spark join with udf fails

[#1]

Hi,

I am trying to do a join in spark using udfs in the join condition, but getting the error shown below:

The joins work fine without udfs. Is is possibel to use udfs in the manner below. The udfs work fine in select etc.

result_df = t1_df.join(t2_df, _udf1(t1_df['col1']) == _udf2(t2_df['col1']), "inner")

File "/usr/hdp/2.3.4.14-9/spark/python/lib/pyspark.zip/pyspark/sql/dataframe.py", line 584, in join File "/usr/hdp/2.3.4.14-9/spark/python/lib/py4j-0.8.2.1-src.zip/py4j/java_gateway.py", line 538, in __call__ File "/usr/hdp/2.3.4.14-9/spark/python/lib/pyspark.zip/pyspark/sql/utils.py", line 36, in deco File "/usr/hdp/2.3.4.14-9/spark/python/lib/py4j-0.8.2.1-src.zip/py4j/protocol.py", line 300, in get_return_value py4j.protocol.Py4JJavaError: An error occurred while calling o79.join. : java.lang.ClassCastException: org.apache.spark.sql.catalyst.plans.logical.Project cannot be cast to org.apache.spark.sql.catalyst.plans.logical.Join at org.apache.spark.sql.DataFrame.join(DataFrame.scala:554) at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62) at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) at java.lang.reflect.Method.invoke(Method.java:498) at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:231) at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:379) at py4j.Gateway.invoke(Gateway.java:259) at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:133) at py4j.commands.CallCommand.execute(CallCommand.java:79) at py4j.GatewayConnection.run(GatewayConnection.java:207) at java.lang.Thread.run(Thread.java:745)

[#2]

@xrcs blue

Looks like you are using Spark python API. The pyspark documentation says:

join :

• on – a string for join column name, a list of column names, , a join expression (Column) or a list of Columns. If on is a string or a list of string indicating the name of the join column(s), the column(s) must exist on both sides, and this performs an equi-join.

Therefore, do the columns exist on both sides of join tables? Also, wondering if you can encode the "condition" separately, then pass it to the join() method, like this:

>>> cond = [df.name == df3.name, df.age == df3.age]
>>> df.join(df3, cond, 'outer')

[#3]

Maybe this issue since I am using v 1.6

https://issues.apache.org/jira/browse/SPARK-12981