CODEWITHSUNDEEP
scalaapache-spark
Mpizos Dimitris
Mpizos Dimitris

Reputation: 4991

Spark: way to join each row of dataframe with all rows of another dataframe

Assuming I am having the following dataframes:

val df1 = sc.parallelize(Seq("a1" -> "a2", "b1" -> "b2", "c1" -> "c2")).toDF("a", "b")
val df2 = sc.parallelize(Seq("aa1" -> "aa2", "bb1" -> "bb2")).toDF("aa", "bb")

And I want the following:

 | a  | b  | aa  | bb  |
 ----------------------
 | a1 | a2 | aa1 | aa2 |
 | a1 | a2 | bb1 | bb2 |
 | b1 | b2 | aa1 | aa2 |
 | b1 | b2 | bb1 | bb2 |
 | c1 | c2 | aa1 | aa2 |
 | c1 | c2 | bb1 | bb2 |

So each row of df1 to map to all of the rows of df2. The way I am doing it is the following:

val df1_dummy = df1.withColumn("dummy_df1", lit("dummy"))
val df2_dummy = df2.withColumn("dummy_df2", lit("dummy"))
val desired_result = df1_dummy
                       .join(df2_dummy, $"dummy_df1" === $"dummy_df2", "left")
                       .drop("dummy_df1")
                       .drop("dummy_df2")

It gives the desired results but it seems a bit of a bad way. Is there a more efficient way of doing that? any recommendation?

Upvotes: 2

Views: 2474

Answers (1)

Tzach Zohar
Tzach Zohar

Reputation: 37822

That's what crossJoin is for:

val result = df1.crossJoin(df2)

result.show()
// +---+---+---+---+
// |a  |b  |aa |bb |
// +---+---+---+---+
// |a1 |a2 |aa1|aa2|
// |a1 |a2 |bb1|bb2|
// |b1 |b2 |aa1|aa2|
// |b1 |b2 |bb1|bb2|
// |c1 |c2 |aa1|aa2|
// |c1 |c2 |bb1|bb2|
// +---+---+---+---+

Upvotes: 7

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