django-bulk-drf 0.1.117

Django REST Framework for synchronous bulk operations

Django Bulk DRF

Advanced operation extensions for Django REST Framework providing intelligent sync/async routing with a clean, unified API design.

Note: This is a complete rewrite with modern architecture and settings. No backwards compatibility with django-drf-extensions.

Installation

pip install django-bulk-drf

Requirements

• Python 3.11+
• Django 4.0+
• Django REST Framework 3.14+

Quick Setup

1. Add to your INSTALLED_APPS:

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

2. (Optional) Configure cache for progress tracking:

CACHES = {
    'default': {
        'BACKEND': 'django.core.cache.backends.locmem.LocMemCache',
        'LOCATION': 'unique-snowflake',
    }
}

Overview

This package extends Django REST Framework with a unified mixin that provides efficient bulk operations for handling large datasets.

Key Features

1. Enhanced Standard Endpoints: Smart bulk operations for immediate results
2. Intelligent Processing: Optimized database operations for large datasets
3. Bulk Operations: Efficient create, update, upsert, and delete operations
4. Progress Tracking: Cache-based monitoring for operations
5. Status Management: Comprehensive operation status and results

Features

• ✅ Unified API Design: Single mixin provides comprehensive bulk capabilities
• ✅ Smart Standard Endpoints: Enhanced ViewSet methods with intelligent array handling
• ✅ Efficient Bulk Operations: Optimized database operations for large datasets
• ✅ Immediate Results: Direct operations with instant feedback
• ✅ Scalable Processing: Efficient handling of large datasets
• ✅ Progress Tracking: Cache-based progress monitoring
• ✅ Comprehensive Error Handling: Detailed validation and error reporting per item
• ✅ Result Persistence: Automatic caching of results for fast retrieval
• ✅ Full Validation: Complete DRF serializer validation ensuring data integrity
• ✅ Transaction Safety: Atomic database operations with rollback on failures

Package Philosophy

This package provides a modern approach to bulk operations by offering:

1. Clean API Design: Enhances existing endpoints rather than creating parallel ones
2. Efficient Processing: Optimized database operations for maximum performance
3. Unified Architecture: Single mixin extends your ViewSets without complexity
4. Production-Ready: Built-in monitoring, error handling, and progress tracking

Usage

Adding Extensions to a ViewSet

from rest_framework import viewsets
from django_bulk_drf.mixins import BulkOperationsMixin

class ContractViewSet(BulkOperationsMixin, viewsets.ModelViewSet):
    """
    Enhanced ViewSet with efficient bulk operations.
    
    Provides:
    - Standard CRUD operations
    - Efficient bulk operations for large datasets
    - Smart upsert capabilities
    """
    queryset = Contract.objects.all()
    serializer_class = ContractSerializer

Your ViewSet now provides these endpoints:

# Standard ModelViewSet endpoints (enhanced for arrays)
GET    /api/contracts/                    # List (enhanced with ?ids= support)
POST   /api/contracts/                    # Create (enhanced with array + ?unique_fields= support)
GET    /api/contracts/{id}/               # Retrieve single
PATCH  /api/contracts/                    # Update (enhanced with array + ?unique_fields= support)
PUT    /api/contracts/                    # Replace (enhanced with array + ?unique_fields= support)
DELETE /api/contracts/{id}/               # Delete single

API Design

Enhanced Standard Endpoints

Smart enhancements to standard ViewSet methods for efficient bulk operations:

Multi-Get

# Retrieve multiple items by IDs
GET /api/contracts/?ids=1,2,3,4,5

# Response
{
  "count": 5,
  "results": [...],
  "operation_type": "multi_get"
}

Bulk Upsert

# Bulk upsert with unique fields
POST /api/contracts/?unique_fields=contract_number,year
Content-Type: application/json
[
  {"contract_number": "C001", "year": 2024, "amount": 1000},
  {"contract_number": "C002", "year": 2024, "amount": 2000}
]

# Response (full data)
[
  {"id": 123, "contract_number": "C001", "year": 2024, "amount": 1000},
  {"id": 124, "contract_number": "C002", "year": 2024, "amount": 2000}
]

# For large datasets, skip serialization for faster response
POST /api/contracts/?unique_fields=contract_number,year
Prefer: return=minimal
Content-Type: application/json
[...10000 records...]

