// Real-Time Backend Connectivity Example
// Event-driven architecture with message queues and real-time data streams
// =====================================================
// PATTERN 1: Real-Time Analytics Dashboard
// =====================================================
@trust("hybrid")
@chain("ethereum")
@ai
service RealTimeAnalyticsDashboard {
// Backend connections
redis_pubsub: any;
kafka_consumer: any;
websocket_server: any;
database: any;
ai_analyzer: any;
event_buffer: Map<String, List<any>>;
active_connections: Map<String, any>;
fn initialize() -> Result<Unit, Error> {
log::info("analytics", {
"service": "RealTimeAnalyticsDashboard",
"status": "initializing"
});
// Initialize Redis for pub/sub messaging
self.redis_pubsub = messaging::create_redis_pubsub({
"host": "localhost",
"port": 6379,
"channels": ["user_events", "system_metrics", "business_events"]
});
// Initialize Kafka consumer for event streaming
self.kafka_consumer = messaging::create_kafka_consumer({
"brokers": ["localhost:9092"],
"group_id": "analytics_dashboard",
"topics": ["user_activities", "system_events", "business_metrics"],
"auto_commit": true
});
// Initialize WebSocket server for real-time client updates
self.websocket_server = web::create_websocket_server({
"port": 8080,
"path": "/analytics",
"max_connections": 1000,
"heartbeat_interval": 30000
});
// Initialize database for persistent storage
self.database = database::connect("postgresql://analytics:password@localhost:5432/analytics_db");
// Initialize AI analyzer for real-time insights
self.ai_analyzer = ai::create_analyzer({
"model": "real_time_analytics",
"features": ["anomaly_detection", "trend_analysis", "prediction"],
"update_interval": 5000 // 5 seconds
});
// Setup event handlers
self.setup_event_handlers();
// Initialize data structures
self.event_buffer = Map::new();
self.active_connections = Map::new();
// Create database tables
self.initialize_database_schema();
log::info("analytics", {
"service": "RealTimeAnalyticsDashboard",
"status": "initialized",
"connections": ["redis", "kafka", "websocket", "postgresql"]
});
return Ok(Unit);
}
fn setup_event_handlers() -> Result<Unit, Error> {
// Redis pub/sub handlers
messaging::on_redis_message(self.redis_pubsub, "user_events", this.handle_user_event);
messaging::on_redis_message(self.redis_pubsub, "system_metrics", this.handle_system_metric);
messaging::on_redis_message(self.redis_pubsub, "business_events", this.handle_business_event);
// Kafka message handlers
messaging::on_kafka_message(self.kafka_consumer, "user_activities", this.handle_user_activity);
messaging::on_kafka_message(self.kafka_consumer, "system_events", this.handle_system_event);
messaging::on_kafka_message(self.kafka_consumer, "business_metrics", this.handle_business_metric);
// WebSocket connection handlers
web::on_websocket_connect(self.websocket_server, this.handle_client_connect);
web::on_websocket_disconnect(self.websocket_server, this.handle_client_disconnect);
web::on_websocket_message(self.websocket_server, this.handle_client_message);
return Ok(Unit);
}
fn initialize_database_schema() -> Result<Unit, Error> {
let schema_sql = "CREATE TABLE events, metrics, insights with indexes";
database::execute_query(self.database, schema_sql);
return Ok(Unit);
}
// ================================================
// EVENT HANDLERS
// ================================================
fn handle_user_event(event_data: any) -> Result<Unit, Error> {
log::info("analytics", {
"event": "user_event_received",
"type": event_data.type,
"user_id": event_data.user_id,
"timestamp": event_data.timestamp
});
// Store event in database
database::execute_query(self.database, "
INSERT INTO events (event_type, event_data, user_id, session_id, timestamp)
VALUES ($1, $2, $3, $4, $5)
", [
"user_event",
json::stringify(event_data),
event_data.user_id,
event_data.session_id,
event_data.timestamp
]);
// Add to processing buffer
if (!self.event_buffer.contains_key("user_events")) {
