MongoDB Implementation

Complete MongoDB-specific implementation guide covering optimizations, indexes, connection pooling, cursor pagination, streaming, and performance tuning for SveltyCMS.

6/27/2026

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Note

Competitive comparisons based on publicly available documentation as of June 2026. Performance data self-measured via bun test tests/benchmarks/.

This guide covers the MongoDB-specific implementation in SveltyCMS, including optimizations, indexing strategy, connection pooling, and advanced features. For database-agnostic architecture, see the Core Infrastructure documentation.

🎯 Production Architecture

MongoDB Version: 6.0+ (latest stable recommended)
Performance Grade: A+ (99/100)
Architecture: Best-practice patterns from day one

Performance Benchmarks

Operation	Response Time	Cache Hit Rate
User lookup (cached)	0.5ms	85-95%
User lookup (uncached)	2ms	-
Collection list (100)	1.2ms	85-95%
Collection list (1K)	1.8ms	85-95%
Collection (cached)	0.5ms	85-95%
Pagination (page 50)	0.1ms	-
Payload size (list)	95 KB	-
Content tree	1.5ms	85-95%
Dashboard (cached)	5ms	85-95%
Streaming 100k records	50MB RAM	-

Performance Improvements

Sub-1s Cold Start: Progressive initialization reaches READY state (API-safe) in <850ms.
Instant Cold Starts (L2): Persistent schema storage in system_content_structure allows booting from DB in <100ms.
Typed Collection Proxy: Fully-typed access via locals.cms.collections.typed.Posts.find().
95-99% faster operations through intelligent caching.
85-95% cache hit rate with dual-layer Redis + MongoDB.
79% smaller payloads via smart field selection.
O(log n) queries through comprehensive indexing.
Automatic Indexing: Background IndexOptimizer ensures fields remain optimized as schemas change.
O(1) pagination with cursor-based navigation.
O(1) memory with streaming for large datasets.
Atomic Versioning: Native $inc support for system:content_version tracking.

📦 Architecture Overview

Performance-First Design

SveltyCMS MongoDB implementation is built with enterprise-scale performance as a core requirement:

Phase 0: Dual-Layer Caching System

Redis (in-memory) + MongoDB (persistent) dual-layer cache
8 cache categories with optimized TTL per use case
85-95% cache hit rate in production
Sub-millisecond response for cached operations
Automatic cache warming during setup
Pattern-based intelligent invalidation

Phase 1: System Model Indexes (29 optimized indexes)

4 TTL indexes for automatic document cleanup (auth_tokens, auth_sessions, auth_users)
25 compound indexes for system collections
Full-text search capabilities on media filenames

Phase 2: Dynamic Collection Indexing

Automatic index creation for all collections
9 essential indexes per collection (status, createdAt, updatedAt, etc.)
Compound indexes for optimized query patterns
Multi-tenant support (tenantId combinations)
Field-specific indexes (unique, indexed, searchable, sortable)
Full-text search on text/textarea fields
Zero configuration required

Phase 3: Connection & Query Optimization

Connection pooling (50 max, 10 min)
Network compression (30-50% bandwidth reduction)
.lean() queries (40% memory optimization)
Query hints support
Smart field selection (50-80% payload reduction)

Phase 4: Advanced Scalability Features

Cursor pagination (O(1) time complexity)
Streaming support (O(1) memory usage)
Intelligent user caching (92% hit rate)
QueryMeta performance tracking

Phase 5: Lazy Activation & Resiliency

Lazy Model Registration: Models are only compiled on-demand and cached in a local Map to prevent Mongoose re-registration errors and reduce cold-start memory.
Dependency Guards (ensure<Feature>): Background services explicitly await the readiness of their specific database sub-systems.
Promise-Based Locking: Internal initialization is protected by a promise map to prevent race conditions during concurrent startup requests.

Phase 6: L2 Persistence & Typed SDK

L2 Schema Persistence: The full Schema object is stored in the database. Cold starts skip the filesystem scan entirely if the DB version matches.
Typed Collection Proxy: Generated types provide full IntelliSense for database operations, reducing developer error and improving speed.
Index Optimizer: A background service that periodically verifies and builds missing indexes based on field configurations.
Native Bulk Operations (NEW): Replaced iterative batching with native bulkWrite, yielding a ~50% improvement in high-throughput data ingestion and updates.

Batch Operation Ordering

MongoDB batch updates now use { ordered: true } in bulkWrite operations to preserve input order. This ensures that when revision ordering depends on write sequence (e.g., counter-based version fields), the final state in the database matches the order in which updates were submitted.

// In batch-module.ts:
const result = await model.bulkWrite(bulkOps, { ordered: true });

Without ordered writes, MongoDB’s bulkWrite may process operations in any order, causing a read-order ≠ write-order mismatch for dependent updates.

Phase 8: Platinum Performance & Zero-Allocation

Zero-Allocation Dates (processDates): Implemented a “dirty-bit” check to prevent object cloning when no date transformation is needed.
Security Fast-Path (bypassSafeQuery): High-performance toggle for selected internal lookups that skip safeQuery allocation entirely. Not used for website-token CRUD — those routes through MongoCrudMethods + safeQuery (soft-delete + tenant injection).
ID Lookup Fast-Path (isLookupQuery): Direct collection access for ID/token queries, bypassing the (inlined) Query IR translation entirely.

🛡️ Agnostic Query IR Translation (inlined)

To maintain strict parity with SQL engines, the MongoDB adapter no longer interacts with raw MongoDB filter objects in the domain modules for complex queries. The Query IR translation logic is inlined directly into the adapter’s mapQuery() (see Query IR slimming refactor).

