MongoDB Distributed Caching and Session Management: High-Performance Web Application State Management and Cache Optimization
Modern web applications require sophisticated caching and session management capabilities that can handle millions of concurrent users while maintaining consistent performance across distributed infrastructure. Traditional caching approaches rely on dedicated cache servers like Redis or Memcached, creating additional infrastructure complexity and potential single points of failure, while session management often involves complex synchronization between application servers and separate session stores.
MongoDB TTL Collections and advanced document modeling provide comprehensive caching and session management capabilities that integrate seamlessly with existing application data, offering automatic expiration, flexible data structures, and distributed consistency without requiring additional infrastructure components. Unlike traditional cache-aside patterns that require complex cache invalidation logic, MongoDB's integrated approach enables intelligent caching strategies with built-in consistency guarantees and sophisticated query capabilities.
The Traditional Caching and Session Challenge
Conventional approaches to distributed caching and session management introduce significant complexity and operational overhead:
-- Traditional PostgreSQL session storage - limited scalability and complex cleanup
CREATE TABLE user_sessions (
session_id VARCHAR(128) PRIMARY KEY,
user_id BIGINT NOT NULL,
session_data JSONB NOT NULL,
-- Session lifecycle management
created_at TIMESTAMPTZ NOT NULL DEFAULT CURRENT_TIMESTAMP,
last_accessed TIMESTAMPTZ NOT NULL DEFAULT CURRENT_TIMESTAMP,
expires_at TIMESTAMPTZ NOT NULL,
-- Session metadata
ip_address INET,
user_agent TEXT,
device_fingerprint VARCHAR(256),
-- Security and fraud detection
login_method VARCHAR(50),
mfa_verified BOOLEAN DEFAULT FALSE,
risk_score DECIMAL(3,2) DEFAULT 0.0,
-- Application state
active BOOLEAN DEFAULT TRUE,
invalidated_at TIMESTAMPTZ,
invalidation_reason VARCHAR(100)
);
-- Manual session cleanup (requires scheduled maintenance)
CREATE OR REPLACE FUNCTION cleanup_expired_sessions()
RETURNS INTEGER AS $$
DECLARE
deleted_count INTEGER;
BEGIN
-- Delete expired sessions
DELETE FROM user_sessions
WHERE expires_at < CURRENT_TIMESTAMP
OR (active = FALSE AND invalidated_at < CURRENT_TIMESTAMP - INTERVAL '1 day');
GET DIAGNOSTICS deleted_count = ROW_COUNT;
-- Log cleanup activity
INSERT INTO session_cleanup_log (cleanup_date, sessions_deleted)
VALUES (CURRENT_TIMESTAMP, deleted_count);
RETURN deleted_count;
END;
$$ LANGUAGE plpgsql;
-- Complex cache table design with manual expiration
CREATE TABLE application_cache (
cache_key VARCHAR(512) PRIMARY KEY,
cache_namespace VARCHAR(100) NOT NULL,
cache_data JSONB NOT NULL,
-- Expiration management
created_at TIMESTAMPTZ NOT NULL DEFAULT CURRENT_TIMESTAMP,
expires_at TIMESTAMPTZ NOT NULL,
last_accessed TIMESTAMPTZ DEFAULT CURRENT_TIMESTAMP,
-- Cache metadata
data_size INTEGER,
cache_tags TEXT[],
cache_version VARCHAR(50),
-- Usage statistics
hit_count BIGINT DEFAULT 0,
miss_count BIGINT DEFAULT 0,
invalidation_count INTEGER DEFAULT 0
);
-- Indexing for cache lookups (expensive maintenance overhead)
CREATE INDEX idx_cache_namespace_key ON application_cache (cache_namespace, cache_key);
CREATE INDEX idx_cache_expires_at ON application_cache (expires_at);
CREATE INDEX idx_cache_tags ON application_cache USING GIN (cache_tags);
CREATE INDEX idx_cache_last_accessed ON application_cache (last_accessed);
-- Manual cache invalidation logic (complex and error-prone)
CREATE OR REPLACE FUNCTION invalidate_cache_by_tags(tag_names TEXT[])
RETURNS INTEGER AS $$
DECLARE
invalidated_count INTEGER;
BEGIN
-- Invalidate cache entries with matching tags
DELETE FROM application_cache
WHERE cache_tags && tag_names;
GET DIAGNOSTICS invalidated_count = ROW_COUNT;
-- Update invalidation statistics
UPDATE cache_statistics
SET tag_invalidations = tag_invalidations + invalidated_count,
last_invalidation = CURRENT_TIMESTAMP
WHERE stat_date = CURRENT_DATE;
RETURN invalidated_count;
END;
$$ LANGUAGE plpgsql;
-- Session data queries with complex join logic
WITH active_sessions AS (
SELECT
us.session_id,
us.user_id,
us.session_data,
us.last_accessed,
us.expires_at,
us.ip_address,
us.device_fingerprint,
-- Calculate session duration
EXTRACT(EPOCH FROM us.last_accessed - us.created_at) / 3600 as session_hours,
-- Determine session freshness
CASE
WHEN us.last_accessed > CURRENT_TIMESTAMP - INTERVAL '15 minutes' THEN 'active'
WHEN us.last_accessed > CURRENT_TIMESTAMP - INTERVAL '1 hour' THEN 'recent'
WHEN us.last_accessed > CURRENT_TIMESTAMP - INTERVAL '6 hours' THEN 'idle'
ELSE 'stale'
END as session_status,
-- Extract user preferences from session data
us.session_data->>'preferences' as user_preferences,
us.session_data->>'shopping_cart' as shopping_cart,
us.session_data->>'last_page' as last_page
FROM user_sessions us
WHERE us.active = TRUE
AND us.expires_at > CURRENT_TIMESTAMP
),
session_analytics AS (
SELECT
COUNT(*) as total_active_sessions,
COUNT(DISTINCT user_id) as unique_users,
AVG(session_hours) as avg_session_duration,
COUNT(*) FILTER (WHERE session_status = 'active') as active_sessions,
COUNT(*) FILTER (WHERE session_status = 'recent') as recent_sessions,
COUNT(*) FILTER (WHERE session_status = 'idle') as idle_sessions,
COUNT(*) FILTER (WHERE session_status = 'stale') as stale_sessions,
-- Risk analysis
COUNT(*) FILTER (WHERE risk_score > 0.7) as high_risk_sessions,
COUNT(DISTINCT ip_address) as unique_ip_addresses,
-- Application state analysis
COUNT(*) FILTER (WHERE shopping_cart IS NOT NULL) as sessions_with_cart,
AVG(CAST(shopping_cart->>'item_count' AS INTEGER)) as avg_cart_items
FROM active_sessions
)
SELECT
'Session Management Report' as report_type,
CURRENT_TIMESTAMP as generated_at,
-- Session statistics
sa.total_active_sessions,
sa.unique_users,
ROUND(sa.avg_session_duration::NUMERIC, 2) as avg_session_hours,
-- Session distribution
JSON_BUILD_OBJECT(
'active', sa.active_sessions,
'recent', sa.recent_sessions,
'idle', sa.idle_sessions,
'stale', sa.stale_sessions
) as session_distribution,
-- Security metrics
sa.high_risk_sessions,
sa.unique_ip_addresses,
ROUND((sa.high_risk_sessions::FLOAT / sa.total_active_sessions * 100)::NUMERIC, 2) as risk_percentage,
-- Business metrics
sa.sessions_with_cart,
ROUND(sa.avg_cart_items::NUMERIC, 1) as avg_cart_items,
ROUND((sa.sessions_with_cart::FLOAT / sa.total_active_sessions * 100)::NUMERIC, 2) as cart_conversion_rate
