Background Job Processing

Overview

The Background Job Processing pattern handles asynchronous, long-running, or resource-intensive work without blocking user-facing operations. This pattern separates immediate response requirements from time-consuming processing, enabling applications to maintain responsiveness while handling complex operations in the background.

Use this pattern when building:

Data processing pipelines and batch operations
Email/notification sending systems
Report generation and analytics processing
Integration with external systems and APIs
Scheduled tasks and maintenance operations
Image/video processing workflows

Architecture Diagram

flowchart TB
    Client[Client Request]
    ServiceAPI[Service API]
    Client --> ServiceAPI

    subgraph Submission ["Job Submission"]
        ServiceAPI --> Validate[Input Validation]
        Validate --> Queue[Queue]
        ServiceAPI --> KvCache["KV Cache<br/>Job Status"]
        ServiceAPI --> JobID[Return Job ID]
    end

    subgraph JobProcessing ["Job Processing"]
        Queue --> Task1[Task Worker 1]
        Queue --> Task2[Task Worker 2]
        Queue --> TaskN[Task Worker N]

        Task1 --> Processing[Job Processing]
        Task2 --> Processing
        TaskN --> Processing
    end

    subgraph Monitoring ["Monitoring & Recovery"]
        Observer[Observer] --> Queue
        Observer --> KvCache
        Observer --> DeadLetter[Dead Letter Queue]
        Processing --> Observer
        Processing --> KvCache
    end

    subgraph Status ["Status & Results"]
        KvCache --> StatusAPI[Status API]
        Processing --> Results[Job Results]
        Results --> KvCache
        StatusAPI --> Client
    end

    JobID --> Client

Components

Queue - Central job distribution with durability, priority handling, and automatic retry mechanisms
Task - Background processing workers that pull jobs and execute work with fault tolerance
Observer - Monitoring component tracking processing metrics, failure patterns, and system health
Service - API gateway handling job submission, validation, and status queries
KV Cache - Fast storage for job metadata, status tracking, and result caching

Logical Flow

Job Submission - Clients submit work requests to Service API which validates parameters and business rules
Queue Insertion - Validated jobs inserted into Queue with priority, metadata, and processing requirements
Job Distribution - Task workers pull jobs from Queue based on capacity and configured job types
Background Processing - Task workers execute job logic, updating progress in KV Cache and handling errors
Status Tracking - Observer monitors progress and updates status; failed jobs retried or moved to dead letter queues
Result Storage - Completed jobs store results in KV Cache with configurable retention policies
Status API - Clients query job status and retrieve results through Service endpoints with real-time updates

Implementation

Configure Queue - Deploy Queue with appropriate throughput settings, durability guarantees, and retry policies
Deploy Task Workers - Create Task components configured for specific job types with resource limits and error handling
Set Up Monitoring - Configure Observer to track key metrics and provide alerts for failures and performance issues
Implement Service API - Create endpoints for job submission, status queries, and result retrieval
Production Setup - Add authentication, rate limiting, job prioritization, resource quotas, and external monitoring integration

application "background_processor" {

  service "job_api" {
  }

  queue "job_queue" {
  }

  task "job_worker" {
  }

  kv_cache "job_status" {
  }

  observer "job_monitor" {
  }

}

application "high_throughput_processor" {

  service "job_api" {
  }

  queue "job_queue" {
  }

  task "job_worker" {
  }

  task "heavy_worker" {
  }

  kv_cache "job_status" {
  }

  kv_cache "job_results" {
  }

  observer "job_monitor" {
  }

}

Best Practices

Make jobs idempotent - Design jobs to produce same results when run multiple times for safe retries
Keep jobs focused - Break large operations into smaller, manageable jobs that can be processed independently
Include sufficient context - Ensure job payloads contain all necessary information without external dependencies
Set appropriate timeouts - Configure realistic timeout values with buffer for processing variability
Implement complete retry logic - Use exponential backoff and maximum retry limits for transient failures
Design dead letter processing - Create workflows for investigating and resolving permanently failed jobs
Log detailed error information - Capture sufficient debug context without exposing sensitive information
Right-size worker resources - Allocate CPU and memory based on actual job requirements and performance
Implement auto-scaling - Configure scaling policies based on queue depth and processing metrics
Use batch processing - Process multiple related jobs together to reduce overhead when appropriate
Track key metrics - Monitor queue depth, processing rates, error rates, and worker health continuously
Set meaningful alerts - Configure alerts for conditions requiring intervention while avoiding false positives