Translation Flow:

Raw Query: { status: 'active', tags: { $in: ['news'] } }
IR Map: Decouples MongoDB-specific operators into a standard LogicalGroup.
Security Injection: Automatically injects tenantId and isDeleted: { $ne: true } filters (matches legacy documents missing the field).
NoSQL Injection Protection: sanitizeMongoQuery() recursively blocks dangerous operators ($where, $function, $expr, $accumulator) and validates $regex patterns against ReDoS attacks before the query reaches the database.
Native Compilation: The mapQuery utility compiles the IR back into an optimized MongoDB filter, with sanitization applied as the final security layer.

This ensures that security audits only need to verify the mapQuery utility rather than every individual CRUD module.

🏗️ Modular Adapter Architecture

The MongoDB adapter has been fully modularized to align with the enterprise-grade architecture used by SveltyCMS’s SQL engines.

Module Breakdown

Module	Responsibility	Optimization
auth-user.ts	Session validation, RBAC, and JIT provisioning.	Platinum Fast-Path for sub-0.5ms validation.
crud-methods.ts	High-speed document operations with name normalization.	`.lean()` with zero-allocation `processDates` logic.
media-methods.ts	GridFS/Filesystem hybrid storage and metadata.	SHA-256 deduplication and metadata extraction.
content-methods.ts	Advanced content tree and structure management.	Optimized recursive lookups for large hierarchies.
website-token-methods.ts	External API credential CRUD (`system_website_tokens`).	SHA-256 at rest, parallel list+count, `safeQuery` soft-delete.

| adapter-core.ts | Unified connection and (inlined) Query IR translation. | Hardened isLookupQuery ID-bypass. |

Explicit Schema Registration

To maintain “Database Agnostic” consistency, SveltyCMS uses String IDs instead of MongoDB’s native ObjectId. To prevent runtime crashes (Cast to ObjectId failed), we use a centralized registration pattern:

// File: src/databases/mongodb/methods/model-registration.ts
export async function registerSystemModels(connection: Connection): Promise<void> {
  // We "force-feed" the correct String-based schemas immediately upon connection
  if (!connection.models.system_content_structure) {
    connection.model("system_content_structure", contentStructureSchema);
  }
  // This ensures 100% parity with SQLite/Postgres UUIDs
}

💎 ACID Transactions (Unified API)

SveltyCMS provides a unified transaction API that works identically across SQL and NoSQL. The MongoDB implementation utilizes native Distributed Sessions.

Implementation Detail

The MongoTransactionModule wraps the native MongoDB session to satisfy the CMS interface:

async execute<T>(fn: (tx: DatabaseTransaction) => Promise<DatabaseResult<T>>) {
  const session = await this.adapter.connection.startSession();
  session.startTransaction();

  const tx = {
    insert: (coll, data) => this.adapter.crud.insert(coll, data, { session }),
    commit: () => session.commitTransaction(),
    // ...
  };

  return await fn(tx);
}

Benefits:

Atomicity: Complex relational seeding either succeeds entirely or rolls back.
Portability: The exact same business logic runs on MongoDB and MariaDB.
Safety: Automated rollback on any unhandled exception within the transaction block.

🗂️ Index Strategy

TTL Indexes (Auto-Cleanup)

TTL (Time-To-Live) indexes automatically delete expired documents, eliminating manual cleanup jobs.

auth_tokens Collection

// File: src/databases/mongodb/models/authToken.ts

// Auto-delete expired tokens immediately
TokenSchema.index({ expires: 1 }, { expireAfterSeconds: 0 });

Benefits:

Automatic cleanup of expired tokens
Zero maintenance overhead
Reduced database bloat
Storage cost savings

auth_sessions Collection

// File: src/databases/mongodb/models/authSession.ts

// Auto-delete expired sessions immediately
SessionSchema.index({ expires: 1 }, { expireAfterSeconds: 0 });

Benefits:

Automatic session cleanup
No manual deletion queries
Better security (stale sessions removed)
Reduced session table size

auth_users Collection

// File: src/databases/mongodb/models/authUser.ts

// Auto-delete expired password reset tokens after 1 hour
UserSchema.index({ "resetToken.expires": 1 }, { sparse: true, expireAfterSeconds: 3600 });

// Auto-delete temporary/unverified users after expiration
UserSchema.index({ expiresAt: 1 }, { sparse: true, expireAfterSeconds: 0 });

Benefits:

Password reset tokens auto-expire
Temporary users auto-deleted
Improved security
Cleaner user database

🔥 Dynamic Collection Indexing (Auto-Applied)

NEW in 2024: SveltyCMS now automatically creates optimized indexes for every user-defined collection based on their schema configuration.

How It Works {#dynamic-indexing-how}

// File: src/databases/mongodb/methods/collectionMethods.ts
// Automatically called when creating any collection

async createModel(schema: Schema): Promise<void> {
  // ... model creation logic ...

  // Auto-create indexes for optimal performance
  await this.createIndexes(model, schema);
}

private async createIndexes(model: Model<unknown>, schema: Schema): Promise<void> {
  const indexes = [
    // Essential indexes (ALL collections)
    { fields: { status: 1 } },
    { fields: { createdAt: -1 } },
    { fields: { updatedAt: -1 } },
    { fields: { createdBy: 1 } },

    // Compound indexes for common patterns
    { fields: { status: 1, createdAt: -1 } },
    { fields: { status: 1, updatedAt: -1 } },

    // Multi-tenant support
    { fields: { tenantId: 1 } },
    { fields: { tenantId: 1, status: 1 } },
    { fields: { tenantId: 1, createdAt: -1 } },
  ];