FROM session_analytics sa;
-- Cache performance queries (limited analytics capabilities)
WITH cache_performance AS (
SELECT
cache_namespace,
COUNT(*) as total_entries,
SUM(data_size) as total_size_bytes,
AVG(data_size) as avg_entry_size,
-- Hit ratio calculation
SUM(hit_count) as total_hits,
SUM(miss_count) as total_misses,
CASE
WHEN SUM(hit_count + miss_count) > 0 THEN
ROUND((SUM(hit_count)::FLOAT / SUM(hit_count + miss_count) * 100)::NUMERIC, 2)
ELSE 0
END as hit_ratio_percent,
-- Expiration analysis
COUNT(*) FILTER (WHERE expires_at <= CURRENT_TIMESTAMP) as expired_entries,
COUNT(*) FILTER (WHERE expires_at > CURRENT_TIMESTAMP + INTERVAL '1 hour') as long_lived_entries,
-- Access patterns
AVG(hit_count) as avg_hits_per_entry,
MAX(last_accessed) as most_recent_access,
MIN(last_accessed) as oldest_access
FROM application_cache
GROUP BY cache_namespace
)
SELECT
cp.cache_namespace,
cp.total_entries,
ROUND((cp.total_size_bytes / 1024.0 / 1024.0)::NUMERIC, 2) as size_mb,
ROUND(cp.avg_entry_size::NUMERIC, 0) as avg_entry_size_bytes,
cp.hit_ratio_percent,
cp.expired_entries,
cp.long_lived_entries,
cp.avg_hits_per_entry,
-- Cache efficiency assessment
CASE
WHEN cp.hit_ratio_percent > 90 THEN 'excellent'
WHEN cp.hit_ratio_percent > 70 THEN 'good'
WHEN cp.hit_ratio_percent > 50 THEN 'acceptable'
ELSE 'poor'
END as cache_efficiency,
-- Recommendations
CASE
WHEN cp.expired_entries > cp.total_entries * 0.1 THEN 'Consider shorter TTL or more frequent cleanup'
WHEN cp.hit_ratio_percent < 50 THEN 'Review caching strategy and key patterns'
WHEN cp.avg_entry_size > 1048576 THEN 'Consider data compression or smaller cache objects'
ELSE 'Cache performance within normal parameters'
END as recommendation
FROM cache_performance cp
ORDER BY cp.total_entries DESC;
-- Problems with traditional cache and session management:
-- 1. Manual expiration cleanup with potential for orphaned data
-- 2. Complex indexing strategies and maintenance overhead
-- 3. Limited scalability for high-concurrency web applications
-- 4. No built-in distributed consistency or replication
-- 5. Complex cache invalidation logic prone to race conditions
-- 6. Separate infrastructure requirements for cache and session stores
-- 7. Limited analytics and monitoring capabilities
-- 8. Manual session lifecycle management and security tracking
-- 9. No automatic compression or storage optimization
-- 10. Complex failover and disaster recovery procedures
MongoDB provides sophisticated distributed caching and session management with automatic TTL handling and advanced capabilities:
// MongoDB Distributed Caching and Session Management System
const { MongoClient, ObjectId } = require('mongodb');
const client = new MongoClient('mongodb://localhost:27017/?replicaSet=rs0');
const db = client.db('distributed_cache_system');
// Comprehensive MongoDB Caching and Session Manager
class AdvancedCacheSessionManager {
constructor(db, config = {}) {
this.db = db;
this.collections = {
userSessions: db.collection('user_sessions'),
applicationCache: db.collection('application_cache'),
cacheStatistics: db.collection('cache_statistics'),
sessionEvents: db.collection('session_events'),
cacheMetrics: db.collection('cache_metrics'),
deviceFingerprints: db.collection('device_fingerprints')
};
// Advanced configuration
this.config = {
// Session management
defaultSessionTTL: config.defaultSessionTTL || 7200, // 2 hours
extendedSessionTTL: config.extendedSessionTTL || 86400, // 24 hours for "remember me"
maxSessionsPerUser: config.maxSessionsPerUser || 10,
sessionCleanupInterval: config.sessionCleanupInterval || 300, // 5 minutes
// Cache management
defaultCacheTTL: config.defaultCacheTTL || 3600, // 1 hour
maxCacheSize: config.maxCacheSize || 16 * 1024 * 1024, // 16MB per entry
enableCompression: config.enableCompression !== false,
enableDistribution: config.enableDistribution !== false,
// Security settings
enableDeviceTracking: config.enableDeviceTracking !== false,
enableRiskScoring: config.enableRiskScoring !== false,
maxRiskScore: config.maxRiskScore || 0.8,
suspiciousActivityThreshold: config.suspiciousActivityThreshold || 5,
// Performance optimization
enableMetrics: config.enableMetrics !== false,
metricsRetentionDays: config.metricsRetentionDays || 30,
enableLazyLoading: config.enableLazyLoading !== false,
cacheWarmupEnabled: config.cacheWarmupEnabled !== false
};
// Initialize TTL collections and indexes
this.initializeCollections();
this.setupMetricsCollection();
this.startMaintenanceTasks();
}
async initializeCollections() {
console.log('Initializing MongoDB TTL collections and indexes...');
try {
// Configure user sessions collection with TTL
await this.collections.userSessions.createIndex(
{ "expiresAt": 1 },
{
expireAfterSeconds: 0,
name: "session_ttl_index"
}
);
// Additional session indexes for performance
await this.collections.userSessions.createIndexes([
{ key: { sessionId: 1 }, unique: true, name: "session_id_unique" },
{ key: { userId: 1, expiresAt: 1 }, name: "user_active_sessions" },
{ key: { deviceFingerprint: 1 }, name: "device_sessions" },
{ key: { ipAddress: 1, createdAt: 1 }, name: "ip_activity" },
{ key: { riskScore: 1 }, name: "risk_analysis" },
{ key: { "sessionData.shoppingCart.items": 1 }, name: "cart_sessions", sparse: true }
]);
// Configure application cache collection with TTL
await this.collections.applicationCache.createIndex(
{ "expiresAt": 1 },
{
expireAfterSeconds: 0,
name: "cache_ttl_index"
}
);
// Cache performance indexes
await this.collections.applicationCache.createIndexes([
{ key: { cacheKey: 1 }, unique: true, name: "cache_key_unique" },
{ key: { namespace: 1, cacheKey: 1 }, name: "namespace_key_lookup" },
{ key: { tags: 1 }, name: "cache_tags" },
{ key: { lastAccessed: 1 }, name: "access_patterns" },
{ key: { dataSize: 1 }, name: "size_analysis" }
]);
// Cache metrics with TTL for automatic cleanup
await this.collections.cacheMetrics.createIndex(
{ "timestamp": 1 },
{
expireAfterSeconds: this.config.metricsRetentionDays * 24 * 3600,
name: "metrics_ttl_index"
}
);
console.log('TTL collections and indexes initialized successfully');
} catch (error) {
console.error('Error initializing collections:', error);
throw error;
}
}
async createUserSession(userId, sessionData, options = {}) {
console.log(`Creating new session for user: ${userId}`);
try {
// Clean up old sessions if user has too many
await this.enforceSessionLimits(userId);
// Generate secure session ID
const sessionId = await this.generateSecureSessionId();
// Calculate expiration time
const ttlSeconds = options.rememberMe ?
