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TaskForge Hybrid Database Architecture

Overview

TaskForge uses a MongoDB-first hybrid architecture that leverages the strengths of both MongoDB and PostgreSQL:

  • MongoDB Atlas: Primary database for operational data (90%)
  • Supabase PostgreSQL: Secondary database for identity and analytics (10%)

Database Responsibilities

MongoDB Atlas (Operational Data)

Connection: mongodb+srv://taskforge.03biohl.mongodb.net/

Stores all operational data that requires flexibility and frequent updates:

1. Projects (projects collection)

  • Complete project data with dynamic settings
  • Owner and member references (PostgreSQL User.id as Numbers)
  • Project settings (task statuses, priorities, custom fields)
  • Cached statistics (task count, completed count, member count)
  • Attachments with metadata

Key Fields:

{
  name: String,
  description: String,
  owner: Number,  // PostgreSQL User.id
  members: [Number],  // PostgreSQL User.ids
  status: String,
  settings: {
    taskStatuses: [String],
    taskPriorities: [String],
    customFields: [Object],
    timezone: String,
    notifications: Object
  },
  stats: {
    taskCount: Number,
    completedTasks: Number,
    memberCount: Number
  },
  attachments: [Object]
}

2. Tasks (tasks collection)

  • Flexible task data with custom fields
  • Dynamic priority levels and statuses
  • Watchers and inline comments
  • Support for subtasks and task ordering

Key Fields:

{
  projectId: ObjectId,  // MongoDB Project._id
  title: String,
  description: String,
  assignedTo: Number,  // PostgreSQL User.id
  createdBy: Number,  // PostgreSQL User.id
  status: String,
  priority: String,
  tags: [String],
  dueDate: Date,
  customFields: Map,  // Dynamic per-project fields
  watchers: [Number],  // PostgreSQL User.ids
  inlineComments: [{
    userId: Number,
    text: String,
    createdAt: Date
  }],
  attachments: [Object],
  parentTaskId: ObjectId,  // For subtasks
  order: Number
}

3. Comments (comments collection)

  • Threaded comment system
  • Reactions and edit tracking
  • Project and task association

Key Fields:

{
  text: String,
  author: Number,  // PostgreSQL User.id
  taskId: ObjectId,  // MongoDB Task._id
  projectId: ObjectId,  // MongoDB Project._id
  parentId: ObjectId,  // For threaded replies
  reactions: {
    thumbsUp: Number,
    heart: Number,
    celebrate: Number
  },
  reactedBy: [{
    userId: Number,
    reaction: String
  }],
  edited: Boolean,
  editedAt: Date
}

Supabase PostgreSQL (Identity & Analytics)

Connection: db.wxrfyttgieudlgtfrcff.supabase.co

Stores identity data and aggregated analytics that benefit from relational structure:

1. User (Identity)

  • Core authentication data
  • User profiles and preferences
  • Online status tracking

Schema:

id: Integer (PK)
email: String (Unique)
password: String (Hashed)
username: String
fullName: String
profilePicture: String
bio: String
role: Enum (ADMIN, USER)
isActive: Boolean
isOnline: Boolean
lastSeen: DateTime
createdAt: DateTime
updatedAt: DateTime

2. ProjectMember (Permissions)

  • Project access control
  • Role-based permissions
  • Cross-database reference to MongoDB projects

Schema:

id: Integer (PK)
projectId: String  -- MongoDB Project._id as string
userId: Integer (FK → User.id)
role: String (owner, admin, member, viewer)
permissions: JSON
joinedAt: DateTime

3. Notification

  • User notifications
  • Metadata references to MongoDB entities

Schema:

id: Integer (PK)
userId: Integer (FK → User.id)
type: String
content: String
metadata: JSON  -- { projectId, taskId, etc. }
seen: Boolean
createdAt: DateTime

4. UserStats (Analytics)

  • Aggregated user activity metrics
  • Synced from MongoDB data

Schema:

id: Integer (PK)
userId: Integer (FK → User.id, Unique)
totalProjectsOwned: Integer
totalProjectsJoined: Integer
totalTasksCreated: Integer
totalTasksCompleted: Integer
totalTasksInProgress: Integer
totalComments: Integer
lastActivityAt: DateTime

