MongoDB Change Data Capture and Real-Time Streaming Applications: Building Event-Driven Architectures with Change Streams and SQL-Style Data Synchronization
Modern applications require real-time responsiveness to data changes, enabling live dashboards, instant notifications, collaborative editing, and synchronized multi-device experiences that react immediately to database modifications. Traditional polling-based approaches for detecting data changes introduce latency, consume unnecessary resources, and create scalability bottlenecks that limit real-time application performance.
MongoDB Change Data Capture (CDC) through Change Streams provides comprehensive real-time data change notification capabilities that enable reactive architectures, event-driven microservices, and live data synchronization across distributed systems. Unlike polling mechanisms that repeatedly query databases for changes, MongoDB Change Streams deliver immediate notifications of data modifications, enabling applications to react instantly to database events with minimal overhead.
The Traditional Polling and Batch Processing Challenge
Conventional approaches to detecting data changes rely on inefficient polling, timestamps, or batch processing that introduce latency and resource waste:
-- Traditional PostgreSQL change detection - inefficient polling and resource-intensive approaches
-- Timestamp-based change tracking with performance limitations
CREATE TABLE orders (
order_id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
customer_id UUID NOT NULL,
product_id UUID NOT NULL,
quantity INTEGER NOT NULL CHECK (quantity > 0),
unit_price DECIMAL(10,2) NOT NULL,
total_amount DECIMAL(10,2) NOT NULL,
order_status VARCHAR(20) NOT NULL DEFAULT 'pending',
shipping_address JSONB,
payment_info JSONB,
-- Change tracking fields (manual maintenance required)
created_at TIMESTAMP WITH TIME ZONE DEFAULT CURRENT_TIMESTAMP,
updated_at TIMESTAMP WITH TIME ZONE DEFAULT CURRENT_TIMESTAMP,
version INTEGER DEFAULT 1,
last_modified_by UUID,
-- Inefficient change flags
is_modified BOOLEAN DEFAULT FALSE,
change_type VARCHAR(10) DEFAULT 'insert',
sync_required BOOLEAN DEFAULT TRUE
);
-- Audit table for change history (storage overhead)
CREATE TABLE order_audit (
audit_id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
order_id UUID NOT NULL,
operation_type VARCHAR(10) NOT NULL, -- 'INSERT', 'UPDATE', 'DELETE'
old_values JSONB,
new_values JSONB,
changed_fields TEXT[],
changed_by UUID,
change_timestamp TIMESTAMP WITH TIME ZONE DEFAULT CURRENT_TIMESTAMP,
change_source VARCHAR(50)
);
-- Trigger-based change tracking (complex maintenance)
CREATE OR REPLACE FUNCTION track_order_changes()
RETURNS TRIGGER AS $$
BEGIN
-- Update timestamp and version
NEW.updated_at = CURRENT_TIMESTAMP;
NEW.version = COALESCE(OLD.version, 0) + 1;
NEW.is_modified = TRUE;
NEW.sync_required = TRUE;
-- Log to audit table
INSERT INTO order_audit (
order_id,
operation_type,
old_values,
new_values,
changed_fields,
changed_by,
change_source
) VALUES (
NEW.order_id,
TG_OP,
CASE WHEN TG_OP = 'UPDATE' THEN row_to_json(OLD) ELSE NULL END,
row_to_json(NEW),
CASE WHEN TG_OP = 'UPDATE' THEN
array_agg(key) FILTER (WHERE (OLD.*)::json->>key IS DISTINCT FROM (NEW.*)::json->>key)
ELSE NULL END,
NEW.last_modified_by,
'database_trigger'
);
RETURN NEW;
END;
$$ LANGUAGE plpgsql;
CREATE TRIGGER order_change_trigger
BEFORE INSERT OR UPDATE ON orders
FOR EACH ROW EXECUTE FUNCTION track_order_changes();
-- Inefficient polling-based change detection
WITH recent_changes AS (
-- Polling approach - expensive and introduces latency
SELECT
o.order_id,
o.customer_id,
o.order_status,
o.total_amount,
o.updated_at,
o.version,
o.is_modified,
o.sync_required,
-- Calculate time since last change
EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - o.updated_at)) as seconds_since_change,
-- Determine if change is recent enough for processing
CASE
WHEN o.updated_at > CURRENT_TIMESTAMP - INTERVAL '5 minutes' THEN 'immediate'
WHEN o.updated_at > CURRENT_TIMESTAMP - INTERVAL '30 minutes' THEN 'batch'
ELSE 'delayed'
END as processing_priority,
-- Get audit information
oa.operation_type,
oa.changed_fields,
oa.change_timestamp
FROM orders o
LEFT JOIN order_audit oa ON o.order_id = oa.order_id
AND oa.change_timestamp = (
SELECT MAX(change_timestamp)
FROM order_audit oa2
WHERE oa2.order_id = o.order_id
)
WHERE
o.is_modified = TRUE
OR o.sync_required = TRUE
OR o.updated_at > CURRENT_TIMESTAMP - INTERVAL '1 hour' -- Polling window
),
change_processing AS (
SELECT
rc.*,
-- Categorize changes for different processing systems
CASE rc.order_status
WHEN 'confirmed' THEN 'inventory_update'
WHEN 'shipped' THEN 'shipping_notification'
WHEN 'delivered' THEN 'delivery_confirmation'
WHEN 'cancelled' THEN 'refund_processing'
ELSE 'general_update'
END as event_type,
-- Calculate processing delay
CASE
WHEN rc.seconds_since_change < 60 THEN 'real_time'
WHEN rc.seconds_since_change < 300 THEN 'near_real_time'
WHEN rc.seconds_since_change < 1800 THEN 'delayed'
ELSE 'stale'
END as data_freshness,
-- Determine notification requirements
ARRAY[
CASE WHEN rc.order_status IN ('shipped', 'delivered') THEN 'customer_sms' END,
CASE WHEN rc.order_status = 'confirmed' THEN 'inventory_system' END,
CASE WHEN rc.total_amount > 1000 THEN 'fraud_monitoring' END,
CASE WHEN rc.changed_fields && ARRAY['shipping_address'] THEN 'logistics_update' END
] as notification_targets,
-- Generate webhook payloads
jsonb_build_object(
'event_type', 'order_updated',
'order_id', rc.order_id,
'customer_id', rc.customer_id,
'status', rc.order_status,
'timestamp', rc.change_timestamp,
'changed_fields', rc.changed_fields,
'version', rc.version
) as webhook_payload
FROM recent_changes rc
),
notification_queue AS (
-- Build notification queue for external systems
SELECT
cp.order_id,
unnest(cp.notification_targets) as target_system,
cp.webhook_payload,
cp.event_type,
cp.data_freshness,
-- Priority scoring for queue processing
CASE cp.event_type
WHEN 'shipping_notification' THEN 5
WHEN 'delivery_confirmation' THEN 5
WHEN 'fraud_monitoring' THEN 10
WHEN 'inventory_update' THEN 7
ELSE 3
END as priority_score,
CURRENT_TIMESTAMP as queued_at,
-- Retry logic configuration
CASE cp.data_freshness
WHEN 'real_time' THEN 3
WHEN 'near_real_time' THEN 2
ELSE 1
END as max_retries
FROM change_processing cp
WHERE cp.notification_targets IS NOT NULL
)
-- Process changes and generate notifications
SELECT
nq.order_id,
nq.target_system,
nq.event_type,
nq.priority_score,
nq.max_retries,
nq.webhook_payload,
-- System-specific endpoint configuration
CASE nq.target_system
WHEN 'customer_sms' THEN 'https://api.sms.service.com/send'
WHEN 'inventory_system' THEN 'https://inventory.internal/api/webhooks'
WHEN 'fraud_monitoring' THEN 'https://fraud.security.com/api/alerts'
WHEN 'logistics_update' THEN 'https://logistics.partner.com/api/updates'
END as webhook_endpoint,
-- Processing metadata
nq.queued_at,
nq.queued_at + INTERVAL '5 minutes' as max_processing_time,
-- Performance impact assessment
'polling_based_change_detection' as detection_method,
EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - cp.change_timestamp)) as detection_latency_seconds
FROM notification_queue nq
JOIN change_processing cp ON nq.order_id = cp.order_id
WHERE nq.target_system IS NOT NULL
ORDER BY nq.priority_score DESC, nq.queued_at ASC;
-- Reset change flags (must be done manually)
UPDATE orders
SET is_modified = FALSE, sync_required = FALSE
WHERE is_modified = TRUE
AND updated_at < CURRENT_TIMESTAMP - INTERVAL '1 minute';
-- Problems with traditional change detection approaches:
-- 1. Polling introduces significant latency between data changes and detection
-- 2. Constant polling consumes database resources even when no changes occur
-- 3. Complex trigger logic that's difficult to maintain and debug