  // Field-specific indexes based on schema
  for (const field of schema.fields) {
    if (field.unique) {
      indexes.push({
        fields: { [field.name]: 1 },
        options: { unique: true, sparse: true }
      });
    }

    if (field.indexed || field.searchable || field.sortable) {
      indexes.push({ fields: { [field.name]: 1 } });
    }

    if (field.searchable && (field.type === 'text' || field.type === 'textarea')) {
      indexes.push({
        fields: { [field.name]: 'text' },
        options: { default_language: 'english' }
      });
    }
  }

  // Create all indexes
  for (const index of indexes) {
    await model.collection.createIndex(index.fields, index.options || {});
  }
}

Index Types Created

1. Essential Indexes (9 per collection)

// Always created for every collection:
{ status: 1 }                      // Filter by status
{ createdAt: -1 }                  // Sort by creation date
{ updatedAt: -1 }                  // Sort by update date
{ createdBy: 1 }                   // Filter by author
{ status: 1, createdAt: -1 }       // Published posts, newest first
{ status: 1, updatedAt: -1 }       // Recently modified drafts
{ tenantId: 1 }                    // Multi-tenant isolation
{ tenantId: 1, status: 1 }         // Tenant + status filter
{ tenantId: 1, createdAt: -1 }     // Tenant + sort by date

2. Field-Specific Indexes

Unique fields:

// Field with unique: true in schema
{
  slug: 1;
} // Unique index with sparse option
{
  email: 1;
} // Unique email addresses

Indexed fields:

// Field with indexed: true in schema
{
  category: 1;
} // Fast category filtering
{
  tags: 1;
} // Fast tag lookups

Searchable fields:

// Text/textarea fields with searchable: true
{
  title: "text";
} // Full-text search
{
  description: "text";
} // Full-text search

Sortable fields:

// fields with sortable: true in schema
{
  price: 1;
} // Sort by price
{
  publishDate: -1;
} // Sort by date

Performance Impact {#dynamic-indexing-perf}

Collection Size	Without Indexes	With Auto-Indexes	Improvement
100 entries	150ms	12ms	12.5x
1,000 entries	1,200ms	18ms	66x
10,000 entries	12,000ms	25ms	480x

Real-World Example: Blog Collection

// Collection schema
{
  name: "Blog Posts",
  fields: [
    { name: "title", type: "text", searchable: true },
    { name: "slug", type: "text", unique: true },
    { name: "body", type: "richtext", searchable: true },
    { name: "category", type: "relation", indexed: true },
    { name: "publishDate", type: "date", sortable: true },
    { name: "featured", type: "boolean", indexed: true }
  ]
}

// Indexes automatically created:
// 1. Essential (9 indexes)
// 2. { slug: 1 } - unique
// 3. { title: 'text' } - searchable
// 4. { body: 'text' } - searchable
// 5. { category: 1 } - indexed
// 6. { publishDate: 1 } - sortable
// 7. { featured: 1 } - indexed
//
// Total: 15 indexes created automatically!

🎯 Field Selection Optimization (NEW)

NEW in 2024: Smart field selection reduces payload sizes by 50-80% for list views.

How It Works {#how-it-works-2}

// File: src/utils/fieldselection.ts

		.slice(0, config?.maxDisplayfields || 5);

	return [...essentialfields, ...displayfields];
}

Integration with Page Loads

// File: src/routes/(app)/[language]/[...collection]/+page.server.ts

if (!editEntryId) {
  // List view: Select only display fields
  const displayfields = getDisplayfields(currentCollection, "list", {
    maxDisplayfields: 5,
  });

  query = query.select(displayfields);
  // Query returns: _id, status, createdAt, title, thumbnail
  // Instead of all 35 fields!
}

Performance Impact {#performance-impact-2}

Collection Config	Before	After	Reduction
20 fields, 5 displayed	450 KB	95 KB	79%
50 fields, 7 displayed	1.2 MB	180 KB	85%
100 entries × 20 fields	2.5 MB	520 KB	79%

Real-World Example:

Blog collection with 35 fields (rich text, metadata, SEO, images)
List view shows: title, status, author, date, thumbnail (5 fields)
Payload: 890 KB → 125 KB = 86% reduction
Page load: 2.3s → 0.4s = 5.75x faster

Compound Indexes (Query Optimization)

Compound indexes dramatically improve multi-field query performance.

auth_tokens (5 indexes)

// File: src/databases/mongodb/models/authToken.ts

// User's active tokens by type
// Query: Find all active reset tokens for user
// Before: 200ms (table scan), After: 5ms
TokenSchema.index({ user_id: 1, type: 1, expires: 1 });

// Email verification/reset queries
// Query: Find reset token by email
// Before: 180ms (table scan), After: 4ms
TokenSchema.index({ email: 1, type: 1, expires: 1 });

// Multi-tenant token queries
// Query: All active tokens for tenant
// Before: 250ms (table scan), After: 6ms
TokenSchema.index({ tenantId: 1, type: 1, expires: 1 });
TokenSchema.index({ tenantId: 1, user_id: 1, type: 1 });

// Active tokens by type (admin queries)
// Query: All non-blocked verification tokens
// Before: 300ms (table scan), After: 8ms
TokenSchema.index({ type: 1, expires: 1, blocked: 1 });

auth_sessions (5 indexes)

// File: src/databases/mongodb/models/authSession.ts

// User's active sessions
// Query: Get all active sessions for user
// Before: 150ms (table scan), After: 3ms
SessionSchema.index({ user_id: 1, expires: 1, rotated: 1 });

// Multi-tenant user sessions
// Query: Get user's sessions in specific tenant
// Before: 180ms (table scan), After: 5ms
SessionSchema.index({ tenantId: 1, user_id: 1, expires: 1 });

// Tenant-wide session queries
// Query: All active sessions for tenant
// Before: 200ms (table scan), After: 6ms
SessionSchema.index({ tenantId: 1, expires: 1, rotated: 1 });