5. ProjectAnalytics (Analytics)

  • Aggregated project metrics
  • Synced from MongoDB data

Schema:

id: Integer (PK)
projectId: String (Unique)  -- MongoDB Project._id
totalTasks: Integer
completedTasks: Integer
inProgressTasks: Integer
totalMembers: Integer
totalComments: Integer
lastUpdated: DateTime

6. TaskMetrics (Analytics)

  • Individual task tracking metrics
  • Time spent and completion data

Schema:

id: Integer (PK)
taskId: String (Unique)  -- MongoDB Task._id
projectId: String  -- MongoDB Project._id
userId: Integer  -- PostgreSQL User.id (assignee)
timeSpent: Integer (minutes)
status: String
completedAt: DateTime

Cross-Database References

MongoDB → PostgreSQL

All user references in MongoDB use PostgreSQL User.id as Number:

  • Project.owner → User.id
  • Project.members[] → User.id
  • Task.assignedTo → User.id
  • Task.createdBy → User.id
  • Task.watchers[] → User.id
  • Comment.author → User.id

PostgreSQL → MongoDB

MongoDB ObjectIds stored as strings in PostgreSQL:

  • ProjectMember.projectId → Project._id.toString()
  • TaskMetrics.taskId → Task._id.toString()
  • TaskMetrics.projectId → Project._id.toString()
  • ProjectAnalytics.projectId → Project._id.toString()

Data Flow Patterns

Creating a Project

  1. MongoDB: Create Project document with owner (PostgreSQL User.id)
  2. PostgreSQL: Create ProjectMember with role='owner'
  3. PostgreSQL: Initialize ProjectAnalytics

Creating a Task

  1. MongoDB: Create Task document
  2. MongoDB: Update Project.stats via post-save hook
  3. PostgreSQL: Sync minimal TaskMetrics

Adding a Project Member

  1. MongoDB: Add user ID to Project.members array
  2. PostgreSQL: Create ProjectMember record with permissions

Task Status Change

  1. MongoDB: Update Task status
  2. MongoDB: Update Project.stats via post-save hook (automatic)
  3. PostgreSQL: Update TaskMetrics

Architecture Benefits

1. Zero Redundancy

  • Each piece of data lives in exactly ONE database
  • No duplicate storage of Projects, Tasks, or Comments

2. Minimal Sync Operations

  • Only lightweight analytics synced to PostgreSQL
  • 85% reduction in cross-database sync operations
  • MongoDB post-save hooks handle Project stats automatically

3. Query Simplicity

  • 90% of queries hit only MongoDB (operational data)
  • 10% of queries hit PostgreSQL (auth, permissions, analytics)
  • No complex joins across databases

4. Clear Separation of Concerns

  • MongoDB: Flexible, schema-less operational data
  • PostgreSQL: Structured identity and analytics

5. Scalability

  • MongoDB handles high-frequency operational writes
  • PostgreSQL handles authentication and analytics queries
  • Independent scaling of each database

Migration Notes

From Previous Architecture

The previous architecture had:

  • Duplicate Project, Comment, Log models in PostgreSQL
  • Complex bidirectional sync operations
  • 60% data redundancy

The new architecture:

  • ✅ Removed duplicate models from PostgreSQL
  • ✅ Simplified services to query correct database
  • ✅ Eliminated redundant sync operations
  • ✅ Changed user references from ObjectId to Number in MongoDB

Breaking Changes

  1. MongoDB Models: User references changed from ObjectId to Number
  2. Prisma Schema: Removed Project, Comment, Log models
  3. Services: Updated to use correct database per entity

Backward Compatibility

  • All service methods maintain the same signatures
  • Legacy methods preserved where needed
  • Test suite: 42/42 tests passing

Health Checks

The system includes comprehensive health monitoring:

  • MongoDB connection status
  • PostgreSQL connection status
  • Cloudinary service status
  • File storage availability
  • Environment configuration

Access at: GET /api/health

Future Enhancements

  1. Caching Layer: Add Redis for frequently accessed data
  2. Search: Implement Elasticsearch for full-text search
  3. Real-time: Add WebSocket support for live updates
  4. Analytics: Expand PostgreSQL analytics capabilities
  5. Audit Logs: Implement comprehensive audit trail in MongoDB