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

Clustering forest cover types

Clustering is an unsupervised family of methods that attempts to find patterns in data without any indication of what a class might be. In other words, the clustering methods find commonalities between records and groups them into clusters, depending on how similar they are to each other, and how dissimilar they are from those found in other clusters.

In this recipe, we will build the most fundamental model of them all—the k-means.

Getting ready

To execute this recipe, you will need a working Spark environment and you would have already loaded the data into the forest DataFrame.

No other prerequisites are required.

How to do it...

The process of building a clustering model in Spark does not deviate significantly from what we have already seen in either the classification or regression examples:

import as clust

vectorAssembler = feat.VectorAssembler(
    , outputCol='features')

kmeans_obj = clust.KMeans(k=7,...