Processing Big Data with Spark - Computer Science Assignment Help

Assignment Task

Task A: Creating RDDs and performing operations on them You like the sound of a faster-and-easier-MapReduce-thingermejigger or Spark as it's normally referred to. You also know a bit about coding in Scala so you're ready to jump right in!

1. In Spark, data is stored in RDDs (Resilient Distributed Datasets). You can think of RDDs as immutable Scala collections whose data is spread across multiple cluster nodes. Each RDD is divided into multiple partitions and each partition is processed by one worker thread. There are two main ways for creating RDDs. The rst way is to use the key word parallelize to create an RDD from a Scala sequence collection. The second way is to load data from an input le. We will focus on the rst way in this task and we will show you the second way in Task C.

2. Open a terminal, then start a Spark shell.

3. During the initialization process a Spark context was created for us and made available in the variable sc. We can use sc to create a new Spark RDD from data which currently only exists outside of Spark. Enter the following line of code to create an RDD from a Scala list.

4. Now lets take a look at what is inside the numbers RDD. We can do this using the collect command which gathers all the RDD contents from the worker into an Array at the master node.

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