spark-issues mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From "James Maki (JIRA)" <j...@apache.org>
Subject [jira] [Updated] (SPARK-20530) "Cannot evaluate expression" when filtering on parquet partition column
Date Sat, 29 Apr 2017 00:18:04 GMT

     [ https://issues.apache.org/jira/browse/SPARK-20530?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

James Maki updated SPARK-20530:
-------------------------------
    Environment: spark-2.1.0-bin-hadoop2.7 Python2  (was: spark-2.1.0-bin-hadoop2.7.tgz Python2)

> "Cannot evaluate expression" when filtering on parquet partition column
> -----------------------------------------------------------------------
>
>                 Key: SPARK-20530
>                 URL: https://issues.apache.org/jira/browse/SPARK-20530
>             Project: Spark
>          Issue Type: Bug
>          Components: PySpark
>    Affects Versions: 2.1.0
>         Environment: spark-2.1.0-bin-hadoop2.7 Python2
>            Reporter: James Maki
>
> In pyspark, when filtering on a parquet partition column, the following error occurs:
> {code}
> py4j.protocol.Py4JJavaError: An error occurred while calling o54.toString.
> : java.lang.UnsupportedOperationException: Cannot evaluate expression: <lambda>(input[0,
int, true])
> {code}
> Reproduce via the following script:
> {code}
> from pyspark.sql import SparkSession
> from pyspark.sql.functions import udf
> from pyspark.sql.types import BooleanType
> if __name__ == '__main__':
>   sql = SparkSession.builder.getOrCreate()
>   data = [(0, 1), (0, 2), (0, 3), (1, 4), (1, 5), (1, 6)]
>   sql.createDataFrame(data, ['key', 'value'])\
>     .write\
>     .partitionBy('key')\
>     .format('parquet')\
>     .save('dest.parquet', mode='overwrite')
>   sql.read.parquet('dest.parquet')\
>     .filter(udf(lambda x: True, BooleanType())('key'))\
>     .explain(extended=True)
> {code}
> Full script output
> {code}
> Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
> Setting default log level to "WARN".
> To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
> 17/04/28 19:45:41 WARN NativeCodeLoader: Unable to load native-hadoop library for your
platform... using builtin-java classes where applicable
> SLF4J: Defaulting to no-operation (NOP) logger implementation
> SLF4J: See http://www.slf4j.org/codes.html#StaticLoggerBinder for further details.
> Traceback (most recent call last):
>   File "udf_filter_partition_bug.py", line 15, in <module>
>     .explain(extended=True)
>   File "C:\build\env\python-2.7\lib\site-packages\pyspark-2.1.0-py2.7.egg\pyspark\sql\dataframe.py",
line 266, in explain
>     print(self._jdf.queryExecution().toString())
>   File "C:\build\env\python-2.7\lib\site-packages\py4j-0.10.4-py2.7.egg\py4j\java_gateway.py",
line 1133, in __call__
>     answer, self.gateway_client, self.target_id, self.name)
>   File "C:\build\env\python-2.7\lib\site-packages\pyspark-2.1.0-py2.7.egg\pyspark\sql\utils.py",
line 63, in deco
>     return f(*a, **kw)
>   File "C:\build\env\python-2.7\lib\site-packages\py4j-0.10.4-py2.7.egg\py4j\protocol.py",
line 319, in get_return_value
>     format(target_id, ".", name), value)
> py4j.protocol.Py4JJavaError: An error occurred while calling o54.toString.
> : java.lang.UnsupportedOperationException: Cannot evaluate expression: <lambda>(input[0,
int, true])
>         at org.apache.spark.sql.catalyst.expressions.Unevaluable$class.eval(Expression.scala:221)
>         at org.apache.spark.sql.execution.python.PythonUDF.eval(PythonUDF.scala:27)
>         at org.apache.spark.sql.catalyst.expressions.InterpretedPredicate$$anonfun$create$1.apply(predicates.scala:34)
>         at org.apache.spark.sql.catalyst.expressions.InterpretedPredicate$$anonfun$create$1.apply(predicates.scala:34)
>         at org.apache.spark.sql.execution.datasources.PartitioningAwareFileIndex$$anonfun$9.apply(PartitioningAwareFileIndex.scala:174)
>         at org.apache.spark.sql.execution.datasources.PartitioningAwareFileIndex$$anonfun$9.apply(PartitioningAwareFileIndex.scala:173)
>         at scala.collection.TraversableLike$$anonfun$filterImpl$1.apply(TraversableLike.scala:248)
>         at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
>         at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