-- 4. Manual synchronization flag management prone to race conditions
-- 5. Audit table storage overhead grows linearly with change volume
-- 6. No real-time notifications - applications must continuously poll
-- 7. Difficult to scale across multiple application instances
-- 8. Poor performance with high-frequency changes or large datasets
-- 9. Complex conflict resolution when multiple systems modify data
-- 10. No built-in filtering or transformation of change events
-- Batch processing approach (high latency)
WITH batch_changes AS (
SELECT
o.order_id,
o.customer_id,
o.order_status,
o.updated_at,
-- Batch processing windows
DATE_TRUNC('hour', o.updated_at) as processing_batch,
-- Change detection via timestamp comparison
CASE
WHEN o.updated_at > (
SELECT COALESCE(MAX(last_processed_at), '1970-01-01'::timestamp)
FROM processing_checkpoints
WHERE system_name = 'order_processor'
) THEN TRUE
ELSE FALSE
END as requires_processing,
-- Lag calculation
EXTRACT(EPOCH FROM (
CURRENT_TIMESTAMP - o.updated_at
)) / 60.0 as processing_delay_minutes
FROM orders o
WHERE o.updated_at > CURRENT_TIMESTAMP - INTERVAL '24 hours'
),
processing_statistics AS (
SELECT
bc.processing_batch,
COUNT(*) as total_changes,
COUNT(*) FILTER (WHERE bc.requires_processing) as unprocessed_changes,
AVG(bc.processing_delay_minutes) as avg_delay_minutes,
MAX(bc.processing_delay_minutes) as max_delay_minutes,
-- Batch processing efficiency
CASE
WHEN COUNT(*) FILTER (WHERE bc.requires_processing) = 0 THEN 'up_to_date'
WHEN AVG(bc.processing_delay_minutes) < 60 THEN 'acceptable_delay'
WHEN AVG(bc.processing_delay_minutes) < 240 THEN 'moderate_delay'
ELSE 'high_delay'
END as processing_status
FROM batch_changes bc
GROUP BY bc.processing_batch
)
SELECT
processing_batch,
total_changes,
unprocessed_changes,
ROUND(avg_delay_minutes::numeric, 2) as avg_delay_minutes,
ROUND(max_delay_minutes::numeric, 2) as max_delay_minutes,
processing_status,
-- Performance assessment
CASE processing_status
WHEN 'high_delay' THEN 'Critical: Real-time requirements not met'
WHEN 'moderate_delay' THEN 'Warning: Consider increasing processing frequency'
WHEN 'acceptable_delay' THEN 'Good: Within acceptable parameters'
ELSE 'Excellent: No backlog'
END as performance_assessment
FROM processing_statistics
WHERE total_changes > 0
ORDER BY processing_batch DESC;
-- Traditional limitations:
-- 1. Batch processing introduces hours of latency for real-time requirements
-- 2. Resource waste from processing empty batches
-- 3. Complex checkpoint management and recovery logic
-- 4. Poor user experience with delayed updates and notifications
-- 5. Difficult horizontal scaling across multiple processing nodes
-- 6. No event ordering guarantees across different data modifications
-- 7. Limited ability to filter events based on content or business logic
-- 8. Manual coordination required between multiple consuming applications
-- 9. High operational overhead for monitoring and maintaining batch jobs
-- 10. Poor integration with modern event-driven and microservices architectures
MongoDB Change Data Capture provides efficient real-time change tracking:
// MongoDB Change Data Capture - real-time event-driven architecture with comprehensive change stream management
const { MongoClient } = require('mongodb');
const { EventEmitter } = require('events');
const client = new MongoClient('mongodb://localhost:27017');
const db = client.db('realtime_commerce_platform');
// Advanced Change Data Capture and event-driven processing system
class AdvancedChangeCaptureEngine extends EventEmitter {
constructor(db, configuration = {}) {
super();
this.db = db;
this.collections = {
orders: db.collection('orders'),
customers: db.collection('customers'),
products: db.collection('products'),
inventory: db.collection('inventory'),
payments: db.collection('payments'),
notifications: db.collection('notifications'),
eventLog: db.collection('event_log')
};
// Advanced CDC configuration
this.config = {
changeStreamConfig: {
fullDocument: 'updateLookup',
fullDocumentBeforeChange: 'whenAvailable',
showExpandedEvents: true,
batchSize: configuration.batchSize || 100,
maxAwaitTimeMS: configuration.maxAwaitTimeMS || 1000
},
// Event processing configuration
eventProcessing: {
enableAsync: true,
enableRetry: true,
retryAttempts: configuration.retryAttempts || 3,
retryDelayMs: configuration.retryDelayMs || 1000,
deadLetterQueue: true,
preserveOrdering: true
},
// Filtering and routing configuration
eventFiltering: {
enableContentFiltering: true,
enableBusinessLogicFiltering: true,
enableUserDefinedFilters: true
},
// Performance optimization
performance: {
enableEventBatching: configuration.enableEventBatching || true,
batchTimeoutMs: configuration.batchTimeoutMs || 500,
enableParallelProcessing: true,
maxConcurrentProcessors: configuration.maxConcurrentProcessors || 10
},
// Monitoring and observability
monitoring: {
enableMetrics: true,
enableTracing: true,
metricsIntervalMs: 30000,
healthCheckIntervalMs: 5000
}
};
// Internal state management
this.changeStreams = new Map();
this.eventProcessors = new Map();
this.processingMetrics = {
eventsProcessed: 0,
eventsFailedProcessing: 0,
averageProcessingTime: 0,
lastProcessedTimestamp: null,
activeChangeStreams: 0
};
// Event routing and transformation
this.eventRouters = new Map();
this.eventTransformers = new Map();
this.businessRuleProcessors = new Map();
this.initializeAdvancedCDC();
}
async initializeAdvancedCDC() {
console.log('Initializing advanced MongoDB Change Data Capture system...');
try {
// Setup comprehensive change stream monitoring
await this.setupCollectionChangeStreams();
// Initialize event processing pipelines
await this.initializeEventProcessors();
// Setup business logic handlers
await this.setupBusinessLogicHandlers();
// Initialize monitoring and health checks
await this.initializeMonitoring();
console.log('Advanced CDC system initialized successfully');
} catch (error) {
console.error('Failed to initialize CDC system:', error);
throw error;
}
}
async setupCollectionChangeStreams() {
console.log('Setting up collection change streams with advanced filtering...');
// Orders collection change stream with comprehensive business logic
const ordersChangeStream = this.collections.orders.watch([
// Stage 1: Filter for relevant order events
{
$match: {
$or: [
// Order status changes
{ 'updateDescription.updatedFields.status': { $exists: true } },
// Payment status changes
{ 'updateDescription.updatedFields.payment.status': { $exists: true } },
// Shipping address changes
{ 'updateDescription.updatedFields.shipping.address': { $exists: true } },
// High-value order insertions
{
operationType: 'insert',
'fullDocument.total': { $gte: 1000 }
},
// Order cancellations or refunds
{ 'updateDescription.updatedFields.cancellation': { $exists: true } },
{ 'updateDescription.updatedFields.refund': { $exists: true } }
]
}
},
// Stage 2: Add enhanced metadata and business context
{
$addFields: {
processedTimestamp: '$$NOW',
changeStreamSource: 'orders_collection',
// Extract key business events
businessEvent: {
$switch: {
branches: [
{
case: {
$and: [
{ $eq: ['$operationType', 'update'] },
{ $eq: ['$updateDescription.updatedFields.status', 'confirmed'] }
]
},
then: 'order_confirmed'
},
{
case: {
$and: [
{ $eq: ['$operationType', 'update'] },
{ $eq: ['$updateDescription.updatedFields.status', 'shipped'] }
]
},
then: 'order_shipped'
},
{
case: {
$and: [
{ $eq: ['$operationType', 'update'] },
{ $eq: ['$updateDescription.updatedFields.payment.status', 'completed'] }
]
},
then: 'payment_completed'
},
{
case: {
$and: [
{ $eq: ['$operationType', 'insert'] },
{ $gte: ['$fullDocument.total', 1000] }
]
},
then: 'high_value_order_created'
}
],
default: 'order_updated'
}
},
// Priority scoring for event processing
eventPriority: {
$switch: {
branches: [
