[jira] [Updated] (SPARK-18084) write.partitionBy() does not recognize nested columns that select() can access

2019-08-19 Thread Nicholas Chammas (Jira) 


 [ 
https://issues.apache.org/jira/browse/SPARK-18084?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
 ]

Nicholas Chammas updated SPARK-18084:
-
Affects Version/s: 2.4.3

Retested and confirmed that this issue is still present in Spark 2.4.3.

> 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, 2.4.3
>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 "", line 1, in 
>   File 
> "/usr/local/Cellar/apache-spark/2.0.1/libexec/python/pyspark/sql/readwriter.py",

[jira] [Updated] (SPARK-18084) write.partitionBy() does not recognize nested columns that select() can access

2018-09-11 Thread Wenchen Fan (JIRA) 


 [ 
https://issues.apache.org/jira/browse/SPARK-18084?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
 ]

Wenchen Fan updated SPARK-18084:

Target Version/s: 3.0.0  (was: 2.4.0)

> 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 "", line 1, in 
>   File 
> "/usr/local/Cellar/apache-spark/2.0.1/libexec/python/pyspark/sql/readwriter.py",
>  line 656, in text
> self._jwrite.text(path)
>   File 
>

[jira] [Updated] (SPARK-18084) write.partitionBy() does not recognize nested columns that select() can access

2018-09-11 Thread Wenchen Fan (JIRA) 


 [ 
https://issues.apache.org/jira/browse/SPARK-18084?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
 ]

Wenchen Fan updated SPARK-18084:

Target Version/s:   (was: 3.0.0)

> 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 "", line 1, in 
>   File 
> "/usr/local/Cellar/apache-spark/2.0.1/libexec/python/pyspark/sql/readwriter.py",
>  line 656, in text
> self._jwrite.text(path)
>   File 
>

[jira] [Updated] (SPARK-18084) write.partitionBy() does not recognize nested columns that select() can access

2018-01-08 Thread Sameer Agarwal (JIRA) 

 [ 
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 "", line 1, in 
>   File 
> "/usr/local/Cellar/apache-spark/2.0.1/libexec/python/pyspark/sql/readwriter.py",
>  line 656, in text
> self._jwrite.text(path)
>   File 
>

[jira] [Updated] (SPARK-18084) write.partitionBy() does not recognize nested columns that select() can access

2017-06-01 Thread Michael Armbrust (JIRA) 

 [ 
https://issues.apache.org/jira/browse/SPARK-18084?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
 ]

Michael Armbrust updated SPARK-18084:
-
Target Version/s: 2.3.0  (was: 2.2.0)

> 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 "", line 1, in 
>   File 
> "/usr/local/Cellar/apache-spark/2.0.1/libexec/python/pyspark/sql/readwriter.py",
>  line 656, in text
> self._jwrite.text(path)
>   File 
>

[jira] [Updated] (SPARK-18084) write.partitionBy() does not recognize nested columns that select() can access

2016-12-15 Thread Michael Armbrust (JIRA) 

 [ 
https://issues.apache.org/jira/browse/SPARK-18084?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
 ]

Michael Armbrust updated SPARK-18084:
-
Target Version/s: 2.2.0

> 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 "", line 1, in 
>   File 
> "/usr/local/Cellar/apache-spark/2.0.1/libexec/python/pyspark/sql/readwriter.py",
>  line 656, in text
> self._jwrite.text(path)
>   File 
>

[jira] [Updated] (SPARK-18084) write.partitionBy() does not recognize nested columns that select() can access

2016-10-24 Thread Nicholas Chammas (JIRA) 

 [ 
https://issues.apache.org/jira/browse/SPARK-18084?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
 ]

Nicholas Chammas updated SPARK-18084:
-
Issue Type: Bug  (was: Improvement)

> 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 "", line 1, in 
>   File 
> "/usr/local/Cellar/apache-spark/2.0.1/libexec/python/pyspark/sql/readwriter.py",
>  line 656, in text
> self._jwrite.text(path)
>   File 
>