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MongoDB: From Fundamentals to Production Excellence
Comprehensive guide to MongoDB architecture, advanced patterns, and enterprise deployment strategies

Overview
MongoDB has evolved from a simple document database to a comprehensive data platform powering modern applications at unprecedented scale.
As organizations embrace cloud-native architectures, microservices, and real-time analytics, MongoDB serves as the backbone for flexible, scalable data solutions.
This comprehensive guide explores MongoDB from foundational concepts to advanced production patterns, covering everything from document modeling principles to distributed system architectures.
Whether you’re architecting a new application, optimizing existing deployments, or implementing enterprise-grade MongoDB solutions, this guide provides the depth and practical insights needed for success.
Modern MongoDB deployments face complex challenges: multi-cloud distribution, real-time synchronization, security compliance, and operational excellence.
Understanding these challenges and implementing appropriate solutions distinguishes successful MongoDB implementations from basic deployments.
2009-2012] A --> C[Distributed Platform
2013-2018] A --> D[Cloud-Native Solution
2019-Present] B --> B1[BSON Documents] B --> B2[Dynamic Schema] B --> B3[Simple Replication] C --> C1[Sharding] C --> C2[Aggregation Framework] C --> C3[WiredTiger Engine] C --> C4[ACID Transactions] D --> D1[Atlas Cloud Platform] D --> D2[Multi-Cloud Clusters] D --> D3[Serverless Instances] D --> D4[Vector Search] D --> D5[Time Series Collections] style A fill:#e3f2fd,stroke:#1976d2,stroke-width:3px style B fill:#fff3e0,stroke:#f57c00,stroke-width:2px style C fill:#e8f5e8,stroke:#388e3c,stroke-width:2px style D fill:#f3e5f5,stroke:#7b1fa2,stroke-width:2px
MongoDB Evolution: From simple document storage to comprehensive cloud-native data platform
MongoDB Architecture Deep Dive
MongoDB’s architecture is designed for horizontal scaling, high availability, and operational simplicity. Understanding its core components enables effective deployment strategies and optimization techniques.
High Availability] A --> C[Sharded Clusters
Horizontal Scaling] A --> D[Config Servers
Metadata Management] A --> E[Query Routers
mongos] B --> B1[Primary Node
Read/Write Operations] B --> B2[Secondary Nodes
Data Replication] B --> B3[Arbiter Nodes
Election Participation] B --> B4[Hidden Members
Analytics/Backup] C --> C1[Shard Key Selection] C --> C2[Chunk Distribution] C --> C3[Balancer Process] C --> C4[Zone Sharding] D --> D1[Cluster Metadata] D --> D2[Shard Mapping] D --> D3[Balancer State] E --> E1[Query Routing] E --> E2[Load Distribution] E --> E3[Connection Pooling] style A fill:#e3f2fd,stroke:#1976d2,stroke-width:3px style B fill:#e8f5e8,stroke:#388e3c,stroke-width:2px style C fill:#fff3e0,stroke:#f57c00,stroke-width:2px style D fill:#f3e5f5,stroke:#7b1fa2,stroke-width:2px style E fill:#ffebee,stroke:#d32f2f,stroke-width:2px
MongoDB Cluster Architecture: Comprehensive view of components and their interactions
Advanced Replica Set Configuration
Modern replica sets require sophisticated configuration for optimal performance, availability, and operational flexibility.
Production-Ready Replica Set Setup
// Advanced replica set configuration
rs.initiate({
_id: "production-replica-set",
version: 1,
members: [
{
_id: 0,
host: "mongo-primary.internal:27017",
priority: 2,
tags: { region: "us-east-1", role: "primary" }
},
{
_id: 1,
host: "mongo-secondary-1.internal:27017",
priority: 1,
tags: { region: "us-east-1", role: "secondary" }
},
{
_id: 2,
host: "mongo-secondary-2.internal:27017",
priority: 1,
tags: { region: "us-west-2", role: "secondary" }
},
{
_id: 3,
host: "mongo-analytics.internal:27017",
priority: 0,
hidden: true,
votes: 0,
tags: { role: "analytics", workload: "reporting" }
},
{
_id: 4,
host: "mongo-delayed.internal:27017",
priority: 0,
hidden: true,
votes: 0,
slaveDelay: 3600, // 1-hour delay for disaster recovery
tags: { role: "delayed-secondary" }
}
],
settings: {
chainingAllowed: true,
heartbeatIntervalMillis: 2000,
heartbeatTimeoutSecs: 10,
electionTimeoutMillis: 10000,
catchUpTimeoutMillis: 60000,
getLastErrorModes: {
"cross-region": { region: 2 },
"majority-plus-analytics": { role: 3 }
},
getLastErrorDefaults: {
w: "majority",
wtimeout: 5000
}
}
});
// Configure read preferences for different workloads
db.getMongo().setReadPref("primaryPreferred", [
{ region: "us-east-1" }
]);
// Analytics workload configuration
db.getMongo().setReadPref("secondary", [
{ role: "analytics" }
]);
Advanced Write Concerns and Read Preferences
// Write concern strategies for different use cases
const writeConfigs = {
// Critical financial transactions
financial: {
w: "majority",
j: true,
wtimeout: 5000
},
// Cross-region durability
crossRegion: {
w: "cross-region",
j: true,
wtimeout: 10000
},
// High-throughput logging
logging: {
w: 1,
j: false,
wtimeout: 1000
},
// Analytics data ingestion
analytics: {
w: "majority-plus-analytics",
j: true,
wtimeout: 15000
}
};
// Application-specific implementations
await db.transactions.insertOne(
{
userId: ObjectId("..."),
amount: 1000.00,
type: "transfer",
timestamp: new Date()
},
{ writeConcern: writeConfigs.financial }
);
// Read preference optimization
const readPreferences = {
// Real-time user data
userProfile: { mode: "primary" },
// Analytics and reporting
analytics: {
mode: "secondary",
tags: [{ role: "analytics" }],
maxStalenessSeconds: 300
},
// Search and discovery
search: {
mode: "secondaryPreferred",
tags: [{ region: "us-east-1" }]
}
};
Sharding Architecture and Optimization
Effective sharding requires careful planning of shard key selection, chunk distribution, and balancing strategies.