self.event_buffer["user_events"] = [];
}
self.event_buffer["user_events"].push(event_data);
// Trigger real-time analysis
self.analyze_user_events();
return Ok(Unit);
}
fn handle_system_metric(metric_data: any) -> Result<Unit, Error> {
// Store metric
database::execute_query(self.database, "
INSERT INTO metrics (metric_name, metric_value, tags, timestamp)
VALUES ($1, $2, $3, $4)
", [
metric_data.name,
metric_data.value,
json::stringify(metric_data.tags),
metric_data.timestamp
]);
// Update real-time dashboard
let change_val = this.calculate_metric_change(metric_data);
self.update_realtime_dashboard("system_metrics", {
"name": metric_data.name,
"value": metric_data.value,
"change": change_val,
"timestamp": metric_data.timestamp
});
// Check for anomalies
let anomaly = ai::detect_anomaly(self.ai_analyzer, "system_metrics", metric_data);
if (anomaly.detected) {
self.handle_metric_anomaly(anomaly);
}
return Ok(Unit);
}
fn handle_business_event(event_data: any) -> Result<Unit, Error> {
// Process business logic
let processed_event = self.process_business_event(event_data);
// Store processed event
database::execute_query(self.database, "
INSERT INTO events (event_type, event_data, user_id, timestamp)
VALUES ($1, $2, $3, $4)
", [
"business_event",
json::stringify(processed_event),
event_data.user_id,
event_data.timestamp
]);
// Update business metrics
self.update_business_metrics(processed_event);
return Ok(Unit);
}
// ================================================
// REAL-TIME ANALYSIS
// ================================================
fn analyze_user_events() -> Result<Unit, Error> {
// let user_events = self.event_buffer["user_events"]
if (user_events.length() == 0) {
return Ok(Unit);
}
// Perform real-time analysis
let analysis = ai::analyze_event_stream(self.ai_analyzer, "user_events", user_events);
// Generate insights
let insights = ai::generate_realtime_insights(self.ai_analyzer, analysis);
// Store insights
for insight in insights {
database::execute_query(self.database, "
INSERT INTO insights (insight_type, insight_data, confidence_score, timestamp)
VALUES ($1, $2, $3, $4)
", [
insight.type,
json::stringify(insight.data),
insight.confidence,
chain::get_block_timestamp()
]);
}
// Broadcast insights to connected clients
self.broadcast_to_clients("insights", {
"insights": insights,
"timestamp": chain::get_block_timestamp()
});
// Clear processed events from buffer
self.event_buffer["user_events"] = [];
return Ok(Unit);
}
fn update_realtime_dashboard(metric_type: String, data: any) -> Result<Unit, Error> {
// Prepare dashboard update
let dashboard_update = {
"type": "dashboard_update",
"metric_type": metric_type,
"data": data,
"timestamp": chain::get_block_timestamp()
};
// Broadcast to all connected clients
self.broadcast_to_clients("dashboard_update", dashboard_update);
return Ok(Unit);
}
// ================================================
// WEBSOCKET CLIENT MANAGEMENT
// ================================================
fn handle_client_connect(connection: any) -> Result<Unit, Error> {
let connection_id = connection.id;
log::info("analytics", {
"event": "client_connected",
"connection_id": connection_id,
"total_connections": self.active_connections.size() + 1
});
// Store connection
self.active_connections[connection_id] = {
"id": connection_id,
"connected_at": chain::get_block_timestamp(),
"subscriptions": []
};
// Send welcome message
web::send_websocket_message(connection, {
"type": "welcome",
"message": "Connected to Real-Time Analytics Dashboard",
"connection_id": connection_id,
"timestamp": chain::get_block_timestamp()
});
return Ok(Unit);
}
fn handle_client_disconnect(connection_id: String) -> Result<Unit, Error> {
log::info("analytics", {
"event": "client_disconnected",
"connection_id": connection_id,
"total_connections": self.active_connections.size() - 1
});
// Remove connection
self.active_connections.remove(connection_id);
return Ok(Unit);
}
fn handle_client_message(connection_id: String, message: any) -> Result<Unit, Error> {
