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

Creating a temporary table

DataFrames can easily be manipulated with SQL queries in Spark.

In this recipe, we will learn how to create a temporary view so you can access the data within DataFrame using SQL.

Getting ready

To execute this recipe, you need to have a working Spark 2.3 environment. You should have gone through the previous recipe, as we will be using the sample_data_schema DataFrame we created there.

There are no other requirements.

How to do it...

We simply use the .createTempView(...) method of a DataFrame:


How it works...

The .createTempView(...) method is the simplest way to create a temporary view that later can be used to query the data. The only required parameter is the name of the view.

Let's see how such a temporary view can now be used to extract data:

    SELECT Model
        , Year
        , RAM
        , HDD
    FROM sample_data_view

We simply use the .sql(...) method of SparkSession, which allows...