Django Batch Processing Options

Based on your scenario of hourly batch jobs processing relatively small data volumes (1000 rows with 10 columns), here are some recommendations:

Using Django's ORM with Batch Operations

Django's bulk_create() method would be ideal for your batch inserts. This approach is efficient and keeps your code within the Django ecosystem:

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from itertools import islice

# Example batch creation
batch_size = 100
objects_to_create = (YourModel(field1=data[i]['field1'], field2=data[i]['field2']) for i in range(1000))

while True:
    batch = list(islice(objects_to_create, batch_size))
    if not batch:
        break
    YourModel.objects.bulk_create(batch, batch_size)

Celery for Task Management

Using Celery for these batch operations is an excellent approach because:

1. It offloads processing from your main application servers
2. It provides scheduling capabilities for your hourly jobs
3. It handles retries and error reporting

This separation ensures your end users won't experience performance degradation during data processing.

Streaming for Larger Datasets

If your data volume grows, you might consider using streaming techniques similar to those used for CSV generation:

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def process_data_in_chunks(data_source):
    # Process data in manageable chunks
    for chunk in data_source.get_chunks():
        process_chunk(chunk)

Database Optimization

Remember to:

• Let the database do the heavy lifting with proper filtering and aggregation
• Use Django's F expressions for field-based operations
• Consider using RawSQL for complex operations if needed

Conclusion

For your specific case (1000 rows, hourly batches), a combination of Django's bulk_create() and Celery would be highly effective. This approach maintains Django's ORM benefits while isolating batch processing from your user-facing application.

Django's "shared-nothing" architecture is designed to scale well, so as long as you're using efficient batch operations and proper task management, your approach should work well without impacting end-user performance.