let connection = self.active_connections[connection_id];
if (message.type == "subscribe") {
// Handle subscription request
connection.subscriptions.push(message.channel);
web::send_websocket_message(
web::get_connection_by_id(connection_id),
{
"type": "subscribed",
"channel": message.channel,
"timestamp": chain::get_block_timestamp()
}
);
} else if (message.type == "unsubscribe" ) {
// Handle unsubscription request
let new_subs = [];
for sub in connection.subscriptions {
if (sub != message.channel ) {
new_subs.push(sub);
}
}
connection.subscriptions = new_subs;
web::send_websocket_message(
web::get_connection_by_id(connection_id),
{
"type": "unsubscribed",
"channel": message.channel,
"timestamp": chain::get_block_timestamp()
}
);
} else if (message.type == "request_historical_data" ) {
// Send historical data
let historical_data = self.get_historical_data(message.metric_type, message.time_range);
web::send_websocket_message(
web::get_connection_by_id(connection_id),
{
"type": "historical_data",
"data": historical_data,
"timestamp": chain::get_block_timestamp()
}
);
}
return Ok(Unit);
}
// ================================================
// BROADCASTING METHODS
// ================================================
fn broadcast_to_clients(message_type: String, data: any) -> Result<Unit, Error> {
let message = {
"type": message_type,
"data": data,
"timestamp": chain::get_block_timestamp()
};
let sent_count = 0;
for connection_id in self.active_connections.keys() {
let connection = self.active_connections[connection_id];
// Check if client is subscribed to this message type
let ws_connection = web::get_connection_by_id(connection_id);
web::send_websocket_message(ws_connection, message);
sent_count = sent_count + 1;
}
log::info("analytics", {
"event": "message_broadcasted",
"type": message_type,
"clients_reached": sent_count,
"total_clients": self.active_connections.size()
});
return Ok(Unit);
}
// ================================================
// DATA RETRIEVAL METHODS
// ================================================
fn get_historical_data(metric_type: String, time_range: any) -> Result<any, Error> {
let query = "
SELECT metric_name, metric_value, tags, timestamp
FROM metrics
WHERE metric_name = $1
AND timestamp BETWEEN $2 AND $3
ORDER BY timestamp DESC
LIMIT 1000
";
let result = database::query(self.database, query, [
metric_type,
time_range.start,
time_range.end
]);
return Ok({
"metric_type": metric_type,
"data_points": result.rows,
"count": result.rows.length(),
"time_range": time_range
});
}
fn get_active_users_count() -> Result<i64, Error> {
// Get active users in last 5 minutes
let five_minutes_ago = chain::get_block_timestamp() - 300;
let result = database::query(self.database, "
SELECT COUNT(DISTINCT user_id) as active_users
FROM events
WHERE timestamp > $1
AND user_id IS NOT NULL
", [five_minutes_ago]);
return Ok(result.rows[0].active_users);
}
fn get_system_health_metrics() -> Result<any, Error> {
let metrics = {
"active_connections": self.active_connections.size(),
"active_users": self.get_active_users_count(),
"event_processing_rate": this.get_event_processing_rate(),
"memory_usage": system::get_memory_usage(),
"cpu_usage": system::get_cpu_usage(),
"uptime": system::get_uptime()
};
return Ok(metrics);
}
// ================================================
// ANOMALY DETECTION
// ================================================
fn handle_metric_anomaly(anomaly: any) -> Result<Unit, Error> {
log::warn("analytics", {
"event": "anomaly_detected",
"metric": anomaly.metric_name,
"value": anomaly.value,
"expected_range": anomaly.expected_range,
"severity": anomaly.severity
});
// Store anomaly
database::execute_query(self.database, "
INSERT INTO insights (insight_type, insight_data, confidence_score, timestamp)
VALUES ($1, $2, $3, $4)
", [
"anomaly",
json::stringify(anomaly),
anomaly.confidence,
chain::get_block_timestamp()
]);
// Send alert to connected clients
self.broadcast_to_clients("alert", {