this.config.extendedSessionTTL :
this.config.defaultSessionTTL;
const expiresAt = new Date(Date.now() + (ttlSeconds * 1000));
// Calculate risk score
const riskScore = await this.calculateSessionRiskScore(userId, sessionData, options);
// Create session document
const session = {
_id: new ObjectId(),
sessionId: sessionId,
userId: userId,
// Session lifecycle
createdAt: new Date(),
lastAccessed: new Date(),
expiresAt: expiresAt,
ttlSeconds: ttlSeconds,
// Session data with flexible structure
sessionData: {
preferences: sessionData.preferences || {},
shoppingCart: sessionData.shoppingCart || { items: [], total: 0 },
navigation: sessionData.navigation || { lastPage: '/', referrer: null },
applicationState: sessionData.applicationState || {},
temporaryData: sessionData.temporaryData || {}
},
// Security and device tracking
ipAddress: options.ipAddress,
userAgent: options.userAgent,
deviceFingerprint: await this.generateDeviceFingerprint(options),
riskScore: riskScore,
// Authentication metadata
loginMethod: options.loginMethod || 'password',
mfaVerified: options.mfaVerified || false,
loginLocation: options.loginLocation,
// Session flags
active: true,
rememberMe: options.rememberMe || false,
// Usage statistics
pageViews: 0,
actionsPerformed: 0,
dataTransferred: 0
};
// Insert session with automatic TTL handling
const result = await this.collections.userSessions.insertOne(session);
// Log session creation event
await this.logSessionEvent(sessionId, 'session_created', {
userId: userId,
riskScore: riskScore,
ttlSeconds: ttlSeconds,
rememberMe: options.rememberMe
});
// Update device fingerprint tracking
if (this.config.enableDeviceTracking) {
await this.updateDeviceTracking(session.deviceFingerprint, userId, sessionId);
}
console.log(`Session created successfully: ${sessionId}`);
return {
sessionId: sessionId,
expiresAt: expiresAt,
riskScore: riskScore,
success: true
};
} catch (error) {
console.error('Error creating user session:', error);
throw error;
}
}
async getSessionData(sessionId, options = {}) {
console.log(`Retrieving session data: ${sessionId}`);
try {
// Find active session
const session = await this.collections.userSessions.findOne({
sessionId: sessionId,
active: true,
expiresAt: { $gt: new Date() }
});
if (!session) {
return { success: false, reason: 'session_not_found' };
}
// Update last accessed timestamp
const updateData = {
$set: { lastAccessed: new Date() },
$inc: {
pageViews: options.incrementPageView ? 1 : 0,
actionsPerformed: options.actionPerformed ? 1 : 0
}
};
await this.collections.userSessions.updateOne(
{ sessionId: sessionId },
updateData
);
// Check for risk score updates
if (this.config.enableRiskScoring && options.updateRiskScore) {
const newRiskScore = await this.calculateSessionRiskScore(
session.userId,
session.sessionData,
options
);
if (newRiskScore > this.config.maxRiskScore) {
await this.flagHighRiskSession(sessionId, newRiskScore);
}
}
return {
success: true,
session: session,
userId: session.userId,
sessionData: session.sessionData,
expiresAt: session.expiresAt,
riskScore: session.riskScore
};
} catch (error) {
console.error('Error retrieving session data:', error);
throw error;
}
}
async updateSessionData(sessionId, updateData, options = {}) {
console.log(`Updating session data: ${sessionId}`);
try {
// Prepare update operations
const update = {
$set: {
lastAccessed: new Date(),
'sessionData.preferences': updateData.preferences,
'sessionData.shoppingCart': updateData.shoppingCart,
'sessionData.navigation': updateData.navigation,
'sessionData.applicationState': updateData.applicationState
}
};
// Optional TTL extension
if (options.extendTTL) {
const newExpirationTime = new Date(Date.now() + (this.config.defaultSessionTTL * 1000));
update.$set.expiresAt = newExpirationTime;
}
// Merge temporary data if provided
if (updateData.temporaryData) {
update.$set['sessionData.temporaryData'] = {
...updateData.temporaryData
};
}
const result = await this.collections.userSessions.updateOne(
{
sessionId: sessionId,
active: true,
expiresAt: { $gt: new Date() }
},
update
);
if (result.matchedCount === 0) {
return { success: false, reason: 'session_not_found' };
}
// Log session update event
await this.logSessionEvent(sessionId, 'session_updated', {
updateFields: Object.keys(updateData),
extendTTL: options.extendTTL
});
return { success: true };
} catch (error) {
console.error('Error updating session data:', error);
throw error;
}
}
async invalidateSession(sessionId, reason = 'user_logout') {
console.log(`Invalidating session: ${sessionId}, reason: ${reason}`);
try {
const result = await this.collections.userSessions.updateOne(
{ sessionId: sessionId },
{
$set: {
active: false,
invalidatedAt: new Date(),
invalidationReason: reason,
// Set immediate expiration for automatic cleanup
expiresAt: new Date()
}
}
);
if (result.matchedCount > 0) {
// Log session invalidation
await this.logSessionEvent(sessionId, 'session_invalidated', {
reason: reason,
invalidatedAt: new Date()
});
}
return { success: result.matchedCount > 0 };
} catch (error) {
console.error('Error invalidating session:', error);
throw error;
}
}
async setCache(cacheKey, data, options = {}) {
console.log(`Setting cache entry: ${cacheKey}`);
try {
// Validate data size
const dataSize = JSON.stringify(data).length;
if (dataSize > this.config.maxCacheSize) {
throw new Error(`Cache data size ${dataSize} exceeds maximum ${this.config.maxCacheSize}`);
}
// Calculate expiration time
const ttlSeconds = options.ttl || this.config.defaultCacheTTL;
const expiresAt = new Date(Date.now() + (ttlSeconds * 1000));
// Prepare cache document
const cacheEntry = {
cacheKey: cacheKey,
namespace: options.namespace || 'default',
data: this.config.enableCompression ? await this.compressData(data) : data,
compressed: this.config.enableCompression,
// Expiration handling
createdAt: new Date(),
expiresAt: expiresAt,
ttlSeconds: ttlSeconds,
lastAccessed: new Date(),
// Metadata
tags: options.tags || [],
version: options.version || '1.0',
dataSize: dataSize,
contentType: options.contentType || 'application/json',
// Usage statistics
hitCount: 0,
accessHistory: [],
// Cache strategy metadata
cacheStrategy: options.strategy || 'default',
invalidationRules: options.invalidationRules || []
};
// Upsert cache entry with automatic TTL
await this.collections.applicationCache.replaceOne(
{ cacheKey: cacheKey },
cacheEntry,
{ upsert: true }
);
// Update cache metrics
await this.updateCacheMetrics('set', cacheKey, {
namespace: cacheEntry.namespace,
dataSize: dataSize,
ttlSeconds: ttlSeconds
});
console.log(`Cache entry set successfully: ${cacheKey}`);
return { success: true, expiresAt: expiresAt };
} catch (error) {
console.error('Error setting cache entry:', error);
throw error;
}
}
async getCache(cacheKey, options = {}) {
console.log(`Getting cache entry: ${cacheKey}`);
try {
const cacheEntry = await this.collections.applicationCache.findOneAndUpdate(
{
cacheKey: cacheKey,
expiresAt: { $gt: new Date() }
},
{
$set: { lastAccessed: new Date() },
$inc: { hitCount: 1 },
$push: {
accessHistory: {
$each: [{ timestamp: new Date(), source: options.source || 'application' }],
$slice: -10 // Keep only last 10 access records
}
}
},
{ returnDocument: 'after' }
);
if (!cacheEntry.value) {
// Record cache miss
await this.updateCacheMetrics('miss', cacheKey, {
namespace: options.namespace || 'default'
});
return { success: false, reason: 'cache_miss' };
}
// Decompress data if needed
const data = cacheEntry.value.compressed ?
