Filter Spark DataFrame based on another DataFrame

问题:

I have a largeDataFrame (multiple columns and billions of rows) and a smallDataFrame (single column and 10,000 rows).

I'd like to filter all the rows from the largeDataFrame whenever the some_identifier column in the largeDataFrame matches one of the rows in the smallDataFrame.

Here's an example:

largeDataFrame

some_idenfitier,first_name
111,bob
123,phil
222,mary
456,sue

smallDataFrame

some_identifier
123
456

desiredOutput

111,bob
222,mary

Here is my ugly solution.

val smallDataFrame2 = smallDataFrame.withColumn("is_bad", lit("bad_row"))
val desiredOutput = largeDataFrame.join(broadcast(smallDataFrame2), Seq("some_identifier"), "left").filter($"is_bad".isNull).drop("is_bad")

Is there a cleaner solution?

回答1:

You'll need to use a left_anti join in this case.

The left anti join is the opposite of a left semi join.

It filters out data from the right table in the left table according to a given key :

largeDataFrame
   .join(smallDataFrame, Seq("some_identifier"),"left_anti")
   .show
// +---------------+----------+
// |some_identifier|first_name|
// +---------------+----------+
// |            222|      mary|
// |            111|       bob|
// +---------------+----------+