How to convert DataFrame to RDD in Scala?

Question

Can someone please share how one can convert a dataframe to an RDD?

Solution 1

Simply:

val rows: RDD[Row] = df.rdd

Solution 2

Use df.map(row => ...) to convert the dataframe to a RDD if you want to map a row to a different RDD element. For example

df.map(row => (row(1), row(2)))

gives you a paired RDD where the first column of the df is the key and the second column of the df is the value.

Solution 3

I was just looking for my answer and found this post.

Jean's answer to absolutely correct,adding on that "df.rdd" will return a RDD[Rows]. I need to apply split() once i get RDD. For that we need to convert RDD[Row} to RDD[String]

val opt=spark.sql("select tags from cvs").map(x=>x.toString()).rdd

Comments

[Boern]

if you get "type not found" for either RDD or Row this might help: val rows: org.apache.spark.rdd.RDD[org.apache.spark.sql.Row] = df.rdd

[Ravi]

To extend Boern's answer, add the following two import commands: import org.apache.spark.rdd.RDD import org.apache.spark.sql.Row

[matanster]

Would this change anything in Spark memory holding the data, or only more lightly create a new object pointing at the same data? I hope it's the lighter of the two but not sure from the source code comments.