Replica Set] B --> E[Shard 2
Replica Set] B --> F[Shard 3
Replica Set] C --> C1[Cluster Metadata] C --> C2[Chunk Mappings] C --> C3[Balancer Configuration] D --> D1[Range: min - 1000] E --> E1[Range: 1000 - 2000] F --> F1[Range: 2000 - max] G[Zone Sharding] --> H[Geographic Distribution] G --> I[Workload Isolation] G --> J[Hardware Optimization] style B fill:#e3f2fd,stroke:#1976d2,stroke-width:2px style C fill:#f3e5f5,stroke:#7b1fa2,stroke-width:2px style D fill:#e8f5e8,stroke:#388e3c,stroke-width:2px style E fill:#e8f5e8,stroke:#388e3c,stroke-width:2px style F fill:#e8f5e8,stroke:#388e3c,stroke-width:2px style G fill:#fff3e0,stroke:#f57c00,stroke-width:2px
Sharded Cluster Architecture: Data distribution and query routing mechanisms
Strategic Shard Key Selection
// Anti-patterns to avoid
const badShardKeys = {
// Monotonically increasing - creates hotspots
timestamp: { _id: 1 },
autoIncrement: { sequence: 1 },
// Low cardinality - limits scaling
status: { status: 1 },
category: { category: 1 },
// Non-queryable - forces scatter-gather
hash: { _id: "hashed" } // without query patterns
};
// Effective shard key strategies
const goodShardKeys = {
// High cardinality + query pattern alignment
userActivity: { userId: 1, timestamp: 1 },
// Compound key for even distribution
ecommerce: { customerId: 1, orderId: 1 },
// Geographic distribution
iot: { deviceRegion: 1, deviceId: 1, timestamp: 1 },
// Hash for even distribution when range isn't suitable
socialMedia: { userId: "hashed" }
};
// Implementation with zone sharding
sh.enableSharding("ecommerce");
// Create zones for geographic distribution
sh.addShardToZone("shard-us-east", "us-east");
sh.addShardToZone("shard-us-west", "us-west");
sh.addShardToZone("shard-europe", "europe");
// Define zone ranges
sh.updateZoneKeyRange(
"ecommerce.orders",
{ region: "us-east", customerId: MinKey },
{ region: "us-east", customerId: MaxKey },
"us-east"
);
// Shard the collection
sh.shardCollection("ecommerce.orders", {
region: 1,
customerId: 1,
orderDate: 1
});
Advanced Balancer Configuration
// Custom balancer settings for different workloads
db.adminCommand({
balancerCollectionStatus: "ecommerce.orders",
defragmentCollection: true
});
// Configure balancer windows
db.settings.updateOne(
{ _id: "balancer" },
{
$set: {
activeWindow: {
start: "02:00", // 2 AM
stop: "06:00" // 6 AM
},
mode: "full"
}
},
{ upsert: true }
);
// Optimize chunk size for specific collections
db.adminCommand({
configureCollectionBalancing: "ecommerce.orders",
chunkSize: 128, // MB
defragmentCollection: true,
enableAutoSplit: true
});
Advanced Data Modeling Patterns
MongoDB’s flexible document model enables sophisticated data modeling patterns that align with application access patterns and performance requirements.
Document Design Strategies
Dynamic Schema Evolution
// Schema versioning for backward compatibility
const userSchemaV1 = {
_id: ObjectId("..."),
schemaVersion: 1,
email: "user@example.com",
name: "John Doe",
created: new Date()
};
const userSchemaV2 = {
_id: ObjectId("..."),
schemaVersion: 2,
email: "user@example.com",
profile: {
firstName: "John",
lastName: "Doe",
displayName: "John Doe"
},
preferences: {
theme: "dark",
notifications: true,
language: "en"
},
created: new Date(),
lastModified: new Date()
};
// Migration strategy with version checks
function migrateUser(user) {
switch (user.schemaVersion || 1) {
case 1:
return {
...user,
schemaVersion: 2,
profile: {
firstName: user.name.split(' ')[0],
lastName: user.name.split(' ')[1] || '',
displayName: user.name
},
preferences: {
theme: "light",
notifications: true,
language: "en"
},
lastModified: new Date()
};
case 2:
return user; // Already current version
default:
throw new Error(`Unknown schema version: ${user.schemaVersion}`);
}
}
Advanced Embedding vs Referencing Patterns
// Product catalog with complex relationships
const productCatalog = {
// Embedded for frequently accessed, bounded data
product: {
_id: ObjectId("..."),
sku: "LAPTOP-001",
name: "Gaming Laptop",
// Embedded specifications (1-to-1, rarely changes)
specifications: {
processor: "Intel i7-12700H",
memory: "32GB DDR4",
storage: "1TB NVMe SSD",
graphics: "RTX 3070",
display: {
size: "15.6 inches",
resolution: "1920x1080",
refreshRate: "144Hz"
}
},
// Embedded pricing (frequently accessed with product)
pricing: {
msrp: 1499.99,
currentPrice: 1299.99,
currency: "USD",
discounts: [
{
type: "seasonal",
amount: 200.00,
validUntil: new Date("2024-12-31")
}
]
},
// Referenced for large, independently managed data
categoryId: ObjectId("category-laptops"),
brandId: ObjectId("brand-gaming"),
// Embedded recent reviews (limited subset)
recentReviews: [
{
_id: ObjectId("..."),
userId: ObjectId("..."),
rating: 5,
title: "Excellent performance",
summary: "Great for gaming and development work...",
date: new Date(),
helpful: 23
}
],
// Referenced for complete review data
