Brian Wheeler created SPARK-12940:
-------------------------------------
Summary: Partition field in SparkSQL WHERE clause causing Exception
Key: SPARK-12940
URL: https://issues.apache.org/jira/browse/SPARK-12940
Project: Spark
Issue Type: Bug
Components: SQL
Affects Versions: 1.5.2
Environment: AWS EMR 4.2
Reporter: Brian Wheeler
I have partitioned Parquet that I am trying to query with Spark SQL. When I
involve a partition column in the {{WHERE}} clause when using {{OR}} I get an
exception.
I have had this issue when using spark-submit on a cluster when the Parquet was
created externally and registered with Hive JDBC-backed metastore externally. I
can also duplicate this behavior with a simplified example in the spark shell.
I will include the simplified example. Note that I am using my hive-site.xml
when I launch the spark-shell so the metastore is set up the same way.
Create some partitioned parquet:
{code}
case class Hit(meta_ts_unix_ms: Long, username: String, srclatitude: Double,
srclongitude: Double, srccity: String, srcregion: String, srccountrycode:
String, metaclass: String)
val rdd = sc.parallelize(Array(Hit(34L, "user1", 45.2, 23.2, "city1", "state1",
"US", "blah, other"), Hit(35L, "user1", 53.2, 11.2, "city2", "state2", "US",
"blah")))
sqlContext.createDataFrame(rdd).registerTempTable("test_table")
sqlContext.sql("select * from test_table where meta_ts_unix_ms =
35").write.parquet("file:///tmp/year=2015/month=12/day=4/hour=1/")
sqlContext.sql("select * from test_table where meta_ts_unix_ms =
34").write.parquet("file:///tmp/year=2015/month=12/day=3/hour=23/")
{code}
Create an external table from the parquet:
{code}
sqlContext.createExternalTable("test_table2", "file:///tmp/year=2015/",
"parquet")
{code}
If I understand correctly the partitions were discovered automatically because
they show up in the describe command even though they were not part of the
schema generated from the case classes:
{code}
+---------------+---------+-------+
| col_name|data_type|comment|
+---------------+---------+-------+
|meta_ts_unix_ms| bigint| |
| username| string| |
| srclatitude| double| |
| srclongitude| double| |
| srccity| string| |
| srcregion| string| |
| srccountrycode| string| |
| metaclass| string| |
| year| int| |
| month| int| |
| day| int| |
| hour| int| |
+---------------+---------+-------+
{code}
This query:
{code}
sqlContext.sql("SELECT
meta_ts_unix_ms,username,srclatitude,srclongitude,srccity,srcregion,srccountrycode
FROM test_table2 WHERE meta_ts_unix_ms IS NOT NULL AND username IS NOT NULL
AND metaclass like '%blah%' OR hour = 1").show()
{code}
Throws this exception:
{noformat}
16/01/20 21:36:46 WARN TaskSetManager: Lost task 0.0 in stage 13.0 (TID 84,
ip-192-168-111-222.ec2.internal):
org.apache.spark.sql.catalyst.errors.package$TreeNodeException: Binding
attribute, tree: metaclass#53
at
org.apache.spark.sql.catalyst.errors.package$.attachTree(package.scala:49)
at
org.apache.spark.sql.catalyst.expressions.BindReferences$$anonfun$bindReference$1.applyOrElse(BoundAttribute.scala:86)
at
org.apache.spark.sql.catalyst.expressions.BindReferences$$anonfun$bindReference$1.applyOrElse(BoundAttribute.scala:85)
at
org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$3.apply(TreeNode.scala:227)
at
org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$3.apply(TreeNode.scala:227)
at
org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:51)
at
org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:226)
at
org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:232)
at
org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:232)
at
org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:249)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
at scala.collection.Iterator$class.foreach(Iterator.scala:727)
at scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
at
scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:48)
at
scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:103)
at
scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:47)
at scala.collection.TraversableOnce$class.to(TraversableOnce.scala:273)
at scala.collection.AbstractIterator.to(Iterator.scala:1157)
at
scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:265)
at scala.collection.AbstractIterator.toBuffer(Iterator.scala:1157)
at
scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:252)
at scala.collection.AbstractIterator.toArray(Iterator.scala:1157)
at
org.apache.spark.sql.catalyst.trees.TreeNode.transformChildren(TreeNode.scala:279)
at
org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:232)