// Find rotated/active sessions
// Query: All non-rotated active sessions
// Before: 120ms (table scan), After: 4ms
SessionSchema.index({ rotated: 1, expires: 1 });

// Session rotation chain lookups
// Query: Find session this rotated to
// Before: 100ms (table scan), After: 3ms
SessionSchema.index({ rotatedTo: 1 });

auth_users (9 indexes)

// File: src/databases/mongodb/models/authUser.ts

// Multi-tenant user lookup - MOST CRITICAL
// Query: Login by email in tenant
// Before: 100ms (table scan), After: 2ms
UserSchema.index({ tenantId: 1, email: 1 });

// Role-based queries per tenant
// Query: All non-blocked editors in tenant
// Before: 150ms (table scan), After: 4ms
UserSchema.index({ tenantId: 1, role: 1, blocked: 1 });

// Username lookup (sparse: only if username exists)
// Query: Find user by username in tenant
// Before: 80ms (table scan), After: 2ms
UserSchema.index({ tenantId: 1, username: 1 }, { sparse: true });

// Recent user activity queries
// Query: Most recently active users in tenant
// Before: 120ms (table scan), After: 3ms
UserSchema.index({ tenantId: 1, lastActiveAt: -1 });

// Admin user management queries
// Query: All registered admin users
// Before: 140ms (table scan), After: 5ms
UserSchema.index({ role: 1, blocked: 1, isRegistered: 1 });

// Auth method tracking
// Query: Users who last used OAuth
// Before: 90ms (table scan), After: 3ms
UserSchema.index({ email: 1, lastAuthMethod: 1 });

// 2FA user queries
// Query: All users with 2FA enabled in tenant
// Before: 110ms (table scan), After: 3ms
UserSchema.index({ is2FAEnabled: 1, tenantId: 1 });

// Security queries
// Query: Users with failed login attempts
// Before: 130ms (table scan), After: 4ms
UserSchema.index({ tenantId: 1, blocked: 1, failedAttempts: -1 });

// Recent registrations
// Query: Newest users in tenant
// Before: 100ms (table scan), After: 3ms
UserSchema.index({ tenantId: 1, createdAt: -1 });

system_content_structure (7 indexes)

// File: src/databases/mongodb/models/contentStructure.ts

// Hierarchical content queries
// Query: All child pages of parent, ordered
// Before: 200ms (table scan), After: 6ms
contentStructureSchema.index({ tenantId: 1, parentId: 1, order: 1 });

// Content type filtering
// Query: All published posts in tenant
// Before: 150ms (table scan), After: 4ms
contentStructureSchema.index({ tenantId: 1, nodeType: 1, status: 1 });

// URL routing - CRITICAL for every page load
// Query: Find page by path
// Before: 120ms (table scan), After: 2ms
contentStructureSchema.index({ tenantId: 1, path: 1 }, { unique: true, sparse: true });

// Slug-based lookups
// Query: Find content by slug in tenant
// Before: 100ms (table scan), After: 3ms
contentStructureSchema.index({ tenantId: 1, slug: 1 }, { sparse: true });

// Multi-language content
// Query: All German posts in tenant
// Before: 180ms (table scan), After: 5ms
contentStructureSchema.index({
  tenantId: 1,
  "translations.languageTag": 1,
  nodeType: 1,
});

// Child node ordering
// Query: All children of node, ordered
// Before: 140ms (table scan), After: 4ms
contentStructureSchema.index({ parentId: 1, order: 1, nodeType: 1 });

// Recent content by type
// Query: Latest 10 blog posts
// Before: 110ms (table scan), After: 3ms
contentStructureSchema.index({ nodeType: 1, updatedAt: -1 });

system_media (6 indexes + full-text)

// File: src/databases/mongodb/models/media.ts

// Unique hash for deduplication - CRITICAL
// Prevents duplicate uploads
mediaSchema.index({ hash: 1 }, { unique: true });

// Folder browsing with status filter
// Query: All published files in folder, newest first
// Before: 180ms (table scan), After: 5ms
mediaSchema.index({ folderId: 1, status: 1, createdAt: -1 });

// User's media library
// Query: All my published uploads
// Before: 150ms (table scan), After: 4ms
mediaSchema.index({ createdBy: 1, status: 1, createdAt: -1 });

// Filter by file type
// Query: All published images
// Before: 120ms (table scan), After: 3ms
mediaSchema.index({ mimeType: 1, status: 1 });

// Recent media by status
// Query: Latest published media
// Before: 100ms (table scan), After: 3ms
mediaSchema.index({ status: 1, updatedAt: -1 });

// Folder + type filtering
// Query: All published images in folder
// Before: 160ms (table scan), After: 5ms
mediaSchema.index({ folderId: 1, mimeType: 1, status: 1 });

// Full-text search on filenames
// Query: Search for "logo"
// Before: 300ms (regex scan), After: 10ms
mediaSchema.index({
  filename: "text",
  originalFilename: "text",
});

content_drafts (5 indexes)

// File: src/databases/mongodb/models/draft.ts

// Latest draft version for content
// Query: Get latest draft of post
// Before: 100ms (table scan), After: 3ms
draftSchema.index({ contentId: 1, version: -1 });

// Author's drafts by status
// Query: My pending drafts, newest first
// Before: 120ms (table scan), After: 4ms
draftSchema.index({ authorId: 1, status: 1, updatedAt: -1 });

// Content draft workflow
// Query: All pending drafts for post
// Before: 140ms (table scan), After: 4ms
draftSchema.index({ contentId: 1, status: 1, updatedAt: -1 });

// Author's recent drafts
// Query: My 10 most recent drafts
// Before: 90ms (table scan), After: 3ms
draftSchema.index({ authorId: 1, createdAt: -1 });

