40 Algorithms Every Programmer Should Know

Implementing data processing in Apache Spark

Let's see how we can create an RDD in Apache Spark and run distributed processing on it across the cluster:

1. For this, first, we need to create a new Spark session, as follows:

from pyspark.sql import SparkSessionspark = SparkSession.builder.appName('cloudanum').getOrCreate()

2. Once we have created a Spark session, we use a CSV file for the source of the RDD. Then, we will run the following function—it will create an RDD that is abstracted as a DataFrame called df. The ability to abstract an RDD as a DataFrame was added in Spark 2.0 and this makes it easier to process the data:

df = spark.read.csv('taxi2.csv',inferSchema=True,header=True)

Let's look into the columns of the DataFrame:

3. Next, ...