How do you use bulk_create, bulk_update, and iterator() to handle large datasets efficiently? बड़े डेटासेट को कुशलता से हैंडल करने के लिए bulk_create, bulk_update और iterator() का उपयोग कैसे करें?

Calling .save() in a loop to create or update many rows issues one database round-trip per object, which is slow at scale. bulk_create() inserts a list of unsaved model instances in a single (or few, batched) SQL statement, and bulk_update() similarly updates many existing instances in one query, both dramatically reducing round-trip overhead — though bulk operations skip save(), so custom save() logic and signals like pre_save/post_save don't fire.

For reading large querysets, plain iteration loads the entire result set into memory at once (via internal caching), which can exhaust memory for very large tables. iterator(chunk_size=2000) streams results from the database in batches instead of loading everything upfront, trading the queryset's result caching for bounded memory usage — the right choice for one-off exports or migrations over millions of rows.

Product.objects.bulk_create([Product(name=f"P{i}") for i in range(10000)])

products = Product.objects.filter(category='clearance')
for p in products:
    p.price *= 0.8
Product.objects.bulk_update(products, ['price'])

for product in Product.objects.all().iterator(chunk_size=2000):
    process(product)