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https://issues.apache.org/jira/browse/SPARK-21546?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16112146#comment-16112146
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Kevin Zhang commented on SPARK-21546:
-------------------------------------
[~zsxwing] in my case I hope to use a watermark to expire the state but not use
the watermark column to filter duplicate elements. i.e. I want to count the
unique access of my website for one day, so I should just store the state of
dropDuplicates for one day and drop the state the next day, meanwhile I want to
use uuid as the key to drop duplicate elements rather than using (uuid,
eventTime) together, but dropDuplicates behaves like the latter right? If so
how can I get the right results as I expected?
> dropDuplicates with watermark 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
> Assignee: Shixiong Zhu
> Fix For: 2.2.1, 2.3.0
>
>
> With today's master...
> The following streaming query with watermark and {{dropDuplicates}} yields
> {{RuntimeException}} due to failure in binding.
> {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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