Python Client for Google BigQuery

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 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 >= 3.7

Unsupported Python Versions

Python == 2.7, Python == 3.5, Python == 3.6.

The last version of this library compatible with Python 2.7 and 3.5 is google-cloud-bigquery==1.28.0.

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)

Instrumenting With OpenTelemetry

This application uses OpenTelemetry to output tracing data from API calls to BigQuery. To enable OpenTelemetry tracing in the BigQuery client the following PyPI packages need to be installed:

pip install google-cloud-bigquery[opentelemetry] opentelemetry-exporter-gcp-trace

After installation, OpenTelemetry can be used in the BigQuery client and in BigQuery jobs. First, however, an exporter must be specified for where the trace data will be outputted to. An example of this can be found here:

from opentelemetry import trace
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.exporter.cloud_trace import CloudTraceSpanExporter
tracer_provider = TracerProvider()
tracer_provider = BatchSpanProcessor(CloudTraceSpanExporter())
trace.set_tracer_provider(TracerProvider())

In this example all tracing data will be published to the Google Cloud Trace console. For more information on OpenTelemetry, please consult the OpenTelemetry documentation.

Logging

This library uses the standard Python logging functionality to log some RPC events that could be of interest for debugging and monitoring purposes. Note the following:

1. Logs may contain sensitive information. Take care to restrict access to the logs if they are saved, whether it be on local storage or on Google Cloud Logging.
2. Google may refine the occurrence, level, and content of various log messages in this library without flagging such changes as breaking. Do not depend on immutability of the logging events.
3. By default, the logging events from this library are not handled. You must explicitly configure log handling using one of the mechanisms below.

Simple, environment-based configuration

To enable logging for this library without any changes in your code, set the GOOGLE_SDK_PYTHON_LOGGING_SCOPE environment variable to a valid Google logging scope. This configures handling of logging events (at level logging.DEBUG or higher) from this library in a default manner, emitting the logged messages in a structured format. It does not currently allow customizing the logging levels captured nor the handlers, formatters, etc. used for any logging event.

A logging scope is a period-separated namespace that begins with google, identifying the Python module or package to log.

• Valid logging scopes: google, google.cloud.asset.v1, google.api, google.auth, etc.
• Invalid logging scopes: foo, 123, etc.

NOTE: If the logging scope is invalid, the library does not set up any logging handlers.

Examples

• Enabling the default handler for all Google-based loggers

export GOOGLE_SDK_PYTHON_LOGGING_SCOPE=google

• Enabling the default handler for a specific Google module (for a client library called google.cloud.bigquery_v2):

export GOOGLE_SDK_PYTHON_LOGGING_SCOPE=google.cloud.bigquery_v2

Advanced, code-based configuration

You can also configure a valid logging scope using Python's standard logging mechanism.

Examples

• Configuring a handler for all Google-based loggers

import logging

from google.cloud.translate_v3 import translate

base_logger = logging.getLogger("google")
base_logger.addHandler(logging.StreamHandler())
base_logger.setLevel(logging.DEBUG)

• Configuring a handler for a specific Google module (for a client library called google.cloud.bigquery_v2):

import logging

from google.cloud.translate_v3 import translate

base_logger = logging.getLogger("google.cloud.bigquery_v2")
base_logger.addHandler(logging.StreamHandler())
base_logger.setLevel(logging.DEBUG)

Logging details

1. Regardless of which of the mechanisms above you use to configure logging for this library, by default logging events are not propagated up to the root logger from the google-level logger. If you need the events to be propagated to the root logger, you must explicitly set logging.getLogger("google").propagate = True in your code.
2. You can mix the different logging configurations above for different Google modules. For example, you may want use a code-based logging configuration for one library, but decide you need to also set up environment-based logging configuration for another library.
   1. If you attempt to use both code-based and environment-based configuration for the same module, the environment-based configuration will be ineffectual if the code -based configuration gets applied first.
3. The Google-specific logging configurations (default handlers for environment-based configuration; not propagating logging events to the root logger) get executed the first time any client library is instantiated in your application, and only if the affected loggers have not been previously configured. (This is the reason for 2.i. above.)