Python idiomatic client for Google Cloud Platform services.
This client supports the following Google Cloud Platform services:
If you need support for other Google APIs, check out the Google APIs Python Client library.
$ pip install gcloud
Google Cloud Datastore is a fully managed, schemaless database for storing non-relational data. Cloud Datastore automatically scales with your users and supports ACID transactions, high availability of reads and writes, strong consistency for reads and ancestor queries, and eventual consistency for all other queries.
See the Google Cloud Datastore docs for more details on how to activate Cloud Datastore for your project.
See the gcloud-python API documentation to learn how to interact with the Cloud Datastore using this Client Library.
from gcloud import datastore
dataset = datastore.get_dataset('dataset-id-here',
'[email protected]',
'/path/to/private.key')
# Then do other things...
query = dataset.query().kind('EntityKind')
entity = dataset.entity('EntityKind')Google Cloud Storage allows you to store data on Google infrastructure with very high reliability, performance and availability, and can be used to distribute large data objects to users via direct download.
You need to create a Google Cloud Storage bucket to use this client library. Follow the steps on the Google Cloud Storage docs to learn how to create a bucket.
See the gcloud-python API documentation to learn how to connect to the Cloud Storage using this Client Library.
import gcloud.storage
bucket = gcloud.storage.get_bucket('bucket-id-here',
'[email protected]',
'/path/to/private.key')
# Then do other things...
key = bucket.get_key('/remote/path/to/file.txt')
print key.get_contents_as_string()
key.set_contents_from_string('New contents!')
bucket.upload_file('/remote/path/storage.txt', '/local/path.txt')Contributions to this library are always welcome and highly encouraged.
See CONTRIBUTING for more information on how to get started.
We support:
We plan to support:
Supported versions can be found in our tox.ini
config.
We explicitly decided not to support Python 2.5 due to decreased usage and lack of continuous integration support.
We also explicitly decided to support Python 3 beginning with version 3.3. Reasons for this include:
- Encouraging use of newest versions of Python 3
- Taking the lead of prominent open-source projects
- Unicode literal support which allows for a cleaner codebase that works in both Python 2 and Python 3.
Apache 2.0 - See LICENSE for more information.