How RDD is created in Spark?

RDDs are created by starting with a file in the Hadoop file system (or any other Hadoop-supported file system), or an existing Scala collection in the driver program, and transforming it. Users may also ask Spark to persist an RDD in memory, allowing it to be reused efficiently across parallel operations.

Which transformation creates a new RDD by picking the elements from the current RDD which pass the function argument?

The “flatMap” transformation will return a new RDD by first applying a function to all elements of this RDD, and then flattening the results.

How does Spark save RDD?

You can save the RDD using saveAsObjectFile and saveAsTextFile method. Whereas you can read the RDD using textFile and sequenceFile function from SparkContext.

Can we create RDD from DataFrame in spark?

From existing DataFrames and DataSet To convert DataSet or DataFrame to RDD just use rdd() method on any of these data types.

Can spark RDD be shared between the spark context?

By design, RDDs cannot be shared between different Spark batch applications because each application has its own SparkContext . However, in some cases, the same RDD might be used by different Spark batch applications.

What is Spark repartition?

The repartition function allows us to change the distribution of the data on the Spark cluster. This distribution change will induce shuffle (physical data movement) under the hood, which is quite an expensive operation.

Can we create RDD using spark session?

If so, then why are we not able to create an rdd by using a spark session instead of a spark context. As shown above, sc. textFile succeeds in creating an RDD but not spark.

Can spark RDD be shared between SparkContexts?

RDDs cannot be shared between SparkContexts (see SparkContext and RDDs). RDDs are a container of instructions on how to materialize big (arrays of) distributed data, and how to split it into partitions so Spark (using executors) can hold some of them.