SQLAlchemy dialect for BigQuery
Project description
SQLAlchemy dialect and API client for BigQuery.
Usage
=====
.. code-block:: python
SQLAchemy
_________
from sqlalchemy import *
from sqlalchemy.engine import create_engine
from sqlalchemy.schema import *
engine = create_engine('bigquery://project')
table = Table('dataset.table', MetaData(bind=engine), autoload=True)
print(select([func.count('*')], from_obj=table).scalar())
API Client
_________
from pybigquery.api import ApiClient
api_client = ApiClient()
print(api_client.dry_run_query(query=sqlstr).total_bytes_processed)
Project
_______
``project`` in ``bigquery://project`` is used to instantiate BigQuery client with the specific project ID. To infer project from the environment, use ``bigquery://`` – without ``project``
Authentication
______________
Follow the `Google Cloud library guide <https://google-cloud-python.readthedocs.io/en/latest/core/auth.html>`_ for authentication. Alternatively, you can provide the path to a service account JSON file in ``create_engine()``:
.. code-block:: python
engine = create_engine('bigquery://', credentials_path='/path/to/keyfile.json')
Table names
___________
To query tables from non-default projects, use the following format for the table name: ``project.dataset.table``, e.g.:
.. code-block:: python
sample_table = Table('bigquery-public-data.samples.natality')
Batch size
__________
By default, ``arraysize`` is set to ``5000``. ``arraysize`` is used to set the batch size for fetching results. To change it, pass ``arraysize`` to ``create_engine()``:
.. code-block:: python
engine = create_engine('bigquery://project', arraysize=1000)
Requirements
============
Install using
- ``pip install pybigquery``
Testing
============
Load sample tables::
./scripts/load_test_data.sh
This will create a dataset ``test_pybigquery`` with tables named ``sample_one_row`` and ``sample``.
Set up an environment and run tests::
pyvenv .env
source .env/bin/activate
pip install -r dev_requirements.txt
pytest
Usage
=====
.. code-block:: python
SQLAchemy
_________
from sqlalchemy import *
from sqlalchemy.engine import create_engine
from sqlalchemy.schema import *
engine = create_engine('bigquery://project')
table = Table('dataset.table', MetaData(bind=engine), autoload=True)
print(select([func.count('*')], from_obj=table).scalar())
API Client
_________
from pybigquery.api import ApiClient
api_client = ApiClient()
print(api_client.dry_run_query(query=sqlstr).total_bytes_processed)
Project
_______
``project`` in ``bigquery://project`` is used to instantiate BigQuery client with the specific project ID. To infer project from the environment, use ``bigquery://`` – without ``project``
Authentication
______________
Follow the `Google Cloud library guide <https://google-cloud-python.readthedocs.io/en/latest/core/auth.html>`_ for authentication. Alternatively, you can provide the path to a service account JSON file in ``create_engine()``:
.. code-block:: python
engine = create_engine('bigquery://', credentials_path='/path/to/keyfile.json')
Table names
___________
To query tables from non-default projects, use the following format for the table name: ``project.dataset.table``, e.g.:
.. code-block:: python
sample_table = Table('bigquery-public-data.samples.natality')
Batch size
__________
By default, ``arraysize`` is set to ``5000``. ``arraysize`` is used to set the batch size for fetching results. To change it, pass ``arraysize`` to ``create_engine()``:
.. code-block:: python
engine = create_engine('bigquery://project', arraysize=1000)
Requirements
============
Install using
- ``pip install pybigquery``
Testing
============
Load sample tables::
./scripts/load_test_data.sh
This will create a dataset ``test_pybigquery`` with tables named ``sample_one_row`` and ``sample``.
Set up an environment and run tests::
pyvenv .env
source .env/bin/activate
pip install -r dev_requirements.txt
pytest
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