Ans: No, When an RDD is created; it belongs to and is completely owned by the Spark context it originated from. RDDs can’t be shared between SparkContexts.

12.       In Spark-Shell, which all contexts are available by default?

Ans: SparkContext and SQLContext

13.       Give few examples , how RDD can be created using SparkContext

Ans: SparkContext allows you to create many different RDDs from input sources like:

·         Scala’s collections: i.e. sc.parallelize(0 to 100)

·         Local or remote filesystems : sc.textFile("README.md")

·         Any Hadoop InputSource : using sc.newAPIHadoopFile

14.       How would you brodcast, collection of values over the Sperk executors?

Ans: sc.broadcast("hello")

15.       What is the advantage of broadcasting values across Spark Cluster?

Ans: Spark transfers the value to Spark executors once, and tasks can share it without incurring repetitive network transmissions when requested multiple times.

16.       Can we broadcast an RDD?

Ans: Yes, you should not broadcast a RDD to use in tasks and Spark will warn you. It will not stop you, though.

17.       How can we distribute JARs to workers?

Ans: The jar you specify with SparkContext.addJar will be copied to all the worker nodes.

18.       How can you stop SparkContext and what is the impact if stopped?

Ans: You can stop a Spark context using SparkContext.stop() method. Stopping a Spark context stops the Spark Runtime Environment and effectively shuts down the entire Spark application.

19.       Which scheduler is used by SparkContext by default?

20 .How would you the amount of memory to allocate to each executor?

Click Below to visit other products as well for Hadoop