$ cat services.json

Celery Implementation

Set up and configure Celery for your application.

Deliverables:

Broker selection (Redis, RabbitMQ)
Task design and implementation
Result backend configuration
Error handling and retries
Monitoring setup

Task Workflow Design

Design complex task workflows with Celery.

Deliverables:

Task chains and groups
Chord patterns
Priority queues
Rate limiting
Task routing

Celery Optimization

Optimize existing Celery deployments.

Deliverables:

Performance analysis
Worker tuning
Memory optimization
Concurrency configuration
Monitoring dashboards

$ man celery

Celery Architecture

Broker - Message transport (Redis, RabbitMQ) Workers - Task executors Result Backend - Store task results Beat - Scheduled task scheduler

Key patterns:

Chains: Sequential task execution
Groups: Parallel task execution
Chords: Group + callback
Retry: Automatic failure recovery

Celery Best Practices

My Celery implementations include:

Idempotent tasks for safe retries
Task timeouts to prevent hangs
Result expiration to manage storage
Priority queues for critical tasks
Dead letter queues for failed tasks
Monitoring with Flower or custom dashboards

$ cat README.md

Celery Task Patterns

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from celery import Celery, chain, group, chord
from celery.exceptions import MaxRetriesExceededError

app = Celery('tasks', broker='redis://localhost:6379/0')

@app.task(bind=True, max_retries=3, default_retry_delay=60)
def process_document(self, document_id: str):
    """Process document with automatic retry."""
    try:
        document = get_document(document_id)
        result = analyze_document(document)
        save_result(document_id, result)
        return result
    except TransientError as e:
        raise self.retry(exc=e)
    except Exception as e:
        # Log and move to dead letter queue
        log_error(document_id, e)
        raise

@app.task
def send_notification(user_id: str, message: str):
    """Send notification after processing."""
    notify_user(user_id, message)

# Workflow: Process then notify
workflow = chain(
    process_document.s(doc_id),
    send_notification.s(user_id)
)

# Parallel processing with callback
parallel_workflow = chord(
    group(process_document.s(id) for id in doc_ids),
    aggregate_results.s()
)

Celery Configuration

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# celery_config.py
broker_url = 'redis://localhost:6379/0'
result_backend = 'redis://localhost:6379/1'

# Task settings
task_serializer = 'json'
result_serializer = 'json'
accept_content = ['json']
timezone = 'UTC'

# Retry settings
task_acks_late = True
task_reject_on_worker_lost = True

# Concurrency
worker_concurrency = 4
worker_prefetch_multiplier = 2

# Result expiration
result_expires = 3600  # 1 hour

# Task routing
task_routes = {
    'tasks.high_priority.*': {'queue': 'high'},
    'tasks.low_priority.*': {'queue': 'low'},
}

Experience:

Case Studies: AI Recruitment Platform | CRM Integration Platform

Related Technologies: Django, Python, Redis, PostgreSQL

$ ls -la projects/

LLM Processing Queue

@ Flowrite

Challenge:

Process async LLM requests without blocking the API.

Solution:

Celery with Redis broker, priority queues for premium users, result caching.

Result:

Scalable async processing, smooth user experience.

Data Processing Pipeline

@ PurpleSlate

Challenge:

Process venue data updates with email notifications.

Solution:

Celery task chains for data processing, email sending, and analytics updates.

Result:

Reliable background processing with full audit trail.

IoT Data Processing

@ Spiio

Challenge:

Process sensor data asynchronously at scale.

Solution:

Celery workers with specialized queues for different data types.

Result:

40,000+ data points processed hourly.

🥬 Celery

$ cat services.json

Celery Implementation

Task Workflow Design

Celery Optimization

$ man celery

Celery Architecture

Celery Best Practices

$ cat README.md

Celery Task Patterns

Celery Configuration

$ ls -la projects/

LLM Processing Queue

Data Processing Pipeline

IoT Data Processing

$ diff me competitors/

Build Your Task Queue