duckdb-engine 0.1.7

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duckdb_engine

Very very very basic sqlalchemy driver for duckdb

Once you install this package, you should be able to just use it, as sqlalchemy does a python path search

from sqlalchemy import Column, Integer, Sequence, String, create_engine
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm.session import Session

Base = declarative_base()


class FakeModel(Base):  # type: ignore
    __tablename__ = "fake"

    id = Column(Integer, Sequence("fakemodel_id_sequence"), primary_key=True)
    name = Column(String)


eng = create_engine("duckdb:///:memory:")
Base.metadata.create_all(eng)
session = Session(bind=eng)

session.add(FakeModel(name="Frank"))
session.commit()

frank = session.query(FakeModel).one()

assert frank.name == "Frank"

Things to keep in mind

Duckdb's SQL parser is based on the PostgreSQL parser, but not all features in PostgreSQL are supported in duckdb. Because the duckdb_engine dialect is derived from the postgresql dialect, sqlalchemy may try to use PostgreSQL-only features. Below are some caveats to look out for.

Auto-incrementing ID columns

When defining an Integer column as a primary key, sqlalchemy uses the SERIAL datatype for PostgreSQL. Duckdb does not yet support this datatype because it's a non-standard PostgreSQL legacy type, so a workaround is to use the sqlalchemy.Sequence() object to auto-increment the key. For more information on sequences, you can find the sqlalchemy Sequence documentation here.

The following example demonstrates how to create an auto-incrementing ID column for a simple table:

>>> import sqlalchemy
>>> engine = sqlalchemy.create_engine('duckdb:////path/to/duck.db')
>>> metadata = sqlalchemy.MetaData(engine)
>>> user_id_seq = sqlalchemy.Sequence('user_id_seq')
>>> users_table = sqlalchemy.Table(
...     'users',
...     metadata,
...     sqlalchemy.Column(
...         'id',
...         sqlalchemy.Integer,
...         user_id_seq,
...         server_default=user_id_seq.next_value(),
...         primary_key=True,
...     ),
... )
>>> metadata.create_all(bind=engine)

Pandas read_sql() chunksize

The pandas.read_sql() method can read tables from duckdb_engine into DataFrames, but the sqlalchemy.engine.result.ResultProxy trips up when fetchmany() is called. Therefore, for now chunksize=None (default) is necessary when reading duckdb tables into DataFrames. For example:

>>> import pandas as pd
>>> import sqlalchemy
>>> engine = sqlalchemy.create_engine('duckdb:////path/to/duck.db')
>>> df = pd.read_sql('users', engine)                ### Works as expected
>>> df = pd.read_sql('users', engine, chunksize=25)  ### Throws an exception