{ case: { $eq: ['$updateDescription.updatedFields.payment.status', 'failed'] }, then: 10 },
{ case: { $eq: ['$updateDescription.updatedFields.status', 'cancelled'] }, then: 8 },
{ case: { $gte: ['$fullDocument.total', 5000] }, then: 7 },
{ case: { $eq: ['$updateDescription.updatedFields.status', 'shipped'] }, then: 6 },
{ case: { $eq: ['$updateDescription.updatedFields.status', 'confirmed'] }, then: 5 }
],
default: 3
}
},
// Determine required downstream actions
requiredActions: {
$switch: {
branches: [
{
case: { $eq: ['$updateDescription.updatedFields.status', 'confirmed'] },
then: ['inventory_update', 'customer_notification', 'logistics_preparation']
},
{
case: { $eq: ['$updateDescription.updatedFields.status', 'shipped'] },
then: ['shipping_notification', 'tracking_activation', 'delivery_estimation']
},
{
case: { $eq: ['$updateDescription.updatedFields.payment.status', 'completed'] },
then: ['receipt_generation', 'accounting_sync', 'loyalty_points_update']
},
{
case: { $gte: ['$fullDocument.total', 1000] },
then: ['fraud_screening', 'vip_handling', 'priority_processing']
}
],
default: ['general_processing']
}
}
}
}
], this.config.changeStreamConfig);
// Register sophisticated event handlers
ordersChangeStream.on('change', async (changeDocument) => {
await this.processOrderChangeEvent(changeDocument);
});
ordersChangeStream.on('error', (error) => {
console.error('Orders change stream error:', error);
this.emit('changeStreamError', { collection: 'orders', error });
});
this.changeStreams.set('orders', ordersChangeStream);
// Inventory collection change stream for real-time stock management
const inventoryChangeStream = this.collections.inventory.watch([
{
$match: {
$or: [
// Stock level changes
{ 'updateDescription.updatedFields.quantity': { $exists: true } },
{ 'updateDescription.updatedFields.reservedQuantity': { $exists: true } },
// Product availability changes
{ 'updateDescription.updatedFields.available': { $exists: true } },
// Low stock alerts
{
operationType: 'update',
'fullDocument.quantity': { $lt: 10 }
}
]
}
},
{
$addFields: {
processedTimestamp: '$$NOW',
changeStreamSource: 'inventory_collection',
// Stock level categorization
stockStatus: {
$switch: {
branches: [
{ case: { $lte: ['$fullDocument.quantity', 0] }, then: 'out_of_stock' },
{ case: { $lte: ['$fullDocument.quantity', 5] }, then: 'critical_low' },
{ case: { $lte: ['$fullDocument.quantity', 20] }, then: 'low_stock' },
{ case: { $gte: ['$fullDocument.quantity', 100] }, then: 'well_stocked' }
],
default: 'normal_stock'
}
},
// Calculate stock velocity and reorder triggers
reorderRequired: {
$cond: {
if: {
$and: [
{ $lt: ['$fullDocument.quantity', '$fullDocument.reorderPoint'] },
{ $ne: ['$fullDocument.reorderStatus', 'pending'] }
]
},
then: true,
else: false
}
},
// Urgency scoring for inventory management
urgencyScore: {
$add: [
{ $cond: [{ $lte: ['$fullDocument.quantity', 0] }, 10, 0] },
{ $cond: [{ $lte: ['$fullDocument.quantity', 5] }, 7, 0] },
{ $cond: [{ $gte: ['$fullDocument.demandForecast', 50] }, 3, 0] },
{ $cond: [{ $eq: ['$fullDocument.category', 'bestseller'] }, 2, 0] }
]
}
}
}
], this.config.changeStreamConfig);
inventoryChangeStream.on('change', async (changeDocument) => {
await this.processInventoryChangeEvent(changeDocument);
});
this.changeStreams.set('inventory', inventoryChangeStream);
// Customer collection change stream for personalization and CRM
const customersChangeStream = this.collections.customers.watch([
{
$match: {
$or: [
// Profile updates
{ 'updateDescription.updatedFields.profile': { $exists: true } },
// Preference changes
{ 'updateDescription.updatedFields.preferences': { $exists: true } },
// Loyalty status changes
{ 'updateDescription.updatedFields.loyalty.tier': { $exists: true } },
// New customer registrations
{ operationType: 'insert' }
]
}
},
{
$addFields: {
processedTimestamp: '$$NOW',
changeStreamSource: 'customers_collection',
// Customer lifecycle events
lifecycleEvent: {
$switch: {
branches: [
{ case: { $eq: ['$operationType', 'insert'] }, then: 'customer_registered' },
{ case: { $ne: ['$updateDescription.updatedFields.loyalty.tier', null] }, then: 'loyalty_tier_changed' },
{ case: { $ne: ['$updateDescription.updatedFields.preferences.marketing', null] }, then: 'communication_preferences_updated' }
],
default: 'customer_profile_updated'
}
},
// Personalization triggers
personalizationActions: {
$cond: {
if: { $eq: ['$operationType', 'insert'] },
then: ['welcome_sequence', 'preference_collection', 'recommendation_initialization'],
else: {
$switch: {
branches: [
{
case: { $ne: ['$updateDescription.updatedFields.preferences.categories', null] },
then: ['recommendation_refresh', 'content_personalization']
},
{
case: { $ne: ['$updateDescription.updatedFields.loyalty.tier', null] },
then: ['tier_benefits_notification', 'exclusive_offers_activation']
}
],
default: ['profile_validation']
}
}
}
}
}
}
], this.config.changeStreamConfig);
customersChangeStream.on('change', async (changeDocument) => {
await this.processCustomerChangeEvent(changeDocument);
});
this.changeStreams.set('customers', customersChangeStream);
this.processingMetrics.activeChangeStreams = this.changeStreams.size;
console.log(`Initialized ${this.changeStreams.size} change streams with advanced filtering`);
}
async processOrderChangeEvent(changeDocument) {
const startTime = Date.now();
try {
console.log(`Processing order change event: ${changeDocument.businessEvent}`);
// Extract key information from the change document
const orderId = changeDocument.documentKey._id;
const operationType = changeDocument.operationType;
const businessEvent = changeDocument.businessEvent;
const eventPriority = changeDocument.eventPriority;
const requiredActions = changeDocument.requiredActions;
const fullDocument = changeDocument.fullDocument;
// Create comprehensive event context
const eventContext = {
eventId: `order_${orderId}_${Date.now()}`,
orderId: orderId,
customerId: fullDocument?.customerId,
operationType: operationType,
businessEvent: businessEvent,
priority: eventPriority,
timestamp: changeDocument.processedTimestamp,
requiredActions: requiredActions,
// Change details
changeDetails: {
updatedFields: changeDocument.updateDescription?.updatedFields,
removedFields: changeDocument.updateDescription?.removedFields,
previousDocument: changeDocument.fullDocumentBeforeChange
},
// Business context
businessContext: {
orderValue: fullDocument?.total,
orderStatus: fullDocument?.status,
customerTier: fullDocument?.customer?.loyaltyTier,
paymentMethod: fullDocument?.payment?.method,
shippingMethod: fullDocument?.shipping?.method
}
};
// Process each required action asynchronously
const actionPromises = requiredActions.map(action =>
this.executeBusinessAction(action, eventContext)
);
if (this.config.eventProcessing.enableAsync) {
// Parallel processing for independent actions
await Promise.allSettled(actionPromises);
} else {
// Sequential processing for dependent actions
for (const actionPromise of actionPromises) {
await actionPromise;
}
}
// Log successful event processing
await this.logEventProcessing(eventContext, 'success');
// Update metrics
this.updateProcessingMetrics(startTime, true);
// Emit success event for monitoring
this.emit('eventProcessed', {
eventId: eventContext.eventId,
businessEvent: businessEvent,
processingTime: Date.now() - startTime
});
} catch (error) {
console.error(`Error processing order change event:`, error);
// Handle retry logic
if (this.config.eventProcessing.enableRetry) {
await this.retryEventProcessing(changeDocument, error);
}
// Update error metrics
this.updateProcessingMetrics(startTime, false);
// Emit error event for monitoring
this.emit('eventProcessingError', {
changeDocument: changeDocument,
error: error,
timestamp: new Date()
});
}
}
async executeBusinessAction(action, eventContext) {