totalReviews: 156,
averageRating: 4.7,
// Embedded inventory (frequently updated)
inventory: {
available: 45,
reserved: 12,
reorderLevel: 10,
lastUpdated: new Date()
}
}
};
// Separate collections for scalable data
const reviews = {
_id: ObjectId("..."),
productId: ObjectId("product-id"),
userId: ObjectId("user-id"),
rating: 5,
title: "Excellent performance",
content: "Detailed review content...",
images: ["review-img-1.jpg", "review-img-2.jpg"],
verified: true,
helpful: 23,
replies: [
{
userId: ObjectId("..."),
content: "Thanks for the detailed review!",
date: new Date()
}
],
created: new Date(),
lastModified: new Date()
};
Polymorphic Data Patterns
// Event-driven architecture with polymorphic events
const eventStore = [
{
_id: ObjectId("..."),
eventType: "UserRegistered",
aggregateId: "user-123",
aggregateVersion: 1,
eventData: {
email: "user@example.com",
registrationSource: "web",
marketingConsent: true
},
metadata: {
ipAddress: "192.168.1.1",
userAgent: "Mozilla/5.0...",
correlationId: "reg-123"
},
timestamp: new Date(),
processed: false
},
{
_id: ObjectId("..."),
eventType: "OrderPlaced",
aggregateId: "order-456",
aggregateVersion: 1,
eventData: {
customerId: "user-123",
items: [
{ productId: "prod-1", quantity: 2, price: 29.99 },
{ productId: "prod-2", quantity: 1, price: 15.50 }
],
totalAmount: 75.48,
paymentMethod: "stripe",
shippingAddress: {
street: "123 Main St",
city: "New York",
state: "NY",
zip: "10001"
}
},
metadata: {
sessionId: "sess-789",
promotionCode: "SAVE10"
},
timestamp: new Date(),
processed: false
}
];
// Polymorphic query patterns
const eventHandlers = {
async handleUserRegistered(event) {
await db.users.insertOne({
_id: event.aggregateId,
email: event.eventData.email,
registrationSource: event.eventData.registrationSource,
marketingConsent: event.eventData.marketingConsent,
created: event.timestamp
});
},
async handleOrderPlaced(event) {
await db.orders.insertOne({
_id: event.aggregateId,
customerId: event.eventData.customerId,
items: event.eventData.items,
totalAmount: event.eventData.totalAmount,
status: "pending",
created: event.timestamp
});
}
};
// Event processing with polymorphic dispatch
async function processEvents() {
const unprocessedEvents = await db.events.find({ processed: false })
.sort({ timestamp: 1 })
.limit(100)
.toArray();
for (const event of unprocessedEvents) {
const handlerName = `handle${event.eventType}`;
const handler = eventHandlers[handlerName];
if (handler) {
try {
await handler(event);
await db.events.updateOne(
{ _id: event._id },
{ $set: { processed: true, processedAt: new Date() } }
);
} catch (error) {
await db.events.updateOne(
{ _id: event._id },
{
$set: {
processingError: error.message,
processingAttempts: (event.processingAttempts || 0) + 1
}
}
);
}
}
}
}
Time Series and IoT Data Patterns
MongoDB’s time series collections optimize storage and queries for time-stamped data.
// Time series collection for IoT sensor data
db.createCollection("sensor_data", {
timeseries: {
timeField: "timestamp",
metaField: "sensor",
granularity: "minutes",
bucketMaxSpanSeconds: 3600,
bucketRoundingSeconds: 60
},
expireAfterSeconds: 31536000 // 1 year retention
});
// Optimized IoT data structure
const sensorReading = {
timestamp: new Date(),
sensor: {
deviceId: "sensor-001",
location: "warehouse-a",
type: "temperature-humidity",
model: "DHT22"
},
measurements: {
temperature: 23.5,
humidity: 65.2,
batteryLevel: 87
},
metadata: {
firmware: "v1.2.3",
signalStrength: -45,
dataQuality: "good"
}
};
// Efficient time series queries
const temperatureAggregation = [
{
$match: {
"timestamp": {
$gte: new Date(Date.now() - 24 * 60 * 60 * 1000) // Last 24 hours
},
"sensor.type": "temperature-humidity"
}
},
{
$group: {
_id: {
deviceId: "$sensor.deviceId",
hour: {
$dateTrunc: {
date: "$timestamp",
unit: "hour"
}
}
},
avgTemperature: { $avg: "$measurements.temperature" },
maxTemperature: { $max: "$measurements.temperature" },
minTemperature: { $min: "$measurements.temperature" },
readingCount: { $sum: 1 }
}
},
{
$sort: { "_id.hour": 1 }
}
];
// Real-time anomaly detection
const anomalyDetection = [
{
$match: {
"timestamp": {
$gte: new Date(Date.now() - 60 * 60 * 1000) // Last hour
}
}
},
{
$setWindowFields: {
partitionBy: "$sensor.deviceId",
sortBy: { timestamp: 1 },
output: {
movingAverage: {
$avg: "$measurements.temperature",
window: {
documents: [-10, 0] // 10-point moving average
}
},
standardDeviation: {
$stdDevSamp: "$measurements.temperature",
window: {
documents: [-20, 0]
}
}
}
}
},
{
$addFields: {
anomaly: {
$gt: [
{
$abs: {
$subtract: ["$measurements.temperature", "$movingAverage"]
}
},
{ $multiply: ["$standardDeviation", 2] } // 2-sigma threshold
]
}
}
},
{
$match: { anomaly: true }
}
];
Performance Optimization and Indexing Strategies
MongoDB performance optimization requires comprehensive indexing strategies, query optimization, and architectural considerations.
