Databasic

A small async database interface built on SQLAlchemy Core and psycopg.

SQLAlchemy Core handles query construction and compilation. Databasic handles connection and transaction lifetimes and executes the resulting queries directly through psycopg.

Usage

import sqlalchemy as sa

from databasic import Databasic


users = sa.Table(
    "users",
    sa.MetaData(),
    sa.Column("id", sa.Integer, primary_key=True),
    sa.Column("name", sa.Text, nullable=False),
)

db = Databasic("postgresql://user:password@localhost/database")

async with db:
    async with db.session() as session:
        rows = await session.fetch_all(
            users.select().where(users.c.name == "Alice")
        )

Databasic can also be connected and disconnected explicitly. Both forms have the same semantics; explicit lifecycle management is useful when the lifetime is controlled by an application or framework.

db = Databasic("postgresql://user:password@localhost/database")

await db.connect()

try:
    async with db.session() as session:
        rows = await session.fetch_all(users.select())
finally:
    await db.disconnect()

A session is a transaction. It commits when the context exits successfully and rolls back when it exits with an exception.

API

Databasic

db = Databasic(conninfo, force_rollback=False)

connect()

Open the connection pool.

await db.connect()

disconnect()

Close the connection pool.

await db.disconnect()

session()

Create a transactional session.

async with db.session() as session:
    ...

Successful exit commits the transaction. An exception rolls it back.

Session

execute()

Execute a statement without returning rows.

await session.execute(
    users.insert().values(name="Alice")
)

execute_many()

Execute the same statement multiple times with different parameters.

query = users.insert().values(
    name=sa.bindparam("name"),
)

await session.execute_many(
    query,
    [
        {"name": "Alice"},
        {"name": "Bob"},
    ],
)

The statement must define its values using explicit bind parameters. Values are supplied exclusively through the parameter mappings.

fetch_one()

Execute a statement and return one row, or None if there is no row.

user = await session.fetch_one(
    users.select().where(users.c.id == 1)
)

A row is simply a dict[str, Any]:

{"id": 1, "name": "Alice"}

There is no result or row wrapper.

fetch_all()

Execute a statement and return all rows as a list of dictionaries.

users = await session.fetch_all(
    users.select()
)

The result has the shape:

[
    {"id": 1, "name": "Alice"},
    {"id": 2, "name": "Bob"},
]

transaction()

Create a nested transaction within the current session.

async with db.session() as session:
    await session.execute(
        users.insert().values(name="Alice")
    )

    async with session.transaction():
        await session.execute(
            users.insert().values(name="Bob")
        )

Nested transactions use database savepoints. An exception rolls back the nested transaction without rolling back earlier work in the session.

Testing

force_rollback=True runs all sessions within an outer transaction that is rolled back when Databasic is disconnected.

async with Databasic(
    "postgresql://user:password@localhost/test_database",
    force_rollback=True,
) as db:
    ...

This allows integration tests to use normal sessions and transactions without leaving persistent database state.