await this.decompressData(cacheEntry.value.data) :
cacheEntry.value.data;
// Record cache hit
await this.updateCacheMetrics('hit', cacheKey, {
namespace: cacheEntry.value.namespace,
dataSize: cacheEntry.value.dataSize
});
return {
success: true,
data: data,
metadata: {
createdAt: cacheEntry.value.createdAt,
expiresAt: cacheEntry.value.expiresAt,
hitCount: cacheEntry.value.hitCount,
tags: cacheEntry.value.tags,
version: cacheEntry.value.version
}
};
} catch (error) {
console.error('Error getting cache entry:', error);
throw error;
}
}
async invalidateCache(criteria, options = {}) {
console.log('Invalidating cache entries with criteria:', criteria);
try {
let query = {};
// Build invalidation query based on criteria
if (criteria.key) {
query.cacheKey = criteria.key;
} else if (criteria.pattern) {
query.cacheKey = { $regex: criteria.pattern };
} else if (criteria.namespace) {
query.namespace = criteria.namespace;
} else if (criteria.tags) {
query.tags = { $in: criteria.tags };
}
// Immediate expiration for automatic cleanup
const result = await this.collections.applicationCache.updateMany(
query,
{
$set: {
expiresAt: new Date(),
invalidatedAt: new Date(),
invalidationReason: options.reason || 'manual_invalidation'
}
}
);
// Update invalidation metrics
await this.updateCacheMetrics('invalidation', null, {
criteriaType: Object.keys(criteria)[0],
entriesInvalidated: result.modifiedCount,
reason: options.reason
});
console.log(`Invalidated ${result.modifiedCount} cache entries`);
return { success: true, invalidatedCount: result.modifiedCount };
} catch (error) {
console.error('Error invalidating cache entries:', error);
throw error;
}
}
async getUserSessionAnalytics(userId, options = {}) {
console.log(`Generating session analytics for user: ${userId}`);
try {
const timeRange = options.timeRange || 24; // hours
const startTime = new Date(Date.now() - (timeRange * 3600 * 1000));
const analytics = await this.collections.userSessions.aggregate([
{
$match: {
userId: userId,
createdAt: { $gte: startTime }
}
},
{
$group: {
_id: '$userId',
// Session counts
totalSessions: { $sum: 1 },
activeSessions: {
$sum: {
$cond: [
{ $and: [
{ $eq: ['$active', true] },
{ $gt: ['$expiresAt', new Date()] }
]},
1,
0
]
}
},
// Duration analysis
averageSessionDuration: {
$avg: {
$divide: [
{ $subtract: ['$lastAccessed', '$createdAt'] },
1000 * 60 // Convert to minutes
]
}
},
// Activity metrics
totalPageViews: { $sum: '$pageViews' },
totalActions: { $sum: '$actionsPerformed' },
// Risk analysis
averageRiskScore: { $avg: '$riskScore' },
highRiskSessions: {
$sum: {
$cond: [{ $gt: ['$riskScore', 0.7] }, 1, 0]
}
},
// Device analysis
uniqueDevices: { $addToSet: '$deviceFingerprint' },
uniqueIpAddresses: { $addToSet: '$ipAddress' },
// Authentication methods
loginMethods: { $addToSet: '$loginMethod' },
mfaUsage: {
$sum: {
$cond: [{ $eq: ['$mfaVerified', true] }, 1, 0]
}
}
}
},
{
$project: {
userId: '$_id',
totalSessions: 1,
activeSessions: 1,
averageSessionDuration: { $round: ['$averageSessionDuration', 2] },
totalPageViews: 1,
totalActions: 1,
averageRiskScore: { $round: ['$averageRiskScore', 3] },
highRiskSessions: 1,
deviceCount: { $size: '$uniqueDevices' },
ipAddressCount: { $size: '$uniqueIpAddresses' },
loginMethods: 1,
mfaUsagePercentage: {
$round: [
{ $multiply: [
{ $divide: ['$mfaUsage', '$totalSessions'] },
100
]},
2
]
}
}
}
]).toArray();
return analytics[0] || null;
} catch (error) {
console.error('Error generating user session analytics:', error);
throw error;
}
}
async getCachePerformanceReport(namespace = null, options = {}) {
console.log('Generating cache performance report...');
try {
const timeRange = options.timeRange || 24; // hours
const startTime = new Date(Date.now() - (timeRange * 3600 * 1000));
// Build match criteria
const matchCriteria = {
timestamp: { $gte: startTime }
};
if (namespace) {
matchCriteria.namespace = namespace;
}
const report = await this.collections.cacheMetrics.aggregate([
{ $match: matchCriteria },
{
$group: {
_id: '$namespace',
// Hit/miss statistics
totalHits: {
$sum: {
$cond: [{ $eq: ['$operation', 'hit'] }, 1, 0]
}
},
totalMisses: {
$sum: {
$cond: [{ $eq: ['$operation', 'miss'] }, 1, 0]
}
},
totalSets: {
$sum: {
$cond: [{ $eq: ['$operation', 'set'] }, 1, 0]
}
},
totalInvalidations: {
$sum: {
$cond: [{ $eq: ['$operation', 'invalidation'] }, '$metadata.entriesInvalidated', 0]
}
},
// Data size statistics
totalDataSize: {
$sum: {
$cond: [{ $eq: ['$operation', 'set'] }, '$metadata.dataSize', 0]
}
},
averageDataSize: {
$avg: {
$cond: [{ $eq: ['$operation', 'set'] }, '$metadata.dataSize', null]
}
},
// TTL analysis
averageTTL: {
$avg: {
$cond: [{ $eq: ['$operation', 'set'] }, '$metadata.ttlSeconds', null]
}
}
}
},
{
$project: {
namespace: '$_id',
// Performance metrics
totalRequests: { $add: ['$totalHits', '$totalMisses'] },
hitRatio: {
$round: [
{
$cond: [
{ $gt: [{ $add: ['$totalHits', '$totalMisses'] }, 0] },
{
$multiply: [
{ $divide: ['$totalHits', { $add: ['$totalHits', '$totalMisses'] }] },
100
]
},
0
]
},
2
]
},
totalHits: 1,
totalMisses: 1,
totalSets: 1,
totalInvalidations: 1,
// Data insights
totalDataSizeMB: {
$round: [{ $divide: ['$totalDataSize', 1024 * 1024] }, 2]
},
averageDataSizeKB: {
$round: [{ $divide: ['$averageDataSize', 1024] }, 2]
},
averageTTLHours: {
$round: [{ $divide: ['$averageTTL', 3600] }, 2]
}
}
}
]).toArray();
return report;
} catch (error) {
console.error('Error generating cache performance report:', error);
throw error;
}
}
// Utility methods
async generateSecureSessionId() {
const crypto = require('crypto');
return crypto.randomBytes(32).toString('hex');
}
async generateDeviceFingerprint(options) {
const crypto = require('crypto');
const fingerprint = `${options.userAgent || ''}-${options.ipAddress || ''}-${Date.now()}`;
return crypto.createHash('sha256').update(fingerprint).digest('hex');
}
async calculateSessionRiskScore(userId, sessionData, options) {
let riskScore = 0.0;
// IP-based risk assessment
if (options.ipAddress) {
const recentSessions = await this.collections.userSessions.countDocuments({
userId: userId,
ipAddress: { $ne: options.ipAddress },
createdAt: { $gte: new Date(Date.now() - 24 * 3600 * 1000) }
