Generate and load BigQuery tables based on Table Schema descriptors.
The package use semantic versioning. It means that major versions could include breaking changes. It's highly recommended to specify
package version range in your
setup/requirements file e.g.
pip install tableschema-bigquery
To start using Google BigQuery service:
- Create a new project - link
- Create a service key - link
- Download json credentials and set
Code examples in this readme requires Python 3.3+ interpreter. You could see even more example in examples directory.
import io import os import json from tableschema import Table from apiclient.discovery import build from oauth2client.client import GoogleCredentials # Prepare BigQuery credentials os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = '.credentials.json' credentials = GoogleCredentials.get_application_default() service = build('bigquery', 'v2', credentials=credentials) project = json.load(io.open('.credentials.json', encoding='utf-8'))['project_id'] # Load and save table to BigQuery table = Table('data.csv', schema='schema.json') table.save('data', storage='bigquery', service=service, project=project, dataset='dataset')
The whole public API of this package is described here and follows semantic versioning rules. Everyting outside of this readme are private API and could be changed without any notification on any new version.
Package implements Tabular Storage interface (see full documentation on the link):
This driver provides an additional API:
Storage(service, project, dataset, prefix='')
service (object)- BigQuery
project (str)- BigQuery project name
dataset (str)- BigQuery dataset name
prefix (str)- prefix for all buckets
The project follows the Open Knowledge International coding standards.
Recommended way to get started is to create and activate a project virtual environment. To install package and development dependencies into active environment:
$ make install
To run tests with linting and coverage:
$ make test
pylama configured in
pylama.ini is used. On this stage it's already
installed into your environment and could be used separately with more fine-grained control
as described in documentation - https://pylama.readthedocs.io/en/latest/.
For example to sort results by error type:
$ pylama --sort <path>
tox configured in
tox.ini is used.
It's already installed into your environment and could be used separately with more fine-grained control as described in documentation - https://testrun.org/tox/latest/.
For example to check subset of tests against Python 2 environment with increased verbosity.
All positional arguments and options after
-- will be passed to
tox -e py27 -- -v tests/<path>
Under the hood
pytest configured in
mock packages. This packages are available only in tox envionments.
Here described only breaking and the most important changes. The full changelog and documentation for all released versions could be found in nicely formatted commit history.
Initial driver implementation.