Topic starter 13/07/2021 1:56 pm
I am using Spark 1.3 and would like to join on multiple columns using python interface (SparkSQL)
The following works:
I first register them as temp tables.
numeric.registerTempTable("numeric")
Ref.registerTempTable("Ref")
test = numeric.join(Ref, numeric.ID == Ref.ID, joinType='inner') I would now like to join them based on multiple columns.
I get SyntaxError: invalid syntax with this:
test = numeric.join(Ref, numeric.ID == Ref.ID AND numeric.TYPE == Ref.TYPE AND numeric.STATUS == Ref.STATUS , joinType='inner')
13/07/2021 3:58 pm
You can use SQL within PySpark
df.spark.sql(""" SQL JOIN STATEMENT """) OR
You can do this purely in PyPspark by the below method
You can use & OR | operators and be careful about operator precedence (== has lower precedence than bitwise AND and OR):
df1 = sqlContext.createDataFrame(
[(1, "a", 2.0), (2, "b", 3.0), (3, "c", 3.0)],
("x1", "x2", "x3"))
df2 = sqlContext.createDataFrame(
[(1, "f", -1.0), (2, "b", 0.0)], ("x1", "x2", "x3"))
df = df1.join(df2, (df1.x1 == df2.x1) & (df1.x2 == df2.x2))
df.show() OUTPUT:
## +---+---+---+---+---+---+ ## | x1| x2| x3| x1| x2| x3| ## +---+---+---+---+---+---+ ## | 2| b|3.0| 2| b|0.0| ## +---+---+---+---+---+---+
Neha liked
