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spark join with udf fails

spark join with udf fails

New Contributor


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/", line 584, in join File "/usr/hdp/", line 538, in __call__ File "/usr/hdp/", line 36, in deco File "/usr/hdp/", 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( at sun.reflect.DelegatingMethodAccessorImpl.invoke( at java.lang.reflect.Method.invoke( at py4j.reflection.MethodInvoker.invoke( at py4j.reflection.ReflectionEngine.invoke( at py4j.Gateway.invoke( at py4j.commands.AbstractCommand.invokeMethod( at py4j.commands.CallCommand.execute( at at


Re: spark join with udf fails

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

Re: spark join with udf fails

New Contributor

Maybe this issue since I am using v 1.6

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