| Name | pydantic-db JSON |
| Version |
0.2.1
JSON |
| download |
| home_page | None |
| Summary | SQL library agnostic data model framework |
| upload_time | 2025-07-30 14:43:13 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.10 |
| license | None |
| keywords |
agnostic
database
model
pydantic
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
pydantic-db aims to be a database framework agnostic modeling library.
Providing functionality to convert database result object(s) into pydantic
model(s). The aim is not to provide an ORM, but to target users who prefer raw
sql interactions over obfuscated ORM object built queries layers.
For those who prefer libraries like pypika to build their queries, this library
can still provide a nice layer between raw query results and database models.
So long as the database library you are using returns result objects that can
be converted to a dictionary, pydantic-db will ineract cleanly with your
results. See unittests for examples with asyncpg, mysql-connector-python,
psycopg2 and sqlite3.
# Usage
All examples assumes the existence of underlying tables and data, they are not
intended to run as is.
## from_result
To convert a single result object into a model, use `Model.from_result`.
```python
import sqlite3
from pydantic_db import Model
class User(Model):
id: int
name: str
db = sqlite3.connect(":memory:")
db.row_factory = sqlite3.Row
stmt = "SELECT * FROM my_user LIMIT 1"
cursor.execute(stmt)
r = cursor.fetchone()
user = User.from_result(r)
```
## from_results
To convert a list of result objects into models, use `Model.from_results`.
```python
import sqlite3
from pydantic_db import Model
class User(Model):
id: int
name: str
db = sqlite3.connect(":memory:")
db.row_factory = sqlite3.Row
stmt = "SELECT * FROM my_user"
cursor.execute(stmt)
results = cursor.fetchall()
users = User.from_results(results)
```
## Nested models
For more complicated queries returning a nested object, models can be nested. To
parse them automatically prefix query fields with `name__` format prefixes.
Say we have a Vehicle table with a reference to an owner (User).
```python
import sqlite3
from pydantic_db import Model
class User(Model):
id: int
name: str
class Vehicle(Model):
id: int
name: str
owner: User
db = sqlite3.connect(":memory:")
db.row_factory = sqlite3.Row
stmt = """
SELECT
v.id,
v.name,
u.id AS owner__id,
u.name AS owner__name
FROM my_vehicle v
JOIN my_user u ON v.owner_id = u.id
"""
cursor.execute(stmt)
results = cursor.fetchall()
vehicles = Vehicle.from_results(results)
```
### Optional nested models
When a nested model is optional i.e. `user: User | None` the library will check
if there is an `id` field by default, and if that field is empty (None), it
will nullify that field.
If your nested model contains a differently named primary key or some other
field that can be relied on to detect that a query has not successfully joined,
and so the nested model should be None. Override the `_skip_prefix_fields` class var.
```python
class User(Model):
primary_key: int
name: str
class Vehicle(Model):
_skip_prefix_fields = {"owner": "primary_key"}
id: int
name: str
owner: User | None
```
Raw data
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"description": "pydantic-db aims to be a database framework agnostic modeling library.\nProviding functionality to convert database result object(s) into pydantic\nmodel(s). The aim is not to provide an ORM, but to target users who prefer raw\nsql interactions over obfuscated ORM object built queries layers.\n\nFor those who prefer libraries like pypika to build their queries, this library\ncan still provide a nice layer between raw query results and database models.\n\nSo long as the database library you are using returns result objects that can\nbe converted to a dictionary, pydantic-db will ineract cleanly with your\nresults. See unittests for examples with asyncpg, mysql-connector-python,\npsycopg2 and sqlite3.\n\n# Usage\n\nAll examples assumes the existence of underlying tables and data, they are not\nintended to run as is.\n\n## from_result\n\nTo convert a single result object into a model, use `Model.from_result`.\n\n```python\nimport sqlite3\n\nfrom pydantic_db import Model\n\n\nclass User(Model):\n id: int\n name: str\n\n\ndb = sqlite3.connect(\":memory:\")\ndb.row_factory = sqlite3.Row\n\nstmt = \"SELECT * FROM my_user LIMIT 1\"\ncursor.execute(stmt)\nr = cursor.fetchone()\n\nuser = User.from_result(r)\n```\n\n## from_results\n\nTo convert a list of result objects into models, use `Model.from_results`.\n\n```python\nimport sqlite3\n\nfrom pydantic_db import Model\n\n\nclass User(Model):\n id: int\n name: str\n\n\ndb = sqlite3.connect(\":memory:\")\ndb.row_factory = sqlite3.Row\n\nstmt = \"SELECT * FROM my_user\"\ncursor.execute(stmt)\nresults = cursor.fetchall()\n\nusers = User.from_results(results)\n```\n\n## Nested models\n\nFor more complicated queries returning a nested object, models can be nested. To\nparse them automatically prefix query fields with `name__` format prefixes.\n\nSay we have a Vehicle table with a reference to an owner (User).\n\n```python\nimport sqlite3\n\nfrom pydantic_db import Model\n\n\nclass User(Model):\n id: int\n name: str\n\n\nclass Vehicle(Model):\n id: int\n name: str\n owner: User\n\ndb = sqlite3.connect(\":memory:\")\ndb.row_factory = sqlite3.Row\n\nstmt = \"\"\"\nSELECT\n v.id,\n v.name,\n u.id AS owner__id,\n u.name AS owner__name\nFROM my_vehicle v\nJOIN my_user u ON v.owner_id = u.id\n\"\"\"\ncursor.execute(stmt)\nresults = cursor.fetchall()\n\nvehicles = Vehicle.from_results(results)\n```\n\n### Optional nested models\n\nWhen a nested model is optional i.e. `user: User | None` the library will check\nif there is an `id` field by default, and if that field is empty (None), it\nwill nullify that field.\n\nIf your nested model contains a differently named primary key or some other\nfield that can be relied on to detect that a query has not successfully joined,\nand so the nested model should be None. Override the `_skip_prefix_fields` class var.\n\n\n```python\nclass User(Model):\n primary_key: int\n name: str\n\n\nclass Vehicle(Model):\n _skip_prefix_fields = {\"owner\": \"primary_key\"}\n\n id: int\n name: str\n owner: User | None\n```\n",
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