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Python Background Jobs

Seth HobsonCommunityCreated by Seth Hobson

  • Skill
  • Guidelines
  • Software development
  • MIT
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From the author

Python background job patterns including task queues, workers, and event-driven architecture. Use when implementing async task processing, job queues, long-running operations, or decoupling work from request/response cycles.
— the description by Seth Hobson

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides a comprehensive guide to decoupling long-running or unreliable tasks from request/response cycles in Python. It covers essential patterns for task queues, worker management, and event-driven architectures, ensuring applications remain responsive while handling heavy workloads. The content focuses on robust design principles such as idempotency, job state management, and error handling strategies like retries and dead-letter queues.

What it does

  • Defines patterns for offloading long-running tasks to background workers.
  • Explains how to implement job state machines for tracking task progress.
  • Provides strategies for ensuring task idempotency to handle retries safely.
  • Outlines methods for task chaining and complex workflow orchestration.
  • Details configuration best practices for task queues like Celery.
  • Describes how to implement status polling endpoints for client feedback.

When to use it

  • When processing tasks that exceed standard request timeout limits.
  • When integrating with unreliable external services or APIs.
  • When building event-driven architectures or asynchronous workflows.
  • When you need to perform data exports, media processing, or bulk notifications.
  • When you need to ensure system reliability through retries and dead-letter queues.

Advantages

  • Improves API responsiveness by offloading heavy processing.
  • Increases system resilience through structured retry logic.
  • Provides clear patterns for managing job visibility and state.
  • Reduces coupling between user-facing services and background operations.

Usage examples

“How do I ensure my background task doesn't process the same payment twice if it retries?”
The skill explains the use of idempotency keys and check-before-write patterns to ensure safe re-execution of tasks.
“What is the best way to handle a task that keeps failing after multiple retries?”
The skill provides a pattern for implementing a Dead Letter Queue (DLQ) to capture failed tasks for manual inspection.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/wshobson/python-background-jobs/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip python-background-jobs.zip -d .agents/skills

Documentation by Seth Hobson

Python Background Jobs & Task Queues

Decouple long-running or unreliable work from request/response cycles. Return immediately to the user while background workers handle the heavy lifting asynchronously.

When to Use This Skill

  • Processing tasks that take longer than a few seconds

  • Sending emails, notifications, or webhooks

  • Generating reports or exporting data

  • Processing uploads or media transformations

  • Integrating with unreliable external services

  • Building event-driven architectures

Core Concepts

1. Task Queue Pattern

API accepts request, enqueues a job, returns immediately with a job ID. Workers process jobs asynchronously.

2. Idempotency

Tasks may be retried on failure. Design for safe re-execution.

3. Job State Machine

Jobs transition through states: pending → running → succeeded/failed.

4. At-Least-Once Delivery

Most queues guarantee at-least-once delivery. Your code must handle duplicates.

Quick Start

This skill uses Celery for examples, a widely adopted task queue. Alternatives like RQ, Dramatiq, and cloud-native solutions (AWS SQS, GCP Tasks) are equally valid choices.

from celery import Celery

app = Celery("tasks", broker="redis://localhost:6379")

@app.task
def send_email(to: str, subject: str, body: str) -> None:
    # This runs in a background worker
    email_client.send(to, subject, body)

# In your API handler
send_email.delay("[email protected]", "Welcome!", "Thanks for signing up")

Fundamental Patterns

Pattern 1: Return Job ID Immediately

For operations exceeding a few seconds, return a job ID and process asynchronously.

from uuid import uuid4
from dataclasses import dataclass
from enum import Enum
from datetime import datetime

class JobStatus(Enum):
    PENDING = "pending"
    RUNNING = "running"
    SUCCEEDED = "succeeded"
    FAILED = "failed"

@dataclass
class Job:
    id: str
    status: JobStatus
    created_at: datetime
    started_at: datetime | None = None
    completed_at: datetime | None = None
    result: dict | None = None
    error: str | None = None

# API endpoint
async def start_export(request: ExportRequest) -> JobResponse:
    """Start export job and return job ID."""
    job_id = str(uuid4())

    # Persist job record
    await jobs_repo.create(Job(
        id=job_id,
        status=JobStatus.PENDING,
        created_at=datetime.utcnow(),
    ))

    # Enqueue task for background processing
    await task_queue.enqueue(
        "export_data",
        job_id=job_id,
        params=request.model_dump(),
    )