at
org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:232)
at
org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:232)
at
org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:249)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
at scala.collection.Iterator$class.foreach(Iterator.scala:727)
at scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
at
scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:48)
at
scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:103)
at
scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:47)
at scala.collection.TraversableOnce$class.to(TraversableOnce.scala:273)
at scala.collection.AbstractIterator.to(Iterator.scala:1157)
at
scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:265)
at scala.collection.AbstractIterator.toBuffer(Iterator.scala:1157)
at
scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:252)
at scala.collection.AbstractIterator.toArray(Iterator.scala:1157)
at
org.apache.spark.sql.catalyst.trees.TreeNode.transformChildren(TreeNode.scala:279)
at
org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:232)
at
org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:232)
at
org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:232)
at
org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:249)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
at scala.collection.Iterator$class.foreach(Iterator.scala:727)
at scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
at
scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:48)
at
scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:103)
at
scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:47)
at scala.collection.TraversableOnce$class.to(TraversableOnce.scala:273)
at scala.collection.AbstractIterator.to(Iterator.scala:1157)
at
scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:265)
at scala.collection.AbstractIterator.toBuffer(Iterator.scala:1157)
at
scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:252)
at scala.collection.AbstractIterator.toArray(Iterator.scala:1157)
at
org.apache.spark.sql.catalyst.trees.TreeNode.transformChildren(TreeNode.scala:279)
at
org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:232)
at
org.apache.spark.sql.catalyst.trees.TreeNode.transform(TreeNode.scala:217)
at
org.apache.spark.sql.catalyst.expressions.BindReferences$.bindReference(BoundAttribute.scala:85)
at
org.apache.spark.sql.catalyst.expressions.InterpretedPredicate$.create(predicates.scala:31)
at
org.apache.spark.sql.execution.SparkPlan.newPredicate(SparkPlan.scala:281)
at
org.apache.spark.sql.execution.Filter$$anonfun$4.apply(basicOperators.scala:114)
at
org.apache.spark.sql.execution.Filter$$anonfun$4.apply(basicOperators.scala:113)
at
org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$17.apply(RDD.scala:710)
at
org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$17.apply(RDD.scala:710)
at
org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:300)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:264)
at
org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:300)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:264)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
at org.apache.spark.scheduler.Task.run(Task.scala:88)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)
at
java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
at
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
at java.lang.Thread.run(Thread.java:745)
Caused by: java.lang.RuntimeException: Couldn't find metaclass#53 in
[meta_ts_unix_ms#45L,username#46,srclatitude#47,srclongitude#48,srccity#49,srcregion#50,srccountrycode#51]
at scala.sys.package$.error(package.scala:27)
at
org.apache.spark.sql.catalyst.expressions.BindReferences$$anonfun$bindReference$1$$anonfun$applyOrElse$1.apply(BoundAttribute.scala:92)
at
org.apache.spark.sql.catalyst.expressions.BindReferences$$anonfun$bindReference$1$$anonfun$applyOrElse$1.apply(BoundAttribute.scala:86)
at
org.apache.spark.sql.catalyst.errors.package$.attachTree(package.scala:48)
... 77 more
{noformat}
This query works fine and returns expected results but it does not involve any
of the partition columns in the {{OR}} portion of the {{WHERE}} clause:
{code}
sqlContext.sql("SELECT
meta_ts_unix_ms,username,srclatitude,srclongitude,srccity,srcregion,srccountrycode
FROM test_table2 WHERE meta_ts_unix_ms IS NOT NULL AND username IS NOT NULL
AND metaclass like '%other%' OR metaclass = 'blah'").show()
{code}
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