Python DynamoDB interface, specialized in single-table design.
Project description
DynamoDB SingleTable
https://pypi.org/project/ddb-single/
Python DynamoDB interface, specialized in single-table design. DynamoDB is high-performance serverless NoSQL, but difficult to disign tables.
Single-table design needs only single table, and few GSIs (Global Secondary Indexes). It makes effective and easy to manage your whole data models for single service.
Getting Started
Install
pip install ddb-single
Start DynamoDB Local
docker run -d --rm -p 8000:8000 amazon/dynamodb-local
Init Table
from ddb_single import Table
table = Table(
table_name="sample",
endpoint_url="http://localhost:8000",
)
table.init()
Data Models
Each model has al least 3 keys
- primary_key ... Hash key for single item. default:
pk: {__model_name__}_{uuid}
- seconday_key ... Range key for item. default:
sk: {__model_name__}_item
- unique_key ... key to identify the item is the same. Mainly used to update item.
And you can set serch_key
to enable search via GSI
from ddb_single import BaseModel, DBField, FieldType
class User(BaseModel):
__table__=table
__model_name__ = "user"
name = DBField(unique_key=True)
email = DBField(search_key=True)
age = DBField(type=FieldType.NUMBER, search_key=True)
description=DBField()
Usage
need "Qurey" object for CRUD
query.model(foo).create
query.model(foo).get
query.model(foo).search
query.model(foo).update
query.model(foo).delete
from ddb_single import Query
query = Query(table)
Create Item
If the item with same value of unique_key
already exist, exist item is updated.
user = User(name="John", email="john@example.com", description="test")
query.model(user).create()
Then, multible items added.
pk | sk | data | name | description | |
---|---|---|---|---|---|
user_xxxx | user_item | John | john@example.com | test | |
user_xxxx | search_user_name | John | |||
user_xxxx | search_user_email | new-john@example.com |
In addition to main item (sk=user_item
), multiple item (sk=search_{__model_name__}_{field_name}
) added to table.
Those "search items" are used to search
The GSI DataSearchIndex
is used to get "search items" to extract target's pk.
Then, batch_get
items by pk.
sk = hash | data = range | pk |
---|---|---|
search_user_name | John | user_xxxx |
search_user_email | new-john@example.com | user_xxxx |
Search Items
user = query.model(User).search(User.name.eq("John"))
print(user)
# -> [{"pk":"user_xxxx", "sk":"user_item", "name":"John", "email":"john@example.com"}]
pk_only=True
to extract pk without batch_get
user_pks = query.model(User).search(User.name.eq("John"), pk_only=True)
print(user_pks)
# -> ["user_xxxx"]
Get single item
get(pk)
to get single item.
user = query.model(User).get("user_xxxx")
print(user)
# -> {"pk":"user_xxxx", "sk":"user_item", "name":"John", "email":"john@example.com"}
get_by_unique
to get item by unique_key
user = query.model(User).get_by_unique("John")
print(user)
# -> {"pk":"user_xxxx", "sk":"user_item", "name":"John", "email":"john@example.com"}
pk_only=True
option in get_by_unique
to get primary key
without get_item
pk = query.model(User).get_by_unique("John", pk_only=True)
print(pk)
# -> "user_xxxx"
Update Item
user = query.model(User).search(User.email.eq("john@example.com"))
new_user = User(**user[0])
new_user.email = "new-john@example.com"
query.model(new_user).update()
Or use unique value to detect exist item.
new_user = User(name="John", email="new-john@example.com")
query.model(new_user).update()
Then, tha value of "main item" and "seach item" changed
pk | sk | data | name | description | |
---|---|---|---|---|---|
user_xxxx | user_item | John | new-john@example.com | test | |
user_xxxx | search_user_name | John | |||
user_xxxx | search_user_email | new-john@example.com |
Delete Item
user = query.model(User).search(User.email.eq("new-john@example.com"))
query.model(user[0]).delete()
primary key
to detect exist item.
query.model(User).delete_by_pk("user_xxxx")
or unique key
query.model(User).delete_by_unique("John")
Batch Writer
table.batch_writer()
to create/update/delete multible items
query.model(foo).create(batch=batch)
query.model(foo).update(batch=batch)
query.model(foo).delete(batch=batch)
Batch Create
with table.batch_writer() as batch:
for i in range(3):
user = User(name=f"test{i}", age=i+10)
query.model(user).create(batch=batch)
res = query.model(User).search(User.name.begins_with("test"))
print([(r["name"], r["age"]) for r in res])
# -> [("test0", 10), ("test1", 11), ("test2", 12)]
Batch Update
with table.batch_writer() as batch:
for i in range(3):
user = User(name=f"test{i}", age=i+20)
query.model(user).update(batch=batch)
res = query.model(User).search(User.name.begins_with("test"))
print([(r["name"], r["age"]) for r in res])
# -> [("test0", 20), ("test1", 21), ("test2", 22)]
Batch Delete
pks = query.model(User).search(User.name.begins_with("test"), pk_only=True)
with table.batch_writer() as batch:
for pk in pks:
query.model(user).delete_by_pk(pk, batch=batch)
res = query.model(User).search(User.name.begins_with("test"))
print(res)
# -> []
Relationship
Create Model
You can sat relationns to other models
relation=BaseModel
to set relation.