});
if (recentSessions > 0) riskScore += 0.2;
}
// Time-based risk assessment
const currentHour = new Date().getHours();
if (currentHour < 6 || currentHour > 23) {
riskScore += 0.1;
}
// Device change assessment
if (this.config.enableDeviceTracking && options.userAgent) {
const knownDevice = await this.collections.deviceFingerprints.findOne({
userId: userId,
userAgent: options.userAgent
});
if (!knownDevice) riskScore += 0.3;
}
return Math.min(riskScore, 1.0);
}
async enforceSessionLimits(userId) {
const sessionCount = await this.collections.userSessions.countDocuments({
userId: userId,
active: true,
expiresAt: { $gt: new Date() }
});
if (sessionCount >= this.config.maxSessionsPerUser) {
// Remove oldest sessions
const sessionsToRemove = await this.collections.userSessions
.find({
userId: userId,
active: true,
expiresAt: { $gt: new Date() }
})
.sort({ lastAccessed: 1 })
.limit(sessionCount - this.config.maxSessionsPerUser + 1)
.toArray();
for (const session of sessionsToRemove) {
await this.invalidateSession(session.sessionId, 'session_limit_exceeded');
}
}
}
async logSessionEvent(sessionId, eventType, eventData) {
if (!this.config.enableMetrics) return;
await this.collections.sessionEvents.insertOne({
sessionId: sessionId,
eventType: eventType,
eventData: eventData,
timestamp: new Date()
});
}
async updateCacheMetrics(operation, cacheKey, metadata) {
if (!this.config.enableMetrics) return;
await this.collections.cacheMetrics.insertOne({
operation: operation,
cacheKey: cacheKey,
namespace: metadata.namespace,
metadata: metadata,
timestamp: new Date()
});
}
async compressData(data) {
const zlib = require('zlib');
const jsonString = JSON.stringify(data);
return zlib.deflateSync(jsonString);
}
async decompressData(compressedData) {
const zlib = require('zlib');
const decompressed = zlib.inflateSync(compressedData);
return JSON.parse(decompressed.toString());
}
async startMaintenanceTasks() {
// TTL collections handle expiration automatically
console.log('Maintenance tasks initialized - TTL collections managing automatic cleanup');
// Optional: Set up additional cleanup for edge cases
if (this.config.sessionCleanupInterval > 0) {
setInterval(async () => {
await this.performMaintenanceCleanup();
}, this.config.sessionCleanupInterval * 1000);
}
}
async performMaintenanceCleanup() {
try {
// Optional cleanup for orphaned records or additional maintenance
const orphanedSessions = await this.collections.userSessions.countDocuments({
active: false,
invalidatedAt: { $lt: new Date(Date.now() - 24 * 3600 * 1000) }
});
if (orphanedSessions > 0) {
console.log(`Found ${orphanedSessions} orphaned sessions for cleanup`);
// TTL will handle automatic cleanup
}
} catch (error) {
console.warn('Error during maintenance cleanup:', error.message);
}
}
}
// Benefits of MongoDB Distributed Caching and Session Management:
// - Automatic TTL expiration with no manual cleanup required
// - Flexible document structure for complex session and cache data
// - Built-in indexing and query optimization for cache and session operations
// - Integrated compression and storage optimization capabilities
// - Sophisticated analytics and metrics collection with automatic retention
// - Advanced security features including risk scoring and device tracking
// - High-performance concurrent access with MongoDB's native concurrency controls
// - Seamless integration with existing MongoDB infrastructure
// - SQL-compatible operations through QueryLeaf for familiar management patterns
// - Distributed consistency and replication support for high availability
module.exports = {
AdvancedCacheSessionManager
};
Understanding MongoDB TTL Collections and Cache Architecture
Advanced TTL Configuration and Performance Optimization
Implement sophisticated TTL strategies for optimal performance and resource management:
// Production-ready MongoDB TTL and cache optimization
class EnterpriseCAcheManager extends AdvancedCacheSessionManager {
constructor(db, enterpriseConfig) {
super(db, enterpriseConfig);
this.enterpriseConfig = {
...enterpriseConfig,
enableTieredStorage: true,
enableCacheWarmup: true,
enablePredictiveEviction: true,
enableLoadBalancing: true,
enableCacheReplication: true,
enablePerformanceOptimization: true
};
this.setupEnterpriseFeatures();
this.initializeAdvancedCaching();
this.setupPerformanceMonitoring();
}
async implementTieredCaching() {
console.log('Implementing enterprise tiered caching strategy...');
const tieredConfig = {
// Hot tier - frequently accessed data
hotTier: {
ttl: 900, // 15 minutes
maxSize: 100 * 1024 * 1024, // 100MB
compressionLevel: 'fast'
},
// Warm tier - moderately accessed data
warmTier: {
ttl: 3600, // 1 hour
maxSize: 500 * 1024 * 1024, // 500MB
compressionLevel: 'balanced'
},
// Cold tier - rarely accessed data
coldTier: {
ttl: 86400, // 24 hours
maxSize: 2048 * 1024 * 1024, // 2GB
compressionLevel: 'maximum'
}
};
return await this.deployTieredCaching(tieredConfig);
}
async setupAdvancedAnalytics() {
console.log('Setting up advanced cache and session analytics...');
const analyticsConfig = {
// Real-time metrics
realtimeMetrics: {
hitRatioThreshold: 0.8,
latencyThreshold: 100, // ms
errorRateThreshold: 0.01,
memoryUsageThreshold: 0.85
},
// Predictive analytics
predictiveAnalytics: {
accessPatternLearning: true,
capacityForecasting: true,
anomalyDetection: true,
performanceOptimization: true
},
// Business intelligence
businessIntelligence: {
userBehaviorAnalysis: true,
conversionTracking: true,
sessionQualityScoring: true,
cacheEfficiencyOptimization: true
}
};
return await this.implementAdvancedAnalytics(analyticsConfig);
}
}
SQL-Style Caching and Session Management with QueryLeaf
QueryLeaf provides familiar SQL syntax for MongoDB TTL collections and advanced caching operations:
-- QueryLeaf advanced caching and session management with SQL-familiar syntax
-- Configure TTL collections for automatic expiration management
CREATE COLLECTION user_sessions
WITH (
ttl_field = 'expiresAt',
expire_after_seconds = 0,
storage_engine = 'wiredTiger',
compression = 'snappy'
);
CREATE COLLECTION application_cache
WITH (
ttl_field = 'expiresAt',
expire_after_seconds = 0,