"type": "anomaly",
"severity": anomaly.severity,
"message": "Anomaly detected",
"data": anomaly,
"timestamp": chain::get_block_timestamp()
});
// Trigger automated response based on severity
if (anomaly.severity == "critical") {
this.trigger_critical_anomaly_response(anomaly);
} else if (anomaly.severity == "high" ) {
this.trigger_high_anomaly_response(anomaly);
}
return Ok(Unit);
}
fn trigger_critical_anomaly_response(anomaly: any) -> Result<Unit, Error> {
// Immediate actions for critical anomalies
log::error("analytics", {
"event": "critical_anomaly_response_triggered",
"anomaly": anomaly.metric_name
});
// Scale up resources if needed
this.scale_up_resources();
// Send emergency notifications
this.send_emergency_notifications(anomaly);
return Ok(Unit);
}
// ================================================
// UTILITY METHODS
// ================================================
fn calculate_metric_change(metric_data: any) -> Float {
let previous_value = 0.0;
let current_value = metric_data.value;
if (previous_value == 0.0 ) {
return 0.0;
}
return ((current_value - previous_value) / previous_value) * 100.0;
}
fn get_event_processing_rate() -> Float {
// Calculate events processed per second in last minute
let one_minute_ago = chain::get_block_timestamp() - 60;
let result = database::query(self.database, "
SELECT COUNT(*) as event_count
FROM events
WHERE timestamp > $1
", [one_minute_ago]);
return result.rows[0].event_count / 60.0;
}
fn process_business_event(event_data: any) -> any {
// Add business logic processing
let processed = {
"original_event": event_data,
"processed_at": chain::get_block_timestamp(),
"business_value": this.calculate_business_value(event_data),
"category": this.categorize_business_event(event_data),
"priority": this.calculate_event_priority(event_data)
};
return processed;
}
fn update_business_metrics(processed_event: any) -> Result<Unit, Error> {
// Update relevant business metrics based on event
let metrics_to_update = this.get_relevant_metrics(processed_event);
for metric in metrics_to_update {
database::execute_query(self.database, "
INSERT INTO metrics (metric_name, metric_value, tags, timestamp)
VALUES ($1, $2, $3, $4)
", [
metric.name,
metric.value,
json::stringify(metric.tags),
chain::get_block_timestamp()
]);
}
return Ok(Unit);
}
fn calculate_business_value(event_data: any) -> Float {
// Calculate monetary value of the event
return event_data.value;
}
fn categorize_business_event(event_data: any) -> String {
// Categorize event for reporting
if (event_data.type.contains("purchase") ) {
return "revenue";
} else if (event_data.type.contains("signup") ) {
return "acquisition";
} else if (event_data.type.contains("support") ) {
return "support";
} else {
return "other";
}
}
fn calculate_event_priority(event_data: any) -> String {
if (event_data.value > 100 ) {
return "medium";
}
return "low";
}
fn get_relevant_metrics(processed_event: any) -> List<any> {
// Return metrics that should be updated based on this event
let metrics = [];
if (processed_event.category == "revenue" ) {
metrics.push({
"name": "total_revenue",
"value": processed_event.business_value,
"tags": { "category": "revenue", "event_type": processed_event.original_event.type }
});
}
if (processed_event.category == "acquisition" ) {
metrics.push({
"name": "new_users",
"value": 1,
"tags": { "category": "acquisition", "source": processed_event.original_event.source }
});
}
return metrics;
}
fn scale_up_resources() -> Result<Unit, Error> {
// Implement auto-scaling logic
log::info("analytics", {
"event": "auto_scaling_triggered",
"reason": "high_resource_usage"
});
// This would integrate with cloud auto-scaling APIs
return Ok(Unit);
}
fn send_emergency_notifications(anomaly: any) -> Result<Unit, Error> {
// Send emergency notifications to on-call personnel
log::error("analytics", {
"event": "emergency_notification_sent",
"anomaly": anomaly.metric_name
});
// This would integrate with notification services
return Ok(Unit);
}
}