// Tenant-wide draft queries
// Query: All drafts in tenant
// Before: 110ms (table scan), After: 4ms
draftSchema.index({ tenantId: 1, status: 1, updatedAt: -1 });

content_revisions (4 indexes)

// File: src/databases/mongodb/models/revision.ts

// Revision history - MOST COMMON
// Query: All revisions for post, newest first
// Before: 150ms (table scan), After: 4ms
revisionSchema.index({ contentId: 1, version: -1, createdAt: -1 });

// User's revision activity
// Query: All my edits, newest first
// Before: 120ms (table scan), After: 4ms
revisionSchema.index({ authorId: 1, createdAt: -1 });

// Content-author revision tracking
// Query: All my edits on specific post
// Before: 140ms (table scan), After: 5ms
revisionSchema.index({ contentId: 1, authorId: 1, createdAt: -1 });

// Recent revisions across all content
// Query: Latest 100 edits system-wide
// Before: 100ms (table scan), After: 3ms
revisionSchema.index({ createdAt: -1 });

system_widgets (3 indexes)

// File: src/databases/mongodb/models/widget.ts

// Active widget lookup
// Query: Get active widget by name
// Before: 80ms (table scan), After: 2ms
widgetSchema.index({ isActive: 1, name: 1 });

// Enforce unique widget names - CRITICAL
// Prevents duplicate widget registration
widgetSchema.index({ name: 1 }, { unique: true });

// Recently modified active widgets
// Query: Recently updated widgets
// Before: 90ms (table scan), After: 3ms
widgetSchema.index({ isActive: 1, updatedAt: -1 });

⚙️ Connection Pool Configuration

Enterprise Connection Pool

The MongoDB adapter uses an optimized connection pool for enterprise-scale performance.

// File: src/databases/mongodb/mongodb-adapter.ts

const connectOptions: mongoose.ConnectOptions = {
  // Connection Pool Settings
  maxPoolSize: 10, // Optimized for CMS workloads
  minPoolSize: 2, // Maintain warm connections

  // Performance Optimizations
  compressors: ["zstd", "snappy"], // Wire compression: 40-60% bandwidth reduction
  heartbeatFrequencyMS: 10000, // Faster topology detection for replica sets

  // Timeout Settings
  serverSelectionTimeoutMS: 5000, // Fail fast on connection issues
  connectTimeoutMS: 5000, // Connection timeout
  socketTimeoutMS: 45000, // Prevent zombie connections (45s idle timeout)

  // Reliability Settings
  retryWrites: true, // Auto-retry failed writes
  retryReads: true, // Auto-retry transient read failures
  w: "majority", // Write concern for data durability
};

Configuration Benefits

Feature	Benefit	Impact
`maxPoolSize: 10`	Balanced for CMS concurrency	Efficient resource use
`minPoolSize: 2`	Pre-warmed connections	Fast first query
`retryWrites/Reads`	Auto-retry on failures	99.9% reliability
`heartbeatFrequency`	10s topology detection	Fast failover
`socketTimeoutMS`	45s zombie connection prevention	No hanging queries

Connection Pool Monitoring

// Monitor connection pool health
mongoose.connection.on("connected", () => {
  console.log("MongoDB connected");
});

mongoose.connection.on("disconnected", () => {
  console.log("MongoDB disconnected");
});

mongoose.connection.on("error", (err) => {
  console.error("MongoDB error:", err);
});

🚀 Advanced Features

Cursor Pagination {#advanced-cursor-pagination}

Cursor pagination provides O(1) time complexity regardless of page number, unlike offset pagination which degrades to O(n).

Implementation {#cursor-pagination-impl}

// File: src/databases/mongodb/MongoQueryBuilder.ts

async paginate(options: PaginateOptions): Promise<DatabaseResult<T[]>> {
  const { pageSize = 20, cursor, sortField = '_id', sortDirection = 'desc' } = options;

  let query = this.buildQuery();

  // If cursor provided, use it for pagination
  if (cursor) {
    const [field, value] = cursor.split(':');
    const operator = sortDirection === 'desc' ? '$lt' : '$gt';
    query = query.where(field)[operator] (value);
  }

  // Apply sorting and limit
  const sort = { [sortField]: sortDirection === 'desc' ? -1 : 1 };
  const results = await query
    .sort(sort)
    .limit(pageSize)
    .lean()
    .exec();

  return {
    success: true,
    data: results,
    meta: {
      pageSize,
      hasMore: results.length === pageSize,
      nextCursor: results.length > 0
        ? `${sortField}:${results[results.length - 1][sortField]}`
        : undefined
    }
  };
}

Usage Example {#cursor-pagination-usage}

// Page 1: No cursor needed
const page1 = await db
  .queryBuilder("posts")
  .paginate({
    pageSize: 20,
    sortField: "_id",
    sortDirection: "desc",
  })
  .execute();

console.log(`Loaded ${page1.data.length} posts`);
console.log(`Has more: ${page1.meta.hasMore}`);

// Page 2: Use cursor from page 1
const page2 = await db
  .queryBuilder("posts")
  .paginate({
    cursor: page1.meta.nextCursor, // '_id:507f1f77bcf86cd799439011'
    pageSize: 20,
    sortDirection: "desc",
  })
  .execute();

// Page 3: Use cursor from page 2
const page3 = await db
  .queryBuilder("posts")
  .paginate({
    cursor: page2.meta.nextCursor,
    pageSize: 20,
    sortDirection: "desc",
  })
  .execute();

Performance Comparison {#cursor-pagination-comparison}

Page Number	Offset Pagination	Cursor Pagination	Improvement
Page 1	10ms	10ms	0%
Page 10	50ms	10ms	80%
Page 50	5000ms	10ms	99.8%
Page 100	15000ms	10ms	99.9%

Key Insight: Cursor pagination maintains constant 10ms performance regardless of page number!

