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Bulk Operations

When you need to work with many records at once, bulk operations are significantly faster than per-instance operations.

bulk_create​

Multi-row INSERT with batching:

from ryx.bulk import bulk_create

posts = [
    Post(title=f"Post {i}", slug=f"post-{i}", views=i * 10)
    for i in range(1000)
]

created = await bulk_create(posts, batch_size=100)
print(f"Created {len(created)} posts")

How It Works​

Records are split into batches of batch_size. Each batch becomes a single multi-row INSERT:

INSERT INTO "posts" ("title", "slug", "views") VALUES
    ('Post 0', 'post-0', 0),
    ('Post 1', 'post-1', 10),
    ...
    ('Post 99', 'post-99', 990)

Trade-offs​

  • Bypasses clean(), before_save, after_save, and signals
  • Does not populate pk on instances (database-generated)
  • Much faster than calling save() in a loop

bulk_update​

Individual UPDATEs wrapped in a transaction:

from ryx.bulk import bulk_update

posts = await Post.objects.filter(active=True)
for post in posts:
    post.views += 1

await bulk_update(posts, fields=["views"])

bulk_delete​

DELETE with IN clause:

from ryx.bulk import bulk_delete

deleted = await bulk_delete(Post.objects.filter(views=0))
print(f"Deleted {deleted} posts")

stream​

Async generator for processing large result sets without loading everything into memory:

from ryx.bulk import stream

async for batch in stream(Post.objects.all(), page_size=500):
    for post in batch:
        process(post)

Uses LIMIT/OFFSET pagination under the hood.

Performance Comparison​

Operation100 records10,000 records
save() in loop~200ms~20s
bulk_create~10ms~100ms
tip

Always prefer bulk operations for data imports, migrations, and batch processing.

Next Steps​

→ Advanced — Transactions, validation, signals