django-bulk-drf 0.1.1

Django REST Framework mixins for asynchronous bulk operations with Celery and Redis

Django Bulk DRF

Asynchronous bulk operations for Django REST Framework using Celery workers and Redis for progress tracking.

Installation

pip install django-bulk-drf

Requirements

• Python 3.11+
• Django 4.0+
• Django REST Framework 3.14+
• Celery 5.2+
• Redis 4.3+
• django-redis 5.2+

Quick Setup

1. Add to your INSTALLED_APPS:

INSTALLED_APPS = [
    # ... your other apps
    'rest_framework',
    'django_bulk_drf',
]

2. Configure Redis cache:

CACHES = {
    'default': {
        'BACKEND': 'django_redis.cache.RedisCache',
        'LOCATION': 'redis://127.0.0.1:6379/1',
        'OPTIONS': {
            'CLIENT_CLASS': 'django_redis.client.DefaultClient',
        }
    }
}

3. Configure Celery:

# settings.py
CELERY_BROKER_URL = 'redis://127.0.0.1:6379/0'
CELERY_RESULT_BACKEND = 'redis://127.0.0.1:6379/0'

This implementation provides asynchronous bulk operations for your Django REST Framework API endpoints using Celery workers and Redis for progress tracking.

Overview

The bulk operations system consists of:

1. Bulk Processing Tasks (django_bulk_drf.bulk_processing) - Celery tasks for handling bulk operations
2. Bulk Mixins (django_bulk_drf.bulk_mixins) - DRF ViewSet mixins to add bulk endpoints
3. Redis Cache (django_bulk_drf.bulk_cache) - Progress tracking and result caching
4. Status Views (django_bulk_drf.bulk_views) - API endpoints to check task status

Features

• ✅ Asynchronous Processing: Long-running bulk operations don't block the API
• ✅ Progress Tracking: Real-time progress updates via Redis
• ✅ Error Handling: Detailed error reporting for failed items
• ✅ Result Caching: Final results cached in Redis for 24 hours
• ✅ Validation: Full DRF serializer validation for all items
• ✅ Atomic Operations: Database transactions ensure data consistency

Available Operations

1. Bulk Create

• Endpoint: POST /api/{model}/bulk/
• Method: POST
• Input: Array of objects to create
• Output: Task ID and status URL

2. Bulk Update (Partial)

• Endpoint: PATCH /api/{model}/bulk/
• Method: PATCH
• Input: Array of objects with id and partial update data
• Output: Task ID and status URL

3. Bulk Replace (Full Update)

• Endpoint: PUT /api/{model}/bulk/
• Method: PUT
• Input: Array of complete objects with id and all required fields
• Output: Task ID and status URL

4. Bulk Delete

• Endpoint: DELETE /api/{model}/bulk/
• Method: DELETE
• Input: Array of IDs to delete
• Output: Task ID and status URL

5. Status Tracking

• Endpoint: GET /api/bulk-operations/{task_id}/status/
• Output: Task status, progress, and results

HTTP Method Differences

• POST: Creates new records (all fields required based on your model)
• PATCH: Partial updates - only include fields you want to change (requires id)
• PUT: Full replacement - all required fields must be provided (requires id)
• DELETE: Removes records (provide array of IDs)

Usage

Adding Bulk Operations to a ViewSet

from django_bulk_drf.bulk_mixins import BulkOperationsMixin

class FinancialTransactionViewSet(BulkOperationsMixin, viewsets.ModelViewSet):
    queryset = FinancialTransaction.objects.all()
    serializer_class = FinancialTransactionSerializer

Example API Calls

Bulk Create

curl -X POST http://localhost:8000/api/financial-transactions/bulk/ \\
  -H "Content-Type: application/json" \\
  -d '[
    {
      "amount": "100.50",
      "description": "Transaction 1",
      "datetime": "2025-01-01T10:00:00Z",
      "financial_account": 1,
      "classification_status": 1
    },
    {
      "amount": "-25.75", 
      "description": "Transaction 2",
      "datetime": "2025-01-01T11:00:00Z",
      "financial_account": 1,
      "classification_status": 1
    }
  ]'

Response:

{
  "message": "Bulk create task started for 2 items",
  "task_id": "abc123-def456-ghi789",
  "total_items": 2,
  "status_url": "/api/bulk-operations/abc123-def456-ghi789/status/"
}

Bulk Update (Partial)

curl -X PATCH http://localhost:8000/api/financial-transactions/bulk/ \\
  -H "Content-Type: application/json" \\
  -d '[
    {
      "id": 1,
      "amount": "150.00",
      "description": "Updated transaction 1"
    },
    {
      "id": 2,
      "description": "Updated transaction 2"
    }
  ]'

