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Posted to issues@spark.apache.org by "Apache Spark (JIRA)" <ji...@apache.org> on 2018/07/09 15:37:00 UTC
[jira] [Assigned] (SPARK-24208) Cannot resolve column in self join
after applying Pandas UDF
[ https://issues.apache.org/jira/browse/SPARK-24208?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Apache Spark reassigned SPARK-24208:
------------------------------------
Assignee: Apache Spark
> Cannot resolve column in self join after applying Pandas UDF
> ------------------------------------------------------------
>
> Key: SPARK-24208
> URL: https://issues.apache.org/jira/browse/SPARK-24208
> Project: Spark
> Issue Type: Bug
> Components: PySpark
> Affects Versions: 2.3.0
> Environment: AWS EMR 5.13.0
> Amazon Hadoop distribution 2.8.3
> Spark 2.3.0
> Pandas 0.22.0
> Reporter: Rafal Ganczarek
> Assignee: Apache Spark
> Priority: Minor
>
> I noticed that after applying Pandas UDF function, a self join of resulted DataFrame will fail to resolve columns. The workaround that I found is to recreate DataFrame with its RDD and schema.
> Below you can find a Python code that reproduces the issue.
> {code:java}
> from pyspark import Row
> import pyspark.sql.functions as F
> @F.pandas_udf('key long, col string', F.PandasUDFType.GROUPED_MAP)
> def dummy_pandas_udf(df):
> return df[['key','col']]
> df = spark.createDataFrame([Row(key=1,col='A'), Row(key=1,col='B'), Row(key=2,col='C')])
> # transformation that causes the issue
> df = df.groupBy('key').apply(dummy_pandas_udf)
> # WORKAROUND that fixes the issue
> # df = spark.createDataFrame(df.rdd, df.schema)
> df.alias('temp0').join(df.alias('temp1'), F.col('temp0.key') == F.col('temp1.key')).show()
> {code}
> If workaround line is commented out, then above code fails with the following error:
> {code:java}
> AnalysisExceptionTraceback (most recent call last)
> <ipython-input-88-8de763656d6d> in <module>()
> 12 # df = spark.createDataFrame(df.rdd, df.schema)
> 13
> ---> 14 df.alias('temp0').join(df.alias('temp1'), F.col('temp0.key') == F.col('temp1.key')).show()
> /usr/lib/spark/python/pyspark/sql/dataframe.py in join(self, other, on, how)
> 929 on = self._jseq([])
> 930 assert isinstance(how, basestring), "how should be basestring"
> --> 931 jdf = self._jdf.join(other._jdf, on, how)
> 932 return DataFrame(jdf, self.sql_ctx)
> 933
> /usr/lib/spark/python/lib/py4j-src.zip/py4j/java_gateway.py in __call__(self, *args)
> 1158 answer = self.gateway_client.send_command(command)
> 1159 return_value = get_return_value(
> -> 1160 answer, self.gateway_client, self.target_id, self.name)
> 1161
> 1162 for temp_arg in temp_args:
> /usr/lib/spark/python/pyspark/sql/utils.py in deco(*a, **kw)
> 67 e.java_exception.getStackTrace()))
> 68 if s.startswith('org.apache.spark.sql.AnalysisException: '):
> ---> 69 raise AnalysisException(s.split(': ', 1)[1], stackTrace)
> 70 if s.startswith('org.apache.spark.sql.catalyst.analysis'):
> 71 raise AnalysisException(s.split(': ', 1)[1], stackTrace)
> AnalysisException: u"cannot resolve '`temp0.key`' given input columns: [temp0.key, temp0.col];;\n'Join Inner, ('temp0.key = 'temp1.key)\n:- AnalysisBarrier\n: +- SubqueryAlias temp0\n: +- FlatMapGroupsInPandas [key#4099L], dummy_pandas_udf(col#4098, key#4099L), [key#4104L, col#4105]\n: +- Project [key#4099L, col#4098, key#4099L]\n: +- LogicalRDD [col#4098, key#4099L], false\n+- AnalysisBarrier\n +- SubqueryAlias temp1\n +- FlatMapGroupsInPandas [key#4099L], dummy_pandas_udf(col#4098, key#4099L), [key#4104L, col#4105]\n +- Project [key#4099L, col#4098, key#4099L]\n +- LogicalRDD [col#4098, key#4099L], false\n"
> {code}
> The same happens, if instead of DataFrame API I use Spark SQL to do a self join:
> {code:java}
> # df is a DataFrame after applying dummy_pandas_udf
> df.createOrReplaceTempView('df')
> spark.sql('''
> SELECT
> *
> FROM df temp0
> LEFT JOIN df temp1 ON
> temp0.key == temp1.key
> ''').show()
> {code}
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