>         at scala.collection.TraversableLike$class.filterImpl(TraversableLike.scala:247)
>         at scala.collection.TraversableLike$class.filter(TraversableLike.scala:259)
>         at scala.collection.AbstractTraversable.filter(Traversable.scala:104)
>         at org.apache.spark.sql.execution.datasources.PartitioningAwareFileIndex.prunePartitions(PartitioningAwareFileIndex.scala:173)
>         at org.apache.spark.sql.execution.datasources.PartitioningAwareFileIndex.listFiles(PartitioningAwareFileIndex.scala:66)
>         at org.apache.spark.sql.execution.FileSourceScanExec.org$apache$spark$sql$execution$FileSourceScanExec$$selectedPartitions$lzycompute(DataSourceScanExec.scala:159)
>         at org.apache.spark.sql.execution.FileSourceScanExec.org$apache$spark$sql$execution$FileSourceScanExec$$selectedPartitions(DataSourceScanExec.scala:159)
>         at org.apache.spark.sql.execution.FileSourceScanExec$$anonfun$17.apply(DataSourceScanExec.scala:244)
>         at org.apache.spark.sql.execution.FileSourceScanExec$$anonfun$17.apply(DataSourceScanExec.scala:243)
>         at scala.Option.map(Option.scala:146)
>         at org.apache.spark.sql.execution.FileSourceScanExec.<init>(DataSourceScanExec.scala:243)
>         at org.apache.spark.sql.execution.datasources.FileSourceStrategy$.apply(FileSourceStrategy.scala:109)
>         at org.apache.spark.sql.catalyst.planning.QueryPlanner$$anonfun$1.apply(QueryPlanner.scala:62)
>         at org.apache.spark.sql.catalyst.planning.QueryPlanner$$anonfun$1.apply(QueryPlanner.scala:62)
>         at scala.collection.Iterator$$anon$12.nextCur(Iterator.scala:434)
>         at scala.collection.Iterator$$anon$12.hasNext(Iterator.scala:440)
>         at scala.collection.Iterator$$anon$12.hasNext(Iterator.scala:439)
>         at org.apache.spark.sql.catalyst.planning.QueryPlanner.plan(QueryPlanner.scala:92)
>         at org.apache.spark.sql.catalyst.planning.QueryPlanner$$anonfun$2$$anonfun$apply$2.apply(QueryPlanner.scala:77)
>         at org.apache.spark.sql.catalyst.planning.QueryPlanner$$anonfun$2$$anonfun$apply$2.apply(QueryPlanner.scala:74)
>         at scala.collection.TraversableOnce$$anonfun$foldLeft$1.apply(TraversableOnce.scala:157)
>         at scala.collection.TraversableOnce$$anonfun$foldLeft$1.apply(TraversableOnce.scala:157)
>         at scala.collection.Iterator$class.foreach(Iterator.scala:893)
>         at scala.collection.AbstractIterator.foreach(Iterator.scala:1336)
>         at scala.collection.TraversableOnce$class.foldLeft(TraversableOnce.scala:157)
>         at scala.collection.AbstractIterator.foldLeft(Iterator.scala:1336)
>         at org.apache.spark.sql.catalyst.planning.QueryPlanner$$anonfun$2.apply(QueryPlanner.scala:74)
>         at org.apache.spark.sql.catalyst.planning.QueryPlanner$$anonfun$2.apply(QueryPlanner.scala:66)
>         at scala.collection.Iterator$$anon$12.nextCur(Iterator.scala:434)
>         at scala.collection.Iterator$$anon$12.hasNext(Iterator.scala:440)
>         at org.apache.spark.sql.catalyst.planning.QueryPlanner.plan(QueryPlanner.scala:92)
>         at org.apache.spark.sql.execution.QueryExecution.sparkPlan$lzycompute(QueryExecution.scala:79)
>         at org.apache.spark.sql.execution.QueryExecution.sparkPlan(QueryExecution.scala:75)
>         at org.apache.spark.sql.execution.QueryExecution.executedPlan$lzycompute(QueryExecution.scala:84)
>         at org.apache.spark.sql.execution.QueryExecution.executedPlan(QueryExecution.scala:84)
>         at org.apache.spark.sql.execution.QueryExecution$$anonfun$toString$3.apply(QueryExecution.scala:232)
>         at org.apache.spark.sql.execution.QueryExecution$$anonfun$toString$3.apply(QueryExecution.scala:232)
>         at org.apache.spark.sql.execution.QueryExecution.stringOrError(QueryExecution.scala:107)
>         at org.apache.spark.sql.execution.QueryExecution.toString(QueryExecution.scala:232)
>         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:497)
>         at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
>         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)
> {code}



--
This message was sent by Atlassian JIRA
(v6.3.15#6346)

---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org


Mime
View raw message