Advanced Indexing Patterns
Compound Index Optimization
// Query pattern analysis for index design
const queryPatterns = {
// User activity lookup
userActivity: {
query: { userId: ObjectId("..."), timestamp: { $gte: new Date() } },
sort: { timestamp: -1 },
limit: 50
},
// Product search with filters
productSearch: {
query: {
category: "electronics",
price: { $gte: 100, $lte: 500 },
inStock: true
},
sort: { popularity: -1, price: 1 }
},
// Geospatial queries
nearbyStores: {
query: {
location: {
$near: {
$geometry: { type: "Point", coordinates: [-73.9857, 40.7484] },
$maxDistance: 5000
}
},
storeType: "retail"
}
}
};
// Optimized index strategies
const indexStrategies = {
// ESR (Equality, Sort, Range) principle
userActivityIndex: {
keys: { userId: 1, timestamp: -1 }, // Equality first, then sort
options: {
name: "idx_user_activity_optimized",
partialFilterExpression: { timestamp: { $exists: true } }
}
},
// Compound index for product filtering
productSearchIndex: {
keys: { category: 1, inStock: 1, price: 1, popularity: -1 },
options: {
name: "idx_product_search_compound",
partialFilterExpression: { inStock: true }
}
},
// Geospatial index
storeLocationIndex: {
keys: { location: "2dsphere", storeType: 1 },
options: { name: "idx_store_location_type" }
},
// Text search with language support
productTextSearch: {
keys: {
name: "text",
description: "text",
tags: "text"
},
options: {
name: "idx_product_text_search",
weights: { name: 10, description: 5, tags: 1 },
default_language: "english"
}
}
};
// Index creation with monitoring
async function createOptimizedIndexes() {
const collections = [
{ name: "user_activity", indexes: [indexStrategies.userActivityIndex] },
{ name: "products", indexes: [
indexStrategies.productSearchIndex,
indexStrategies.productTextSearch
]},
{ name: "stores", indexes: [indexStrategies.storeLocationIndex] }
];
for (const collection of collections) {
for (const index of collection.indexes) {
try {
const result = await db[collection.name].createIndex(
index.keys,
index.options
);
console.log(`Created index ${index.options.name}: ${result}`);
} catch (error) {
console.error(`Failed to create index ${index.options.name}:`, error);
}
}
}
}
Sparse and Partial Indexes for Optimization
// Sparse indexes for optional fields
db.users.createIndex(
{ socialSecurityNumber: 1 },
{
sparse: true,
name: "idx_users_ssn_sparse",
unique: true
}
);
// Partial indexes for specific conditions
db.orders.createIndex(
{ customerId: 1, orderDate: -1 },
{
partialFilterExpression: {
status: { $in: ["pending", "processing"] }
},
name: "idx_orders_active_customer_date"
}
);
// TTL indexes for automatic document expiration
db.sessions.createIndex(
{ lastAccessed: 1 },
{ expireAfterSeconds: 3600 } // 1 hour
);
db.logs.createIndex(
{ createdAt: 1 },
{ expireAfterSeconds: 2592000 } // 30 days
);
// Compound TTL with partial filter
db.temporaryData.createIndex(
{ expiresAt: 1 },
{
expireAfterSeconds: 0,
partialFilterExpression: {
temporary: true
}
}
);
Query Optimization Techniques
Aggregation Pipeline Optimization
// Inefficient aggregation pipeline
const inefficientPipeline = [
{ $match: { status: "completed" } },
{ $lookup: {
from: "products",
localField: "productId",
foreignField: "_id",
as: "product"
}
},
{ $unwind: "$product" },
{ $match: { "product.category": "electronics" } }, // Should be earlier
{ $group: {
_id: "$customerId",
totalSpent: { $sum: "$amount" }
}
},
{ $sort: { totalSpent: -1 } },
{ $limit: 100 }
];
// Optimized aggregation pipeline
const optimizedPipeline = [
// Early filtering reduces documents in pipeline
{ $match: {
status: "completed",
amount: { $gt: 0 } // Additional early filter
}
},
// Index hint for optimal query plan
{ $hint: "idx_orders_status_customer_amount" },
// Efficient lookup with pipeline for filtering
{ $lookup: {
from: "products",
let: { productId: "$productId" },
pipeline: [
{ $match: {
$expr: { $eq: ["$_id", "$$productId"] },
category: "electronics" // Filter in subpipeline
}
},
{ $project: { name: 1, category: 1 } } // Minimal projection
],
as: "product"
}
},
// Filter out documents without matching products
{ $match: { "product.0": { $exists: true } } },
// Group with optimized accumulator
{ $group: {
_id: "$customerId",
totalSpent: { $sum: "$amount" },
orderCount: { $sum: 1 },
avgOrderValue: { $avg: "$amount" }
}
},
// Sort and limit for top customers
{ $sort: { totalSpent: -1 } },
{ $limit: 100 },
// Final projection for clean output
{ $project: {
customerId: "$_id",
totalSpent: 1,
orderCount: 1,
avgOrderValue: { $round: ["$avgOrderValue", 2] },
_id: 0
}
}
];
// Performance monitoring wrapper
async function executeOptimizedAggregation(pipeline, options = {}) {
const startTime = Date.now();
try {
const result = await db.orders.aggregate(pipeline, {
allowDiskUse: true,
cursor: { batchSize: 1000 },
...options
}).toArray();
const executionTime = Date.now() - startTime;
// Log performance metrics
await db.query_performance.insertOne({
query: "customer_electronics_analysis",
executionTime,
resultCount: result.length,
timestamp: new Date(),
pipeline: pipeline.length
});
return result;
} catch (error) {
console.error("Aggregation failed:", error);
throw error;
}
}
Query Plan Analysis and Optimization
// Query performance analysis
async function analyzeQueryPerformance() {
const query = {
category: "electronics",
price: { $gte: 100, $lte: 1000 },
inStock: true
};
// Explain query execution
const explanation = await db.products.find(query)
.sort({ popularity: -1 })
.limit(20)
.explain("executionStats");
console.log("Query Execution Stats:");
console.log(`Execution Time: ${explanation.executionStats.executionTimeMillis}ms`);
console.log(`Documents Examined: ${explanation.executionStats.totalDocsExamined}`);
console.log(`Documents Returned: ${explanation.executionStats.totalDocsReturned}`);
console.log(`Index Used: ${explanation.executionStats.executionStages.indexName || 'COLLSCAN'}`);
// Calculate selectivity
const selectivity = explanation.executionStats.totalDocsReturned /
explanation.executionStats.totalDocsExamined;
if (selectivity < 0.1) {
console.warn("Low selectivity detected. Consider index optimization.");
}
return explanation;
}
// Index usage monitoring
async function monitorIndexUsage() {
const indexStats = await db.products.aggregate([
{ $indexStats: {} }
]).toArray();
console.log("Index Usage Statistics:");
indexStats.forEach(stat => {
console.log(`Index: ${stat.name}`);
console.log(` Accesses: ${stat.accesses.ops}`);
console.log(` Since: ${stat.accesses.since}`);
});
// Identify unused indexes
const unusedIndexes = indexStats.filter(stat => stat.accesses.ops === 0);
if (unusedIndexes.length > 0) {
console.warn("Unused indexes detected:", unusedIndexes.map(idx => idx.name));
}
}
Enterprise Patterns and Distributed Systems
Modern MongoDB deployments often serve as the data layer for distributed systems, requiring sophisticated patterns for consistency, scalability, and reliability.
