pydynox 🐍⚙️
A fast, async-first DynamoDB ORM for Python with a Rust core.
🎉 v1.0 is here! The API is stable and fully async. Production-ready with a Rust core for speed.
Why pydynox?
Py(thon) + Dyn(amoDB) + Ox(ide/Rust)
Key features
- Async-first - Async by default, sync with
sync_prefix. True non-blocking I/O with Rust's tokio - Fast - Rust core for serialization, compression, and encryption. Zero Python runtime dependencies
- Simple API - Class-based models like PynamoDB. Define once, use everywhere
- Type-safe - Full type hints for IDE autocomplete and type checkers
- Pydantic support - Use your existing Pydantic models with DynamoDB
- Batteries included - TTL, hooks, auto-generate, optimistic locking, rate limiting, encryption, compression, S3 attributes, PartiQL, observability
Installation
pydynox supports Python 3.11–3.15. Wheels are built for Linux (glibc and musl) and macOS on x86_64 and ARM64.
pip install pydynox
Optional extras:
pip install pydynox[pydantic] # Pydantic integration
pip install pydynox[opentelemetry] # OpenTelemetry tracing
Quick start
Define a model
from pydynox import Model, ModelConfig
from pydynox.attributes import StringAttribute, NumberAttribute
class User(Model):
model_config = ModelConfig(table="users")
pk = StringAttribute(partition_key=True)
sk = StringAttribute(sort_key=True)
name = StringAttribute()
age = NumberAttribute(default=0)
Async operations (default)
Async methods have no prefix. This is the default.
import asyncio
async def main():
# Create
user = User(pk="USER#123", sk="PROFILE", name="John")
await user.save()
# Read
user = await User.get(pk="USER#123", sk="PROFILE")
# Update
await user.update(name="Jane", age=30)
# Query
async for user in User.query(partition_key="USER#123"):
print(user.name)
# Delete
await user.delete()
asyncio.run(main())
Sync operations (use sync_ prefix)
For scripts, CLI tools, or code that doesn't need async.
# Create
user = User(pk="USER#123", sk="PROFILE", name="John")
user.sync_save()
# Read
user = User.sync_get(pk="USER#123", sk="PROFILE")
# Update
user.sync_update(name="Jane", age=30)
# Query
for user in User.sync_query(partition_key="USER#123"):
print(user.name)
# Delete
user.sync_delete()
Async-first API
pydynox is async-first. Methods without prefix are async, methods with sync_ prefix are sync.
| Async (default) | Sync |
|---|---|
await model.save() |
model.sync_save() |
await model.delete() |
model.sync_delete() |
await model.update() |
model.sync_update() |
await Model.get() |
Model.sync_get() |
async for x in Model.query() |
for x in Model.sync_query() |
async for x in Model.scan() |
for x in Model.sync_scan() |
await Model.batch_get() |
Model.sync_batch_get() |
async with BatchWriter() |
with SyncBatchWriter() |
Why async? Python's GIL blocks threads during I/O. With async, your app can handle other work while waiting for DynamoDB. pydynox releases the GIL during network calls, so async operations are truly non-blocking.
