If during the execution of a job, the user persists/cache an RDD then information about that RDD can be retrieved on this tab. It can be accessed at http://localhost:4040/storage/.

Let's launch Spark shell again, read a file, and run an action on it. However, this time we will cache the file before running an action on it.

Initially, when you launch Spark shell, the Storage tab appears blank.

Let's read the file using SparkContext, as follows:

scala>val file=sc.textFile("/usr/local/spark/examples/src/main/resources/people.txt")
file: org.apache.spark.rdd.RDD[String] = /usr/local/spark/examples/src/main/resources/people.txt MapPartitionsRDD[1] at textFile at <console>:24

This time we will cache this RDD. By default, it will be cached in memory:

scala>file.cache
res0: file.type = /usr/local/spark/examples/src/main/resources/people.txt MapPartitionsRDD[1] at textFile at <console>:24

As explained earlier, the DAG of transformations will only be executed when an action is performed, so the cache step will also be executed when we run an action on the RDD. So let's run a collect on it:

scala>file.collect
res1: Array[String] = Array(Michael, 29, Andy, 30, Justin, 19)

Now, you can find information about an RDD being cached on the Storage tab.

If you click on the RDD name, it provides information about the partitions on the RDD along with the address of the host on which the RDD is stored.