pydynantic 1.0.0

Single-table design for DynamoDB in Python, with typed entities on top of Pydantic v2.

pydynantic

Single-table design for DynamoDB in Python, with typed entities on top of Pydantic v2.

pydynantic is the Pythonic answer to JavaScript's ElectroDB and DynamoDB-Toolbox: model many entities in a single DynamoDB table, compose keys from attributes, declare named & typed access patterns, and never hand-write an UpdateExpression again.

• 🧩 Declarative entities built on Pydantic v2 (validation, defaults, nested models).
• 🔑 Automatic key composition from templates like "ORG#{org_id}" — primary keys and GSIs.
• 🔎 Typed, named access patterns instead of raw expressions.
• 🔁 Transparent marshalling for str, int, float, Decimal, bool, bytes, datetime, date, UUID, Enum, list, dict, set, and nested Pydantic models.
• 🧱 Single-table primitives: collections, batch get/write, transactions, optimistic locking, cursor pagination.
• ✅ Type-safe: ships py.typed, passes mypy --strict.

The library never opens its own connections — you inject a boto3 client, which keeps credentials, region, endpoint, and testing fully in your control.

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Installation

pip install pydynantic

Requires Python 3.10+ and boto3 / pydantic>=2.

Quick start

import boto3
from datetime import datetime, timezone
from pydynantic import Table, Entity, Field, key, F

# 1. Describe the physical table (one instance per real table).
table = Table(
    name="app-prod",
    pk="PK",
    sk="SK",
    indexes={"GSI1": {"pk": "GSI1PK", "sk": "GSI1SK"}},
    client=boto3.client("dynamodb"),   # injected; testable
)

# 2. Declare entities. Attributes use plain Pydantic syntax; keys are templates.
class User(Entity, table=table, name="user"):
    user_id: str
    org_id: str
    email: str
    name: str
    status: str = "active"
    login_count: int = 0
    created_at: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))

    class Meta:
        primary = key(pk="ORG#{org_id}", sk="USER#{user_id}")
        by_email = key(index="GSI1", pk="EMAIL#{email}", sk="USER#{user_id}")

# 3. CRUD — no expressions by hand.
User.put(User(user_id="u1", org_id="acme", email="[e-mail]", name="Ana"))

user = User.get(org_id="acme", user_id="u1")          # -> User | None

User.update(
    org_id="acme", user_id="u1",
    set={"name": "Ana B.", "status": "inactive"},
    add={"login_count": 1},
)

User.delete(org_id="acme", user_id="u1")

Queries & access patterns

# Partition + begins_with on the sort key:
users = (User.query.primary(org_id="acme")
                   .begins_with("USER#")
                   .limit(50)
                   .all())                              # -> list[User]

# Access by GSI:
user = User.query.by_email(email="[e-mail]").one_or_none()  # -> User | None

# Sort-key operators: .eq .begins_with .between .gt .gte .lt .lte
# Terminals: .all() .one() .one_or_none() .first() .iter() .page(cursor=...) .count()

# Count server-side (no items materialised):
active = User.query.primary(org_id="acme").filter(F("status") == "active").count()

# Projection — fetch only some attributes (omitted ones fall back to defaults):
User.query.primary(org_id="acme").attributes(["user_id", "name"]).all()
User.get(org_id="acme", user_id="u1", attributes=["user_id", "name"])

Scan

# Full-table scan, automatically restricted to this entity's items:
all_users = User.scan().all()
User.scan().filter(F("status") == "active").count()
# Same builder surface as query: .filter .limit .attributes .all .first .iter .page .count

Filters & conditions

User.query.primary(org_id="acme").filter(
    (F("status") == "active") & (F("login_count") > 0)
).all()

# Create-only put:
from pydynantic import attr_not_exists
User.put(user, condition=attr_not_exists("PK"))

Operators: == != < <= > >=, .between(), .begins_with(), .contains(), .exists(), .not_exists(), .is_in([...]), combined with &, |, ~. ExpressionAttributeNames/Values are managed for you with no placeholder collisions.

Pagination

page = User.query.primary(org_id="acme").limit(25).page()
page.items        # list[User]
page.cursor       # opaque, JSON/HTTP-safe token (None when exhausted)

next_page = User.query.primary(org_id="acme").limit(25).page(cursor=page.cursor)

Batch

# Chunks of 25 (write) / 100 (get) and UnprocessedItems/Keys retried with backoff.
users = User.batch_get([("acme", "u1"), ("acme", "u2")])
User.batch_write(puts=[...], deletes=[("acme", "u3")])

Transactions

from pydynantic import transaction

with transaction(table) as tx:
    tx.put(Order(...))
    tx.update(User, key=("acme", "u1"), add={"orders": 1})
    tx.condition_check(Account, key=("acme",), condition=F("balance") >= 100)
# Commits on a clean exit; a failed condition cancels everything atomically.

Collections (several entities, one partition)

from pydynantic import Collection

class OrgData(Collection):
    members = [User, Membership, Invoice]   # share PK = ORG#{org_id}

result = OrgData.query(org_id="acme").all()
result.users        # list[User]
result.invoices     # list[Invoice]

Items are discriminated by a reserved __entity__ attribute written on every item.

Optimistic locking

from pydynantic import version_attr

class Order(Entity, table=table, name="order"):
    order_id: str
    customer_id: str
    total: float = 0.0
    version: int = version_attr()        # marks the version field

    class Meta:
        primary = key(pk="CUSTOMER#{customer_id}", sk="ORDER#{order_id}")

Each versioned put/update increments version and guards on the previous value; a lost race raises OptimisticLockError.

Error handling

All errors derive from PydynanticError (raw ClientError is never leaked):

PydynanticError
├── ItemNotFoundError
├── ConditionCheckFailedError
│   └── OptimisticLockError
├── ValidationError
├── TransactionCanceledError    (.reasons per action)
├── KeyTemplateError
└── MultipleResultsError

Marshalling notes

• Numbers are stored as Decimal to avoid floating-point drift.
• datetime/date are stored as ISO-8601 strings.
• None values and empty sets are omitted from items (DynamoDB cannot store them).

Development

pip install -e ".[dev]"
pytest                 # unit tests on moto (in-memory DynamoDB)
pytest --cov=pydynantic --cov-report=term-missing
mypy                   # strict
ruff check .

Scope

In scope (v1): single-table modelling, keys, CRUD, queries, filters/conditions, pagination, batch, transactions, collections, optimistic locking. Synchronous only.

Out of scope: table/infra provisioning (use CDK/Terraform), data migrations, multi-table joins, async (planned for v2 on aioboto3).

License

MIT © Robert Ruben