Streaming API

Streaming provides O(1) memory usage for processing large datasets, unlike fetching all records which uses O(n) memory.

Implementation {#implementation-2}

// File: src/databases/mongodb/MongoQueryBuilder.ts

async stream(): Promise<DatabaseResult<AsyncIterable<T>>> {
  try {
    const query = this.buildQuery();
    const cursor = query.cursor();

    // Async generator for streaming
    async function* streamGenerator() {
      for await (const doc of cursor) {
        yield processDates(doc.toObject()) as T;
      }
    }

    return {
      success: true,
      data: streamGenerator()
    };
  } catch (error) {
    return {
      success: false,
      error: error.message
    };
  }
}

Usage Examples {#streaming-usage}

Export Large Dataset

// Export 100,000 media files
const stream = await db.queryBuilder("media").where({ status: "published" }).stream();

if (stream.success) {
  let count = 0;

  for await (const media of stream.data) {
    await exportToFile(media); // Process one at a time
    count++;

    if (count % 1000 === 0) {
      console.log(`Exported ${count} files...`);
    }
  }

  console.log(`Total exported: ${count}`);
}

// Memory usage: ~50MB (constant)
// vs fetching all: ~2GB (scales with data)

Batch Processing

// Process all users for email campaign
const stream = await db
  .queryBuilder("users")
  .where({ emailVerified: true, blocked: false })
  .stream();

if (stream.success) {
  const batchSize = 100;
  let batch: User[] = [];

  for await (const user of stream.data) {
    batch.push(user);

    if (batch.length >= batchSize) {
      await sendEmailBatch(batch); // Send 100 at a time
      batch = [];
    }
  }

  // Send remaining
  if (batch.length > 0) {
    await sendEmailBatch(batch);
  }
}

Real-time Data Migration

// Migrate content to new schema
const stream = await db.queryBuilder("posts").stream();

if (stream.success) {
  for await (const post of stream.data) {
    // Transform data
    const migratedPost = {
      ...post,
      newField: computeNewField(post),
      // ... more transformations
    };

    // Update in database
    await db.update("posts", post.id, migratedPost);
  }
}

// Processes millions of records without memory issues

Performance Comparison {#performance-comparison-2}

Records	Fetch All (Memory)	Streaming (Memory)	Savings
10,000	200MB	50MB	75%
100,000	2GB	50MB	97.5%
1,000,000	20GB	50MB	99.75%

Query Hints

Query hints force MongoDB to use specific indexes or set execution limits.

Implementation {#implementation-3}

// File: src/databases/mongodb/MongoQueryBuilder.ts

hint(hints: OptimizationHints): this {
  this.optimizationHints = hints;
  return this;
}

private async executeQuery(): Promise<T[]> {
  let mongoQuery = this.buildQuery();

  // Apply hints
  if (this.optimizationHints) {
    if (this.optimizationHints.useIndex?.length) {
      mongoQuery = mongoQuery.hint(this.optimizationHints.useIndex[0]);
    }
    if (this.optimizationHints.maxExecutionTime) {
      mongoQuery = mongoQuery.maxTimeMS(this.optimizationHints.maxExecutionTime);
    }
  }

  return mongoQuery.lean().exec();
}

Usage Examples {#usage-examples-2}

Force Specific Index

// Force use of compound index for better performance
const users = await db
  .queryBuilder("users")
  .where({ tenantId: "acme", role: "editor" })
  .hint({ useIndex: ["tenantId_1_role_1_blocked_1"] })
  .execute();

// MongoDB will use the specified index instead of choosing automatically

Set Query Timeout

// Limit query execution time to prevent long-running queries
const results = await db
  .queryBuilder("analytics")
  .where({ date: { $gte: startDate, $lte: endDate } })
  .hint({ maxExecutionTime: 5000 }) // 5 second max
  .execute();

// Query aborts after 5 seconds, preventing resource exhaustion

Complex Query Optimization

// Force optimal index for complex multi-tenant query
const content = await db
  .queryBuilder("content")
  .where({
    tenantId: "acme",
    "translations.languageTag": "de",
    nodeType: "post",
    status: "published",
  })
  .hint({
    useIndex: ["tenantId_1_translations.languageTag_1_nodeType_1"],
    maxExecutionTime: 3000,
  })
  .execute();

.lean() Queries

All MongoDB queries use .lean() for optimal performance.

What is .lean()?

.lean() returns plain JavaScript objects instead of Mongoose documents, eliminating overhead.

// Without .lean() - Full Mongoose Document
const user = await User.findOne({ email: "user@example.com" });
// Returns: MongooseDocument with methods, getters, virtuals
// Memory: ~100KB per document
// Access: user.name (with getters/setters)

// With .lean() - Plain Object
const user = await User.findOne({ email: "user@example.com" }).lean();
// Returns: Plain JavaScript object
// Memory: ~60KB per document (40% less!)
// Access: user.name (direct property access)

Implementation {#implementation-4}

All CRUD methods in crudMethods.ts use .lean():

// File: src/databases/mongodb/crudMethods.ts

async findOne(query: FilterQuery<T>): Promise<T | null> {
  const result = await this.model
    .findOne(query)
    .lean()  // ✅ Always lean
    .exec();
  return processDates(result) as T;
}

async find(query: FilterQuery<T>): Promise<T[]> {
  const results = await this.model
    .find(query)
    .lean()  // ✅ Always lean
    .exec();
  return results.map(r => processDates(r) as T);
}

Performance Impact {#performance-impact-3}

Operation	Without .lean()	With .lean()	Improvement
Memory/doc	100KB	60KB	40%
Query time	50ms	30ms	40%
Iteration	10ms/1000	3ms/1000	70%

📊 Performance Monitoring

QueryMeta Tracking

All queries return metadata for performance monitoring.

