dataflows-elasticsearch 0.1.2

A utility library for working with data flows in Python and ElasticSearch

dataflows-elasticsearch

Dataflows's processors to work with ElasticSearch

Features

• dump_to_elasticsearch processor

Contents

• Getting Started
  • Installation
  • Examples
• Documentation
  • dump_to_es
• 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-elasticsearch

Examples

These processors have to be used as a part of a dataflows Flow. For example:

flow = Flow(
    load('data/data.csv'),
    dump_to_es(
        engine='localhost:9200',
    ),
)
flow.process()

Documentation

dump_to_es

Saves the Flow to an ElasticSearch Index.

Parameters

• indexes - Mapping of indexe names to resource names, e.g.

{
  'index-name-1': {
    'resource-name': 'resource-name-1',
  },
  'index-name-2': {
    'resource-name': 'resource-name-2',
  },
  # ...
}

• mapper_cls - Class to be used to map json table schema types into ElasticSearch types
• index_settings - Options to be used when creating the ElasticSearch index
• engine - Connection string for connecting the ElasticSearch instance, or an Elasticsearch object. Can also be of the form env://ENV_VAR, in which case the connection string will be fetched from the environment variable ENV_VAR.
• elasticsearch_options - Options to be used when creating the Elasticsearch object (in case it wasn't provided)

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

The full changelog and documentation for all released versions can be found in the nicely formatted commit history.