Guide

Requirements

Package	Required	Purpose
oridecon	Yes	Core framework
oridecon-contracts	Yes	Protocol definitions
oridecon-queue	Optional	Task distribution
oridecon-resilience	Optional	Retry policies

The Problem

Your web application needs to send confirmation emails, generate PDF reports, sync data with external APIs, or run nightly maintenance — all without blocking HTTP requests. Background task processing is the standard solution, but most Python tools (Celery, RQ, Huey) require separate worker processes, complex configuration, and manual wiring into your DI container.

oridecon-tasks solves this by providing task definitions, queue backends, worker pools, and job scheduling — all integrated with the Oridecon DI container under a single TasksModule with zero separate processes.

---

Mental Model

┌────────────┐     ┌──────────────┐     ┌──────────────┐
│  @task     │ ──► │  TaskQueue    │ ──► │  WorkerPool  │
│  @scheduled│     │  (Protocol)   │     │  (N workers) │
│  handlers  │     │               │     │              │
└────────────┘     │ memory/      │     │  Handler     │
                   │ redis/       │     │  Registry    │
                   │ rabbitmq/    │     │  ─► handler  │
                   │ postgres     │     │  ─► handler  │
                   └──────┬───────┘     └──────────────┘
                          │
                   ┌──────▼───────┐
                   │  TaskScheduler│
                   │  (cron)      │
                   └──────────────┘

Three layers:

1. Task definitions (@task, @scheduled) — decorate async functions with metadata (name, priority, retries, timeout)
2. Queue backends (TaskQueueProtocol) — MemoryTaskQueue, RedisTaskQueue, RabbitMQTaskQueue. Stores pending jobs.
3. Execution (WorkerPool + HandlerRegistry) — N workers poll the queue, look up registered handlers, and execute. Optionally a TaskScheduler enqueues jobs on a cron schedule.

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Core Concepts

Tasks (@task)

The @task decorator converts an async function into a task descriptor with queue-aware methods:

from oridecon.tasks.decorators import task
from oridecon.tasks.types import Priority


@task(name="send-invoice", priority=Priority.HIGH, max_retries=5, timeout=60.0)
async def send_invoice(invoice_id: str, user_email: str) -> None:
    """Generate and email an invoice PDF."""
    ...


# Create a job signature without enqueuing
signature = send_invoice.signature("inv-42", user_email="[email protected]")

# Enqueue via a TaskQueueProtocol instance
await send_invoice.apply_async(queue, "inv-42", user_email="[email protected]")

# Shorthand for signature()
sig = send_invoice.s("inv-42", user_email="[email protected]")

Scheduled Tasks (@scheduled)

The @scheduled decorator extends @task with a cron expression. The TaskScheduler enqueues the task automatically when its cron fires:

from oridecon.tasks.decorators import scheduled


@scheduled(cron="0 3 * * *", name="daily-cleanup", max_retries=2)
async def cleanup_expired_sessions() -> None:
    """Remove expired sessions from the database nightly at 3 AM."""
    ...


# The task is also callable on-demand — the scheduler doesn't block direct use
await cleanup_expired_sessions()

TaskQueueProtocol

The core interface for queue backends. Three built-in implementations:

Backend	Class	Persistence	Best For
Memory	MemoryTaskQueue	None (volatile)	Development, tests
Redis	RedisTaskQueue	Redis list	Single-node production
RabbitMQ	RabbitMQTaskQueue	AMQP queue	Distributed deployments

TaskProvider

The TaskProvider is the DI provider that registers TaskQueueProtocol, TaskExecutorProtocol, HandlerRegistry, WorkerPool, and TaskScheduler into the container. It manages the full lifecycle:

• register() — binds services, creates backends, discovers entry-point backends
• boot() — starts the worker pool, connects queues, starts the scheduler
• shutdown() — stops scheduler, stops workers, closes queues

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Typical Usage

1. Wired via TasksModule (recommended)

from oridecon import Application
from oridecon.tasks.module import TasksModule
from oridecon.tasks.decorators import task


@task(name="process-order")
async def process_order(order_id: str) -> None:
    ...


async def main():
    async with Application.boot(
        name="order-service",
        modules=[TasksModule.configure(worker_count=4)],
    ) as app:
        # Enqueue from anywhere with a resolved queue
        from oridecon.contracts.infra.tasks import TaskQueueProtocol

        queue = await app.container.resolve(TaskQueueProtocol)
        await process_order.apply_async(queue, "ord-123")

        await asyncio.sleep(1)

2. With a Redis backend

from oridecon.tasks.backends.redis import RedisTaskQueue

async with Application.boot(
    name="order-service",
    modules=[
        TasksModule.configure(
            queue=RedisTaskQueue("redis://localhost:6379", queue_name="orders"),
            worker_count=8,
        )
    ],
) as app:
    ...

3. Multi-backend (named queues)

Declare multiple named queues via TaskConfig for workload isolation:

from oridecon.tasks.config import TaskConfig, NamedTaskConfig

config = TaskConfig(
    backends=[
        NamedTaskConfig(name="urgent", primary=True, type="redis", redis_url="redis://..."),
        NamedTaskConfig(name="batch", type="rabbitmq", amqp_url="amqp://..."),
    ],
)

Inject by name via Annotated[TaskQueueProtocol, Named("batch")].

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Common Patterns

Result Store

Task results can be stored and retrieved later. The provider uses InMemoryResultStore by default and upgrades to CacheBackendResultStore when a CacheBackendProtocol is available in the container.

Progress Tracking

Long-running tasks can report progress via ProgressTracker / ProgressTrackerProtocol:

from oridecon.tasks.progress import ProgressTracker

tracker = ProgressTracker(task_id)
await tracker.update(progress=50, message="Processing batch 5/10")

Middleware

The TaskMiddlewarePipeline supports pre- and post-execution hooks:

from oridecon.tasks.middleware import LoggingMiddleware, MetricsMiddleware, TimeoutMiddleware

# Added automatically by the provider; custom middleware extends TaskMiddleware

Workflows

Chain, group, and chord tasks for multi-step processing:

from oridecon.tasks.workflows import chain, TaskStep

workflow = chain(
    TaskStep(name="validate", handler=validate_order),
    TaskStep(name="charge", handler=charge_payment),
    TaskStep(name="ship", handler=schedule_shipping),
)
result = await workflow.run(order_id="ord-42")

Dead-Letter Queue

Failed tasks after exhausting retries are moved to a dead-letter queue (DeadLetterQueue / FailureRecord) for inspection and replay.

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Best Practices

• ✅ Use @task() for on-demand work; use @scheduled() for cron-based recurring work
• ✅ Set explicit timeout per task to prevent hung workers
• ✅ Set max_retries based on idempotency — retry transient failures, don’t retry validation errors
• ✅ Use idempotency_key for exactly-once processing semantics
• ✅ Pin production deployments to RedisTaskQueue or RabbitMQTaskQueue — MemoryTaskQueue loses jobs on restart
• ❌ Don’t store large payloads in job args — enqueue IDs and let the handler fetch data
• ❌ Don’t use @task().delay() — it raises NotImplementedError; use .apply_async(queue, ...) or TaskProvider.enqueue_job()

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Next Steps

• Architecture — internal design, contracts, extension points
• How-Tos — Redis backend, task chaining, scheduling, progress tracking
• Configuration — every config key with defaults and env vars