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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.

Related Libraries

For higher-level data analysis and DataFrame operations, consider using one of the following connector libraries:

Library

Description

Documentation

BigQuery DataFrames (bigframes)

Flexible pandas-like and scikit-learn-like API powered by BigQuery engine for large-scale data analysis and ML.

BigFrames Docs

pandas-gbq

Convenient integration allowing pandas DataFrames to load from and write to BigQuery.

pandas-gbq Docs

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 >= 3.10

Unsupported Python Versions

Python <= 3.9.

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.

Project links

Key dates

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  • Released:
Latest release

3 maintainers

PyPI data
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Avatar for gcloudpypi from gravatar.com gcloudpypi Avatar for google_opensource from gravatar.com google_opensource Avatar for TimSwast from gravatar.com TimSwast

Credits

Author: Google LLC

License

Apache 2.0

Requires

Python >=3.10

Provides Extra

bqstorage pandas ipywidgets geopandas ipython matplotlib tqdm opentelemetry bigquery-v2 all

Classifiers

Report project as malware

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