storage_engine = 'wiredTiger',
compression = 'zlib'
);
-- Advanced session management with TTL and complex queries
WITH session_analytics AS (
SELECT
user_id,
session_id,
created_at,
last_accessed,
expires_at,
ip_address,
device_fingerprint,
risk_score,
-- Session duration analysis
EXTRACT(EPOCH FROM last_accessed - created_at) / 60 as session_duration_minutes,
-- Session status classification
CASE
WHEN last_accessed > CURRENT_TIMESTAMP - INTERVAL '5 minutes' THEN 'active'
WHEN last_accessed > CURRENT_TIMESTAMP - INTERVAL '30 minutes' THEN 'recent'
WHEN last_accessed > CURRENT_TIMESTAMP - INTERVAL '2 hours' THEN 'idle'
ELSE 'stale'
END as session_status,
-- Shopping cart analysis
CAST(JSON_EXTRACT(session_data, '$.shoppingCart.items') AS JSONB) as cart_items,
CAST(JSON_EXTRACT(session_data, '$.shoppingCart.total') AS DECIMAL(10,2)) as cart_total,
-- User preferences analysis
JSON_EXTRACT(session_data, '$.preferences.theme') as preferred_theme,
JSON_EXTRACT(session_data, '$.preferences.language') as preferred_language,
-- Navigation patterns
JSON_EXTRACT(session_data, '$.navigation.lastPage') as last_page,
JSON_EXTRACT(session_data, '$.navigation.referrer') as referrer,
-- Activity metrics
page_views,
actions_performed,
data_transferred,
-- Security indicators
mfa_verified,
login_method,
-- Risk assessment
CASE
WHEN risk_score > 0.8 THEN 'high'
WHEN risk_score > 0.5 THEN 'medium'
WHEN risk_score > 0.2 THEN 'low'
ELSE 'minimal'
END as risk_level
FROM user_sessions
WHERE active = true
AND expires_at > CURRENT_TIMESTAMP
),
session_aggregations AS (
SELECT
-- Overall session metrics
COUNT(*) as total_active_sessions,
COUNT(DISTINCT user_id) as unique_active_users,
AVG(session_duration_minutes) as avg_session_duration,
-- Session status distribution
COUNT(*) FILTER (WHERE session_status = 'active') as active_sessions,
COUNT(*) FILTER (WHERE session_status = 'recent') as recent_sessions,
COUNT(*) FILTER (WHERE session_status = 'idle') as idle_sessions,
COUNT(*) FILTER (WHERE session_status = 'stale') as stale_sessions,
-- Business metrics
COUNT(*) FILTER (WHERE JSON_ARRAY_LENGTH(cart_items) > 0) as sessions_with_cart,
AVG(cart_total) FILTER (WHERE cart_total > 0) as avg_cart_value,
COUNT(*) FILTER (WHERE JSON_ARRAY_LENGTH(cart_items) > 5) as high_volume_carts,
-- Security metrics
COUNT(*) FILTER (WHERE risk_level = 'high') as high_risk_sessions,
COUNT(*) FILTER (WHERE mfa_verified = true) as mfa_verified_sessions,
COUNT(DISTINCT device_fingerprint) as unique_devices,
COUNT(DISTINCT ip_address) as unique_ip_addresses,
-- Activity analysis
SUM(page_views) as total_page_views,
SUM(actions_performed) as total_actions,
AVG(page_views) as avg_page_views_per_session,
AVG(actions_performed) as avg_actions_per_session,
-- Performance insights
SUM(data_transferred) / (1024 * 1024) as total_data_mb_transferred
FROM session_analytics
),
cache_performance AS (
SELECT
namespace,
cache_key,
created_at,
expires_at,
last_accessed,
data_size,
hit_count,
tags,
-- Cache efficiency metrics
EXTRACT(EPOCH FROM expires_at - created_at) / 3600 as ttl_hours,
EXTRACT(EPOCH FROM last_accessed - created_at) / 60 as lifetime_minutes,
EXTRACT(EPOCH FROM CURRENT_TIMESTAMP - last_accessed) / 60 as idle_minutes,
-- Data size analysis
CASE
WHEN data_size > 1024 * 1024 THEN 'large'
WHEN data_size > 100 * 1024 THEN 'medium'
WHEN data_size > 10 * 1024 THEN 'small'
ELSE 'tiny'
END as size_category,
-- Access pattern analysis
CASE
WHEN hit_count > 100 THEN 'high_traffic'
WHEN hit_count > 10 THEN 'medium_traffic'
WHEN hit_count > 0 THEN 'low_traffic'
ELSE 'unused'
END as traffic_level,
-- Cache effectiveness
CASE
WHEN hit_count = 0 THEN 'ineffective'
WHEN hit_count / GREATEST(lifetime_minutes / 60, 1) > 10 THEN 'highly_effective'
WHEN hit_count / GREATEST(lifetime_minutes / 60, 1) > 1 THEN 'effective'
ELSE 'moderately_effective'
END as effectiveness_rating
FROM application_cache
WHERE expires_at > CURRENT_TIMESTAMP
),
cache_analytics AS (
SELECT
namespace,
-- Volume metrics
COUNT(*) as total_entries,
SUM(data_size) / (1024 * 1024) as total_size_mb,
AVG(data_size) as avg_entry_size_bytes,
-- Performance metrics
SUM(hit_count) as total_hits,
AVG(hit_count) as avg_hits_per_entry,
COUNT(*) FILTER (WHERE hit_count = 0) as unused_entries,
-- TTL analysis
AVG(ttl_hours) as avg_ttl_hours,
COUNT(*) FILTER (WHERE ttl_hours > 24) as long_lived_entries,
COUNT(*) FILTER (WHERE ttl_hours < 1) as short_lived_entries,
-- Size distribution
COUNT(*) FILTER (WHERE size_category = 'large') as large_entries,
COUNT(*) FILTER (WHERE size_category = 'medium') as medium_entries,
COUNT(*) FILTER (WHERE size_category = 'small') as small_entries,
COUNT(*) FILTER (WHERE size_category = 'tiny') as tiny_entries,
-- Traffic analysis
COUNT(*) FILTER (WHERE traffic_level = 'high_traffic') as high_traffic_entries,
COUNT(*) FILTER (WHERE traffic_level = 'medium_traffic') as medium_traffic_entries,
COUNT(*) FILTER (WHERE traffic_level = 'low_traffic') as low_traffic_entries,
COUNT(*) FILTER (WHERE traffic_level = 'unused') as unused_traffic_entries,
-- Effectiveness distribution
COUNT(*) FILTER (WHERE effectiveness_rating = 'highly_effective') as highly_effective_entries,
COUNT(*) FILTER (WHERE effectiveness_rating = 'effective') as effective_entries,
COUNT(*) FILTER (WHERE effectiveness_rating = 'moderately_effective') as moderately_effective_entries,
COUNT(*) FILTER (WHERE effectiveness_rating = 'ineffective') as ineffective_entries
FROM cache_performance
GROUP BY namespace
)
-- Comprehensive session and cache monitoring dashboard
SELECT
'System Performance Dashboard' as dashboard_title,
CURRENT_TIMESTAMP as report_generated_at,
-- Session management metrics
JSON_OBJECT(
'total_active_sessions', sa.total_active_sessions,
'unique_active_users', sa.unique_active_users,
'avg_session_duration_minutes', ROUND(sa.avg_session_duration::NUMERIC, 1),
'session_distribution', JSON_OBJECT(
'active', sa.active_sessions,
'recent', sa.recent_sessions,
'idle', sa.idle_sessions,
'stale', sa.stale_sessions
),
'security_metrics', JSON_OBJECT(
'high_risk_sessions', sa.high_risk_sessions,
'mfa_verified_sessions', sa.mfa_verified_sessions,
'unique_devices', sa.unique_devices,