# Response (minimal - much faster!)
{
  "message": "Successfully upserted 10000 instances",
  "count": 10000,
  "serialization_skipped": true
}

Operation Types

Bulk Operations

• Best for: Any size dataset that needs efficient processing
• Use cases: Data imports, batch processing, CSV uploads, API integrations
• Response: Immediate results with full data
• Endpoints: Enhanced standard ViewSet methods

Configuration

Custom Settings

# Core Settings
BULK_DRF_CHUNK_SIZE = 100                    # Items per processing chunk
BULK_DRF_MAX_RECORDS = 10000                 # Maximum records per operation
BULK_DRF_BATCH_SIZE = 1000                   # Database batch size
BULK_DRF_CACHE_TIMEOUT = 86400               # Cache timeout (24 hours)
BULK_DRF_PROGRESS_UPDATE_INTERVAL = 10       # Progress update frequency

# Sync Operation Settings
BULK_DRF_SYNC_UPSERT_MAX_ITEMS = 50          # Max items for sync upsert
BULK_DRF_SYNC_UPSERT_BATCH_SIZE = 1000       # Batch size for sync operations
BULK_DRF_SYNC_UPSERT_TIMEOUT = 30            # Timeout for sync operations (seconds)

# Advanced Settings
BULK_DRF_USE_OPTIMIZED_TASKS = True          # Enable task optimizations
BULK_DRF_AUTO_OPTIMIZE_QUERIES = True        # Auto-optimize database queries
BULK_DRF_QUERY_TIMEOUT = 300                 # Query timeout (5 minutes)
BULK_DRF_ENABLE_METRICS = False              # Enable performance metrics

Example Usage

Basic Contract Management

# Bulk upsert operations
curl -X POST "/api/contracts/?unique_fields=contract_number" \
  -H "Content-Type: application/json" \
  -d '[
    {"contract_number": "C001", "amount": 1000},
    {"contract_number": "C002", "amount": 2000}
  ]'

# Bulk create operations
curl -X POST "/api/contracts/" \
  -H "Content-Type: application/json" \
  -d '[...500 contracts...]'

Migration from Previous Versions

If you're coming from older versions:

# Old (separate mixins)
class ContractViewSet(SyncUpsertMixin, BulkOperationsMixin, viewsets.ModelViewSet):
    queryset = Contract.objects.all()
    serializer_class = ContractSerializer

# New (unified mixin)
class ContractViewSet(BulkModelViewSet, viewsets.ModelViewSet):
    queryset = Contract.objects.all()
    serializer_class = ContractSerializer

Error Handling

The system provides comprehensive error handling:

• Validation Errors: Field-level validation using DRF serializers
• Size Limits: Automatic routing suggestion for oversized sync requests
• Database Errors: Transaction rollback on failures
• Task Failures: Detailed error reporting in async task status

Performance Considerations

• Database Efficiency: Uses optimized database operations for all bulk processing
• Memory Management: Processes large datasets in configurable chunks
• Efficient Processing: Direct database operations for maximum performance
• Progress Tracking: Cache-based monitoring without database overhead
• Result Caching: Efficient caching of operation results

Performance Optimization Tips

1. Use Prefer: return=minimal for Large Operations

When upserting/creating large datasets (1000+ records), skip response serialization:

# Python requests example
headers = {'Prefer': 'return=minimal'}
response = requests.post(
    '/api/contracts/?unique_fields=contract_number',
    json=data,
    headers=headers
)
# Response: {"message": "Successfully upserted 10000 instances", "count": 10000}

Performance impact: Can reduce response time by 80%+ for large operations (e.g., 55s → 9s for 10,000 records)

2. Optimize Serializers for Bulk Operations

If your serializer has related fields, optimize response serialization:

class ContractSerializer(BulkModelSerializer):
    class Meta:
        model = Contract
        fields = ['id', 'contract_number', 'amount']
        
    def get_queryset(self):
        # Use select_related/prefetch_related to avoid N+1 queries
        return Contract.objects.select_related('customer', 'account')

3. Batch Size Configuration

Adjust batch sizes based on your data:

# For smaller records (few fields)
BULK_DRF_BATCH_SIZE = 2000

# For larger records (many fields, large text)
BULK_DRF_BATCH_SIZE = 500

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License.