Database and Caching Module

Overview

The Database and Caching Module is responsible for efficient data storage, retrieval, and caching in PDeck. This module ensures that the application can handle large amounts of data while maintaining high performance and responsiveness.

Key Responsibilities

Manage database connections and operations
Implement efficient data models and schemas
Handle data migrations and versioning
Implement caching strategies for frequently accessed data
Manage cache invalidation and consistency
Provide an abstraction layer for database and cache operations
Implement data backup and recovery strategies

Technology Stack

MongoDB for primary data storage
Mongoose as an ODM (Object Document Mapper) for MongoDB
Redis for caching and as a secondary data store
Node.js for the core implementation

Implementation Guidelines

1. Setting Up the Database Module

Create a new module for managing database connections and operations:

// services/databaseService.js
const mongoose = require('mongoose')
const Redis = require('ioredis')
 
class DatabaseService {
  constructor() {
    this.mongoose = mongoose
    this.redis = new Redis(process.env.REDIS_URL)
  }
 
  async connect() {
    await this.mongoose.connect(process.env.MONGODB_URI, {
      useNewUrlParser: true,
      useUnifiedTopology: true,
      useCreateIndex: true,
      useFindAndModify: false
    })
    console.log('Connected to MongoDB')
  }
 
  async disconnect() {
    await this.mongoose.disconnect()
    await this.redis.quit()
    console.log('Disconnected from MongoDB and Redis')
  }
 
  // ... (other methods)
}
 
module.exports = new DatabaseService()

2. Implementing Data Models

Create Mongoose schemas and models for PDeck entities:

// models/Task.js
const mongoose = require('mongoose')
 
const taskSchema = new mongoose.Schema({
  title: { type: String, required: true },
  description: String,
  userId: { type: mongoose.Schema.Types.ObjectId, ref: 'User', required: true },
  priority: { type: Number, default: 0 },
  dueDate: Date,
  status: { type: String, enum: ['TODO', 'IN_PROGRESS', 'DONE'], default: 'TODO' },
  tags: [String],
  createdAt: { type: Date, default: Date.now },
  updatedAt: { type: Date, default: Date.now }
})
 
taskSchema.index({ userId: 1, priority: -1 })
taskSchema.index({ userId: 1, dueDate: 1 })
 
module.exports = mongoose.model('Task', taskSchema)
 
// models/User.js
const mongoose = require('mongoose')
 
const userSchema = new mongoose.Schema({
  email: { type: String, required: true, unique: true },
  password: { type: String, required: true },
  name: String,
  preferences: {
    theme: { type: String, default: 'light' },
    notifications: { type: Boolean, default: true }
  },
  createdAt: { type: Date, default: Date.now }
})
 
module.exports = mongoose.model('User', userSchema)
 
// Add more models as needed (e.g., Project, Integration, etc.)

3. Implementing Data Access Layer

Create a data access layer to abstract database operations:

// services/taskService.js
const Task = require('../models/Task')
const cacheService = require('./cacheService')
 
class TaskService {
  async createTask(taskData) {
    const task = new Task(taskData)
    await task.save()
    await cacheService.invalidateUserTasks(taskData.userId)
    return task
  }
 
  async getUserTasks(userId) {
    const cacheKey = `user:${userId}:tasks`
    const cachedTasks = await cacheService.get(cacheKey)
    if (cachedTasks) {
      return JSON.parse(cachedTasks)
    }
 
    const tasks = await Task.find({ userId }).sort({ priority: -1 })
    await cacheService.set(cacheKey, JSON.stringify(tasks), 300) // Cache for 5 minutes
    return tasks
  }
 
  async updateTask(taskId, updateData) {
    const task = await Task.findByIdAndUpdate(taskId, updateData, { new: true })
    if (task) {
      await cacheService.invalidateUserTasks(task.userId)
    }
    return task
  }
 
  async deleteTask(taskId) {
    const task = await Task.findByIdAndDelete(taskId)
    if (task) {
      await cacheService.invalidateUserTasks(task.userId)
    }
    return task
  }
 
  // ... (other methods)
}
 
module.exports = new TaskService()

4. Implementing Caching Service

Create a caching service to manage Redis operations:

