As of January 1, 2020 this library no longer supports Python 2 on the latest released version. Library versions released prior to that date will continue to be available. For more information please visit Python 2 support on Google Cloud.

Python Client for Google BigQuery

GA pypi versions

Querying massive datasets can be time consuming and expensive without the right hardware and infrastructure. Google BigQuery solves this problem by enabling super-fast, SQL queries against append-mostly tables, using the processing power of Google’s infrastructure.

Quick Start

In order to use this library, you first need to go through the following steps:

  1. Select or create a Cloud Platform project.

  2. Enable billing for your project.

  3. Enable the Google Cloud BigQuery API.

  4. Setup Authentication.

Installation

Install this library in a virtualenv using pip. virtualenv is a tool to create isolated Python environments. The basic problem it addresses is one of dependencies and versions, and indirectly permissions.

With virtualenv, it’s possible to install this library without needing system install permissions, and without clashing with the installed system dependencies.

Supported Python Versions

Python >= 3.5

Deprecated Python Versions

Python == 2.7. Python 2.7 support will be removed on January 1, 2020.

Mac/Linux

pip install virtualenv
virtualenv <your-env>
source <your-env>/bin/activate
<your-env>/bin/pip install google-cloud-bigquery

Windows

pip install virtualenv
virtualenv <your-env>
<your-env>\Scripts\activate
<your-env>\Scripts\pip.exe install google-cloud-bigquery

Example Usage

Perform a query

from google.cloud import bigquery

client = bigquery.Client()

# Perform a query.
QUERY = (
    'SELECT name FROM `bigquery-public-data.usa_names.usa_1910_2013` '
    'WHERE state = "TX" '
    'LIMIT 100')
query_job = client.query(QUERY)  # API request
rows = query_job.result()  # Waits for query to finish

for row in rows:
    print(row.name)

Note

Because the BigQuery client uses the third-party requests library by default and the BigQuery-Storage client uses grpcio library, both are safe to share instances across threads. In multiprocessing scenarios, the best practice is to create client instances after multiprocessing.Pool or multiprocessing.Process invokes os.fork().