Conditions
# Save only if item doesn't exist
await user.save(condition=User.pk.not_exists())
# Delete with condition
await user.delete(condition=User.version == 5)
# Combine with & (AND) and | (OR)
await user.save(condition=User.pk.not_exists() | (User.version == 1))
Atomic updates
# Increment
await user.update(atomic=[User.age.add(1)])
# Append to list
await user.update(atomic=[User.tags.append(["verified"])])
# Multiple operations
await user.update(atomic=[
User.age.add(1),
User.tags.append(["premium"]),
])
Batch operations
from pydynox import BatchWriter, SyncBatchWriter, DynamoDBClient
client = DynamoDBClient()
# Async (default)
async with BatchWriter(client, "users") as batch:
for i in range(100):
batch.put({"pk": f"USER#{i}", "sk": "PROFILE", "name": f"User {i}"})
# Sync
with SyncBatchWriter(client, "users") as batch:
batch.put({"pk": "USER#1", "sk": "PROFILE", "name": "John"})
Global Secondary Index
from pydynox.indexes import GlobalSecondaryIndex
class User(Model):
model_config = ModelConfig(table="users")
pk = StringAttribute(partition_key=True)
email = StringAttribute()
email_index = GlobalSecondaryIndex(
index_name="email-index",
partition_key="email",
)
# Async
async for user in User.email_index.query(partition_key="john@test.com"):
print(user.name)
# Sync
for user in User.email_index.sync_query(partition_key="john@test.com"):
print(user.name)
Transactions
from pydynox import DynamoDBClient, Transaction
client = DynamoDBClient()
async with Transaction(client) as tx:
tx.put("users", {"pk": "USER#1", "sk": "PROFILE", "name": "John"})
tx.put("orders", {"pk": "ORDER#1", "sk": "DETAILS", "user": "USER#1"})
Pydantic integration
from pydantic import BaseModel, EmailStr
from pydynox import DynamoDBClient
from pydynox.integrations.pydantic import dynamodb_model
client = DynamoDBClient()
@dynamodb_model(table="users", partition_key="pk", sort_key="sk", client=client)
class User(BaseModel):
pk: str
sk: str
name: str
email: EmailStr
# Async (default)
user = User(pk="USER#123", sk="PROFILE", name="John", email="john@test.com")
await user.save()
user = await User.get(pk="USER#123", sk="PROFILE")
# Sync
user.sync_save()
user = User.sync_get(pk="USER#123", sk="PROFILE")
S3 attribute (large files)
DynamoDB has a 400KB item limit. S3Attribute stores files in S3 and keeps metadata in DynamoDB.
from pydynox.attributes import S3Attribute, S3File
class Document(Model):
model_config = ModelConfig(table="documents")
pk = StringAttribute(partition_key=True)
content = S3Attribute(bucket="my-bucket", prefix="docs/")
# Upload
doc = Document(pk="DOC#1")
doc.content = S3File(b"...", name="report.pdf", content_type="application/pdf")
await doc.save()
# Download (async)
data = await doc.content.get_bytes()
await doc.content.save_to("/path/to/file.pdf")
url = await doc.content.presigned_url(3600)
# Download (sync)
data = doc.content.sync_get_bytes()
doc.content.sync_save_to("/path/to/file.pdf")
Table management
# Async (default)
await User.create_table(wait=True)
if await User.table_exists():
print("Table exists")
# Sync
User.sync_create_table(wait=True)
if User.sync_table_exists():
print("Table exists")
GenAI contributions 🤖
I believe GenAI is transforming how we build software. It's a powerful tool that accelerates development when used by developers who understand what they're doing.
To support both humans and AI agents, I created:
.ai/folder - Guidelines for agentic IDEs (Cursor, Windsurf, Kiro, etc.)ADR/folder - Architecture Decision Records for humans to understand the "why" behind decisions
If you're contributing with AI help:
- Understand what the AI generated before submitting
- Make sure the code follows the project patterns
- Test your changes
I reserve the right to reject low-quality PRs where project patterns are not followed and it's clear that GenAI was driving instead of the developer.
Documentation
Full documentation: https://ferrumio.github.io/pydynox
License
Apache 2.0 License
Inspirations
- PynamoDB - The ORM-style API and model design
- Pydantic - Data validation patterns
- dynarust - Rust DynamoDB client patterns
- dyntastic - Pydantic + DynamoDB integration ideas
Building from source
# Clone
git clone https://github.com/ferrumio/pydynox.git
cd pydynox
# Build (requires Python 3.11+, Rust 1.70+)
pip install maturin
maturin develop
# Test
pip install -e ".[dev]"
pytest
1 maintainer
Metadata
1 maintainer
Release files for pydynox 1.7.0
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