// services/cacheService.js
const Redis = require('ioredis')
 
class CacheService {
  constructor() {
    this.redis = new Redis(process.env.REDIS_URL)
  }
 
  async get(key) {
    return await this.redis.get(key)
  }
 
  async set(key, value, expirationInSeconds) {
    await this.redis.set(key, value, 'EX', expirationInSeconds)
  }
 
  async del(key) {
    await this.redis.del(key)
  }
 
  async invalidateUserTasks(userId) {
    await this.del(`user:${userId}:tasks`)
  }
 
  // ... (other methods)
}
 
module.exports = new CacheService()

5. Implementing Data Migrations

Use a migration tool like migrate-mongo to manage database schema changes:

// migrations/20230615120000-add-task-tags.js
module.exports = {
  async up(db) {
    await db.collection('tasks').updateMany({}, {
      $set: { tags: [] }
    })
  },
 
  async down(db) {
    await db.collection('tasks').updateMany({}, {
      $unset: { tags: "" }
    })
  }
}

6. Implementing Backup and Recovery

Create scripts for database backup and recovery:

// scripts/backup.js
const { exec } = require('child_process')
const fs = require('fs')
 
const backupDir = './backups'
if (!fs.existsSync(backupDir)) {
  fs.mkdirSync(backupDir)
}
 
const timestamp = new Date().toISOString().replace(/:/g, '-')
const backupFile = `${backupDir}/backup-${timestamp}.gz`
 
const command = `mongodump --uri "${process.env.MONGODB_URI}" --gzip --archive=${backupFile}`
 
exec(command, (error, stdout, stderr) => {
  if (error) {
    console.error(`Backup failed: ${error}`)
    return
  }
  console.log(`Backup completed: ${backupFile}`)
})
 
// scripts/restore.js
const { exec } = require('child_process')
 
const backupFile = process.argv[2]
if (!backupFile) {
  console.error('Please provide a backup file path')
  process.exit(1)
}
 
const command = `mongorestore --uri "${process.env.MONGODB_URI}" --gzip --archive=${backupFile}`
 
exec(command, (error, stdout, stderr) => {
  if (error) {
    console.error(`Restore failed: ${error}`)
    return
  }
  console.log('Restore completed successfully')
})

7. Implementing Database Monitoring

Create a service for monitoring database health and performance:

// services/databaseMonitorService.js
const mongoose = require('mongoose')
 
class DatabaseMonitorService {
  async getStatus() {
    return {
      isConnected: mongoose.connection.readyState === 1,
      dbName: mongoose.connection.name,
      host: mongoose.connection.host,
      port: mongoose.connection.port
    }
  }
 
  async getCollectionStats() {
    const stats = {}
    const collections = await mongoose.connection.db.listCollections().toArray()
    for (const collection of collections) {
      stats[collection.name] = await mongoose.connection.db.collection(collection.name).stats()
    }
    return stats
  }
 
  async runHealthCheck() {
    try {
      await mongoose.connection.db.admin().ping()
      return { status: 'ok', message: 'Database is responsive' }
    } catch (error) {
      return { status: 'error', message: error.message }
    }
  }
 
  // ... (other monitoring methods)
}
 
module.exports = new DatabaseMonitorService()

Best Practices

Use indexes wisely to optimize query performance.
Implement data validation at the schema level to ensure data integrity.
Use transactions for operations that involve multiple documents or collections.
Implement proper error handling and logging for all database operations.
Use connection pooling to manage database connections efficiently.
Implement a caching strategy that balances between data freshness and performance.
Regularly monitor and optimize database performance.
Implement data partitioning or sharding for large-scale data.
Use read replicas to distribute read operations and improve performance.
Implement proper security measures, including data encryption and access controls.

Next Steps

Implement more advanced caching strategies, such as write-through or write-behind caching.
Develop a system for automatically scaling database resources based on load.
Implement a data archiving strategy for old or infrequently accessed data.
Create a dashboard for real-time monitoring of database and cache performance.
Implement data analytics and reporting features using aggregation pipelines.
Develop a strategy for handling eventual consistency in a distributed system.
Implement a system for managing and rotating database credentials securely.

Task Prioritization Engine Front End