console.log(`Executing business action: ${action} for event: ${eventContext.eventId}`);
try {
switch (action) {
case 'inventory_update':
await this.updateInventoryForOrder(eventContext);
break;
case 'customer_notification':
await this.sendCustomerNotification(eventContext);
break;
case 'logistics_preparation':
await this.prepareLogistics(eventContext);
break;
case 'shipping_notification':
await this.sendShippingNotification(eventContext);
break;
case 'tracking_activation':
await this.activateOrderTracking(eventContext);
break;
case 'payment_processing':
await this.processPayment(eventContext);
break;
case 'fraud_screening':
await this.performFraudScreening(eventContext);
break;
case 'loyalty_points_update':
await this.updateLoyaltyPoints(eventContext);
break;
case 'analytics_update':
await this.updateAnalytics(eventContext);
break;
default:
console.warn(`Unknown business action: ${action}`);
}
} catch (actionError) {
console.error(`Error executing business action ${action}:`, actionError);
throw actionError;
}
}
async updateInventoryForOrder(eventContext) {
console.log(`Updating inventory for order: ${eventContext.orderId}`);
try {
// Get order details
const order = await this.collections.orders.findOne(
{ _id: eventContext.orderId }
);
if (!order || !order.items) {
throw new Error(`Order ${eventContext.orderId} not found or has no items`);
}
// Process inventory updates for each order item
const inventoryUpdates = order.items.map(async (item) => {
const inventoryUpdate = {
$inc: {
reservedQuantity: item.quantity,
availableQuantity: -item.quantity
},
$push: {
reservations: {
orderId: eventContext.orderId,
quantity: item.quantity,
reservedAt: new Date(),
status: 'active'
}
},
$set: {
lastUpdated: new Date(),
lastUpdateReason: 'order_confirmed'
}
};
return this.collections.inventory.updateOne(
{ productId: item.productId },
inventoryUpdate
);
});
// Execute all inventory updates
await Promise.all(inventoryUpdates);
console.log(`Inventory updated successfully for order: ${eventContext.orderId}`);
} catch (error) {
console.error(`Failed to update inventory for order ${eventContext.orderId}:`, error);
throw error;
}
}
async sendCustomerNotification(eventContext) {
console.log(`Sending customer notification for event: ${eventContext.businessEvent}`);
try {
// Get customer information
const customer = await this.collections.customers.findOne(
{ _id: eventContext.customerId }
);
if (!customer) {
throw new Error(`Customer ${eventContext.customerId} not found`);
}
// Determine notification content based on business event
const notificationConfig = this.getNotificationConfig(
eventContext.businessEvent,
eventContext.businessContext
);
// Create notification document
const notification = {
customerId: eventContext.customerId,
orderId: eventContext.orderId,
type: notificationConfig.type,
channel: this.selectNotificationChannel(customer.preferences),
content: {
subject: notificationConfig.subject,
message: this.personalizeMessage(
notificationConfig.template,
customer,
eventContext.businessContext
),
actionUrl: notificationConfig.actionUrl,
imageUrl: notificationConfig.imageUrl
},
priority: eventContext.priority,
scheduledFor: this.calculateDeliveryTime(notificationConfig.timing),
metadata: {
eventId: eventContext.eventId,
businessEvent: eventContext.businessEvent,
createdAt: new Date()
}
};
// Store notification for delivery
const result = await this.collections.notifications.insertOne(notification);
// Trigger immediate delivery for high-priority notifications
if (eventContext.priority >= 7) {
await this.deliverNotificationImmediately(notification);
}
console.log(`Notification created successfully: ${result.insertedId}`);
} catch (error) {
console.error(`Failed to send customer notification:`, error);
throw error;
}
}
async processInventoryChangeEvent(changeDocument) {
const startTime = Date.now();
try {
console.log(`Processing inventory change event: ${changeDocument.stockStatus}`);
const productId = changeDocument.documentKey._id;
const stockStatus = changeDocument.stockStatus;
const urgencyScore = changeDocument.urgencyScore;
const reorderRequired = changeDocument.reorderRequired;
const fullDocument = changeDocument.fullDocument;
const eventContext = {
eventId: `inventory_${productId}_${Date.now()}`,
productId: productId,
stockStatus: stockStatus,
urgencyScore: urgencyScore,
reorderRequired: reorderRequired,
currentQuantity: fullDocument?.quantity,
changeDetails: changeDocument.updateDescription
};
// Handle critical stock situations
if (stockStatus === 'out_of_stock' || stockStatus === 'critical_low') {
await this.handleCriticalStockSituation(eventContext);
}
// Trigger reorder process if needed
if (reorderRequired) {
await this.initiateReorderProcess(eventContext);
}
// Update product availability in real-time
await this.updateProductAvailability(eventContext);
// Notify relevant stakeholders
await this.notifyStakeholders(eventContext);
this.updateProcessingMetrics(startTime, true);
} catch (error) {
console.error(`Error processing inventory change event:`, error);
this.updateProcessingMetrics(startTime, false);
}
}
async processCustomerChangeEvent(changeDocument) {
const startTime = Date.now();
try {
console.log(`Processing customer change event: ${changeDocument.lifecycleEvent}`);
const customerId = changeDocument.documentKey._id;
const lifecycleEvent = changeDocument.lifecycleEvent;
const personalizationActions = changeDocument.personalizationActions;
const fullDocument = changeDocument.fullDocument;
const eventContext = {
eventId: `customer_${customerId}_${Date.now()}`,
customerId: customerId,
lifecycleEvent: lifecycleEvent,
personalizationActions: personalizationActions,
customerData: fullDocument
};
// Execute personalization actions
for (const action of personalizationActions) {
await this.executePersonalizationAction(action, eventContext);
}
this.updateProcessingMetrics(startTime, true);
} catch (error) {
console.error(`Error processing customer change event:`, error);
this.updateProcessingMetrics(startTime, false);
}
}
async initializeEventProcessors() {
console.log('Initializing specialized event processors...');
// Order fulfillment processor
this.eventProcessors.set('order_fulfillment', {
process: async (eventContext) => {
await this.processOrderFulfillment(eventContext);
},
concurrency: 5,
retryPolicy: { maxAttempts: 3, backoffMs: 1000 }
});
// Payment processor
this.eventProcessors.set('payment_processing', {
process: async (eventContext) => {
await this.processPaymentEvent(eventContext);
},
concurrency: 10,
retryPolicy: { maxAttempts: 5, backoffMs: 2000 }
});
// Notification processor
this.eventProcessors.set('notification_delivery', {
process: async (eventContext) => {
await this.processNotificationDelivery(eventContext);
},
concurrency: 20,
retryPolicy: { maxAttempts: 3, backoffMs: 500 }
});
}
async logEventProcessing(eventContext, status) {
try {
const logEntry = {
eventId: eventContext.eventId,
timestamp: new Date(),
status: status,
eventType: eventContext.businessEvent || eventContext.lifecycleEvent,
processingTime: Date.now() - new Date(eventContext.timestamp).getTime(),
context: eventContext,
metadata: {
changeStreamSource: eventContext.changeStreamSource,
priority: eventContext.priority
}
};
await this.collections.eventLog.insertOne(logEntry);
} catch (logError) {
console.error('Failed to log event processing:', logError);
// Don't throw - logging failures shouldn't break event processing
}
}
updateProcessingMetrics(startTime, success) {
this.processingMetrics.eventsProcessed++;
if (success) {
const processingTime = Date.now() - startTime;
this.processingMetrics.averageProcessingTime =
(this.processingMetrics.averageProcessingTime + processingTime) / 2;
this.processingMetrics.lastProcessedTimestamp = new Date();
} else {
this.processingMetrics.eventsFailedProcessing++;