interface QueryMeta {
  executionTime?: number; // Query execution time in ms
  cached?: boolean; // Was result from cache?
  indexUsed?: string; // Which index was used
  documentsScanned?: number; // How many docs scanned
}

Usage Example {#usage-example-2}

const result = await db.queryBuilder("users").where({ tenantId: "acme", role: "admin" }).execute();

console.log("Execution time:", result.meta?.executionTime, "ms");
console.log("Cached:", result.meta?.cached);
console.log("Index used:", result.meta?.indexUsed);

// Monitor slow queries
if (result.meta?.executionTime > 100) {
  Logger.warn(`Slow query detected: ${result.meta.executionTime}ms`);
}

Cache Metrics Integration

MongoDB queries integrate with the cache system for monitoring.

import { cacheMetrics } from "@src/databases/CacheMetrics";

// Get cache performance metrics
const metrics = cacheMetrics.getMetrics();

console.log("Cache hit rate:", metrics.hitRate);
console.log("Average response time:", metrics.averageResponseTime);
console.log("Total hits:", metrics.totalHits);
console.log("Total misses:", metrics.totalMisses);

// Per-category metrics
const userMetrics = cacheMetrics.getCategoryMetrics("USER");
console.log("User cache hit rate:", userMetrics.hitRate);

🎯 Best Practices

DO ✅

1. Use Cursor Pagination for Large Datasets

// ✅ Good: Cursor pagination (constant O(1) performance)
const page = await db
  .queryBuilder("posts")
  .paginate({
    cursor: lastCursor,
    pageSize: 20,
    sortField: "_id",
    sortDirection: "desc",
  })
  .execute();

// ❌ Bad: Offset pagination (degrades to O(n))
const page = await db
  .find("posts")
  .skip(pageNumber * 20) // Scans all skipped records
  .limit(20)
  .execute();

2. Use Streaming for Large Exports

// ✅ Good: Streaming (constant ~50MB memory)
const stream = await db.queryBuilder("media").stream();
for await (const item of stream.data) {
  await processItem(item);
}

// ❌ Bad: Load all at once (scales with data size)
const allMedia = await db.find("media", {}); // 2GB+ memory!
for (const item of allMedia) {
  await processItem(item);
}

3. Leverage Compound Indexes

// ✅ Good: Uses compound index (2ms)
const users = await db.find("users", {
  tenantId: "acme",
  role: "editor",
  blocked: false,
});
// Uses index: tenantId_1_role_1_blocked_1

// ❌ Bad: Only uses first field (50ms)
const users = await db.find("users", {
  role: "editor", // No index starts with 'role'
  tenantId: "acme",
  blocked: false,
});

4. Cache Frequently Accessed Data

// ✅ Good: Check cache first
const user = await CacheService.get("USER", `user:${userId}`);
if (!user) {
  const result = await db.findOne("users", { id: userId });
  if (result.success) {
    await CacheService.set("USER", `user:${userId}`, result.data);
  }
}

// ❌ Bad: Always hit database
const user = await db.findOne("users", { id: userId });

5. Monitor Query Performance

// ✅ Good: Monitor and alert on slow queries
const result = await db.queryBuilder("posts").execute();
if (result.meta?.executionTime > 100) {
  Logger.warn("Slow query", {
    collection: "posts",
    time: result.meta.executionTime,
    indexUsed: result.meta.indexUsed,
  });
}

DON’T ❌

1. Don’t Use Offset Pagination for Deep Pages

// ❌ Bad: Page 100 takes 15 seconds
const posts = await db
  .find("posts")
  .skip(100 * 20) // Scans 2000 records
  .limit(20)
  .execute();

2. Don’t Load Large Datasets into Memory

// ❌ Bad: 10GB memory for 1M records
const allUsers = await db.find("users", {});
for (const user of allUsers) {
  await sendEmail(user); // OOM crash!
}

3. Don’t Use Regex Without Indexes

// ❌ Bad: Full table scan (500ms)
const users = await db.find("users", {
  email: { $regex: /.*@example\.com/ },
});

// ✅ Good: Use text index (10ms)
// First add text index: userSchema.index({ email: 'text' })
const users = await db.find("users", {
  $text: { $search: "example.com" },
});

4. Don’t Forget Cache Invalidation

// ❌ Bad: Stale cache after update
await db.update("users", userId, { role: "admin" });
// Cache still has old role!

// ✅ Good: Invalidate cache after mutation
await db.update("users", userId, { role: "admin" });
await CacheService.delete("USER", `user:${userId}`);

5. Don’t Ignore Connection Pool Limits

// ❌ Bad: Creating 100 parallel connections
for (let i = 0; i < 100; i++) {
  promises.push(db.find("posts", {})); // Exceeds pool!
}

// ✅ Good: Batch with concurrency limit
import pLimit from "p-limit";
const limit = pLimit(10); // Max 10 concurrent
for (let i = 0; i < 100; i++) {
  promises.push(limit(() => db.find("posts", {})));
}

🔧 Maintenance & Monitoring

Index Health Check

// Check index usage statistics
const indexStats = await mongoose.connection.db
  .collection("auth_users")
  .aggregate([{ $indexStats: {} }])
  .toArray();

console.log("Index statistics:", indexStats);

// Look for:
// - Unused indexes (ops: 0)
// - High accesses (frequently used indexes)

Slow Query Logging

# Enable MongoDB slow query log
# In mongod.conf:
systemLog:
  verbosity: 1
  component:
    query:
      verbosity: 2  # Log slow queries

# View slow queries
tail -f /var/log/mongodb/mongod.log | grep "slow query"

Database Statistics

// Get collection statistics
const stats = await mongoose.connection.db.collection("auth_users").stats();

console.log("Total documents:", stats.count);
console.log("Average document size:", stats.avgObjSize);
console.log("Total index size:", stats.totalIndexSize);
console.log("Storage size:", stats.storageSize);

📈 Performance Benchmarks

For detailed performance metrics, stress test results, and cross-database comparisons, please refer to the Performance Benchmarks document.

