dyntastic 0.9.0

A DynamoDB library on top of Pydantic and boto3.

dyntastic

A DynamoDB library on top of Pydantic and boto3.

Installation

pip3 install dyntastic

If the Pydantic binaries are too large for you (they can exceed 90MB), use the following:

pip3 uninstall pydantic  # if pydantic is already installed
pip3 install dyntastic --no-binary pydantic

Usage

The core functionality of this library is provided by the Dyntastic class.

Dyntastic is a subclass of Pydantic's BaseModel, so can be used in all the same places a Pydantic model can be used (FastAPI, etc).

import uuid
from datetime import datetime
from typing import Optional

from dyntastic import Dyntastic
from pydantic import Field

class Product(Dyntastic):
    __table_name__ = "products"
    __hash_key__ = "product_id"

    product_id: str = Field(default_factory=lambda: str(uuid.uuid4()))
    name: str
    description: Optional[str] = None
    price: float
    tax: Optional[float] = None


class Event(Dyntastic):
    __table_name__ = "events"
    __hash_key__ = "event_id"
    __range_key__ = "timestamp"

    event_id: str
    timestamp: datetime
    data: dict

# All your favorite pydantic functionality still works:

p = Product(name="bread", price=3.49)
# Product(product_id='d2e91c30-e701-422f-b71b-465b02749f18', name='bread', description=None, price=3.49, tax=None)

p.dict()
# {'product_id': 'd2e91c30-e701-422f-b71b-465b02749f18', 'name': 'bread', 'description': None, 'price': 3.49, 'tax': None}

p.json()
# '{"product_id": "d2e91c30-e701-422f-b71b-465b02749f18", "name": "bread", "description": null, "price": 3.49, "tax": null}'

To explicitly define an AWS region or DynamoDB endpoint url (for using a local dynamodb docker instance, for example), set __table_region__ or __table_host__

Inserting into DynamoDB

Using the Product example from above, simply:

product = Product(name="bread", description="Sourdough Bread", price=3.99)
product.product_id
# d2e91c30-e701-422f-b71b-465b02749f18

# Nothing is written to DynamoDB until .save() is called:
product.save()

Getting Items from DynamoDB

Product.get("d2e91c30-e701-422f-b71b-465b02749f18")
# Product(product_id='d2e91c30-e701-422f-b71b-465b02749f18', name='bread', description="Sourdough Bread", price=3.99, tax=None)

The range key must be provided if one is defined:

Event.get("d2e91c30-e701-422f-b71b-465b02749f18", "2022-02-12T18:27:55.837Z")

Consistent reads are supported:

Event.get(..., consistent_read=True)

A DoesNotExist error is raised by get if a key is not found:

Product.get("nonexistent")
# Traceback (most recent call last):
#   ...
# dyntastic.exceptions.DoesNotExist

Use safe_get instead to return None if the key is not found:

Product.safe_get("nonexistent")
# None

Querying Items in DynamoDB

# A is shorthand for the Attr class (i.e. attribute)
from dyntastic import A

# auto paging iterable
for event in Event.query("some_event_id"):
    print(event)


Event.query("some_event_id", per_page=10)
Event.query("some_event_id")
Event.query("some_event_id", range_key_condition=A.timestamp < datetime(2022, 2, 13))
Event.query("some_event_id", filter_condition=A.some_field == "foo")

# query an index
Event.query(A.my_other_field == 12345, index="my_other_field-index")

# note: Must provide a condition expression rather than just the value
Event.query(123545, index="my_other_field-index")  # errors!

# query an index with an optional filter expression
filter_expression = None
if filter_value:
    filter_expression = A('filter_field').eq(filter_value)
Event.query(
    A.my_other_field == 12345, 
    index="my_other_field-index",
    filter_expression=filter_expression
)

# consistent read
Event.query("some_event_id", consistent_read=True)

If you need to manually handle pagination, use query_page:

page = Event.query_page(...)
page.items
# [...]
page.has_more
# True
page.last_evaluated_key
# {"event_id": "some_event_id", "timestamp": "..."}

Event.query_page(..., last_evaluated_key=page.last_evaluated_key)

Scanning Items in DynamoDB

Scanning is done identically to querying, except there are no hash key or range key conditions.

