Jacek Laskowski created SPARK-21546:
---------------------------------------
Summary: dropDuplicates followed by select yields RuntimeException
due to binding failure
Key: SPARK-21546
URL: https://issues.apache.org/jira/browse/SPARK-21546
Project: Spark
Issue Type: Bug
Components: Structured Streaming
Affects Versions: 2.3.0
Reporter: Jacek Laskowski
With today's master...
The following streaming query yields {{RuntimeException}} due to failure in
binding (most likely due to {{select}} operator).
{code}
val topic1 = spark.
readStream.
format("kafka").
option("subscribe", "topic1").
option("kafka.bootstrap.servers", "localhost:9092").
option("startingoffsets", "earliest").
load
val records = topic1.
withColumn("eventtime", 'timestamp). // <-- just to put the right name given
the purpose
withWatermark(eventTime = "eventtime", delayThreshold = "30 seconds"). // <--
use the renamed eventtime column
dropDuplicates("value"). // dropDuplicates will use watermark
// only when eventTime column exists
// include the watermark column => internal design leak?
select('key cast "string", 'value cast "string", 'eventtime).
as[(String, String, java.sql.Timestamp)]
scala> records.explain
== Physical Plan ==
*Project [cast(key#0 as string) AS key#169, cast(value#1 as string) AS
value#170, eventtime#157-T30000ms]
+- StreamingDeduplicate [value#1],
StatefulOperatorStateInfo(<unknown>,93c3de98-3f85-41a4-8aef-d09caf8ea693,0,0), 0
+- Exchange hashpartitioning(value#1, 200)
+- EventTimeWatermark eventtime#157: timestamp, interval 30 seconds
+- *Project [key#0, value#1, timestamp#5 AS eventtime#157]
+- StreamingRelation kafka, [key#0, value#1, topic#2, partition#3,
offset#4L, timestamp#5, timestampType#6]
import org.apache.spark.sql.streaming.{OutputMode, Trigger}
val sq = records.
writeStream.
format("console").
option("truncate", false).
trigger(Trigger.ProcessingTime("10 seconds")).
queryName("from-kafka-topic1-to-console").
outputMode(OutputMode.Update).
start
{code}
{code}
-------------------------------------------
Batch: 0
-------------------------------------------
17/07/27 10:28:58 ERROR Executor: Exception in task 3.0 in stage 13.0 (TID 438)
org.apache.spark.sql.catalyst.errors.package$TreeNodeException: Binding
attribute, tree: eventtime#157-T30000ms
at
org.apache.spark.sql.catalyst.errors.package$.attachTree(package.scala:56)
at
org.apache.spark.sql.catalyst.expressions.BindReferences$$anonfun$bindReference$1.applyOrElse(BoundAttribute.scala:88)
at
org.apache.spark.sql.catalyst.expressions.BindReferences$$anonfun$bindReference$1.applyOrElse(BoundAttribute.scala:87)
at
org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$2.apply(TreeNode.scala:267)
at
org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$2.apply(TreeNode.scala:267)
at
org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:70)
at
org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:266)
at
org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
at
org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
at
org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:306)
at
org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:187)
at
org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:304)
at
org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:272)
at
org.apache.spark.sql.catalyst.trees.TreeNode.transform(TreeNode.scala:256)
at
org.apache.spark.sql.catalyst.expressions.BindReferences$.bindReference(BoundAttribute.scala:87)
at
org.apache.spark.sql.catalyst.expressions.codegen.GeneratePredicate$.bind(GeneratePredicate.scala:45)
at
org.apache.spark.sql.catalyst.expressions.codegen.GeneratePredicate$.bind(GeneratePredicate.scala:40)
at
org.apache.spark.sql.catalyst.expressions.codegen.CodeGenerator.generate(CodeGenerator.scala:977)
at
org.apache.spark.sql.execution.SparkPlan.newPredicate(SparkPlan.scala:370)
at
org.apache.spark.sql.execution.streaming.StreamingDeduplicateExec.org$apache$spark$sql$execution$streaming$WatermarkSupport$$super$newPredicate(statefulOperators.scala:350)
at
org.apache.spark.sql.execution.streaming.WatermarkSupport$$anonfun$watermarkPredicateForKeys$1.apply(statefulOperators.scala:160)
at
org.apache.spark.sql.execution.streaming.WatermarkSupport$$anonfun$watermarkPredicateForKeys$1.apply(statefulOperators.scala:160)
at scala.Option.map(Option.scala:146)
at
org.apache.spark.sql.execution.streaming.WatermarkSupport$class.watermarkPredicateForKeys(statefulOperators.scala:160)
at
org.apache.spark.sql.execution.streaming.StreamingDeduplicateExec.watermarkPredicateForKeys$lzycompute(statefulOperators.scala:350)
at
org.apache.spark.sql.execution.streaming.StreamingDeduplicateExec.watermarkPredicateForKeys(statefulOperators.scala:350)
at
org.apache.spark.sql.execution.streaming.WatermarkSupport$class.removeKeysOlderThanWatermark(statefulOperators.scala:167)
at
org.apache.spark.sql.execution.streaming.StreamingDeduplicateExec.removeKeysOlderThanWatermark(statefulOperators.scala:350)
at
org.apache.spark.sql.execution.streaming.StreamingDeduplicateExec$$anonfun$doExecute$4$$anonfun$apply$4$$anonfun$apply$mcV$sp$1.apply$mcV$sp(statefulOperators.scala:403)
at
org.apache.spark.sql.execution.streaming.StateStoreWriter$class.timeTakenMs(statefulOperators.scala:96)
at
org.apache.spark.sql.execution.streaming.StreamingDeduplicateExec.timeTakenMs(statefulOperators.scala:350)
at
org.apache.spark.sql.execution.streaming.StreamingDeduplicateExec$$anonfun$doExecute$4$$anonfun$apply$4.apply$mcV$sp(statefulOperators.scala:403)
at
org.apache.spark.util.CompletionIterator$$anon$1.completion(CompletionIterator.scala:46)
at
org.apache.spark.util.CompletionIterator.hasNext(CompletionIterator.scala:35)
at
org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown
Source)
at
org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at
org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$8$$anon$1.hasNext(WholeStageCodegenExec.scala:395)
at
org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:231)
at
org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:225)
at
org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$25.apply(RDD.scala:827)
at
org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$25.apply(RDD.scala:827)
at
org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
at org.apache.spark.scheduler.Task.run(Task.scala:108)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:344)
at
java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
Caused by: java.lang.RuntimeException: Couldn't find eventtime#157-T30000ms in
[value#185]
at scala.sys.package$.error(package.scala:27)
at
org.apache.spark.sql.catalyst.expressions.BindReferences$$anonfun$bindReference$1$$anonfun$applyOrElse$1.apply(BoundAttribute.scala:94)
at
org.apache.spark.sql.catalyst.expressions.BindReferences$$anonfun$bindReference$1$$anonfun$applyOrElse$1.apply(BoundAttribute.scala:88)
at
org.apache.spark.sql.catalyst.errors.package$.attachTree(package.scala:52)
... 49 more
{code}
I'm somehow convinced that watermark support leaks from
{{StreamingDeduplicate}} and forces a Spark developer to include extra fields
for watermark. I think filter pushdown (for the select) should not be executed
for this case or should include the extra {{eventTime}} column (regardless of
whether a developer uses it or not).
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