Key MongoDB highlights include:

95-99% performance improvement across all operations compared to unoptimized queries.
Dual-Layer Caching (Redis + MongoDB) delivering sub-millisecond response times for 92% of requests.
O(1) deep pagination and streaming support for large datasets.

🚀 Quick Reference

Cursor Pagination {#cursor-pagination-2}

const page = await db
  .queryBuilder("posts")
  .paginate({ cursor: lastCursor, pageSize: 20 })
  .execute();

Streaming

const stream = await db.queryBuilder("media").stream();
for await (const item of stream.data) {
  await processItem(item);
}

Query Hints {#query-hints-2}

const result = await db
  .queryBuilder("users")
  .hint({ useIndex: ["tenantId_1_email_1"] })
  .execute();

Cache Integration

const cached = await CacheService.get("USER", key);
if (!cached) {
  const result = await db.findOne("users", { id });
  await CacheService.set("USER", key, result.data);
}

Performance Monitoring

const result = await db.queryBuilder("posts").execute();
console.log("Time:", result.meta?.executionTime, "ms");
console.log("Cached:", result.meta?.cached);

📚 Related Documentation

Core Infrastructure - Database-agnostic architecture
Cache System - Dual-layer caching strategy (Redis + MongoDB)
Authentication System - Auth & session management

Summary

The MongoDB implementation provides:

✅ Dual-Layer Caching (Redis + MongoDB, 85-95% hit rate, 56x-160x faster)
✅ 29 System Indexes (4 TTL + 25 compound for auth/content/media)
✅ Dynamic Collection Indexing (9+ indexes per collection, auto-applied)
✅ Field Selection Optimization (50-80% payload reduction for list views)
✅ Enterprise Connection Pool (50 max, 10 min, compression)
✅ Cursor Pagination (O(1) time, 99.9% faster for deep pages)
✅ Streaming API (O(1) memory, 97% memory savings)
✅ Query Hints (Force indexes, set timeouts)
✅ .lean() Queries (40% memory reduction, 40% faster)
✅ Cache Integration (Redis primary, MongoDB fallback)
✅ Performance Monitoring (QueryMeta tracking)

Result: 95-99% performance improvement across all operations, enterprise-ready for production at scale! 🚀

Recent Enhancements

Dual-Layer Caching System: Redis + MongoDB with 8 category-specific TTLs
Dynamic Collection Indexing: Every user collection gets optimized indexes automatically
Field Selection: List views load only necessary fields (5-7 instead of 20+)
Cache Warming: Zero-restart setup with pre-cached first load
Combined Impact: 10x-160x faster operations depending on cache state

Security Hardening

NoSQL Injection Protection: sanitizeMongoQuery() in src/utils/security/mongo-sanitize.ts recursively blocks dangerous MongoDB operators ($where, $function, $expr, $accumulator) at the mapQuery level — every query, including fast-path ID lookups, is sanitized before reaching the database.
ReDoS Prevention: $regex patterns validated to maximum 500 characters, preventing catastrophic backtracking attacks.
Soft-Delete Filter Enforcement: safeQuery() now always applies the isDeleted: { $ne: true } filter unless explicitly opted out — closing a gap where callers could bypass soft-deletion by setting isDeleted in their query.
Website Token Parity: website-token-methods.ts declares tenantId and isDeleted on the Mongoose schema (strict: true), routes all CRUD through safeQuery (no bypassSafeQuery), and aligns with the SQL ISystemAdapter.websiteTokens contract. See Credential Storage.

Runtime Compatibility

Bun Runtime Alignment: The MongoDB adapter now runs natively under bun test — no need for vitest/Node.js. A v8 shim in src/utils/v8-shim.ts patches process.getBuiltinModule('v8') to handle the bson package’s isBuildingSnapshot() call, which Bun (JavaScriptCore) doesn’t implement. All 4 database adapters (MariaDB, PostgreSQL, SQLite, MongoDB) use bun test uniformly.
Reserved Key Warning Suppression: The generic Mongoose schema in adapter-core.ts now passes suppressReservedKeysWarning: true to avoid warnings when content documents contain a field named collection (a Mongoose reserved pathname).
Self-Healing Shutdown Guard: During intentional system reinitialization (POST /api/system/reinitialize), the __SYSTEM_SHUTTING_DOWN__ flag prevents the resilience hooks from scheduling competing auto-reconnections, eliminating MongoNotConnectedError race conditions during state-machine stress tests.

Performance Summary

MongoDB delivers enterprise-ready performance with sub-1ms CRUD (0.798 ms FIND ONE, 0.853 ms INSERT) and significant optimizations for large-scale content management. For the full breakdown of metrics, see the Performance Benchmarks page.

For detailed implementation information, see:

Cache System Architecture
Performance Architecture
✅ Query Hints (Force indexes, set timeouts)
✅ .lean() Queries (40% memory reduction, 40% faster)
✅ Cache Integration (92% hit rate)
✅ Performance Monitoring (QueryMeta tracking)

Result: Enterprise-ready performance architecture delivering 95-99% improvement across all operations 🚀

Production Architecture (2024)

Dual-Layer Caching: Redis + MongoDB cache with 85-95% hit rate
Dynamic Collection Indexing: Every collection gets optimized indexes automatically
Field Selection: Smart payload optimization (50-80% reduction)
Combined Performance: Sub-1ms CRUD: 0.798 ms FIND ONE, 0.853 ms INSERT

For complete architecture details, see: