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https://issues.apache.org/jira/browse/SPARK-12940?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Brian Wheeler updated SPARK-12940:
----------------------------------
Attachment: spark-12940.txt
These commands can be run in the spark shell to duplicate this big. I assume in
this example that /tmp is your temp directory.
> Partition field in Spark SQL 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, 1.6.0
> Environment: AWS EMR 4.2, OSX
> Reporter: Brian Wheeler
> Attachments: spark-12940.txt
>
>
> 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.
> I also tried this locally with the same results on a Mac laptop with 1.6.0.
> 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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