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Python Client for Google Cloud Storage

stable pypi versions

Google Cloud Storage is a managed service for storing unstructured data. Cloud Storage allows world-wide storage and retrieval of any amount of data at any time. You can use Cloud Storage for a range of scenarios including serving website content, storing data for archival and disaster recovery, or distributing large data objects to users via direct download.

A comprehensive list of changes in each version may be found in the CHANGELOG.

Certain control plane and long-running operations for Cloud Storage (including Folder and Managed Folder operations) are supported via the Storage Control Client. The Storage Control API creates one space to perform metadata-specific, control plane, and long-running operations apart from the Storage API.

Read more about the client libraries for Cloud APIs, including the older Google APIs Client Libraries, in Client Libraries Explained.

Quick Start

In order to use this library, you first need to go through the following steps. A step-by-step guide may also be found in Get Started with Client Libraries.

  1. Select or create a Cloud Platform project.
  2. Enable billing for your project.
  3. Enable the Google Cloud Storage API.
  4. Setup Authentication.

Installation

Install this library in a virtual environment using venv. venv is a tool that creates isolated Python environments. These isolated environments can have separate versions of Python packages, which allows you to isolate one project's dependencies from the dependencies of other projects.

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

Code samples and snippets

Code samples and snippets live in the samples/ folder.

Supported Python Versions

Our client libraries are compatible with all current active and maintenance versions of Python.

Python >= 3.7

Unsupported Python Versions

Python <= 3.6

If you are using an end-of-life version of Python, we recommend that you update as soon as possible to an actively supported version.

Mac/Linux

python3 -m venv <your-env>
source <your-env>/bin/activate
pip install google-cloud-storage

Windows

py -m venv <your-env>
.\<your-env>\Scripts\activate
pip install google-cloud-storage

Tracing With OpenTelemetry

This is a PREVIEW FEATURE: Coverage and functionality are still in development and subject to change.

This library can be configured to use OpenTelemetry to generate traces on calls to Google Cloud Storage. For information on the benefits and utility of tracing, read the Cloud Trace Overview.

To enable OpenTelemetry tracing in the Cloud Storage client, first install OpenTelemetry:

pip install google-cloud-storage[tracing]

Set the ENABLE_GCS_PYTHON_CLIENT_OTEL_TRACES environment variable to selectively opt-in tracing for the Cloud Storage client:

export ENABLE_GCS_PYTHON_CLIENT_OTEL_TRACES=True

You will also need to tell OpenTelemetry which exporter to use. An example to export traces to Google Cloud Trace can be found below.

# Install the Google Cloud Trace exporter and propagator, however you can use any exporter of your choice.
pip install opentelemetry-exporter-gcp-trace opentelemetry-propagator-gcp

# [Optional] Install the OpenTelemetry Requests Instrumentation to trace the underlying HTTP requests.
pip install opentelemetry-instrumentation-requests
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.add_span_processor(BatchSpanProcessor(CloudTraceSpanExporter()))
trace.set_tracer_provider(tracer_provider)

# Optional yet recommended to instrument the requests HTTP library
from opentelemetry.instrumentation.requests import RequestsInstrumentor
RequestsInstrumentor().instrument(tracer_provider=tracer_provider)

In this example, tracing data will be published to the Google Cloud Trace console. Tracing is most effective when many libraries are instrumented to provide insight over the entire lifespan of a request. For a list of libraries that can be instrumented, refer to the OpenTelemetry Registry.

Next Steps