Bulk Replace (Full Update)

curl -X PUT http://localhost:8000/api/financial-transactions/bulk/ \\
  -H "Content-Type: application/json" \\
  -d '[
    {
      "id": 1,
      "amount": "200.00",
      "description": "Completely replaced transaction 1",
      "datetime": "2025-01-01T15:00:00Z",
      "financial_account": 1,
      "classification_status": 2
    },
    {
      "id": 2,
      "amount": "75.50",
      "description": "Completely replaced transaction 2",
      "datetime": "2025-01-01T16:00:00Z",
      "financial_account": 1,
      "classification_status": 1
    }
  ]'

Bulk Delete

curl -X DELETE http://localhost:8000/api/financial-transactions/bulk/ \\
  -H "Content-Type: application/json" \\
  -d '[1, 2, 3, 4, 5]'

Check Status

curl http://localhost:8000/api/bulk-operations/abc123-def456-ghi789/status/

Response:

{
  "task_id": "abc123-def456-ghi789",
  "state": "SUCCESS",
  "result": {
    "task_id": "abc123-def456-ghi789",
    "total_items": 2,
    "operation_type": "bulk_create",
    "success_count": 2,
    "error_count": 0,
    "errors": [],
    "created_ids": [10, 11],
    "updated_ids": [],
    "deleted_ids": []
  },
  "progress": {
    "current": 2,
    "total": 2,
    "percentage": 100.0,
    "message": "Creating instances in database..."
  },
  "status": "Task completed successfully"
}

Task States

• PENDING: Task is waiting to be executed
• PROGRESS: Task is currently running (includes progress data)
• SUCCESS: Task completed successfully
• FAILURE: Task failed with an error

Progress Tracking

Progress is tracked in Redis and updated every 10 items processed. The progress object includes:

{
  "current": 50,
  "total": 100,
  "percentage": 50.0,
  "message": "Validated 50/100 items"
}

Error Handling

Individual item errors are captured and included in the result:

{
  "errors": [
    {
      "index": 5,
      "error": "amount: This field is required.",
      "data": {"description": "Missing amount"}
    }
  ]
}

Configuration

Redis Settings

Make sure your Django settings include Redis configuration:

# Redis cache for bulk operations
CACHES = {
    'default': {
        'BACKEND': 'django_redis.cache.RedisCache',
        'LOCATION': REDIS_URL,
        'OPTIONS': {
            'CLIENT_CLASS': 'django_redis.client.DefaultClient',
        }
    }
}

Celery Settings

Your Celery configuration should include:

# Celery settings for bulk operations
CELERY_TASK_TIME_LIMIT = 5 * 60  # 5 minutes
CELERY_TASK_SOFT_TIME_LIMIT = 60  # 1 minute
CELERY_WORKER_SEND_TASK_EVENTS = True
CELERY_TASK_SEND_SENT_EVENT = True

Starting Workers

To process bulk operations, start Celery workers:

# Start Celery worker
celery -A config.celery_app worker -l info

# Start Celery beat (for periodic tasks)
celery -A config.celery_app beat -l info

# Start Flower (monitoring - optional)
celery -A config.celery_app flower

Performance Considerations

1. Batch Size: Large arrays are processed in chunks to avoid memory issues
2. Database Connections: Use connection pooling for high-volume operations
3. Redis Memory: Monitor Redis memory usage for large result sets
4. Worker Scaling: Scale Celery workers based on load

Monitoring

• Use Flower for Celery task monitoring: http://localhost:5555
• Monitor Redis usage with redis-cli info memory
• Check Django logs for task execution details
• Use the status endpoint for real-time progress tracking

Security Considerations

1. Authentication: Ensure bulk endpoints require proper authentication
2. Rate Limiting: Implement rate limiting for bulk operations
3. Input Validation: All input is validated through DRF serializers
4. Permission Checks: Add custom permission classes as needed

Extending the System

Custom Bulk Operations

You can create custom bulk operations by:

1. Creating new Celery tasks in bulk_processing.py
2. Adding new action methods to the mixins
3. Updating the status view if needed

Custom Progress Tracking

Override the progress tracking by extending BulkOperationCache:

from django_bulk_drf.bulk_cache import BulkOperationCache

class CustomBulkCache(BulkOperationCache):
    @classmethod
    def set_custom_metric(cls, task_id: str, metric_data: dict):
        # Custom metric tracking
        pass

This bulk operations system provides a robust, scalable solution for handling large data operations asynchronously while keeping your API responsive and providing real-time feedback to users.