Microservices Data Patterns
Database per Service with Event Sourcing
// User Service - Event Store Implementation
class UserEventStore {
constructor(db) {
this.db = db;
this.collection = db.collection('user_events');
}
async appendEvent(streamId, expectedVersion, events) {
const session = this.db.client.startSession();
try {
await session.withTransaction(async () => {
// Check current version for optimistic concurrency
const currentEvents = await this.collection
.find({ streamId }, { session })
.sort({ version: -1 })
.limit(1)
.toArray();
const currentVersion = currentEvents.length > 0 ?
currentEvents[0].version : 0;
if (currentVersion !== expectedVersion) {
throw new Error(`Concurrency conflict. Expected ${expectedVersion}, got ${currentVersion}`);
}
// Append new events
const eventsToInsert = events.map((event, index) => ({
_id: new ObjectId(),
streamId,
version: expectedVersion + index + 1,
eventType: event.type,
eventData: event.data,
metadata: {
...event.metadata,
timestamp: new Date(),
correlationId: event.metadata?.correlationId || new ObjectId().toString()
}
}));
await this.collection.insertMany(eventsToInsert, { session });
return eventsToInsert;
});
} finally {
await session.endSession();
}
}
async getEvents(streamId, fromVersion = 0) {
return await this.collection
.find({
streamId,
version: { $gt: fromVersion }
})
.sort({ version: 1 })
.toArray();
}
}
// Saga Orchestration Pattern
class OrderSagaOrchestrator {
constructor(db) {
this.db = db;
this.sagaCollection = db.collection('order_sagas');
this.stepCollection = db.collection('saga_steps');
}
async startOrderSaga(orderData) {
const sagaId = new ObjectId();
const saga = {
_id: sagaId,
sagaType: 'ProcessOrder',
state: 'Started',
currentStep: 0,
data: orderData,
steps: [
{ name: 'ReserveInventory', status: 'Pending' },
{ name: 'ProcessPayment', status: 'Pending' },
{ name: 'CreateShipment', status: 'Pending' },
{ name: 'SendConfirmation', status: 'Pending' }
],
createdAt: new Date(),
updatedAt: new Date()
};
await this.sagaCollection.insertOne(saga);
await this.executeNextStep(sagaId);
return sagaId;
}
async executeNextStep(sagaId) {
const saga = await this.sagaCollection.findOne({ _id: sagaId });
if (!saga || saga.currentStep >= saga.steps.length) {
return;
}
const currentStep = saga.steps[saga.currentStep];
try {
// Execute step based on type
const result = await this.executeStep(currentStep.name, saga.data);
// Update step status
await this.sagaCollection.updateOne(
{ _id: sagaId },
{
$set: {
[`steps.${saga.currentStep}.status`]: 'Completed',
[`steps.${saga.currentStep}.result`]: result,
currentStep: saga.currentStep + 1,
updatedAt: new Date()
}
}
);
// Continue to next step
await this.executeNextStep(sagaId);
} catch (error) {
// Handle step failure
await this.handleStepFailure(sagaId, saga.currentStep, error);
}
}
async handleStepFailure(sagaId, failedStepIndex, error) {
// Mark step as failed
await this.sagaCollection.updateOne(
{ _id: sagaId },
{
$set: {
[`steps.${failedStepIndex}.status`]: 'Failed',
[`steps.${failedStepIndex}.error`]: error.message,
state: 'Compensating',
updatedAt: new Date()
}
}
);
// Execute compensation actions
await this.executeCompensation(sagaId, failedStepIndex);
}
async executeCompensation(sagaId, fromStep) {
const saga = await this.sagaCollection.findOne({ _id: sagaId });
// Compensate completed steps in reverse order
for (let i = fromStep - 1; i >= 0; i--) {
const step = saga.steps[i];
if (step.status === 'Completed') {
try {
await this.compensateStep(step.name, step.result, saga.data);
await this.sagaCollection.updateOne(
{ _id: sagaId },
{
$set: {
[`steps.${i}.compensated`]: true,
updatedAt: new Date()
}
}
);
} catch (compensationError) {
console.error(`Compensation failed for step ${step.name}:`, compensationError);
// Handle compensation failure (manual intervention required)
}
}
}
await this.sagaCollection.updateOne(
{ _id: sagaId },
{ $set: { state: 'Failed', updatedAt: new Date() } }
);
}
}
CQRS with MongoDB
// Command Side - Write Model
class OrderCommandHandler {
constructor(eventStore, db) {
this.eventStore = eventStore;
this.db = db;
}
async createOrder(command) {
const orderId = new ObjectId().toString();
const events = [
{
type: 'OrderCreated',
data: {
orderId,
customerId: command.customerId,
items: command.items,
totalAmount: command.totalAmount
},
metadata: {
commandId: command.id,
userId: command.userId,
timestamp: new Date()
}
}
];
await this.eventStore.appendEvent(`order-${orderId}`, 0, events);
return orderId;
}
async updateOrderStatus(command) {
const events = await this.eventStore.getEvents(`order-${command.orderId}`);
const order = this.reconstructOrderFromEvents(events);
if (!order) {
throw new Error('Order not found');
}
const newEvent = {
type: 'OrderStatusChanged',
data: {
orderId: command.orderId,
previousStatus: order.status,
newStatus: command.newStatus,
reason: command.reason
},
metadata: {
commandId: command.id,
userId: command.userId,
timestamp: new Date()
}
};
await this.eventStore.appendEvent(
`order-${command.orderId}`,
events.length,
[newEvent]
);
}
reconstructOrderFromEvents(events) {
let order = null;
for (const event of events) {
switch (event.eventType) {
case 'OrderCreated':
order = {
id: event.eventData.orderId,
customerId: event.eventData.customerId,
items: event.eventData.items,
totalAmount: event.eventData.totalAmount,
status: 'Created',
createdAt: event.metadata.timestamp
};
break;
case 'OrderStatusChanged':
if (order) {
order.status = event.eventData.newStatus;
order.lastModified = event.metadata.timestamp;
}
break;
}
}
return order;
}
}
// Query Side - Read Model Projections
class OrderProjectionHandler {
constructor(db) {
this.db = db;
this.orderSummaryCollection = db.collection('order_summary');
this.customerOrdersCollection = db.collection('customer_orders');
}
async handleOrderCreated(event) {
const orderSummary = {
_id: event.eventData.orderId,
customerId: event.eventData.customerId,
itemCount: event.eventData.items.length,
totalAmount: event.eventData.totalAmount,
status: 'Created',
createdAt: event.metadata.timestamp,
lastModified: event.metadata.timestamp
};
await this.orderSummaryCollection.insertOne(orderSummary);
// Update customer orders aggregation
await this.customerOrdersCollection.updateOne(
{ _id: event.eventData.customerId },
{
$inc: {
totalOrders: 1,
totalSpent: event.eventData.totalAmount
},
$set: { lastOrderDate: event.metadata.timestamp },
$setOnInsert: { firstOrderDate: event.metadata.timestamp }
},
{ upsert: true }
);
}
async handleOrderStatusChanged(event) {
await this.orderSummaryCollection.updateOne(
{ _id: event.eventData.orderId },
{
$set: {
status: event.eventData.newStatus,
lastModified: event.metadata.timestamp
}
}
);
// Update additional projections based on status
if (event.eventData.newStatus === 'Completed') {
await this.customerOrdersCollection.updateOne(
{ _id: event.eventData.customerId },
{ $inc: { completedOrders: 1 } }
);
}
}
}
Multi-Tenant Architecture Patterns
Tenant Isolation Strategies
Security and Compliance
Enterprise MongoDB deployments require comprehensive security measures addressing authentication, authorization, encryption, and compliance requirements.
