For access control details, see Access Control Guide.

Data Protection¶

pub struct DataProtection {
    pub encryption: EncryptionConfig,
    pub masking: DataMaskingRules,
}

Monitoring¶

System Metrics¶

#[derive(Debug)]
pub struct SystemMetrics {
    pub cpu_usage: f64,
    pub memory_usage: f64,
    pub disk_io: DiskMetrics,
    pub network_io: NetworkMetrics,
}

Health Checks¶

pub async fn check_analytics_health() -> Result<HealthStatus, HealthCheckError> {
    // Implementation details
}

Configuration Examples¶

Development¶

[analytics]
environment = "development"
log_level = "debug"
metrics_enabled = true

[analytics.collection]
interval = 60
batch_size = 1000

Production¶

[analytics]
environment = "production"
log_level = "info"
metrics_enabled = true

[analytics.collection]
interval = 15
batch_size = 5000

Best Practices¶

1. Data Collection
2. Use appropriate sampling rates
3. Implement data validation
4. Handle missing data

5. Optimize storage

6. Processing

7. Batch processing when possible
8. Implement caching
9. Use efficient algorithms

10. Handle errors gracefully

11. Visualization

12. Use appropriate chart types
13. Implement responsive design
14. Optimize rendering
15. Handle large datasets

Support¶

For additional support:

• Technical Support
• Security Issues
• Feature Requests
• Bug Reports

Last updated: 2025-06-02