pyspark-config 0.0.2.16

Configurable data pipeline with Pyspark

Pyspark-config

Pyspark-Config is a Python module for data processing in Pyspark by means of a configuration file, granting access to build distributed data piplines with configurable inputs, transformations and outputs.

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.

Installation

To install the current release (Ubuntu and Windows):

$ pip install pyspark_config

Dependencies

• Python (>= 3.6)
• Pyspark (>= 2.4.5)
• PyYaml (>= 5.3.1)
• Dataclasses (>= 0.0.0)

Example

Given the yaml configuration file '../example.yaml':

input:
  sources:
    - type: 'Parquet'
      label: 'parquet'
      parquet_path: '../table.parquet'

transformations:
  - type: "Select"
    cols: ['A', 'B']
  - type: "Concatenate"
    cols: ['A', 'B']
    name: 'Concatenation_AB'
    delimiter: "-"

output:
  - type: 'Parquet'
    name: "example"
    path: "../outputs"

With the input source saved in '../table.parquet', the following code can then be applied:

from pyspark_config import Config

from pyspark_config.transformations.transformations import *
from pyspark_config.output import *
from pyspark_config.input import *

config_path="../example.yaml"
configuration=Config()
configuration.load(config_path)

configuration.apply()

The output will then be saved in '../outputs/example.parquet'.

Changelog

See the changelog for a history of notable changes to pyspark-config.

License

This project is distributed under the 3-Clause BSD license. - see the LICENSE.md file for details.