}
}
// Additional utility methods for comprehensive CDC functionality
getNotificationConfig(businessEvent, businessContext) {
const notificationConfigs = {
order_confirmed: {
type: 'order_confirmation',
subject: 'Order Confirmed - Thank You!',
template: 'order_confirmation_template',
timing: 'immediate',
actionUrl: '/orders/{orderId}',
imageUrl: '/images/order-confirmed.png'
},
order_shipped: {
type: 'shipping_notification',
subject: 'Your Order is On the Way!',
template: 'shipping_notification_template',
timing: 'immediate',
actionUrl: '/orders/{orderId}/tracking',
imageUrl: '/images/package-shipped.png'
},
payment_completed: {
type: 'payment_confirmation',
subject: 'Payment Received',
template: 'payment_confirmation_template',
timing: 'immediate',
actionUrl: '/orders/{orderId}/receipt',
imageUrl: '/images/payment-success.png'
}
};
return notificationConfigs[businessEvent] || {
type: 'general_notification',
subject: 'Order Update',
template: 'general_update_template',
timing: 'delayed'
};
}
selectNotificationChannel(customerPreferences) {
if (!customerPreferences) return 'email';
if (customerPreferences.notifications?.push?.enabled) return 'push';
if (customerPreferences.notifications?.sms?.enabled) return 'sms';
return 'email';
}
personalizeMessage(template, customer, businessContext) {
// Simplified personalization - in production, use a templating engine
return template
.replace('{customerName}', customer.profile?.firstName || 'Valued Customer')
.replace('{orderId}', businessContext.orderId)
.replace('{orderValue}', businessContext.orderValue);
}
async retryEventProcessing(changeDocument, error) {
console.log(`Retrying event processing for change document: ${changeDocument._id}`);
// Implement exponential backoff retry logic
// This is a simplified version - production should use a proper retry queue
}
async initializeMonitoring() {
console.log('Initializing CDC monitoring and health checks...');
// Set up periodic health checks
setInterval(() => {
this.performHealthCheck();
}, this.config.monitoring.healthCheckIntervalMs);
// Set up metrics collection
setInterval(() => {
this.collectMetrics();
}, this.config.monitoring.metricsIntervalMs);
}
performHealthCheck() {
// Check change stream health
let healthyStreams = 0;
this.changeStreams.forEach((stream, name) => {
if (!stream.closed) {
healthyStreams++;
} else {
console.warn(`Change stream ${name} is closed - attempting reconnection`);
// Implement reconnection logic
}
});
const healthStatus = {
timestamp: new Date(),
totalStreams: this.changeStreams.size,
healthyStreams: healthyStreams,
processingMetrics: this.processingMetrics
};
this.emit('healthCheck', healthStatus);
}
collectMetrics() {
const metrics = {
timestamp: new Date(),
...this.processingMetrics,
changeStreamStatus: Array.from(this.changeStreams.entries()).map(([name, stream]) => ({
name: name,
closed: stream.closed,
hasNext: stream.hasNext()
}))
};
this.emit('metricsCollected', metrics);
}
}
// Benefits of MongoDB Change Data Capture:
// - Real-time data change notifications without polling overhead
// - Comprehensive change document information including before/after states
// - Built-in filtering and transformation capabilities within change streams
// - Automatic ordering and delivery guarantees for change events
// - Horizontal scalability with replica set and sharded cluster support
// - Integration with MongoDB's operational capabilities (backup, monitoring)
// - Event-driven architecture enablement for microservices and reactive systems
// - Minimal performance impact on primary database operations
// - Rich metadata and context information for intelligent event processing
// - Native MongoDB driver integration with automatic reconnection handling
module.exports = {
AdvancedChangeCaptureEngine
};
Understanding MongoDB Change Streams Architecture
Advanced Event Processing and Business Logic Integration
Implement sophisticated change data capture strategies for production real-time applications:
// Production-ready MongoDB Change Data Capture with enterprise-grade event processing
class EnterpriseChangeDataCaptureSystem extends AdvancedChangeCaptureEngine {
constructor(db, enterpriseConfig) {
super(db, enterpriseConfig);
this.enterpriseConfig = {
...enterpriseConfig,
// Advanced event sourcing
eventSourcing: {
enableEventStore: true,
eventRetentionDays: 365,
snapshotFrequency: 1000,
enableReplay: true
},
// Distributed processing
distributedProcessing: {
enableClusterMode: true,
nodeId: process.env.NODE_ID || 'node-1',
coordinationDatabase: 'cdc_coordination',
leaderElection: true
},
// Advanced monitoring
observability: {
enableDistributedTracing: true,
enableCustomMetrics: true,
alertingThresholds: {
processingLatency: 5000,
errorRate: 0.05,
backlogSize: 1000
}
}
};
this.setupEnterpriseFeatures();
}
async setupEventSourcingCapabilities() {
console.log('Setting up enterprise event sourcing capabilities...');
// Event store for complete audit trail
const eventStore = this.db.collection('event_store');
await eventStore.createIndex({ aggregateId: 1, version: 1 }, { unique: true });
await eventStore.createIndex({ eventType: 1, timestamp: -1 });
await eventStore.createIndex({ timestamp: -1 });
// Snapshots for performance optimization
const snapshots = this.db.collection('aggregate_snapshots');
await snapshots.createIndex({ aggregateId: 1, version: -1 });
return { eventStore, snapshots };
}
async implementAdvancedEventRouting() {
console.log('Implementing advanced event routing and transformation...');
// Dynamic event routing based on content and business rules
const routingRules = [
{
name: 'high_value_order_routing',
condition: (event) => event.businessContext?.orderValue > 5000,
destinations: ['fraud_detection', 'vip_processing', 'management_alerts'],
transformation: this.transformHighValueOrder.bind(this)
},
{
name: 'inventory_critical_routing',
condition: (event) => event.stockStatus === 'critical_low',
destinations: ['procurement', 'sales_alerts', 'website_updates'],
transformation: this.transformInventoryAlert.bind(this)
},
{
name: 'customer_lifecycle_routing',
condition: (event) => event.lifecycleEvent === 'customer_registered',
destinations: ['marketing_automation', 'personalization_engine', 'crm_sync'],
transformation: this.transformCustomerEvent.bind(this)
}
];
return routingRules;
}
async setupDistributedProcessing() {
console.log('Setting up distributed CDC processing...');
// Implement leader election for coordinated processing
const coordination = {
leaderElection: await this.setupLeaderElection(),
workloadDistribution: await this.setupWorkloadDistribution(),
failoverHandling: await this.setupFailoverHandling()
};
return coordination;
}
}
SQL-Style Change Data Capture with QueryLeaf
QueryLeaf provides familiar SQL syntax for MongoDB Change Data Capture and real-time streaming operations:
-- QueryLeaf advanced change data capture with SQL-familiar syntax
-- Create change data capture streams with comprehensive filtering and transformation
CREATE CHANGE STREAM order_events
ON orders
WITH (
full_document = 'update_lookup',
full_document_before_change = 'when_available',
show_expanded_events = true
)
AS
SELECT
change_id() as event_id,
operation_type(),
document_key() as order_id,
cluster_time() as event_timestamp,
-- Enhanced change document information
full_document() as current_order,
full_document_before_change() as previous_order,
update_description() as change_details,
-- Business event classification
CASE
WHEN operation_type() = 'insert' THEN 'order_created'
WHEN JSON_EXTRACT(update_description(), '$.updatedFields.status') = 'confirmed' THEN 'order_confirmed'
WHEN JSON_EXTRACT(update_description(), '$.updatedFields.status') = 'shipped' THEN 'order_shipped'
WHEN JSON_EXTRACT(update_description(), '$.updatedFields.status') = 'delivered' THEN 'order_delivered'
