DynamORM is a Python object relation mapping library for Amazon's DynamoDB service.
Project description
DynamORM
This package is a work in progress – Feedback / Suggestions / Etc welcomed!
DynamORM (pronounced Dynamo-R-M) is a Python object relation mapping library for Amazon’s DynamoDB service.
The project has two goals:
Abstract away the interaction with the underlying DynamoDB libraries. Python access to the DynamoDB service has evolved quickly, from Dynamo v1 in boto to Dynamo v2 in boto and then the new resource model in boto3. By providing a consistent interface that will feel familiar to users of other Python ORMs (SQLAlchemy, Django, Peewee, etc) means that we can always provide best-practices for queries and take advantages of new features without needing to refactor any application logic.
Delegate schema validation and serialization to more focused libraries. Building ORM semantics is “easy”, doing data validation and serialization is not. We support both Marshmallow and Schematics for building your object schemas. You can take advantage of the full power of these libraries as they are transparently exposed in your code.
Supported Versions
Schematics >= 2.0
Marshmallow >= 2.0
Example
from dynamorm import DynaModel
# In this example we'll use Marshmallow, but you can also use Schematics too!
# You can see that you have to import the schema library yourself, it is not abstracted at all
from marshmallow import fields
# Our objects are defined as DynaModel classes
class Book(DynaModel):
# Define our DynamoDB properties
class Table:
name = 'prod-books'
hash_key = 'isbn'
read = 25
write = 5
# Define our data schema, each property here will become a property on instances of the Book class
class Schema:
isbn = fields.String(validate=validate_isbn)
title = fields.String()
author = fields.String()
publisher = fields.String()
year = fields.Number()
# Store new documents directly from dictionaries
Book.put({
"isbn": "12345678910",
"title": "Foo",
"author": "Mr. Bar",
"publisher": "Publishorama"
})
# Work with the classes as objects. You can pass attributes from the schema to the constructor
foo = Book(isbn="12345678910", title="Foo", author="Mr. Bar",
publisher="Publishorama")
foo.save()
# Or assign attributes
foo = Book()
foo.isbn = "12345678910"
foo.title = "Foo"
foo.author = "Mr. Bar"
foo.publisher = "Publishorama"
# In all cases they go through Schema validation, calls to .put or .save can result in ValidationError
foo.save()
# You can then fetch, query and scan your tables.
# Get on the hash key, and/or range key
book = Book.get(isbn="12345678910")
# Update items, with conditions
# Here our condition ensures we don't have a race condition where someone else updates the title first
book.update(title='Corrected Foo', conditions=(title=book.title,))
# Query based on the keys
Book.query(isbn__begins_with="12345")
# Scan based on attributes
Book.scan(author="Mr. Bar")
Book.scan(author__ne="Mr. Bar")Documentation
Full documentation can be found online at:
TODO
These are broken down by milestone release.
0.3.0
Indexes – Currently there is no support for indexes.
Partial updates on save()
1.0.0
Schema Migrations
Release history Release notifications | RSS feed
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