Advanced Security Implementation
Role-Based Access Control (RBAC)
// Custom role definitions for enterprise security
const securityRoles = {
// Application service roles
userServiceRole: {
role: "userServiceRole",
privileges: [
{
resource: { db: "userdb", collection: "users" },
actions: ["find", "insert", "update", "remove"]
},
{
resource: { db: "userdb", collection: "user_sessions" },
actions: ["find", "insert", "remove"]
}
],
roles: []
},
// Analytics read-only role
analyticsRole: {
role: "analyticsRole",
privileges: [
{
resource: { db: "", collection: "" }, // All databases
actions: ["find", "listCollections", "listIndexes"]
}
],
roles: []
},
// Audit role with special permissions
auditRole: {
role: "auditRole",
privileges: [
{
resource: { cluster: true },
actions: ["auditLogRotate", "viewRole", "viewUser"]
},
{
resource: { db: "audit", collection: "" },
actions: ["find", "insert", "listCollections"]
}
],
roles: []
}
};
// User creation with role assignment
async function createApplicationUsers(adminDb) {
// Create custom roles
for (const [roleName, roleConfig] of Object.entries(securityRoles)) {
try {
await adminDb.runCommand({
createRole: roleConfig.role,
privileges: roleConfig.privileges,
roles: roleConfig.roles
});
console.log(`Created role: ${roleConfig.role}`);
} catch (error) {
if (error.code !== 51002) { // Role already exists
console.error(`Failed to create role ${roleConfig.role}:`, error);
}
}
}
// Create application users
const applicationUsers = [
{
user: "userService",
pwd: process.env.USER_SERVICE_PASSWORD,
roles: ["userServiceRole"]
},
{
user: "analyticsService",
pwd: process.env.ANALYTICS_SERVICE_PASSWORD,
roles: ["analyticsRole"]
},
{
user: "auditService",
pwd: process.env.AUDIT_SERVICE_PASSWORD,
roles: ["auditRole"]
}
];
for (const userConfig of applicationUsers) {
try {
await adminDb.runCommand({
createUser: userConfig.user,
pwd: userConfig.pwd,
roles: userConfig.roles
});
console.log(`Created user: ${userConfig.user}`);
} catch (error) {
if (error.code !== 51003) { // User already exists
console.error(`Failed to create user ${userConfig.user}:`, error);
}
}
}
}
Field-Level Encryption
// Client-side field level encryption setup
const { MongoClient } = require('mongodb');
const { ClientEncryption } = require('mongodb-client-encryption');
class FieldLevelEncryption {
constructor(uri, keyVaultNamespace) {
this.uri = uri;
this.keyVaultNamespace = keyVaultNamespace;
this.kmsProviders = {
local: {
key: Buffer.from(process.env.MASTER_KEY, 'base64')
}
};
}
async initialize() {
// Create key vault client
this.keyVaultClient = new MongoClient(this.uri);
await this.keyVaultClient.connect();
// Create client encryption
this.clientEncryption = new ClientEncryption(
this.keyVaultClient,
{
keyVaultNamespace: this.keyVaultNamespace,
kmsProviders: this.kmsProviders
}
);
// Create data encryption keys
this.dataKeys = {
ssn: await this.createDataKey('SSN_KEY'),
creditCard: await this.createDataKey('CREDIT_CARD_KEY'),
personalData: await this.createDataKey('PERSONAL_DATA_KEY')
};
}
async createDataKey(altName) {
try {
return await this.clientEncryption.createDataKey('local', {
keyAltNames: [altName]
});
} catch (error) {
// Key might already exist, try to find it
const keyVault = this.keyVaultClient
.db(this.keyVaultNamespace.split('.')[0])
.collection(this.keyVaultNamespace.split('.')[1]);
const existingKey = await keyVault.findOne({
keyAltNames: altName
});
return existingKey ? existingKey._id : null;
}
}
async encryptField(value, fieldType, algorithm = 'AEAD_AES_256_CBC_HMAC_SHA_512-Deterministic') {
const keyId = this.dataKeys[fieldType];
if (!keyId) {
throw new Error(`No encryption key found for field type: ${fieldType}`);
}
return await this.clientEncryption.encrypt(value, {
keyId,
algorithm
});
}
async decryptField(encryptedValue) {
return await this.clientEncryption.decrypt(encryptedValue);
}
// Create encrypted client with automatic encryption
async createEncryptedClient() {
const schemaMap = {
"healthcare.patients": {
bsonType: "object",
properties: {
ssn: {
encrypt: {
keyId: this.dataKeys.ssn,
bsonType: "string",
algorithm: "AEAD_AES_256_CBC_HMAC_SHA_512-Deterministic"
}
},
medicalRecord: {
encrypt: {
keyId: this.dataKeys.personalData,
bsonType: "object",
algorithm: "AEAD_AES_256_CBC_HMAC_SHA_512-Random"
}
}
}
}
};
const client = new MongoClient(this.uri, {
autoEncryption: {
keyVaultNamespace: this.keyVaultNamespace,
kmsProviders: this.kmsProviders,
schemaMap
}
});
await client.connect();
return client;
}
}
Comprehensive Audit Logging
// Advanced audit logging configuration
const auditConfiguration = {
// Enable audit logging
auditLog: {
destination: "file",
path: "/var/log/mongodb/audit.json",
format: "JSON",
filter: {
// Audit all authentication events
$or: [
{ "atype": "authenticate" },
{ "atype": "authCheck" },
{ "atype": "logout" },
// Audit administrative operations
{ "atype": "createCollection" },
{ "atype": "dropCollection" },
{ "atype": "createIndex" },
{ "atype": "dropIndex" },
// Audit data modification on sensitive collections
{
$and: [
{ "atype": { $in: ["insert", "update", "remove"] } },
{ "param.ns": { $regex: "^(users|payments|medical)" } }
]
},
// Audit role and user management
{ "atype": "createUser" },
{ "atype": "dropUser" },
{ "atype": "createRole" },
{ "atype": "dropRole" },
{ "atype": "grantRolesToUser" },
{ "atype": "revokeRolesFromUser" }
]
}
}
};
// Custom audit event handler
class AuditEventProcessor {
constructor(db) {
this.db = db;
this.auditCollection = db.collection('security_audit_events');
}
async processAuditEvent(auditEvent) {
const processedEvent = {
...auditEvent,
processedAt: new Date(),
riskScore: this.calculateRiskScore(auditEvent),
category: this.categorizeEvent(auditEvent)