WHEN JSON_EXTRACT(update_description(), '$.updatedFields.status') = 'cancelled' THEN 'order_cancelled'
WHEN JSON_EXTRACT(update_description(), '$.updatedFields.payment.status') = 'completed' THEN 'payment_completed'
WHEN JSON_EXTRACT(update_description(), '$.updatedFields.payment.status') = 'failed' THEN 'payment_failed'
ELSE 'order_updated'
END as business_event,
-- Priority scoring for event processing
CASE
WHEN JSON_EXTRACT(full_document(), '$.total') > 5000 THEN 10
WHEN JSON_EXTRACT(update_description(), '$.updatedFields.payment.status') = 'failed' THEN 9
WHEN JSON_EXTRACT(update_description(), '$.updatedFields.status') = 'cancelled' THEN 8
WHEN JSON_EXTRACT(update_description(), '$.updatedFields.status') = 'shipped' THEN 7
WHEN JSON_EXTRACT(update_description(), '$.updatedFields.status') = 'confirmed' THEN 6
ELSE 3
END as event_priority,
-- Required downstream actions
CASE
WHEN JSON_EXTRACT(update_description(), '$.updatedFields.status') = 'confirmed' THEN
JSON_ARRAY('inventory_update', 'customer_notification', 'logistics_preparation')
WHEN JSON_EXTRACT(update_description(), '$.updatedFields.status') = 'shipped' THEN
JSON_ARRAY('shipping_notification', 'tracking_activation', 'delivery_estimation')
WHEN JSON_EXTRACT(update_description(), '$.updatedFields.payment.status') = 'completed' THEN
JSON_ARRAY('receipt_generation', 'accounting_sync', 'loyalty_points_update')
WHEN JSON_EXTRACT(full_document(), '$.total') > 1000 THEN
JSON_ARRAY('fraud_screening', 'vip_handling', 'priority_processing')
ELSE JSON_ARRAY('general_processing')
END as required_actions,
-- Customer and business context
JSON_OBJECT(
'customer_id', JSON_EXTRACT(full_document(), '$.customerId'),
'order_value', JSON_EXTRACT(full_document(), '$.total'),
'order_status', JSON_EXTRACT(full_document(), '$.status'),
'payment_method', JSON_EXTRACT(full_document(), '$.payment.method'),
'shipping_method', JSON_EXTRACT(full_document(), '$.shipping.method'),
'customer_tier', JSON_EXTRACT(full_document(), '$.customer.loyaltyTier'),
'order_items_count', JSON_LENGTH(JSON_EXTRACT(full_document(), '$.items'))
) as business_context
WHERE
-- Filter for relevant business events
(
operation_type() = 'insert' OR
JSON_EXTRACT(update_description(), '$.updatedFields.status') IS NOT NULL OR
JSON_EXTRACT(update_description(), '$.updatedFields.payment.status') IS NOT NULL OR
JSON_EXTRACT(update_description(), '$.updatedFields.shipping.address') IS NOT NULL OR
JSON_EXTRACT(update_description(), '$.updatedFields.cancellation') IS NOT NULL
)
-- Additional business logic filters
AND (
operation_type() != 'insert' OR
JSON_EXTRACT(full_document(), '$.total') >= 10 -- Only track orders above minimum value
);
-- Advanced change stream processing with business logic and real-time actions
WITH real_time_order_processing AS (
SELECT
oe.*,
-- Calculate processing urgency
CASE
WHEN oe.event_priority >= 8 THEN 'critical'
WHEN oe.event_priority >= 6 THEN 'high'
WHEN oe.event_priority >= 4 THEN 'normal'
ELSE 'low'
END as processing_urgency,
-- Determine notification channels
CASE oe.business_event
WHEN 'order_confirmed' THEN JSON_ARRAY('email', 'push_notification')
WHEN 'order_shipped' THEN JSON_ARRAY('email', 'sms', 'push_notification')
WHEN 'order_delivered' THEN JSON_ARRAY('email', 'push_notification', 'in_app')
WHEN 'payment_failed' THEN JSON_ARRAY('email', 'sms', 'priority_alert')
WHEN 'order_cancelled' THEN JSON_ARRAY('email', 'refund_processing')
ELSE JSON_ARRAY('email')
END as notification_channels,
-- Generate webhook payloads for external systems
JSON_OBJECT(
'event_type', oe.business_event,
'event_id', oe.event_id,
'order_id', oe.order_id,
'timestamp', oe.event_timestamp,
'priority', oe.event_priority,
'customer_context', oe.business_context,
'change_details', oe.change_details
) as webhook_payload,
-- Real-time analytics updates
CASE oe.business_event
WHEN 'order_created' THEN 'increment_daily_orders'
WHEN 'payment_completed' THEN 'increment_revenue'
WHEN 'order_cancelled' THEN 'increment_cancellations'
WHEN 'order_delivered' THEN 'increment_completions'
ELSE 'general_metric_update'
END as analytics_action
FROM order_events oe
),
-- Inventory change stream for real-time stock management
inventory_events AS (
SELECT
change_id() as event_id,
operation_type(),
document_key() as product_id,
cluster_time() as event_timestamp,
full_document() as current_inventory,
update_description() as change_details,
-- Stock status classification
CASE
WHEN JSON_EXTRACT(full_document(), '$.quantity') <= 0 THEN 'out_of_stock'
WHEN JSON_EXTRACT(full_document(), '$.quantity') <= 5 THEN 'critical_low'
WHEN JSON_EXTRACT(full_document(), '$.quantity') <= 20 THEN 'low_stock'
WHEN JSON_EXTRACT(full_document(), '$.quantity') >= 100 THEN 'well_stocked'
ELSE 'normal_stock'
END as stock_status,
-- Reorder trigger detection
CASE
WHEN JSON_EXTRACT(full_document(), '$.quantity') < JSON_EXTRACT(full_document(), '$.reorderPoint')
AND JSON_EXTRACT(full_document(), '$.reorderStatus') != 'pending' THEN true
ELSE false
END as reorder_required,
-- Urgency scoring for inventory alerts
(
CASE WHEN JSON_EXTRACT(full_document(), '$.quantity') <= 0 THEN 10 ELSE 0 END +
CASE WHEN JSON_EXTRACT(full_document(), '$.quantity') <= 5 THEN 7 ELSE 0 END +
CASE WHEN JSON_EXTRACT(full_document(), '$.demandForecast') >= 50 THEN 3 ELSE 0 END +
CASE WHEN JSON_EXTRACT(full_document(), '$.category') = 'bestseller' THEN 2 ELSE 0 END
) as urgency_score
FROM CHANGE_STREAM(inventory)
WHERE JSON_EXTRACT(update_description(), '$.updatedFields.quantity') IS NOT NULL
OR JSON_EXTRACT(update_description(), '$.updatedFields.reservedQuantity') IS NOT NULL
OR JSON_EXTRACT(update_description(), '$.updatedFields.available') IS NOT NULL
),
-- Customer lifecycle change stream for personalization and CRM
customer_events AS (
SELECT
change_id() as event_id,
operation_type(),
document_key() as customer_id,
cluster_time() as event_timestamp,
full_document() as current_customer,
update_description() as change_details,
-- Lifecycle event classification
CASE
WHEN operation_type() = 'insert' THEN 'customer_registered'
WHEN JSON_EXTRACT(update_description(), '$.updatedFields.loyalty.tier') IS NOT NULL THEN 'loyalty_tier_changed'
WHEN JSON_EXTRACT(update_description(), '$.updatedFields.preferences.marketing') IS NOT NULL THEN 'communication_preferences_updated'
WHEN JSON_EXTRACT(update_description(), '$.updatedFields.profile') IS NOT NULL THEN 'profile_updated'
ELSE 'customer_updated'
END as lifecycle_event,
-- Personalization trigger actions
CASE
WHEN operation_type() = 'insert' THEN
JSON_ARRAY('welcome_sequence', 'preference_collection', 'recommendation_initialization')
WHEN JSON_EXTRACT(update_description(), '$.updatedFields.preferences.categories') IS NOT NULL THEN
JSON_ARRAY('recommendation_refresh', 'content_personalization')
WHEN JSON_EXTRACT(update_description(), '$.updatedFields.loyalty.tier') IS NOT NULL THEN
JSON_ARRAY('tier_benefits_notification', 'exclusive_offers_activation')
ELSE JSON_ARRAY('profile_validation')
END as personalization_actions
FROM CHANGE_STREAM(customers)
WHERE operation_type() = 'insert'
OR JSON_EXTRACT(update_description(), '$.updatedFields.profile') IS NOT NULL
OR JSON_EXTRACT(update_description(), '$.updatedFields.preferences') IS NOT NULL
OR JSON_EXTRACT(update_description(), '$.updatedFields.loyalty.tier') IS NOT NULL
)
-- Comprehensive real-time event processing with cross-collection coordination
SELECT
-- Event identification and metadata
'order' as event_source,
rtop.event_id,
rtop.business_event as event_type,
rtop.event_timestamp,
rtop.processing_urgency,
-- Business context and payload
rtop.business_context,
rtop.webhook_payload,
rtop.required_actions,
rtop.notification_channels,
-- Real-time processing instructions
JSON_OBJECT(
'immediate_actions', rtop.required_actions,
