Back-End Modules
Asynchronous Processing

Asynchronous Processing Module

Overview

The Asynchronous Processing Module is responsible for managing and executing background jobs and long-running tasks in PDeck. This module ensures that resource-intensive operations don't block the main application thread, maintaining responsiveness and scalability of the system.

Key Responsibilities

  1. Manage job queues for various types of background tasks
  2. Schedule and execute jobs asynchronously
  3. Handle job priorities and concurrency
  4. Provide job status updates and error handling
  5. Implement retry mechanisms for failed jobs
  6. Manage distributed job processing across multiple workers

Technology Stack

  • Node.js for the core implementation
  • Bull for job queue management
  • Redis for storing job data and as a message broker
  • PM2 or similar for process management and scaling

Implementation Guidelines

1. Setting Up the Asynchronous Processing Module

Create a new module for managing asynchronous jobs:

// services/asyncProcessingService.js
const Bull = require('bull')
const Redis = require('ioredis')
 
class AsyncProcessingService {
  constructor() {
    this.redis = new Redis(process.env.REDIS_URL)
    this.queues = {}
    this.setupQueues()
  }
 
  setupQueues() {
    const queueConfigs = [
      { name: 'taskPrioritization', concurrency: 5 },
      { name: 'dataSync', concurrency: 3 },
      { name: 'notificationDispatch', concurrency: 10 },
      { name: 'reportGeneration', concurrency: 2 }
    ]
 
    queueConfigs.forEach(config => {
      this.queues[config.name] = new Bull(config.name, {
        redis: this.redis,
        defaultJobOptions: {
          removeOnComplete: true,
          removeOnFail: false
        }
      })
      this.setupQueueProcessor(config.name, config.concurrency)
    })
  }
 
  setupQueueProcessor(queueName, concurrency) {
    this.queues[queueName].process(concurrency, async (job) => {
      const processorPath = `./jobProcessors/${queueName}Processor`
      const processor = require(processorPath)
      return await processor(job.data)
    })
  }
 
  // ... (other methods)
}
 
module.exports = new AsyncProcessingService()

2. Implementing Job Addition and Management

Create methods to add jobs to queues and manage their lifecycle:

class AsyncProcessingService {
  // ... (previous code)
 
  async addJob(queueName, data, options = {}) {
    if (!this.queues[queueName]) {
      throw new Error(`Queue ${queueName} not found`)
    }
    return await this.queues[queueName].add(data, options)
  }
 
  async getJobStatus(queueName, jobId) {
    const queue = this.queues[queueName]
    const job = await queue.getJob(jobId)
    if (!job) {
      return null
    }
    const state = await job.getState()
    return {
      id: job.id,
      state,
      progress: job.progress(),
      failedReason: job.failedReason,
      stacktrace: job.stacktrace,
      returnvalue: job.returnvalue
    }
  }
 
  async removeJob(queueName, jobId) {
    const queue = this.queues[queueName]
    const job = await queue.getJob(jobId)
    if (job) {
      await job.remove()
      return true
    }
    return false
  }
 
  async pauseQueue(queueName) {
    await this.queues[queueName].pause()
  }
 
  async resumeQueue(queueName) {
    await this.queues[queueName].resume()
  }
}

3. Implementing Job Processors

Create separate files for each job processor:

// jobProcessors/taskPrioritizationProcessor.js
const TaskPrioritizationEngine = require('../services/taskPrioritizationEngine')
 
module.exports = async (data) => {
  const { userId } = data
  await TaskPrioritizationEngine.prioritizeTasks(userId)
  return { success: true, message: 'Tasks prioritized successfully' }
}
 
// jobProcessors/dataSyncProcessor.js
const ExternalIntegrationsService = require('../services/externalIntegrationsService')
 
module.exports = async (data) => {
  const { userId, integrationType } = data
  await ExternalIntegrationsService.syncData(userId, integrationType)
  return { success: true, message: 'Data synced successfully' }
}
 
