google-cloud-bigquery 1.6.0

Google BigQuery API client library

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.

• Client Library Documentation

• Product Documentation

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 Datastore 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.

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

Create a dataset

from google.cloud import bigquery
from google.cloud.bigquery import Dataset

client = bigquery.Client()

dataset_ref = client.dataset('dataset_name')
dataset = Dataset(dataset_ref)
dataset.description = 'my dataset'
dataset = client.create_dataset(dataset)  # API request

Load data from CSV

import csv

from google.cloud import bigquery
from google.cloud.bigquery import LoadJobConfig
from google.cloud.bigquery import SchemaField

client = bigquery.Client()

SCHEMA = [
    SchemaField('full_name', 'STRING', mode='required'),
    SchemaField('age', 'INTEGER', mode='required'),
]
table_ref = client.dataset('dataset_name').table('table_name')

load_config = LoadJobConfig()
load_config.skip_leading_rows = 1
load_config.schema = SCHEMA

# Contents of csv_file.csv:
#     Name,Age
#     Tim,99
with open('csv_file.csv', 'rb') as readable:
    client.load_table_from_file(
        readable, table_ref, job_config=load_config)  # API request

Perform a query

# 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)