'unique_ip_addresses', sa.unique_ip_addresses
),
'business_metrics', JSON_OBJECT(
'sessions_with_cart', sa.sessions_with_cart,
'avg_cart_value', ROUND(sa.avg_cart_value::NUMERIC, 2),
'high_volume_carts', sa.high_volume_carts,
'cart_conversion_rate',
ROUND((sa.sessions_with_cart::FLOAT / sa.total_active_sessions * 100)::NUMERIC, 2)
)
) as session_metrics,
-- Cache performance metrics by namespace
JSON_OBJECT_AGG(
ca.namespace,
JSON_OBJECT(
'total_entries', ca.total_entries,
'total_size_mb', ROUND(ca.total_size_mb::NUMERIC, 2),
'avg_entry_size_kb', ROUND((ca.avg_entry_size_bytes / 1024)::NUMERIC, 1),
'total_hits', ca.total_hits,
'avg_hits_per_entry', ROUND(ca.avg_hits_per_entry::NUMERIC, 1),
'unused_entry_percentage',
ROUND((ca.unused_entries::FLOAT / ca.total_entries * 100)::NUMERIC, 1),
'cache_efficiency',
CASE
WHEN ca.unused_entries::FLOAT / ca.total_entries > 0.5 THEN 'poor'
WHEN ca.unused_entries::FLOAT / ca.total_entries > 0.2 THEN 'fair'
WHEN ca.unused_entries::FLOAT / ca.total_entries > 0.1 THEN 'good'
ELSE 'excellent'
END,
'size_distribution', JSON_OBJECT(
'large', ca.large_entries,
'medium', ca.medium_entries,
'small', ca.small_entries,
'tiny', ca.tiny_entries
),
'effectiveness_distribution', JSON_OBJECT(
'highly_effective', ca.highly_effective_entries,
'effective', ca.effective_entries,
'moderately_effective', ca.moderately_effective_entries,
'ineffective', ca.ineffective_entries
)
)
) as cache_metrics_by_namespace,
-- System health indicators
JSON_OBJECT(
'session_system_health',
CASE
WHEN sa.high_risk_sessions::FLOAT / sa.total_active_sessions > 0.1 THEN 'critical'
WHEN sa.avg_session_duration < 5 THEN 'warning'
WHEN sa.unique_active_users::FLOAT / sa.total_active_sessions < 0.5 THEN 'warning'
ELSE 'healthy'
END,
'cache_system_health',
CASE
WHEN AVG(ca.unused_entries::FLOAT / ca.total_entries) > 0.5 THEN 'critical'
WHEN AVG(ca.total_size_mb) > 1024 THEN 'warning'
WHEN AVG(ca.avg_hits_per_entry) < 5 THEN 'warning'
ELSE 'healthy'
END,
'overall_system_status',
CASE
WHEN sa.high_risk_sessions > 10 OR AVG(ca.unused_entries::FLOAT / ca.total_entries) > 0.5 THEN 'needs_attention'
WHEN sa.avg_session_duration > 30 AND AVG(ca.avg_hits_per_entry) > 10 THEN 'optimal'
ELSE 'normal'
END
) as system_health,
-- Operational recommendations
ARRAY[
CASE WHEN sa.high_risk_sessions > sa.total_active_sessions * 0.05
THEN 'Review session security policies and risk scoring algorithms' END,
CASE WHEN AVG(ca.unused_entries::FLOAT / ca.total_entries) > 0.3
THEN 'Optimize cache TTL settings and review caching strategies' END,
CASE WHEN sa.avg_session_duration < 5
THEN 'Investigate user engagement issues and session timeout settings' END,
CASE WHEN AVG(ca.total_size_mb) > 512
THEN 'Consider cache data compression and size optimization' END,
CASE WHEN sa.sessions_with_cart::FLOAT / sa.total_active_sessions < 0.1
THEN 'Review shopping cart functionality and user experience' END
]::TEXT[] as recommendations
FROM session_aggregations sa
CROSS JOIN cache_analytics ca
GROUP BY sa.total_active_sessions, sa.unique_active_users, sa.avg_session_duration,
sa.active_sessions, sa.recent_sessions, sa.idle_sessions, sa.stale_sessions,
sa.high_risk_sessions, sa.mfa_verified_sessions, sa.unique_devices, sa.unique_ip_addresses,
sa.sessions_with_cart, sa.avg_cart_value, sa.high_volume_carts;
-- Advanced cache invalidation with pattern matching and conditional logic
UPDATE application_cache
SET expires_at = CURRENT_TIMESTAMP,
invalidated_at = CURRENT_TIMESTAMP,
invalidation_reason = 'product_update_cascade'
WHERE
-- Pattern-based invalidation
(cache_key LIKE 'product:%' OR cache_key LIKE 'catalog:%')
AND
-- Conditional invalidation based on tags
('product_catalog' = ANY(tags) OR 'inventory' = ANY(tags))
AND
-- Time-based invalidation criteria
created_at < CURRENT_TIMESTAMP - INTERVAL '1 hour'
AND
-- Size-based invalidation for large entries
data_size > 1024 * 1024; -- 1MB
-- Session cleanup with advanced criteria and security considerations
UPDATE user_sessions
SET active = false,
expires_at = CURRENT_TIMESTAMP,
invalidated_at = CURRENT_TIMESTAMP,
invalidation_reason = 'security_cleanup'
WHERE
-- Risk-based cleanup
risk_score > 0.8
OR
-- Inactive session cleanup
(last_accessed < CURRENT_TIMESTAMP - INTERVAL '2 hours' AND remember_me = false)
OR
-- Device anomaly cleanup
(device_fingerprint NOT IN (
SELECT device_fingerprint
FROM user_sessions
WHERE user_id = user_sessions.user_id
AND created_at >= CURRENT_TIMESTAMP - INTERVAL '30 days'
GROUP BY device_fingerprint
HAVING COUNT(*) > 5
))
OR
-- Geographic anomaly cleanup
(ip_address NOT SIMILAR TO (
SELECT STRING_AGG(DISTINCT SUBSTRING(ip_address, 1, POSITION('.' IN ip_address, POSITION('.' IN ip_address) + 1)), '|')
FROM user_sessions recent_sessions
WHERE recent_sessions.user_id = user_sessions.user_id
AND recent_sessions.created_at >= CURRENT_TIMESTAMP - INTERVAL '7 days'
));
-- Real-time TTL and performance monitoring
CREATE VIEW cache_session_health_monitor AS
WITH real_time_metrics AS (
SELECT
-- Current timestamp for dashboard refresh
CURRENT_TIMESTAMP as monitor_timestamp,
-- Active session metrics
(SELECT COUNT(*) FROM user_sessions
WHERE active = true AND expires_at > CURRENT_TIMESTAMP) as current_active_sessions,
(SELECT COUNT(DISTINCT user_id) FROM user_sessions
WHERE active = true AND expires_at > CURRENT_TIMESTAMP) as current_unique_users,
-- Cache metrics
(SELECT COUNT(*) FROM application_cache
WHERE expires_at > CURRENT_TIMESTAMP) as current_cache_entries,
(SELECT SUM(data_size) / (1024 * 1024) FROM application_cache
WHERE expires_at > CURRENT_TIMESTAMP) as current_cache_size_mb,
-- Recent activity (last 5 minutes)
(SELECT COUNT(*) FROM user_sessions
WHERE last_accessed >= CURRENT_TIMESTAMP - INTERVAL '5 minutes') as recent_session_activity,
(SELECT COUNT(*) FROM application_cache
WHERE last_accessed >= CURRENT_TIMESTAMP - INTERVAL '5 minutes') as recent_cache_activity,
-- TTL efficiency metrics
(SELECT COUNT(*) FROM user_sessions
WHERE expires_at <= CURRENT_TIMESTAMP + INTERVAL '1 minute') as sessions_expiring_soon,
(SELECT COUNT(*) FROM application_cache
WHERE expires_at <= CURRENT_TIMESTAMP + INTERVAL '1 minute') as cache_expiring_soon,