class BlogPost(BaseModel):
__model_name__ = "blogpost"
__table__=table
name = DBField(unique_key=True)
content = DBField()
author = DBField(reletion=User)
Create Item
blogpost = BlogPost(
name="Hello",
content="Hello world",
author=self.user
)
query.model(blogpost).create()
Then, tha value "reletion item" added
pk | sk | data | name | author | content |
---|---|---|---|---|---|
user_xxxx | user_item | John | |||
user_xxxx | search_user_name | John | |||
blogpost_xxxx | blogpost_item | Hello | John | Hello world | |
blogpost_xxxx | search_blogpost_title | Hello | |||
blogpost_xxxx | rel_user_xxxx | author |
In addition to main item (sk=blogpost_item
), relation item (sk=rel_{primary_key}
) added to table. The GSI DataSearchIndex
is used to get "relation items" to extract target's pk.
Then, batch_get
items by pk.
sk = hash | data = range | pk |
---|---|---|
rel_user_xxxx | author | blogpost_xxxx |
Search Relations
get_relation(model=Basemodel)
to search relations
blogpost = query.model(BlogPost).get_by_unique("Hello")
blogpost = BlogPost(**blogpost)
user = query.model(blogpost).get_relation(model=User)
print(user)
# -> [{"pk":"user_xxxx", "sk":"user_item", "name":"John"}]
Also get_relation(field=DBField)
to specify field
user = query.model(blogpost).get_relation(field=BlogPost.author)
print(user)
# -> [{"pk":"user_xxxx", "sk":"user_item", "name":"John"}]
Search Reference
In this library, "reference" is antonym to relation
get_reference(model=Basemodel)
to search items related to the item
user = query.model(User).get_by_unique("John")
user = User(**blogpost)
blogpost = query.model(blogpost).get_reference(model=BlogPost)
print(blogpost)
# -> [{"pk":"blogpost_xxxx", "sk":"blogpost_item", "name":"Hello"}]
Also get_reference(field=DBField)
to specify field
blogpost = query.model(user).get_reference(field=BlogPost.author)
print(blogpost)
# -> [{"pk":"blogpost_xxxx", "sk":"blogpost_item", "name":"Hello"}]
Update Relation
If relation key's value changed, relationship also changed.
new_user = User(name="Michael")
blogpost = query.model(BlogPost).get_by_unique("Hello")
blogpost["author"] = new_user
blogpost = BlogPost(**blogpost)
query.model(blogpost).update()
Then, "reletion item" changed
pk | sk | data | name | author | content |
---|---|---|---|---|---|
user_xxxx | user_item | John | |||
user_xxxx | search_user_name | John | |||
user_yyyy | user_item | Michael | |||
user_yyyy | search_user_name | Michael | |||
blogpost_xxxx | blogpost_item | Hello | Michael | Hello world | |
blogpost_xxxx | search_blogpost_title | Hello | |||
blogpost_xxxx | rel_user_yyyy | author |
Delete Relation
If related item deleted, relationship also deleted
query.model(user).delete_by_unique("Michael")
Then, "reletion item" deleted. But main item's value is not chenged.
pk | sk | data | name | author | content |
---|---|---|---|---|---|
user_xxxx | user_item | John | |||
user_xxxx | search_user_name | John | |||
blogpost_xxxx | blogpost_item | Hello | Michael | Hello world | |
blogpost_xxxx | search_blogpost_title | Hello |
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
File details
Details for the file ddb_single-0.4.15.tar.gz
.
File metadata
- Download URL: ddb_single-0.4.15.tar.gz
- Upload date:
- Size: 14.5 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: poetry/1.8.3 CPython/3.12.6 Linux/6.8.0-1014-azure
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 418d82091f1f9ece70ec5cd37244d8a853aab4e5a5dbed045f0d7200d278e563 |
|
MD5 | 61e02379910c0f3ce59bf51f7e983e40 |
|
BLAKE2b-256 | acf7ae7e2252b8cae51a3cf4fb7df97c08d6b4b0d9745ecfb8489f8f0e69860f |
File details
Details for the file ddb_single-0.4.15-py3-none-any.whl
.
File metadata
- Download URL: ddb_single-0.4.15-py3-none-any.whl
- Upload date:
- Size: 16.6 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: poetry/1.8.3 CPython/3.12.6 Linux/6.8.0-1014-azure
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 34a2b1a584e7a99987b42bfd399e3aed59ad43099c22b1f001fbf687dc804ef2 |
|
MD5 | 96bd9e5bfc10c389a648e7d7843aa115 |
|
BLAKE2b-256 | 66fdec0c58ee25c681b2bf422d074886229dfe0f0cbc409e2a46e71b021135a7 |