# auto paging iterable
for event in Event.scan():
    pass

Event.scan((A.my_field < 5) & (A.some_other_field.is_in(["a", "b", "c"])))
Event.scan(..., consistent_read=True)

Updating Items in DynamoDB

Examples:

my_item.update(A.my_field.set("new_value"))
my_item.update(A.my_field.set(A.another_field))
my_item.update(A.my_int.set(A.another_int - 10))
my_item.update(A.my_int.plus(1))
my_item.update(A.my_list.append("new_element"))
my_item.update(A.some_attribute.set_default("value_if_not_already_present"))

my_item.update(A.my_field.remove())
my_item.update(A.my_list.remove(2))  # remove by index

my_item.update(A.my_string_set.add("new_element"))
my_item.update(A.my_string_set.add({"new_1", "new_2"}))
my_item.update(A.my_string_set.delete("element_to_remove"))
my_item.update(A.my_string_set.delete({"remove_1", "remove_2"}))

The data is automatically refreshed after the update request. To disable this behavior, pass refresh=False:

my_item.update(..., refresh=False)

Supports conditions:

my_item.update(..., condition=A.my_field == "something")

By default, if the condition is not met, the update call will be a noop. To instead error in this situation, pass require_condition=True:

my_item.update(..., require_condition=True)

Batch Reads

Multiple items can be read from a table at the same time using the batch_get function.

Note that DynamoDB limits the number of items that can be read at one time to 100 items or 16MB, whichever comes first.

Note that if any of the provided keys are missing from dynamo, they will simply be excluded in the result set.

MyModel.batch_get(["hash_key_1", "hash_key_2", "hash_key_3"])
# => [MyModel(...), MyModel(...)]

For models with a range key defined:

MyModel.batch_get([("hash_key_1", "range_key_1"), ("hash_key_2", "range_key_2")])
# => [MyModel(...), MyModel(...)]

Batch Writes

Save and delete operations may also be performed in batches.

Note that DynamoDB limits the number of items that can be written in a single batch to 25 items or 16MB, whichever comes first. Dyntastic will automatically batch in chunks of 25, or less if desired.

with MyModel.batch_writer():
    MyModel(id="0").delete()
    MyModel(id="1").save()
    MyModel(id="2").save()

# all operations are performed once the `with` context is exited

To configure a smaller batch size, for example when each item is relatively large:

with MyModel.batch_writer(batch_size=2):
    MyModel(id="1").save()
    MyModel(id="2").save()
    # the previous two models are written immediately, since the batch size was reached
    MyModel(id="3).save()

# The final operation is performed here now that the `with` context has exited

Create a DynamoDB Table

This functionality is currently meant only for use in unit tests as it does not support configuring throughput.

To create a table with no secondary indexes:

MyModel.create_table()

# Do not wait until the table creation is complete (subsequent operations
# may error if they are performed before the table creation is finished)
MyModel.create_table(wait=False)

To define global secondary indexes (creating local secondary indexes is not currently supported):

# All of the following are equivalent
index1 = "my_field"
index1 = Index("my_field")
index1 = Index("my_field", index_name="my_field-index")

# Range keys are also supported
index2 = Index("my_field", "my_second_field")
index2 = Index("my_field", "my_second_field", index_name="my_field_my_second_field-index")

MyModel.create_table(index1, index2)

Changelog

0.9.0 2023-04-15

• Add support for __table_host__ for local testing

0.8.2 2022-11-12

• Make mypy linting more strict

0.8.1 2022-11-08

• Fixed batch_read to support non-string hash keys

0.8.0 2022-10-12

• Add py.typed marker to indicate this library ships with type hints

0.7.0 2022-10-11

• No changes since 0.7.0a1

0.7.0a1 2022-10-08

• Change dependency version pinning to be more flexible
• Only require importlib_metadata for python3.7 and earlier

0.6.0 2022-09-17

• Added support for __table_name__ being a Callable[[], str] to allow dynamic table name
• Added support for batch reads and writes
• Fixed consistent_read behavior for safe_get (previously was always set to True)

0.5.0 2022-05-09

• Added support for multiple subclasses within one table (get_model function)

0.4.1 2022-04-26

• Fixed serialization of dynamo types when using Pydantic aliases

0.4.0 2022-04-26

• Fixed compatibility with Pydantic aliases

0.3.0 2022-04-25

• Added support for nested attribute conditions and update expressions
• Fixed bug where refresh() would cause nested Pydantic models to be converted to dictionaries instead of loaded into their models
• Added Pydantic aliases (models will all be dumped using pydantic's by_alias=True flag).

0.2.0 2022-04-23

BREAKING: Accessing attributes after calling update(..., refresh=False) will trigger a ValueError. Read below for more information.

• Added built in safety for unrefreshed instances after an update. Any attribute accesses on an instance that was updated with refresh=False will raise a ValueError. This can be fixed by calling refresh() to get the most up-to-date data of the item, or by calling ignore_unrefreshed() to explicitly opt-in to using stale data.

0.1.0 2022-02-13

• Initial release