// Similar processors for notificationDispatch and reportGeneration

4. Implementing Error Handling and Retries

Add error handling and retry logic:

class AsyncProcessingService {
  // ... (previous code)
 
  setupQueueProcessor(queueName, concurrency) {
    this.queues[queueName].process(concurrency, async (job) => {
      const processorPath = `./jobProcessors/${queueName}Processor`
      const processor = require(processorPath)
      try {
        return await processor(job.data)
      } catch (error) {
        console.error(`Error processing job ${job.id} in queue ${queueName}:`, error)
        throw error // This will trigger a retry if retries are configured
      }
    })
 
    this.queues[queueName].on('failed', (job, err) => {
      console.error(`Job ${job.id} in queue ${queueName} failed with error:`, err)
    })
  }
 
  async addJob(queueName, data, options = {}) {
    const defaultOptions = {
      attempts: 3,
      backoff: {
        type: 'exponential',
        delay: 1000
      }
    }
    return await this.queues[queueName].add(data, { ...defaultOptions, ...options })
  }
}

5. Implementing Job Progress Tracking

Add methods to track and update job progress:

class AsyncProcessingService {
  // ... (previous code)
 
  async updateJobProgress(queueName, jobId, progress) {
    const queue = this.queues[queueName]
    const job = await queue.getJob(jobId)
    if (job) {
      await job.progress(progress)
      return true
    }
    return false
  }
}
 
// Usage in a job processor
module.exports = async (data) => {
  const { jobId } = data
  await AsyncProcessingService.updateJobProgress('taskPrioritization', jobId, 10)
  // Do some work
  await AsyncProcessingService.updateJobProgress('taskPrioritization', jobId, 50)
  // Do more work
  await AsyncProcessingService.updateJobProgress('taskPrioritization', jobId, 100)
  return { success: true, message: 'Job completed successfully' }
}

6. Implementing Scheduled Jobs

Add support for scheduled and recurring jobs:

class AsyncProcessingService {
  // ... (previous code)
 
  async scheduleRecurringJob(queueName, data, cronExpression) {
    return await this.queues[queueName].add(data, {
      repeat: { cron: cronExpression }
    })
  }
 
  async scheduleDelayedJob(queueName, data, delay) {
    return await this.queues[queueName].add(data, {
      delay: delay
    })
  }
 
  async removeRecurringJob(queueName, jobId) {
    const queue = this.queues[queueName]
    const repeatableJobs = await queue.getRepeatableJobs()
    const job = repeatableJobs.find(j => j.id === jobId)
    if (job) {
      await queue.removeRepeatableByKey(job.key)
      return true
    }
    return false
  }
}

7. Implementing Queue Monitoring

Add methods to monitor queue health and performance:

class AsyncProcessingService {
  // ... (previous code)
 
  async getQueueStats(queueName) {
    const queue = this.queues[queueName]
    const [
      jobCounts,
      completedCount,
      failedCount,
      delayedCount,
      activeCount,
      waitingCount,
      pausedCount
    ] = await Promise.all([
      queue.getJobCounts(),
      queue.getCompletedCount(),
      queue.getFailedCount(),
      queue.getDelayedCount(),
      queue.getActiveCount(),
      queue.getWaitingCount(),
      queue.getPausedCount()
    ])
 
    return {
      jobCounts,
      completedCount,
      failedCount,
      delayedCount,
      activeCount,
      waitingCount,
      pausedCount
    }
  }
 
  async cleanOldJobs(queueName, grace, status) {
    return await this.queues[queueName].clean(grace, status)
  }
}

Best Practices

  1. Use appropriate job priorities to ensure critical tasks are processed first.
  2. Implement proper error handling and logging for all job processors.
  3. Use reasonable retry strategies to handle transient failures.
  4. Monitor queue depths and processing times to identify bottlenecks.
  5. Implement job timeouts to prevent stuck jobs from blocking the queue.
  6. Use separate queues for different types of jobs to prevent slow jobs from blocking fast ones.
  7. Implement a dead-letter queue for jobs that consistently fail after retries.

Next Steps

  1. Implement a user interface for monitoring job queues and managing jobs.
  2. Develop more sophisticated retry strategies based on error types.
  3. Implement job dependencies to allow for complex workflows.
  4. Create a system for dynamically scaling worker processes based on queue load.
  5. Implement job result caching for frequently run jobs with consistent results.
  6. Develop a testing framework for job processors to ensure reliability.
  7. Implement a notification system for critical job failures.
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