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Abstraction layer for cloud blobstores.

Project description

# cloud-blobstore

This library provides an abstraction layer for the most basic functionality provided by the cloud providers. These are the basic functions required by the [Human Cell Atlas](https://www.humancellatlas.org/) [Data Storage Service](https://github.com/HumanCellAtlas/data-store).

## Development

### Set up your python environment Before you start, you may wish to set up a [virtualenv](https://virtualenv.pypa.io/en/stable/). Run pip install -r requirements-dev.txt. Then set up the following environment variables:

Environment variable | Explanation |
———————– | ———– |
S3_BUCKET | Points to the AWS S3 bucket where new files will be written. It may be advisable to set up a cleanup policy for this bucket. |
S3_BUCKET_FIXTURES | Points to the AWS S3 bucket where static fixtures are stored. |
S3_BUCKET_US_EAST_1 | Points to an AWS S3 bucket in the US_EAST_1 region. This can overlap with one of the other buckets. |
S3_BUCKET_NON_US_EAST_1 | Points to an AWS S3 bucket _not_ in the US_EAST_1 region. This can overlap with one of the other buckets. |
GS_BUCKET | Points to the GCP GS bucket where new files will be written. It may be advisable to set up a cleanup policy for this bucket. |
GS_BUCKET_FIXTURES | Points to the GCP GS bucket where static fixtures are stored. |

### Set up test fixtures. Run python tests/fixtures/populate.py –s3-bucket $S3_BUCKET_FIXTURES –gs-bucket $GS_BUCKET_FIXTURES

#### Running tests Run make test in the top-level directory.

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