rls 0.1.0

fastapi sqlalchemy rls integration package

rls

a package to provide row level security seamlessly to your python app by extending sqlalchemy and alembic.

---

Installation

Package

pip install rls

or if you are using poetry

poetry add rls

Source Code

After cloning the repo use it as you would use the package but import from your local cloned files

---

Usage Example

Creating Policies

from sqlalchemy import Column, Integer, String, ForeignKey
from sqlalchemy.orm import relationship, declarative_base
from rls.schemas import (
    Permissive,
    ExpressionTypes,
    Command,
)


Base = declarative_base()

class User(Base):
    __tablename__ = "users"

    id = Column(Integer, primary_key=True, index=True)
    username = Column(String, unique=True, index=True)



class Item(Base):
    __tablename__ = "items"

    id = Column(Integer, primary_key=True, index=True)
    title = Column(String, index=True)
    description = Column(String)
    owner_id = Column(Integer, ForeignKey("users.id"))

    owner = relationship("User")

    __rls_policies__ = [
        Permissive(
            condition_args=[
                {
                    "comparator_name": "account_id",
                    "type": ExpressionTypes.integer,
                }
            ],
            cmd=[Command.all],
            custom_expr="owner_id > {0}",
        )
    ]

class Item1():
    __tablename__ = "items1"

    id = Column(Integer, primary_key=True, index=True)
    title = Column(String, index=True)
    description = Column(String)
    owner_id = Column(Integer, ForeignKey("users.id"))

    owner = relationship("User")

    __rls_policies__ = [
        Permissive(
            condition_args=[
                {
                    "comparator_name": "sub",
                    "operation": Operation.equality,
                    "type": ExpressionTypes.integer,
                    "column_name": "owner_id",
                },
                {
                    "comparator_name": "title",
                    "operation": Operation.equality,
                    "type": ExpressionTypes.text,
                    "column_name": "title",
                }
            ],
            cmd=[Command.all],
            joined_expr="{0} OR {1}",
        )
    ]


class Item2():
    __tablename__ = "items2"

    id = Column(Integer, primary_key=True, index=True)
    title = Column(String, index=True)
    description = Column(String)
    owner_id = Column(Integer, ForeignKey("users.id"))

    owner = relationship("User")

    __rls_policies__ = [
        Permissive(
            condition_args=[
                {
                    "comparator_name": "sub",
                    "operation": Operation.equality,
                    "type": ExpressionTypes.integer,
                    "column_name": "owner_id",
                }
            ],
            cmd=[Command.select],
        )
    ]

Note

alembic must be initialized to be used when creating policies

Now then, there are multiple way you can add expressions:

• plain expressions: where you just fill the fields in the condition args and don't specify an expr input so it takes the first value in condition args only as Item2 table policy.
• joined expressions: where you specify multiple condition args elements and input a parametrized joined_expr that has 0 indexed expression numbers e.g: {0} and their joining operations as Item1 table policy.
• custom expressions: where you write expression as you wish but supply us through custom_expr with the session variables as 0 indexed parameters e.g: {0} as Item table policy.

the rls policies are registered as metadata info and can be used with alembic but first in alembic env.py before setting

target_metadata = Base.metadata

call our rls base wrapper instead

from rls.alembic_rls import rls_base_wrapper

target_metadata = rls_base_wrapper(Base).metadata

which returns a base that its rls policies metadata set.

Now all you have to do is create a revision and run upgrade head with alembic for the policies to be created or dropped.

Using the policies

now that we have created the policies how are we going to use it?

we have a custom sqlalchemy session class called RlsSession which must be used or extended.

and you have to pass the context which the session variables values will be taken from which should extend a pydantic Base Model and bind an engine to it.

class MyContext(BaseModel):
    account_id: int
    provider_id: int


context = MyContext(account_id=1, provider_id=2)
session = RlsSession(context=context, bind=engine)

res = session.execute(text("SELECT * FROM users")).fetchall()

you can use this session to talk to your db directly or you can create a session factory for which we provide our RlsSessioner.

which takes two arguments:

• sessionmaker: your own created session maker from our RlsSession or its subclass
• context_getter: an instance of a class that extends ContextGetter that has the get context function implemented from which you can extract values from args or kwargs and assign it to your context variables.

for which you have

from sqlalchemy.orm import sessionmaker
from rls.rls_session import RlsSession
from rls.rls_sessioner import RlsSessioner, ContextGetter
from pydantic import BaseModel
from test.engines import sync_engine as engine
from sqlalchemy import text


class ExampleContext(BaseModel):
    account_id: int
    provider_id: int


# Concrete implementation of ContextGetter
class ExampleContextGetter(ContextGetter):
    def get_context(self, *args, **kwargs) -> ExampleContext:
        account_id = kwargs.get("account_id", 1)
        provider_id = kwargs.get("provider_id", 2)
        return ExampleContext(account_id=account_id, provider_id=provider_id)


my_context = ExampleContextGetter()

session_maker = sessionmaker(
    class_=RlsSession, autoflush=False, autocommit=False, bind=engine
)

my_sessioner = RlsSessioner(sessionmaker=session_maker, context_getter=my_context)



with  my_sessioner(account_id=22, provider_id=99) as session:
    res = session.execute(text("SELECT * FROM users")).fetchall()
    print(res) # output: List of users with account_id = 22 and provider_id = 99


with  my_sessioner(account_id=11, provider_id=44) as session:
    res = session.execute(text("SELECT * FROM users")).fetchall()
    print(res) # output: List of users with account_id = 11 and provider_id = 44

---

Frameworks

Fastapi

if you are trying to use the RlsSessioner with fastapi you may face some difficulties so that's why there is a ready made function for this integration to be injected in your request handler

from rls.rls_sessioner import fastapi_dependency_function
from fastapi import Request

app = FastAPI()

class ExampleContext(BaseModel):
    account_id: int
    provider_id: int


# Concrete implementation of ContextGetter
class ExampleContextGetter(ContextGetter):
    def get_context(self, *args, **kwargs) -> ExampleContext:
        request: Request = kwargs.get('request')

        account_id = request.headers.get('account_id')
        provider_id = request.headers.get('provider_id')

        return ExampleContext(account_id=account_id, provider_id=provider_id)


my_context = ExampleContextGetter()

session_maker = sessionmaker(
    class_=RlsSession, autoflush=False, autocommit=False, bind=engine
)



rls_sessioner = RlsSessioner(sessionmaker=session_maker, context_getter=my_context)
my_session = Depends(fastapi_dependency_function(rls_sessioner))

@app.get("/users")
async def get_users(db: Session = my_session):
    result = db.execute(text("SELECT * FROM users")).all()
    return dict(result)