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Enhanced Python Dataframes for Spark/PySpark

Project Description

Enhanced Python Dataframes for Spark/PySpark

Motivation

Spark DataFrames provide a nice interface to datasets that have a schema. Getting data from your code into a DataFrame in Python means creating a Row() object with field names and respective values. Given that you already have a schema with data types per field, it would be nice to easily take an object that represents the row and create the Row() object automatically.

Smartframes allow you to define a class by just creating the schema that represents the fields and datatypes. You can then create an object and set the values like any other Python class. When you are ready to store that in a DataFrame, just call the createRow() method.

The createRow() method will coerce any values into the correct data types, for example, if a field is defined as an IntegerType and the value set in the class is a String, it will attempt to convert the string to an Integer before creating the Row().

This was written when creating Row()’s with Long datatypes and finding that Spark did not handle converting integers as longs when passing values to the JVM. I needed a consistent manner to create Row() for all of my DataFrames.

Installation

pip install smartframes

Example

Simply create a class that extends from SmartFrame and define the schema as a sorted list of StructFields. It’s important that the schema is sorted as Spark gets upset if the Row() object is created with fields that are in a different order. Strange, but true.

The skipSelectedFields is a list of field names that you normally would not select when creating a select() statement.

class SimpleTable(SmartFrames):
    schema = StructType( sorted(
        [
        StructField("pk_id", IntegerType()),
        StructField("first_name", StringType()),
        ],
        key = lambda x: x.name))
    skipSelectedFields = []

...

        s1 = SimpleTable()
        s1.pk_id = 1
        s1.first_name = 'Don'

        s2 = SimpleTable()
        s2.pk_id = 2
        s2.first_name = 'Dan'
        df = self.sqlCtx.createDataFrame(self.sc.parallelize([s1.createRow(), s2.createRow()]), s1.schema)

Releases

Version Date Notes
1.1.0 10/6/2015 Performance improvements
1.0.1 10/3/2015 First release of smartframes
Release History

Release History

This version
History Node

1.1.0

History Node

1.0.1

History Node

1.0.0

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smartframes-1.1.0.tar.gz (8.3 kB) Copy SHA256 Checksum SHA256 Source Oct 7, 2015

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