};
await this.auditCollection.insertOne(processedEvent);
// Check for suspicious patterns
if (processedEvent.riskScore > 7) {
await this.triggerSecurityAlert(processedEvent);
}
}
calculateRiskScore(event) {
let score = 0;
// Failed authentication attempts
if (event.atype === 'authenticate' && event.result === 18) {
score += 3;
}
// Administrative operations outside business hours
const hour = new Date(event.ts).getHours();
if (['createUser', 'dropUser', 'createRole', 'dropRole'].includes(event.atype) &&
(hour < 6 || hour > 22)) {
score += 5;
}
// Bulk data access
if (event.atype === 'find' && event.param?.filter === {}) {
score += 4;
}
// Unusual source IP
if (event.remote && !this.isKnownIP(event.remote)) {
score += 3;
}
return Math.min(score, 10);
}
categorizeEvent(event) {
const categories = {
'authenticate': 'AUTHENTICATION',
'authCheck': 'AUTHORIZATION',
'insert': 'DATA_MODIFICATION',
'update': 'DATA_MODIFICATION',
'remove': 'DATA_MODIFICATION',
'find': 'DATA_ACCESS',
'createUser': 'USER_MANAGEMENT',
'dropUser': 'USER_MANAGEMENT',
'createRole': 'ROLE_MANAGEMENT',
'dropRole': 'ROLE_MANAGEMENT'
};
return categories[event.atype] || 'OTHER';
}
async triggerSecurityAlert(event) {
const alert = {
alertId: new ObjectId(),
type: 'HIGH_RISK_AUDIT_EVENT',
severity: 'HIGH',
event: event,
triggeredAt: new Date(),
status: 'OPEN',
assignedTo: null
};
await this.db.collection('security_alerts').insertOne(alert);
// Send notification to security team
await this.notifySecurityTeam(alert);
}
async notifySecurityTeam(alert) {
// Implementation for security team notification
console.log(`🚨 Security Alert: ${alert.type} - Risk Score: ${alert.event.riskScore}`);
}
isKnownIP(ip) {
const knownIPRanges = [
'10.0.0.0/8',
'172.16.0.0/12',
'192.168.0.0/16'
];
// Implementation for IP range checking
return true; // Simplified for example
}
}
Production Operations and Monitoring
Enterprise MongoDB deployments require comprehensive operational excellence covering monitoring, alerting, backup strategies, and performance optimization.
Performance Monitoring and Alerting
// Comprehensive performance monitoring system
class MongoDBPerformanceMonitor {
constructor(db, alertManager) {
this.db = db;
this.alertManager = alertManager;
this.metricsCollection = db.collection('performance_metrics');
this.alertCollection = db.collection('performance_alerts');
}
async collectPerformanceMetrics() {
const metrics = {
timestamp: new Date(),
server: await this.getServerStatus(),
database: await this.getDatabaseMetrics(),
queries: await this.getSlowQueryMetrics(),
replication: await this.getReplicationMetrics(),
sharding: await this.getShardingMetrics()
};
await this.metricsCollection.insertOne(metrics);
await this.analyzeMetrics(metrics);
return metrics;
}
async getServerStatus() {
const serverStatus = await this.db.admin().serverStatus();
return {
uptime: serverStatus.uptime,
memory: {
resident: serverStatus.mem.resident,
virtual: serverStatus.mem.virtual,
mapped: serverStatus.mem.mapped
},
connections: {
current: serverStatus.connections.current,
available: serverStatus.connections.available,
totalCreated: serverStatus.connections.totalCreated
},
network: {
bytesIn: serverStatus.network.bytesIn,
bytesOut: serverStatus.network.bytesOut,
numRequests: serverStatus.network.numRequests
},
opcounters: serverStatus.opcounters,
wiredTiger: serverStatus.wiredTiger ? {
cacheSize: serverStatus.wiredTiger.cache['bytes currently in the cache'],
cacheDirtySize: serverStatus.wiredTiger.cache['tracked dirty bytes in the cache'],
cacheReadIntoSize: serverStatus.wiredTiger.cache['bytes read into cache'],
cacheWrittenFromSize: serverStatus.wiredTiger.cache['bytes written from cache']
} : null
};
}
async getDatabaseMetrics() {
const dbStats = await this.db.stats();
return {
collections: dbStats.collections,
objects: dbStats.objects,
avgObjSize: dbStats.avgObjSize,
dataSize: dbStats.dataSize,
storageSize: dbStats.storageSize,
indexSize: dbStats.indexSize,
indexes: dbStats.indexes
};
}
async getSlowQueryMetrics() {
// Enable profiling for slow operations
await this.db.runCommand({ profile: 2, slowms: 100 });
const slowQueries = await this.db.collection('system.profile')
.find({ ts: { $gte: new Date(Date.now() - 5 * 60 * 1000) } }) // Last 5 minutes
.sort({ ts: -1 })
.limit(100)
.toArray();
return {
count: slowQueries.length,
avgDuration: slowQueries.reduce((sum, q) => sum + q.millis, 0) / slowQueries.length || 0,
queries: slowQueries.slice(0, 10) // Top 10 slowest
};
}
async getReplicationMetrics() {
try {
const replStatus = await this.db.admin().replSetGetStatus();
return {
state: replStatus.myState,
members: replStatus.members.map(member => ({
name: member.name,
state: member.state,
health: member.health,
uptime: member.uptime,
optime: member.optime,
syncSourceHost: member.syncSourceHost
})),
replicationLag: this.calculateReplicationLag(replStatus.members)
};
} catch (error) {
return { error: 'Not in replica set or insufficient permissions' };
}
}
async getShardingMetrics() {
try {
const shardingStatus = await this.db.admin().runCommand({ listShards: 1 });
const configDB = this.db.getSiblingDB('config');
const chunks = await configDB.chunks.countDocuments();
const balancerState = await configDB.settings.findOne({ _id: 'balancer' });
return {
shards: shardingStatus.shards.length,
chunks: chunks,
balancerEnabled: balancerState ? balancerState.stopped !== true : true
};
} catch (error) {
return { error: 'Not in sharded cluster or insufficient permissions' };
}
}
calculateReplicationLag(members) {
const primary = members.find(m => m.state === 1);
if (!primary) return null;
const secondaries = members.filter(m => m.state === 2);
const maxLag = Math.max(...secondaries.map(s =>
