Skip to main content

Generate BigQuery tables, load and extract data, based on JSON Table Schema descriptors.

Project description

tableschema-bigquery-py
=======================

| |Travis|
| |Coveralls|
| |PyPi|
| |Gitter|

Generate and load BigQuery tables based on `Table
Schema <http://specs.frictionlessdata.io/table-schema/>`__ descriptors.

Features
--------

- implements ``tableschema.Storage`` interface

Getting Started
---------------

Installation
~~~~~~~~~~~~

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.
``package>=1.0,<2.0``.

.. code:: bash

pip install tableschema-bigquery

To start using Google BigQuery service:

- Create a new project -
`link <https://console.developers.google.com/home/dashboard>`__
- Create a service key -
`link <https://console.developers.google.com/apis/credentials>`__
- Download json credentials and set ``GOOGLE_APPLICATION_CREDENTIALS``
environment variable

Examples
~~~~~~~~

Code examples in this readme requires Python 3.3+ interpreter. You could
see even more example in
`examples <https://github.com/frictionlessdata/tableschema-bigquery-py/tree/master/examples>`__
directory.

.. code:: python

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')

Documentation
-------------

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.

Storage
~~~~~~~

Package implements `Tabular
Storage <https://github.com/frictionlessdata/tableschema-py#storage>`__
interface (see full documentation on the link):

|Storage|

This driver provides an additional API:

``Storage(service, project, dataset, prefix='')``
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

- ``service (object)`` - BigQuery ``Service`` object
- ``project (str)`` - BigQuery project name
- ``dataset (str)`` - BigQuery dataset name
- ``prefix (str)`` - prefix for all buckets

Contributing
------------

The project follows the `Open Knowledge International coding
standards <https://github.com/okfn/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:

.. code:: bash

$ make test

| For linting ``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:

.. code:: bash

$ pylama --sort <path>

| For testing ``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
``py.test``:

.. code:: bash

tox -e py27 -- -v tests/<path>

| Under the hood ``tox`` uses ``pytest`` configured in ``pytest.ini``,
``coverage``
| and ``mock`` packages. This packages are available only in tox
envionments.

Changelog
---------

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 <https://github.com/frictionlessdata/tableschema-bigquery-py/commits/master>`__.

v0.x
~~~~

Initial driver implementation.

.. |Travis| image:: https://img.shields.io/travis/frictionlessdata/tableschema-bigquery-py/master.svg
:target: https://travis-ci.org/frictionlessdata/tableschema-bigquery-py
.. |Coveralls| image:: http://img.shields.io/coveralls/frictionlessdata/tableschema-bigquery-py.svg?branch=master
:target: https://coveralls.io/r/frictionlessdata/tableschema-bigquery-py?branch=master
.. |PyPi| image:: https://img.shields.io/pypi/v/tableschema-bigquery.svg
:target: https://pypi-hypernode.com/pypi/tableschema-bigquery
.. |Gitter| image:: https://img.shields.io/gitter/room/frictionlessdata/chat.svg
:target: https://gitter.im/frictionlessdata/chat
.. |Storage| image:: https://i.imgur.com/RQgrxqp.png

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

tableschema-bigquery-0.6.3.tar.gz (10.2 kB view details)

Uploaded Source

Built Distribution

tableschema_bigquery-0.6.3-py2.py3-none-any.whl (11.3 kB view details)

Uploaded Python 2 Python 3

File details

Details for the file tableschema-bigquery-0.6.3.tar.gz.

File metadata

File hashes

Hashes for tableschema-bigquery-0.6.3.tar.gz
Algorithm Hash digest
SHA256 486dfbcff7672e211e3f003036b1a57dd24c9bf927338af808e6268ccb2243be
MD5 8591cf8eabea81173f539c5fec144c0c
BLAKE2b-256 c8f613971120135b49f53e2c9a92855f097a8f84cdf957617c7c68a0ceab99a9

See more details on using hashes here.

Provenance

File details

Details for the file tableschema_bigquery-0.6.3-py2.py3-none-any.whl.

File metadata

File hashes

Hashes for tableschema_bigquery-0.6.3-py2.py3-none-any.whl
Algorithm Hash digest
SHA256 457034c8d4e856585ae7ac15735dc2dd99f6d8bd096eab558ebf0b83fa5bae90
MD5 8e2a815722e54963638e5d42443c8374
BLAKE2b-256 1e9dcd596d3b52bbdf393e657537e3f11624472587748bd1285623c03f8245eb

See more details on using hashes here.

Provenance

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page