Optimizing Large QuerySets with Materialized Views in Django

When dealing with 10 million records, performance optimization is crucial. Based on your situation, there are several approaches to combine your materialized view data with the additional fields needed by your application.

Using RawSQL with Annotations

You can use Django's RawSQL expression to incorporate your materialized view data while still maintaining access to your model's fields:

PYTHONCopied
from django.db.models.expressions import RawSQL
from django.db.models import F

queryset = YourModel.objects.annotate(
    mv_field1=RawSQL("SELECT field1 FROM your_materialized_view WHERE your_materialized_view.id = %s", 
                     [F('id')]),
    mv_field2=RawSQL("SELECT field2 FROM your_materialized_view WHERE your_materialized_view.id = %s", 
                     [F('id')])
)

Using Raw SQL with Model Population

For more complex scenarios, you might need to use raw SQL:

PYTHONCopied
from django.db import connection

def get_optimized_data():
    with connection.cursor() as cursor:
        cursor.execute("""
            SELECT m.*, mv.field1, mv.field2
            FROM your_model m
            JOIN your_materialized_view mv ON m.id = mv.id
        """)
        columns = [col[0] for col in cursor.description]
        return [dict(zip(columns, row)) for row in cursor.fetchall()]

Chunking Large Results

When dealing with 10 million records, you should process them in chunks to avoid memory issues:

PYTHONCopied
from django.db.models import Q

# Process in chunks of 1000
chunk_size = 1000
last_pk = 0

while True:
    # Get a chunk
    queryset = YourModel.objects.filter(
        Q(pk__gt=last_pk)
    ).order_by('pk')[:chunk_size]
    
    # Convert to list to execute the query
    items = list(queryset)
    
    if not items:
        break
        
    # Process items
    for item in items:
        # Your processing logic here
        
    # Update last_pk for the next iteration
    last_pk = items[-1].pk

Using iterator() for Memory Efficiency

For large querysets, use the iterator() method to process results in batches:

PYTHONCopied
# Specify a chunk size appropriate for your data
for item in YourModel.objects.all().iterator(chunk_size=2000):
    # Process each item
    pass

Combining with select_related() or prefetch_related()

If your additional fields are related objects, use these methods to optimize loading:

PYTHONCopied
queryset = YourModel.objects.select_related('related_model').prefetch_related('many_related')

Remember that optimizing for 10 million records often requires a combination of these techniques, and you may need to adjust your approach based on your specific database, schema, and query patterns.