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Posted to issues@spark.apache.org by "Hyukjin Kwon (JIRA)" <ji...@apache.org> on 2019/04/01 01:08:02 UTC
[jira] [Commented] (SPARK-27331) Schema mismatch using
MicroBatchReader with columns pruning
[ https://issues.apache.org/jira/browse/SPARK-27331?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16806318#comment-16806318 ]
Hyukjin Kwon commented on SPARK-27331:
--------------------------------------
Datasource V2 is under heavy developement, and not meant to be exposed yet. Would you be able to test this against the current master?
> Schema mismatch using MicroBatchReader with columns pruning
> -----------------------------------------------------------
>
> Key: SPARK-27331
> URL: https://issues.apache.org/jira/browse/SPARK-27331
> Project: Spark
> Issue Type: Bug
> Components: Spark Core
> Affects Versions: 2.3.1
> Environment: spark 2.3.1
> Reporter: Kineret
> Priority: Major
> Labels: datasource
>
> I'm writing a custom Spark streaming source. I want to support columns pruning, I did something like this:
>
> {code:java}
> class MyMicroBatchReader(...) extends MicroBatchReader with SupportsPushDownRequiredColumns {
> var schema: StructType = createSchema()
> def readSchema(): StructType = schema
> def pruneColumns(requiredSchema: StructType): Unit = {
> schema = requiredSchema
> }
> ...
> }
> {code}
>
> if I run a streaming query selecting some columns, the job fails. For example, running:
> {code:java}
> spark.readStream().format("mysource").load().select("Id").writeStream().format("console").start()
> {code}
> I obtain the following exception (in the second iteration):
> {code:java}
> 18/06/29 15:50:01 ERROR MicroBatchExecution: Query [id = 59c13195-9d63-42c9-8f92-eb9d67e8b26c, runId = 72124019-1ab3-48a9-9503-0cf1c7d26fb9] terminated with error java.lang.AssertionError: assertion failed: Invalid batch: fieldA#0,fieldB#1,fieldC,Id#3,fieldD#4,fieldE#5 != Id#52 at scala.Predef$.assert(Predef.scala:170) at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$2$$anonfun$applyOrElse$4.apply(MicroBatchExecution.scala:417) at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$2$$anonfun$applyOrElse$4.apply(MicroBatchExecution.scala:416) at scala.Option.map(Option.scala:146) at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$2.applyOrElse(MicroBatchExecution.scala:416) at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$2.applyOrElse(MicroBatchExecution.scala:414) 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$$anonfun$4.apply(TreeNode.scala:306) at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:187) at org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:304) at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:272) at org.apache.spark.sql.catalyst.trees.TreeNode.transform(TreeNode.scala:256) at org.apache.spark.sql.execution.streaming.MicroBatchExecution.org$apache$spark$sql$execution$streaming$MicroBatchExecution$$runBatch(MicroBatchExecution.scala:414) at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$runActivatedStream$1$$anonfun$apply$mcZ$sp$1.apply$mcV$sp(MicroBatchExecution.scala:133) at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$runActivatedStream$1$$anonfun$apply$mcZ$sp$1.apply(MicroBatchExecution.scala:121) at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$runActivatedStream$1$$anonfun$apply$mcZ$sp$1.apply(MicroBatchExecution.scala:121) at org.apache.spark.sql.execution.streaming.ProgressReporter$class.reportTimeTaken(ProgressReporter.scala:271) at org.apache.spark.sql.execution.streaming.StreamExecution.reportTimeTaken(StreamExecution.scala:58) at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$runActivatedStream$1.apply$mcZ$sp(MicroBatchExecution.scala:121) at org.apache.spark.sql.execution.streaming.ProcessingTimeExecutor.execute(TriggerExecutor.scala:56) at org.apache.spark.sql.execution.streaming.MicroBatchExecution.runActivatedStream(MicroBatchExecution.scala:117) at org.apache.spark.sql.execution.streaming.StreamExecution.org$apache$spark$sql$execution$streaming$StreamExecution$$runStream(StreamExecution.scala:279) at org.apache.spark.sql.execution.streaming.StreamExecution$$anon$1.run(StreamExecution.scala:189)
> {code}
> Can you please help?
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