Skip to main content

Google BigQuery API client library

Reason this release was yanked:

undeclared dependency on pyarrow

Project description

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 >= 3.7

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.

Project details


Release history Release notifications | RSS feed

Download files

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

Source Distribution

google-cloud-bigquery-3.20.0.tar.gz (441.9 kB view details)

Uploaded Source

Built Distribution

google_cloud_bigquery-3.20.0-py2.py3-none-any.whl (233.4 kB view details)

Uploaded Python 2 Python 3

File details

Details for the file google-cloud-bigquery-3.20.0.tar.gz.

File metadata

File hashes

Hashes for google-cloud-bigquery-3.20.0.tar.gz
Algorithm Hash digest
SHA256 b18c597cbccd522a2894292f393d1d9712a3bd208d9cd36b65f69c46b4c8519f
MD5 5b3b488e4821a0a0201264cf7c111c42
BLAKE2b-256 e85791fae7d2affcee13bcd398388dabdc677ef80e076c44eb1a9aa5bb31f4ce

See more details on using hashes here.

Provenance

File details

Details for the file google_cloud_bigquery-3.20.0-py2.py3-none-any.whl.

File metadata

File hashes

Hashes for google_cloud_bigquery-3.20.0-py2.py3-none-any.whl
Algorithm Hash digest
SHA256 ce6f5385bd62f0b6c62e6c4b87241d9dae4eafddf034b4dee1f909f1e9368f57
MD5 a5dd58fec07a6a2b5012e42fc143fdb6
BLAKE2b-256 da64330c4a9a4ab847d6be7161f9f9bb7e4d9438b66b9bb11ebeccd92ce89f33

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