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Posted to issues@spark.apache.org by "Mark Hamstra (JIRA)" <ji...@apache.org> on 2016/01/29 19:30:39 UTC
[jira] [Created] (SPARK-13087) Grouping by a complex expression may
lead to incorrect AttributeReferences in aggregations
Mark Hamstra created SPARK-13087:
------------------------------------
Summary: Grouping by a complex expression may lead to incorrect AttributeReferences in aggregations
Key: SPARK-13087
URL: https://issues.apache.org/jira/browse/SPARK-13087
Project: Spark
Issue Type: Bug
Components: SQL
Affects Versions: 1.6.0
Reporter: Mark Hamstra
This is a regression from 1.5.
An example of the failure:
Working with this table...
{code}
0: jdbc:hive2://10.1.3.203:10000> DESCRIBE csd_0ae1abc1_a3af_4c63_95b0_9599faca6c3d;
+-----------------------+------------+----------+--+
| col_name | data_type | comment |
+-----------------------+------------+----------+--+
| c_date | timestamp | NULL |
| c_count | int | NULL |
| c_location_fips_code | string | NULL |
| c_airtemp | float | NULL |
| c_dewtemp | float | NULL |
| c_pressure | int | NULL |
| c_rain | float | NULL |
| c_snow | float | NULL |
+-----------------------+------------+----------+--+
{code}
...and this query (which isn't necessarily all that sensical or useful, but has been adapted from a similarly failing query that uses a custom UDF where the Spark SQL built-in `day` function has been substituted into this query)...
{code}
SELECT day ( c_date ) AS c_date, percentile_approx(c_rain, 0.5) AS c_expr_1256887735 FROM csd_0ae1abc1_a3af_4c63_95b0_9599faca6c3d GROUP BY day ( c_date ) ORDER BY c_date;
{code}
Spark 1.5 produces the expected results without error.
In Spark 1.6, this plan is produced...
{code}
Exchange rangepartitioning(c_date#63009 ASC,16), None
+- SortBasedAggregate(key=[dayofmonth(cast(c_date#63011 as date))#63020], functions=[(hiveudaffunction(HiveFunctionWrapper(org.apache.hadoop.hive.ql.udf.generic.GenericUDAFPercentileApprox,org.apache.hadoop.hive.ql.udf.generic.Gene
ricUDAFPercentileApprox@6f211801),c_rain#63017,0.5,false,0,0),mode=Complete,isDistinct=false)], output=[c_date#63009,c_expr_1256887735#63010])
+- ConvertToSafe
+- !Sort [dayofmonth(cast(c_date#63011 as date))#63020 ASC], false, 0
+- !TungstenExchange hashpartitioning(dayofmonth(cast(c_date#63011 as date))#63020,16), None
+- ConvertToUnsafe
+- HiveTableScan [c_date#63011,c_rain#63017], MetastoreRelation default, csd_0ae1abc1_a3af_4c63_95b0_9599faca6c3d, None
{code}
...which fails with a TreeNodeException and stack traces that include this...
{code}
Caused by: ! org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 2842.0 failed 4 times, most recent failure: Lost task 0.3 in stage 2842.0 (TID 15007, ip-10-1-1-59.dev.clearstory.com): org.apache.spark.sql.catalyst.errors.package$TreeNodeException: Binding attribute, tree: dayofmonth(cast(c_date#63011 as date))#63020
at org.apache.spark.sql.catalyst.errors.package$.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$$anonfun$3.apply(TreeNode.scala:259)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$3.apply(TreeNode.scala:259)
at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:69)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:258)
at org.apache.spark.sql.catalyst.trees.TreeNode.transform(TreeNode.scala:249)
at org.apache.spark.sql.catalyst.expressions.BindReferences$.bindReference(BoundAttribute.scala:85)
at org.apache.spark.sql.catalyst.expressions.InterpretedMutableProjection$$anonfun$$init$$2.apply(Projection.scala:62)
at org.apache.spark.sql.catalyst.expressions.InterpretedMutableProjection$$anonfun$$init$$2.apply(Projection.scala:62)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
at scala.collection.immutable.List.foreach(List.scala:318)
at scala.collection.TraversableLike$class.map(TraversableLike.scala:244)
at scala.collection.AbstractTraversable.map(Traversable.scala:105)
at org.apache.spark.sql.catalyst.expressions.InterpretedMutableProjection.<init>(Projection.scala:62)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$newMutableProjection$1.apply(SparkPlan.scala:254)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$newMutableProjection$1.apply(SparkPlan.scala:254)
at org.apache.spark.sql.execution.Exchange.org$apache$spark$sql$execution$Exchange$$getPartitionKeyExtractor$1(Exchange.scala:196)
at org.apache.spark.sql.execution.Exchange$$anonfun$3.apply(Exchange.scala:208)
at org.apache.spark.sql.execution.Exchange$$anonfun$3.apply(Exchange.scala:207)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$21.apply(RDD.scala:728)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$21.apply(RDD.scala:728)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:73)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41)
at org.apache.spark.scheduler.Task.run(Task.scala:89)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)
Caused by: java.lang.RuntimeException: Couldn't find dayofmonth(cast(c_date#63011 as date))#63020 in [c_date#63011,c_rain#63017]
at scala.sys.package$.error(package.scala:27)
at org.apache.spark.sql.catalyst.expressions.BindReferences$$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)
... 33 more
{code}
It is possible to work around the problem by adding a Project node in case an aggregation is relying on aliases missing in the child plan (https://github.com/mbautin/spark/commit/2e99064b42a6dddf6b94b989c744a1308aacaee2), but it seems there should be a deeper fix that prevents the problem instead of covering for it.
[~yhuai] I think this problem crept in with the changes for SPARK-9830
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