    # Return immediately with job ID
    return JobResponse(
        job_id=job_id,
        status="pending",
        poll_url=f"/jobs/{job_id}",
    )
Pattern 2: Celery Task Configuration

Configure Celery tasks with proper retry and timeout settings.

from celery import Celery

app = Celery("tasks", broker="redis://localhost:6379")

# Global configuration
app.conf.update(
    task_time_limit=3600,          # Hard limit: 1 hour
    task_soft_time_limit=3000,      # Soft limit: 50 minutes
    task_acks_late=True,            # Acknowledge after completion
    task_reject_on_worker_lost=True,
    worker_prefetch_multiplier=1,   # Don't prefetch too many tasks
)

@app.task(
    bind=True,
    max_retries=3,
    default_retry_delay=60,
    autoretry_for=(ConnectionError, TimeoutError),
)
def process_payment(self, payment_id: str) -> dict:
    """Process payment with automatic retry on transient errors."""
    try:
        result = payment_gateway.charge(payment_id)
        return {"status": "success", "transaction_id": result.id}
    except PaymentDeclinedError as e:
        # Don't retry permanent failures
        return {"status": "declined", "reason": str(e)}
    except TransientError as e:
        # Retry with exponential backoff
        raise self.retry(exc=e, countdown=2 ** self.request.retries * 60)
Pattern 3: Make Tasks Idempotent

Workers may retry on crash or timeout. Design for safe re-execution.

@app.task(bind=True)
def process_order(self, order_id: str) -> None:
    """Process order idempotently."""
    order = orders_repo.get(order_id)

    # Already processed? Return early
    if order.status == OrderStatus.COMPLETED:
        logger.info("Order already processed", order_id=order_id)
        return

    # Already in progress? Check if we should continue
    if order.status == OrderStatus.PROCESSING:
        # Use idempotency key to avoid double-charging
        pass

    # Process with idempotency key
    result = payment_provider.charge(
        amount=order.total,
        idempotency_key=f"order-{order_id}",  # Critical!
    )

    orders_repo.update(order_id, status=OrderStatus.COMPLETED)

Idempotency Strategies:

  1. Check-before-write: Verify state before action

  2. Idempotency keys: Use unique tokens with external services

  3. Upsert patterns: INSERT ... ON CONFLICT UPDATE

  4. Deduplication window: Track processed IDs for N hours

Pattern 4: Job State Management

Persist job state transitions for visibility and debugging.

class JobRepository:
    """Repository for managing job state."""

    async def create(self, job: Job) -> Job:
        """Create new job record."""
        await self._db.execute(
            """INSERT INTO jobs (id, status, created_at)
               VALUES ($1, $2, $3)""",
            job.id, job.status.value, job.created_at,
        )
        return job

    async def update_status(
        self,
        job_id: str,
        status: JobStatus,
        **fields,
    ) -> None:
        """Update job status with timestamp."""
        updates = {"status": status.value, **fields}

        if status == JobStatus.RUNNING:
            updates["started_at"] = datetime.utcnow()
        elif status in (JobStatus.SUCCEEDED, JobStatus.FAILED):
            updates["completed_at"] = datetime.utcnow()

        await self._db.execute(
            "UPDATE jobs SET status = $1, ... WHERE id = $2",
            updates, job_id,
        )

        logger.info(
            "Job status updated",
            job_id=job_id,
            status=status.value,
        )

Detailed worked examples and patterns

Detailed sections (starting with ## Advanced Patterns) live in references/details.md. Read that file when the navigation summary above is insufficient.

Best Practices Summary

  1. Return immediately - Don't block requests for long operations

  2. Persist job state - Enable status polling and debugging

  3. Make tasks idempotent - Safe to retry on any failure

  4. Use idempotency keys - For external service calls

  5. Set timeouts - Both soft and hard limits

  6. Implement DLQ - Capture permanently failed tasks

  7. Log transitions - Track job state changes

  8. Retry appropriately - Exponential backoff for transient errors

  9. Don't retry permanent failures - Validation errors, invalid credentials

  10. Monitor queue depth - Alert on backlog growth

Files

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs Python

Security

No issues found by our static scan· No issues found

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Attribution and licence

Created by
Seth Hobson
Publisher
Seth Hobson (Community)
Source
wshobson/agents · plugins/python-development/skills/python-background-jobs · commit 4bde316
Licence
MIT License (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
Synced

AutoGineer is not affiliated with or endorsed by Seth Hobson or Seth Hobson.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download python-background-jobs.zip