'notification_config', JSON_OBJECT(
'channels', rtop.notification_channels,
'priority', rtop.event_priority,
'urgency', rtop.processing_urgency
),
'webhook_config', JSON_OBJECT(
'payload', rtop.webhook_payload,
'priority', rtop.event_priority,
'retry_policy', CASE rtop.processing_urgency
WHEN 'critical' THEN JSON_OBJECT('max_attempts', 5, 'backoff_ms', 1000)
WHEN 'high' THEN JSON_OBJECT('max_attempts', 3, 'backoff_ms', 2000)
ELSE JSON_OBJECT('max_attempts', 2, 'backoff_ms', 5000)
END
),
'analytics_config', JSON_OBJECT(
'action', rtop.analytics_action,
'metrics_update', rtop.business_context
)
) as processing_configuration
FROM real_time_order_processing rtop
UNION ALL
SELECT
-- Inventory events
'inventory' as event_source,
ie.event_id,
CONCAT('inventory_', ie.stock_status) as event_type,
ie.event_timestamp,
CASE
WHEN ie.urgency_score >= 8 THEN 'critical'
WHEN ie.urgency_score >= 5 THEN 'high'
ELSE 'normal'
END as processing_urgency,
-- Inventory context
JSON_OBJECT(
'product_id', ie.product_id,
'stock_status', ie.stock_status,
'current_quantity', JSON_EXTRACT(ie.current_inventory, '$.quantity'),
'urgency_score', ie.urgency_score,
'reorder_required', ie.reorder_required
) as business_context,
-- Inventory webhook payload
JSON_OBJECT(
'event_type', CONCAT('inventory_', ie.stock_status),
'product_id', ie.product_id,
'stock_status', ie.stock_status,
'quantity', JSON_EXTRACT(ie.current_inventory, '$.quantity'),
'reorder_required', ie.reorder_required,
'timestamp', ie.event_timestamp
) as webhook_payload,
-- Inventory-specific actions
CASE
WHEN ie.stock_status = 'out_of_stock' THEN
JSON_ARRAY('website_update', 'sales_alert', 'emergency_reorder')
WHEN ie.stock_status = 'critical_low' THEN
JSON_ARRAY('reorder_trigger', 'low_stock_alert', 'sales_notification')
WHEN ie.reorder_required THEN
JSON_ARRAY('procurement_notification', 'supplier_contact', 'reorder_automation')
ELSE JSON_ARRAY('inventory_update')
END as required_actions,
-- Inventory notification channels
CASE ie.stock_status
WHEN 'out_of_stock' THEN JSON_ARRAY('email', 'slack', 'sms', 'dashboard_alert')
WHEN 'critical_low' THEN JSON_ARRAY('email', 'slack', 'dashboard_alert')
ELSE JSON_ARRAY('email', 'dashboard_alert')
END as notification_channels,
-- Inventory processing configuration
JSON_OBJECT(
'immediate_actions', CASE
WHEN ie.stock_status = 'out_of_stock' THEN
JSON_ARRAY('website_update', 'sales_alert', 'emergency_reorder')
WHEN ie.stock_status = 'critical_low' THEN
JSON_ARRAY('reorder_trigger', 'low_stock_alert')
ELSE JSON_ARRAY('inventory_sync')
END,
'notification_config', JSON_OBJECT(
'channels', CASE ie.stock_status
WHEN 'out_of_stock' THEN JSON_ARRAY('email', 'slack', 'sms')
ELSE JSON_ARRAY('email', 'slack')
END,
'urgency', CASE
WHEN ie.urgency_score >= 8 THEN 'critical'
WHEN ie.urgency_score >= 5 THEN 'high'
ELSE 'normal'
END
),
'reorder_config', CASE
WHEN ie.reorder_required THEN JSON_OBJECT(
'automatic_reorder', true,
'supplier_notification', true,
'quantity_calculation', 'demand_based'
)
ELSE NULL
END
) as processing_configuration
FROM inventory_events ie
UNION ALL
SELECT
-- Customer events
'customer' as event_source,
ce.event_id,
ce.lifecycle_event as event_type,
ce.event_timestamp,
CASE ce.lifecycle_event
WHEN 'customer_registered' THEN 'high'
WHEN 'loyalty_tier_changed' THEN 'high'
ELSE 'normal'
END as processing_urgency,
-- Customer context
JSON_OBJECT(
'customer_id', ce.customer_id,
'lifecycle_event', ce.lifecycle_event,
'customer_tier', JSON_EXTRACT(ce.current_customer, '$.loyalty.tier'),
'registration_date', JSON_EXTRACT(ce.current_customer, '$.createdAt'),
'preferences', JSON_EXTRACT(ce.current_customer, '$.preferences')
) as business_context,
-- Customer webhook payload
JSON_OBJECT(
'event_type', ce.lifecycle_event,
'customer_id', ce.customer_id,
'timestamp', ce.event_timestamp,
'customer_data', ce.current_customer
) as webhook_payload,
ce.personalization_actions as required_actions,
-- Customer notification channels
CASE ce.lifecycle_event
WHEN 'customer_registered' THEN JSON_ARRAY('email', 'welcome_kit')
WHEN 'loyalty_tier_changed' THEN JSON_ARRAY('email', 'push_notification', 'in_app')
ELSE JSON_ARRAY('email')
END as notification_channels,
-- Customer processing configuration
JSON_OBJECT(
'immediate_actions', ce.personalization_actions,
'personalization_config', JSON_OBJECT(
'update_recommendations', true,
'refresh_preferences', true,
'trigger_campaigns', CASE ce.lifecycle_event
WHEN 'customer_registered' THEN true
ELSE false
END
),
'crm_sync_config', JSON_OBJECT(
'sync_required', true,
'priority', CASE ce.lifecycle_event
WHEN 'customer_registered' THEN 'high'
ELSE 'normal'
END
)
) as processing_configuration
FROM customer_events ce
ORDER BY
CASE processing_urgency
WHEN 'critical' THEN 1
WHEN 'high' THEN 2
WHEN 'normal' THEN 3
ELSE 4
END,
event_timestamp ASC;
-- Real-time analytics and monitoring for change data capture performance
WITH cdc_performance_metrics AS (
SELECT
DATE_TRUNC('minute', event_timestamp) as time_bucket,
event_source,
event_type,
processing_urgency,
-- Event volume metrics
COUNT(*) as events_per_minute,
COUNT(DISTINCT CASE event_source
WHEN 'order' THEN JSON_EXTRACT(business_context, '$.customer_id')
WHEN 'customer' THEN JSON_EXTRACT(business_context, '$.customer_id')
ELSE NULL
END) as unique_customers_affected,
-- Processing priority distribution
COUNT(*) FILTER (WHERE processing_urgency = 'critical') as critical_events,
COUNT(*) FILTER (WHERE processing_urgency = 'high') as high_priority_events,
COUNT(*) FILTER (WHERE processing_urgency = 'normal') as normal_events,
-- Business event analysis
COUNT(*) FILTER (WHERE event_type LIKE 'order_%') as order_events,
COUNT(*) FILTER (WHERE event_type LIKE 'inventory_%') as inventory_events,
COUNT(*) FILTER (WHERE event_type LIKE 'customer_%') as customer_events,
-- Revenue impact tracking
SUM(
CASE
WHEN event_type = 'payment_completed' THEN
CAST(JSON_EXTRACT(business_context, '$.order_value') AS DECIMAL(10,2))
ELSE 0
END
) as revenue_processed,
-- Alert generation tracking
COUNT(*) FILTER (WHERE processing_urgency IN ('critical', 'high')) as alerts_generated
FROM (
-- Use the main change stream query results
SELECT * FROM (
SELECT
'order' as event_source,
rtop.business_event as event_type,
rtop.event_timestamp,
CASE
WHEN rtop.event_priority >= 8 THEN 'critical'
WHEN rtop.event_priority >= 6 THEN 'high'
ELSE 'normal'
END as processing_urgency,
rtop.business_context
FROM real_time_order_processing rtop
UNION ALL
SELECT
'inventory' as event_source,
CONCAT('inventory_', ie.stock_status) as event_type,
ie.event_timestamp,
CASE
WHEN ie.urgency_score >= 8 THEN 'critical'
WHEN ie.urgency_score >= 5 THEN 'high'
ELSE 'normal'
END as processing_urgency,
JSON_OBJECT(
'product_id', ie.product_id,
'stock_status', ie.stock_status
) as business_context
FROM inventory_events ie
UNION ALL
SELECT
'customer' as event_source,
ce.lifecycle_event as event_type,
ce.event_timestamp,
CASE ce.lifecycle_event
WHEN 'customer_registered' THEN 'high'
WHEN 'loyalty_tier_changed' THEN 'high'
ELSE 'normal'
END as processing_urgency,
JSON_OBJECT(
'customer_id', ce.customer_id
) as business_context
FROM customer_events ce
) all_events
WHERE event_timestamp >= CURRENT_TIMESTAMP - INTERVAL '1 hour'
) recent_events
GROUP BY
DATE_TRUNC('minute', event_timestamp),
event_source,
event_type,
processing_urgency
),
-- Real-time system health monitoring
system_health_metrics AS (
SELECT
CURRENT_TIMESTAMP as health_check_time,
-- Change stream performance indicators
COUNT(*) as total_events_last_minute,
AVG(events_per_minute) as avg_events_per_minute,
MAX(events_per_minute) as peak_events_per_minute,
-- Alert and priority distribution
SUM(critical_events) as total_critical_events,
SUM(high_priority_events) as total_high_priority_events,
SUM(alerts_generated) as total_alerts_generated,