primary.optime.ts.getTime() - s.optime.ts.getTime()
));
return maxLag / 1000; // Convert to seconds
}
async analyzeMetrics(metrics) {
const alerts = [];
// Connection alert
if (metrics.server.connections.current / metrics.server.connections.available > 0.8) {
alerts.push({
type: 'HIGH_CONNECTION_USAGE',
severity: 'WARNING',
message: `Connection usage at ${Math.round(metrics.server.connections.current / metrics.server.connections.available * 100)}%`,
metrics: {
current: metrics.server.connections.current,
available: metrics.server.connections.available
}
});
}
// Memory alert
if (metrics.server.memory.resident > 8000) { // 8GB threshold
alerts.push({
type: 'HIGH_MEMORY_USAGE',
severity: 'WARNING',
message: `Resident memory usage: ${metrics.server.memory.resident}MB`,
metrics: { resident: metrics.server.memory.resident }
});
}
// Slow query alert
if (metrics.queries.count > 50) {
alerts.push({
type: 'HIGH_SLOW_QUERY_RATE',
severity: 'CRITICAL',
message: `${metrics.queries.count} slow queries in last 5 minutes`,
metrics: {
count: metrics.queries.count,
avgDuration: metrics.queries.avgDuration
}
});
}
// Replication lag alert
if (metrics.replication.replicationLag > 10) {
alerts.push({
type: 'HIGH_REPLICATION_LAG',
severity: 'CRITICAL',
message: `Replication lag: ${metrics.replication.replicationLag} seconds`,
metrics: { lag: metrics.replication.replicationLag }
});
}
// Process and store alerts
for (const alert of alerts) {
await this.processAlert(alert, metrics.timestamp);
}
}
async processAlert(alert, timestamp) {
const alertDocument = {
...alert,
timestamp,
status: 'ACTIVE',
acknowledged: false,
resolvedAt: null
};
await this.alertCollection.insertOne(alertDocument);
await this.alertManager.sendAlert(alertDocument);
}
}
// Automated backup and disaster recovery
class BackupManager {
constructor(mongoUri, s3Config) {
this.mongoUri = mongoUri;
this.s3Config = s3Config;
}
async createBackup(databases = []) {
const timestamp = new Date().toISOString().replace(/[:.]/g, '-');
const backupId = `backup-${timestamp}`;
try {
for (const dbName of databases) {
await this.backupDatabase(dbName, backupId);
}
await this.uploadToS3(backupId);
await this.recordBackup(backupId, databases);
return backupId;
} catch (error) {
console.error(`Backup failed for ${backupId}:`, error);
throw error;
}
}
async backupDatabase(dbName, backupId) {
const backupPath = `/tmp/backups/${backupId}/${dbName}`;
// Use mongodump for consistent backup
const { execSync } = require('child_process');
const command = `mongodump --uri="${this.mongoUri}" --db=${dbName} --out=${backupPath}`;
execSync(command, { stdio: 'inherit' });
}
async uploadToS3(backupId) {
// Implementation for S3 upload
console.log(`Uploading backup ${backupId} to S3...`);
}
async recordBackup(backupId, databases) {
const client = new MongoClient(this.mongoUri);
await client.connect();
const adminDb = client.db('admin');
await adminDb.collection('backups').insertOne({
backupId,
databases,
createdAt: new Date(),
status: 'COMPLETED',
location: `s3://${this.s3Config.bucket}/backups/${backupId}`
});
await client.close();
}
}
Conclusion
MongoDB has evolved from a simple document database into a comprehensive data platform capable of supporting the most demanding enterprise applications. This comprehensive exploration demonstrates that successful MongoDB implementations require deep understanding of architectural patterns, performance optimization strategies, and operational excellence practices.
Key Success Factors for Production MongoDB:
Architecture Excellence: Implementing appropriate replica set configurations, sharding strategies, and distributed system patterns ensures scalability and reliability at enterprise scale.
Data Modeling Mastery: Leveraging MongoDB’s flexible document model while applying proper normalization strategies, indexing patterns, and schema evolution techniques optimizes performance and maintainability.
Security Implementation: Comprehensive security measures including RBAC, field-level encryption, audit logging, and compliance frameworks protect sensitive data and meet regulatory requirements.
Operational Excellence: Proactive monitoring, automated alerting, robust backup strategies, and performance optimization enable reliable production operations.
Modern Integration Patterns: Event sourcing, CQRS, saga patterns, and microservices architectures leverage MongoDB’s strengths in distributed, cloud-native environments.
Future Considerations:
As MongoDB continues evolving with features like vector search for AI workloads, enhanced time series capabilities, and improved multi-cloud operations, the fundamental principles explored in this guide provide a solid foundation for embracing new capabilities while maintaining operational excellence.
The investment in understanding MongoDB’s advanced capabilities and implementation patterns enables organizations to build scalable, secure, and maintainable data architectures that can adapt to changing business requirements and technological landscapes.
Whether implementing greenfield applications or optimizing existing MongoDB deployments, these patterns and practices provide the depth needed for production success at any scale.
References and Further Reading
- MongoDB Official Documentation
- MongoDB Production Best Practices
- MongoDB Performance Best Practices
- MongoDB Security Checklist
- Building with Patterns: MongoDB Data Modeling
- MongoDB Architecture Guide
- Designing Data-Intensive Applications - Martin Kleppmann
- MongoDB: The Definitive Guide - Shannon Bradshaw
- Event Sourcing with MongoDB
- MongoDB University
- MongoDB Community Forums
- MongoDB GitHub Repository
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