Join two DataFrames where the join key is different and only select some columns

Question

What I would like to do is:

Join two DataFrames A and B using their respective id columns a_id and b_id. I want to select all columns from A and two specific columns from B

I tried something like what I put below with different quotation marks but still not working. I feel in pyspark, there should have a simple way to do this.

A_B = A.join(B, A.id == B.id).select(A.*, B.b1, B.b2)

I know you could write

A_B = sqlContext.sql("SELECT A.*, B.b1, B.b2 FROM A JOIN B ON A.a_id = B.b_id")

to do this but I would like to do it more like the pseudo code above.

Answer 1

Your pseudocode is basically correct. This slightly modified version would work if the id column existed in both DataFrames:

A_B = A.join(B, on="id").select("A.*", "B.b1", "B.b2")

From the docs for pyspark.sql.DataFrame.join():

If on is a string or a list of strings indicating the name of the join column(s), the column(s) must exist on both sides, and this performs an equi-join.

Since the keys are different, you can just use withColumn() (or withColumnRenamed()) to created a column with the same name in both DataFrames:

A_B = A.withColumn("id", col("a_id")).join(B.withColumn("id", col("b_id")), on="id")\
    .select("A.*", "B.b1", "B.b2")

If your DataFrames have long complicated names, you could also use alias() to make things easier:

A_B = long_data_frame_name1.alias("A").withColumn("id", col("a_id"))\
    .join(long_data_frame_name2.alias("B").withColumn("id", col("b_id")), on="id")\
    .select("A.*", "B.b1", "B.b2")