Book Image

PySpark Cookbook

By : Denny Lee, Tomasz Drabas
Book Image

PySpark Cookbook

By: Denny Lee, Tomasz Drabas

Overview of this book

Apache Spark is an open source framework for efficient cluster computing with a strong interface for data parallelism and fault tolerance. The PySpark Cookbook presents effective and time-saving recipes for leveraging the power of Python and putting it to use in the Spark ecosystem. You’ll start by learning the Apache Spark architecture and how to set up a Python environment for Spark. You’ll then get familiar with the modules available in PySpark and start using them effortlessly. In addition to this, you’ll discover how to abstract data with RDDs and DataFrames, and understand the streaming capabilities of PySpark. You’ll then move on to using ML and MLlib in order to solve any problems related to the machine learning capabilities of PySpark and use GraphFrames to solve graph-processing problems. Finally, you will explore how to deploy your applications to the cloud using the spark-submit command. By the end of this book, you will be able to use the Python API for Apache Spark to solve any problems associated with building data-intensive applications.
Table of Contents (13 chapters)
Title Page
Packt Upsell
Contributors
Preface
Index

Building a clustering models


Often, it is hard to get our hands on data that is labeled. Also, sometimes you might want to find underlying patterns in your dataset. In this recipe, we will learn how to build the popular k-means clustering model in Spark.

Getting ready

To execute this recipe, you need to have a working Spark environment. You should have already gone through the Standardizing the data recipe where we standardized the encoded census data.

No other prerequisites are required.

How to do it...

Just like with classification or regression models, building clustering models is pretty straightforward in Spark. Here's the code that aims to find patterns in the census data:

import pyspark.mllib.clustering as clu

model = clu.KMeans.train(
    final_data.map(lambda row: row[1])
    , 2
    , initializationMode='random'
    , seed=666
)

How it works...

First, we need to import the clustering submodule of MLlib. Just like before, we first create the clustering estimator object, KMeans. The .train...