dataflows-aws

Dataflows's processors to work with AWS

Features

• dump_to_s3 processor
• change_acl_on_s3 processor

Contents

• Getting Started
  • Installation
  • Examples
• Documentation
  • dump_to_s3
  • change_acl_on_s3
• Contributing
• Changelog

Getting Started

Installation

The package use semantic versioning. It means that major versions could include breaking changes. It's recommended to specify package version range in your setup/requirements file e.g. package>=1.0,<2.0.

$ pip install dataflows-aws

Examples

These processors have to be used as a part of data flow. For example:

flow = Flow(
    load('data/data.csv'),
    dump_to_s3(
        bucket=bucket,
        acl='private',
        path='my/datapackage',
        endpoint_url=os.environ['S3_ENDPOINT_URL'],
    ),
)
flow.process()

Documentation

dump_to_s3

Saves the DataPackage to AWS S3.

Parameters

• bucket - Name of the bucket where DataPackage will be stored (should already be created!)
• acl - ACL to provide the uploaded files. Default is 'public-read' (see boto3 docs for more info).
• path - Path (key/prefix) to the DataPackage. May contain format string available for datapackage.json Eg: my/example/path/{owner}/{name}/{version}
• content_type - content type to use when storing files in S3. Defaults to text/plain (usual S3 default is binary/octet-stream but we prefer text/plain).
• endpoint_url - api endpoint to allow using S3 compatible services (e.g. 'https://ams3.digitaloceanspaces.com')

change_acl_on_s3

Changes ACL of object in given Bucket with given path aka prefix.

Parameters

• bucket - Name of the bucket where objects are stored
• acl - Available options 'private'|'public-read'|'public-read-write'|'authenticated-read'|'aws-exec-read'|'bucket-owner-read'|'bucket-owner-full-control'
• path - Path (key/prefix) to the DataPackage.
• endpoint_url - api endpoint to allow using S3 compatible services (e.g. 'https://ams3.digitaloceanspaces.com')

Contributing

The project follows the Open Knowledge International coding standards.

The recommended way to get started is to create and activate a project virtual environment. To install package and development dependencies into your active environment:

$ make install

To run tests with linting and coverage:

$ make test

For linting, pylama (configured in pylama.ini) is used. At this stage it's already installed into your environment and could be used separately with more fine-grained control as described in documentation - https://pylama.readthedocs.io/en/latest/.

For example to sort results by error type:

$ pylama --sort <path>

For testing, tox (configured in tox.ini) is used. It's already installed into your environment and could be used separately with more fine-grained control as described in documentation - https://testrun.org/tox/latest/.

For example to check subset of tests against Python 2 environment with increased verbosity. All positional arguments and options after -- will be passed to py.test:

tox -e py37 -- -v tests/<path>

Under the hood tox uses pytest (configured in pytest.ini), coverage and mock packages. These packages are available only in tox envionments.

Changelog

Here described only breaking and the most important changes. The full changelog and documentation for all released versions can be found in the nicely formatted commit history.

v0.x

• an initial processors implementation