-- Risk indicators
(SELECT COUNT(*) FROM user_sessions
WHERE active = true AND risk_score > 0.7) as high_risk_active_sessions
)
SELECT
monitor_timestamp,
-- Session health indicators
current_active_sessions,
current_unique_users,
ROUND(current_unique_users::FLOAT / NULLIF(current_active_sessions, 0), 2) as user_session_ratio,
recent_session_activity,
sessions_expiring_soon,
-- Cache health indicators
current_cache_entries,
ROUND(current_cache_size_mb::NUMERIC, 2) as cache_size_mb,
recent_cache_activity,
cache_expiring_soon,
-- Security indicators
high_risk_active_sessions,
CASE
WHEN high_risk_active_sessions > current_active_sessions * 0.1 THEN 'critical'
WHEN high_risk_active_sessions > current_active_sessions * 0.05 THEN 'warning'
ELSE 'normal'
END as security_status,
-- Performance indicators
CASE
WHEN current_active_sessions > 10000 THEN 'high_load'
WHEN current_active_sessions > 5000 THEN 'medium_load'
WHEN current_active_sessions > 1000 THEN 'normal_load'
ELSE 'low_load'
END as system_load,
-- Cache efficiency
CASE
WHEN recent_cache_activity::FLOAT / NULLIF(current_cache_entries, 0) > 0.1 THEN 'highly_active'
WHEN recent_cache_activity::FLOAT / NULLIF(current_cache_entries, 0) > 0.05 THEN 'moderately_active'
ELSE 'low_activity'
END as cache_activity_level,
-- TTL management effectiveness
CASE
WHEN sessions_expiring_soon + cache_expiring_soon > 100 THEN 'high_turnover'
WHEN sessions_expiring_soon + cache_expiring_soon > 50 THEN 'moderate_turnover'
ELSE 'stable_turnover'
END as ttl_turnover_rate
FROM real_time_metrics;
-- QueryLeaf provides comprehensive MongoDB TTL and caching capabilities:
-- 1. Automatic TTL expiration management with SQL-familiar syntax
-- 2. Advanced session lifecycle management with security features
-- 3. Intelligent cache invalidation patterns and strategies
-- 4. Real-time performance monitoring and health assessments
-- 5. Flexible document structure support for complex cache and session data
-- 6. Built-in compression and storage optimization capabilities
-- 7. Sophisticated analytics and business intelligence integration
-- 8. Advanced security features including risk scoring and anomaly detection
-- 9. High-performance concurrent access with MongoDB's native capabilities
-- 10. Enterprise-grade scalability and distributed consistency support
Best Practices for Production Caching and Session Management
TTL Collection Strategy Design
Essential principles for effective MongoDB TTL implementation:
- TTL Configuration: Design TTL strategies that balance performance, storage costs, and data availability requirements
- Index Optimization: Implement appropriate indexing strategies for cache and session access patterns
- Data Compression: Use MongoDB compression features to optimize storage for large cache and session data
- Security Integration: Implement comprehensive security measures including risk scoring and device tracking
- Performance Monitoring: Deploy real-time monitoring and alerting for cache and session system health
- Scalability Planning: Design caching architecture that can scale with user growth and data volume increases
Enterprise Deployment Considerations
Optimize caching and session management for production environments:
- High Availability: Implement distributed session and cache management across multiple nodes
- Data Consistency: Ensure cache and session consistency across distributed infrastructure
- Disaster Recovery: Design backup and recovery procedures for critical session and cache data
- Compliance Integration: Meet regulatory requirements for session data handling and cache security
- Cost Optimization: Monitor and optimize caching costs while maintaining performance requirements
- Operational Integration: Integrate with existing monitoring, alerting, and operational workflows
Conclusion
MongoDB TTL Collections provide comprehensive distributed caching and session management capabilities that eliminate the complexity of traditional cache servers and session stores while offering superior flexibility, performance, and integration with existing application infrastructure. Native TTL expiration, advanced document modeling, and integrated analytics enable sophisticated caching strategies without requiring additional operational overhead.
Key MongoDB caching and session management benefits include:
- Automatic TTL Management: Built-in expiration handling with no manual cleanup or maintenance required
- Flexible Data Models: Support for complex nested session and cache data structures with efficient querying
- Integrated Security: Comprehensive security features including risk scoring, device tracking, and anomaly detection
- High Performance: Native MongoDB performance optimizations for concurrent cache and session operations
- Advanced Analytics: Sophisticated metrics and business intelligence capabilities for optimization insights
- SQL Accessibility: Familiar SQL-style operations through QueryLeaf for accessible cache and session management
Whether you're building high-traffic web applications, e-commerce platforms, IoT systems, or enterprise applications requiring sophisticated state management, MongoDB TTL Collections with QueryLeaf's familiar SQL interface provide the foundation for reliable, scalable, and efficient distributed caching and session management.
QueryLeaf Integration: QueryLeaf automatically translates SQL-style TTL and caching operations into MongoDB's native TTL collections and indexing strategies, making advanced caching and session management accessible to SQL-oriented development teams. Complex cache invalidation patterns, session analytics, and performance optimization are seamlessly handled through familiar SQL constructs, enabling sophisticated distributed state management without requiring deep MongoDB TTL expertise.
The combination of MongoDB's robust TTL capabilities with SQL-style caching operations makes it an ideal platform for applications requiring both high-performance state management and familiar database interaction patterns, ensuring your distributed systems can maintain optimal performance and user experience as they scale and evolve.