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https://github.com/mause/duckdb_engine
SQLAlchemy driver for DuckDB
https://github.com/mause/duckdb_engine
duckdb duckdb-engine python sql sqlalchemy
Last synced: 1 day ago
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SQLAlchemy driver for DuckDB
- Host: GitHub
- URL: https://github.com/mause/duckdb_engine
- Owner: Mause
- License: mit
- Created: 2020-09-28T07:09:14.000Z (about 4 years ago)
- Default Branch: main
- Last Pushed: 2024-11-04T23:46:35.000Z (10 days ago)
- Last Synced: 2024-11-05T00:28:42.746Z (10 days ago)
- Topics: duckdb, duckdb-engine, python, sql, sqlalchemy
- Language: Python
- Homepage:
- Size: 2.13 MB
- Stars: 351
- Watchers: 4
- Forks: 40
- Open Issues: 45
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE.txt
- Code of conduct: CODE_OF_CONDUCT.md
Awesome Lists containing this project
README
# duckdb_engine
[](https://pypi.org/project/duckdb-engine/) [](https://badge.fury.io/py/duckdb-engine) [](https://pypi.org/project/duckdb-engine/) [](https://codecov.io/gh/Mause/duckdb_engine)
Basic SQLAlchemy driver for [DuckDB](https://duckdb.org/)
* [duckdb_engine](#duckdb_engine)
* [Installation](#installation)
* [Usage](#usage)
* [Usage in IPython/Jupyter](#usage-in-ipythonjupyter)
* [Configuration](#configuration)
* [How to register a pandas DataFrame](#how-to-register-a-pandas-dataframe)
* [Things to keep in mind](#things-to-keep-in-mind)
* [Auto-incrementing ID columns](#auto-incrementing-id-columns)
* [Pandas read_sql() chunksize](#pandas-read_sql-chunksize)
* [Unsigned integer support](#unsigned-integer-support)
* [Alembic Integration](#alembic-integration)
* [Preloading extensions (experimental)](#preloading-extensions-experimental)
* [The name](#the-name)
## Installation
```sh
$ pip install duckdb-engine
```
DuckDB Engine also has a conda feedstock available, the instructions for the use of which are available in it's [repository](https://github.com/conda-forge/duckdb-engine-feedstock).
## Usage
Once you've installed this package, you should be able to just use it, as SQLAlchemy does a python path search
```python
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"
```
## Usage in IPython/Jupyter
With IPython-SQL and DuckDB-Engine you can query DuckDB natively in your notebook! Check out [DuckDB's documentation](https://duckdb.org/docs/guides/python/jupyter) or
Alex Monahan's great demo of this on [his blog](https://alex-monahan.github.io/2021/08/22/Python_and_SQL_Better_Together.html#an-example-workflow-with-duckdb).
## Configuration
You can configure DuckDB by passing `connect_args` to the create_engine function
```python
create_engine(
'duckdb:///:memory:',
connect_args={
'read_only': False,
'config': {
'memory_limit': '500mb'
}
}
)
```
The supported configuration parameters are listed in the [DuckDB docs](https://duckdb.org/docs/sql/configuration)
## How to register a pandas DataFrame
```python
conn = create_engine("duckdb:///:memory:").connect()
# with SQLAlchemy 1.3
conn.execute("register", ("dataframe_name", pd.DataFrame(...)))
# with SQLAlchemy 1.4+
conn.execute(text("register(:name, :df)"), {"name": "test_df", "df": df})
conn.execute("select * from dataframe_name")
```
## 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](https://docs.sqlalchemy.org/en/14/core/defaults.html#associating-a-sequence-as-the-server-side-default).
The following example demonstrates how to create an auto-incrementing ID column for a simple table:
```python
>>> 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
**NOTE**: this is no longer an issue in versions `>=0.5.0` of `duckdb`
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:
```python
>>> 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
```
### Unsigned integer support
Unsigned integers are supported by DuckDB, and are available in [`duckdb_engine.datatypes`](duckdb_engine/datatypes.py).
## Alembic Integration
SQLAlchemy's companion library `alembic` can optionally be used to manage database migrations.
This support can be enabling by adding an Alembic implementation class for the `duckdb` dialect.
```python
from alembic.ddl.impl import DefaultImpl
class AlembicDuckDBImpl(DefaultImpl):
"""Alembic implementation for DuckDB."""
__dialect__ = "duckdb"
```
After loading this class with your program, Alembic will no longer raise an error when generating or applying migrations.
## Preloading extensions (experimental)
> DuckDB 0.9.0+ includes builtin support for autoinstalling and autoloading of extensions, see [the extension documentation](http://duckdb.org/docs/archive/0.9.0/extensions/overview#autoloadable-extensions) for more information.
Until the DuckDB python client allows you to natively preload extensions, I've added experimental support via a `connect_args` parameter
```python
from sqlalchemy import create_engine
create_engine(
'duckdb:///:memory:',
connect_args={
'preload_extensions': ['https'],
'config': {
's3_region': 'ap-southeast-1'
}
}
)
```
## The name
Yes, I'm aware this package should be named `duckdb-driver` or something, I wasn't thinking when I named it and it's too hard to change the name now