-- Business impact metrics
SUM(revenue_processed) as total_revenue_processed,
SUM(unique_customers_affected) as total_customers_affected,
-- Event type distribution
SUM(order_events) as total_order_events,
SUM(inventory_events) as total_inventory_events,
SUM(customer_events) as total_customer_events,
-- Performance assessment
CASE
WHEN MAX(events_per_minute) > 1000 THEN 'high_load'
WHEN MAX(events_per_minute) > 500 THEN 'moderate_load'
WHEN MAX(events_per_minute) > 100 THEN 'normal_load'
ELSE 'low_load'
END as system_load_status,
-- Alert status assessment
CASE
WHEN SUM(critical_events) > 50 THEN 'critical_alerts_high'
WHEN SUM(critical_events) > 10 THEN 'critical_alerts_moderate'
WHEN SUM(critical_events) > 0 THEN 'critical_alerts_low'
ELSE 'no_critical_alerts'
END as alert_status,
-- Recommendations for system optimization
CASE
WHEN MAX(events_per_minute) > 1000 AND SUM(critical_events) > 50 THEN
'Scale up processing capacity and review alert thresholds'
WHEN MAX(events_per_minute) > 1000 THEN
'Consider horizontal scaling for change stream processing'
WHEN SUM(critical_events) > 50 THEN
'Review alert sensitivity and business rule configuration'
ELSE 'System operating within normal parameters'
END as optimization_recommendation
FROM cdc_performance_metrics
WHERE time_bucket >= CURRENT_TIMESTAMP - INTERVAL '1 hour'
)
-- Final comprehensive CDC monitoring dashboard
SELECT
shm.health_check_time,
shm.total_events_last_minute,
ROUND(shm.avg_events_per_minute, 1) as avg_events_per_minute,
shm.peak_events_per_minute,
shm.total_critical_events,
shm.total_high_priority_events,
ROUND(shm.total_revenue_processed, 2) as revenue_processed_usd,
shm.total_customers_affected,
shm.system_load_status,
shm.alert_status,
shm.optimization_recommendation,
-- Event distribution summary
JSON_OBJECT(
'order_events', shm.total_order_events,
'inventory_events', shm.total_inventory_events,
'customer_events', shm.total_customer_events
) as event_distribution,
-- Performance indicators
JSON_OBJECT(
'events_per_second', ROUND(shm.avg_events_per_minute / 60.0, 2),
'peak_throughput', shm.peak_events_per_minute,
'alert_rate', ROUND((shm.total_alerts_generated / NULLIF(shm.total_events_last_minute, 0)) * 100, 2),
'critical_event_percentage', ROUND((shm.total_critical_events / NULLIF(shm.total_events_last_minute, 0)) * 100, 2)
) as performance_indicators,
-- Business impact summary
JSON_OBJECT(
'revenue_velocity', ROUND(shm.total_revenue_processed / 60.0, 2),
'customer_engagement_rate', shm.total_customers_affected,
'business_event_diversity', (
CASE WHEN shm.total_order_events > 0 THEN 1 ELSE 0 END +
CASE WHEN shm.total_inventory_events > 0 THEN 1 ELSE 0 END +
CASE WHEN shm.total_customer_events > 0 THEN 1 ELSE 0 END
)
) as business_impact,
-- Trend analysis from recent performance metrics
(
SELECT JSON_OBJECT(
'event_trend', CASE
WHEN COUNT(*) > 1 AND
(MAX(events_per_minute) - MIN(events_per_minute)) / NULLIF(MIN(events_per_minute), 0) > 0.2
THEN 'increasing'
WHEN COUNT(*) > 1 AND
(MIN(events_per_minute) - MAX(events_per_minute)) / NULLIF(MAX(events_per_minute), 0) > 0.2
THEN 'decreasing'
ELSE 'stable'
END,
'alert_trend', CASE
WHEN SUM(critical_events) > LAG(SUM(critical_events)) OVER (ORDER BY time_bucket)
THEN 'increasing'
ELSE 'stable'
END
)
FROM cdc_performance_metrics
WHERE time_bucket >= CURRENT_TIMESTAMP - INTERVAL '15 minutes'
ORDER BY time_bucket DESC
LIMIT 1
) as trend_analysis
FROM system_health_metrics shm;
-- QueryLeaf provides comprehensive change data capture capabilities:
-- 1. Real-time change stream processing with SQL-familiar syntax
-- 2. Advanced event filtering, classification, and routing
-- 3. Business logic integration for intelligent event processing
-- 4. Multi-collection coordination for complex business workflows
-- 5. Comprehensive monitoring and performance analytics
-- 6. Enterprise-grade event sourcing and audit trail capabilities
-- 7. Distributed processing support for high-availability scenarios
-- 8. SQL-style syntax for change stream configuration and management
-- 9. Integration with MongoDB's native change stream capabilities
-- 10. Production-ready scalability and operational monitoring
Best Practices for Change Data Capture Implementation
Event Processing Strategy Design
Essential principles for effective MongoDB Change Data Capture deployment:
- Event Filtering: Design comprehensive filtering strategies to process only relevant business events
- Business Logic Integration: Embed business rules directly into change stream pipelines for immediate processing
- Error Handling: Implement robust retry mechanisms and dead letter queues for failed event processing
- Performance Optimization: Configure change streams for optimal throughput with appropriate batch sizes
- Monitoring Strategy: Deploy comprehensive monitoring for change stream health and event processing metrics
- Scalability Planning: Design for horizontal scaling with distributed processing capabilities
Production Implementation
Optimize MongoDB Change Data Capture for enterprise-scale deployments:
- Distributed Processing: Implement leader election and workload distribution for high availability
- Event Sourcing: Maintain complete audit trails with event store and snapshot capabilities
- Real-time Analytics: Integrate change streams with analytics pipelines for immediate insights
- Security Implementation: Ensure proper authentication and authorization for change stream access
- Disaster Recovery: Plan for change stream recovery and replay capabilities
- Integration Patterns: Design microservices integration with event-driven architecture patterns
Conclusion
MongoDB Change Data Capture through Change Streams provides comprehensive real-time data change notification capabilities that enable responsive, event-driven applications without the performance overhead and latency of traditional polling approaches. The native MongoDB integration ensures that change capture benefits from the same reliability, scalability, and operational features as core database operations.
Key MongoDB Change Data Capture benefits include:
- Real-Time Responsiveness: Immediate notification of data changes without polling latency or resource waste
- Comprehensive Change Information: Complete change documents including before/after states and modification details
- Advanced Filtering: Sophisticated change stream filtering and transformation capabilities within the database
- Event Ordering: Guaranteed ordering and delivery of change events for consistent event processing
- Horizontal Scalability: Native support for replica sets and sharded clusters with distributed change stream processing
- Production Ready: Enterprise-grade reliability with automatic reconnection, resume tokens, and operational monitoring
Whether you're building real-time dashboards, event-driven microservices, collaborative applications, or reactive user experiences, MongoDB Change Data Capture with QueryLeaf's familiar SQL interface provides the foundation for responsive, event-driven architectures.
QueryLeaf Integration: QueryLeaf automatically manages MongoDB Change Streams while providing SQL-familiar syntax for change data capture configuration, event processing, and real-time monitoring. Advanced event routing, business logic integration, and distributed processing patterns are seamlessly handled through familiar SQL constructs, making sophisticated real-time capabilities accessible to SQL-oriented development teams.
The combination of MongoDB's robust change stream capabilities with SQL-style event processing makes it an ideal platform for modern applications that require both real-time responsiveness and familiar database interaction patterns, ensuring your event-driven solutions can scale efficiently while remaining maintainable and feature-rich.