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Posted to issues@spark.apache.org by "Sameer Agarwal (JIRA)" <ji...@apache.org> on 2018/01/08 20:38:00 UTC
[jira] [Updated] (SPARK-18084) write.partitionBy() does not
recognize nested columns that select() can access
[ https://issues.apache.org/jira/browse/SPARK-18084?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Sameer Agarwal updated SPARK-18084:
-----------------------------------
Target Version/s: 2.4.0 (was: 2.3.0)
> write.partitionBy() does not recognize nested columns that select() can access
> ------------------------------------------------------------------------------
>
> Key: SPARK-18084
> URL: https://issues.apache.org/jira/browse/SPARK-18084
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 2.0.0, 2.0.1
> Reporter: Nicholas Chammas
> Priority: Minor
>
> Here's a simple repro in the PySpark shell:
> {code}
> from pyspark.sql import Row
> rdd = spark.sparkContext.parallelize([Row(a=Row(b=5))])
> df = spark.createDataFrame(rdd)
> df.printSchema()
> df.select('a.b').show() # works
> df.write.partitionBy('a.b').text('/tmp/test') # doesn't work
> {code}
> Here's what I see when I run this:
> {code}
> >>> from pyspark.sql import Row
> >>> rdd = spark.sparkContext.parallelize([Row(a=Row(b=5))])
> >>> df = spark.createDataFrame(rdd)
> >>> df.printSchema()
> root
> |-- a: struct (nullable = true)
> | |-- b: long (nullable = true)
> >>> df.show()
> +---+
> | a|
> +---+
> |[5]|
> +---+
> >>> df.select('a.b').show()
> +---+
> | b|
> +---+
> | 5|
> +---+
> >>> df.write.partitionBy('a.b').text('/tmp/test')
> Traceback (most recent call last):
> File "/usr/local/Cellar/apache-spark/2.0.1/libexec/python/pyspark/sql/utils.py", line 63, in deco
> return f(*a, **kw)
> File "/usr/local/Cellar/apache-spark/2.0.1/libexec/python/lib/py4j-0.10.3-src.zip/py4j/protocol.py", line 319, in get_return_value
> py4j.protocol.Py4JJavaError: An error occurred while calling o233.text.
> : org.apache.spark.sql.AnalysisException: Partition column a.b not found in schema StructType(StructField(a,StructType(StructField(b,LongType,true)),true));
> at org.apache.spark.sql.execution.datasources.PartitioningUtils$$anonfun$partitionColumnsSchema$1$$anonfun$apply$10.apply(PartitioningUtils.scala:368)
> at org.apache.spark.sql.execution.datasources.PartitioningUtils$$anonfun$partitionColumnsSchema$1$$anonfun$apply$10.apply(PartitioningUtils.scala:368)
> at scala.Option.getOrElse(Option.scala:121)
> at org.apache.spark.sql.execution.datasources.PartitioningUtils$$anonfun$partitionColumnsSchema$1.apply(PartitioningUtils.scala:367)
> at org.apache.spark.sql.execution.datasources.PartitioningUtils$$anonfun$partitionColumnsSchema$1.apply(PartitioningUtils.scala:366)
> at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
> at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
> at scala.collection.Iterator$class.foreach(Iterator.scala:893)
> at scala.collection.AbstractIterator.foreach(Iterator.scala:1336)
> at scala.collection.IterableLike$class.foreach(IterableLike.scala:72)
> at scala.collection.AbstractIterable.foreach(Iterable.scala:54)
> at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
> at scala.collection.AbstractTraversable.map(Traversable.scala:104)
> at org.apache.spark.sql.execution.datasources.PartitioningUtils$.partitionColumnsSchema(PartitioningUtils.scala:366)
> at org.apache.spark.sql.execution.datasources.PartitioningUtils$.validatePartitionColumn(PartitioningUtils.scala:349)
> at org.apache.spark.sql.execution.datasources.DataSource.write(DataSource.scala:458)
> at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:211)
> at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:194)
> at org.apache.spark.sql.DataFrameWriter.text(DataFrameWriter.scala:534)
> 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:237)
> at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
> at py4j.Gateway.invoke(Gateway.java:280)
> at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
> at py4j.commands.CallCommand.execute(CallCommand.java:79)
> at py4j.GatewayConnection.run(GatewayConnection.java:214)
> at java.lang.Thread.run(Thread.java:745)
> During handling of the above exception, another exception occurred:
> Traceback (most recent call last):
> File "<stdin>", line 1, in <module>
> File "/usr/local/Cellar/apache-spark/2.0.1/libexec/python/pyspark/sql/readwriter.py", line 656, in text
> self._jwrite.text(path)
> File "/usr/local/Cellar/apache-spark/2.0.1/libexec/python/lib/py4j-0.10.3-src.zip/py4j/java_gateway.py", line 1133, in __call__
> File "/usr/local/Cellar/apache-spark/2.0.1/libexec/python/pyspark/sql/utils.py", line 69, in deco
> raise AnalysisException(s.split(': ', 1)[1], stackTrace)
> pyspark.sql.utils.AnalysisException: 'Partition column a.b not found in schema StructType(StructField(a,StructType(StructField(b,LongType,true)),true));'
> {code}
> I don't understand why there is an {{AnalysisException}} when referring to {{'a.b'}} in the {{write.partitionBy()}} operation, but not when we do a {{select()}}.
> Is this expected behavior somehow?
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