[SPARK-20530] "Cannot evaluate expression" when filtering on parquet partition column - ASF JIRA

Details

Type: Bug
Status: Resolved
Priority: Major
Resolution: Incomplete
Affects Version/s: 2.1.0
Fix Version/s: None
Component/s: PySpark
Labels:
- bulk-closed
Environment:

spark-2.1.0-bin-hadoop2.7 Python2

Description

In pyspark, when filtering on a parquet partition column, the following error occurs:

py4j.protocol.Py4JJavaError: An error occurred while calling o54.toString.
: java.lang.UnsupportedOperationException: Cannot evaluate expression: <lambda>(input[0, int, true])

Reproduce via the following script:

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)

Full script output

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)

"Cannot evaluate expression" when filtering on parquet partition column

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