[
https://issues.apache.org/jira/browse/SPARK-27564?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Kineret updated SPARK-27564:
----------------------------
Description:
I get 'No plan for EventTimeWatermark' error while doing a query with columns
pruning using structured streaming with a custom data source that implements
Spark datasource v2.
My data source implementation that handles the schemas includes the following:
{{class MyDataSourceReader extends DataSourceReader with
SupportsPushDownRequiredColumns \{ var schema: StructType = createSchema()
override def readSchema(): StructType = schema override def
pruneColumns(requiredSchema: StructType) = { this.schema = requiredSchema } }}
and then:
{{class MyDataSourceReaderStream extends MyDataSourceReader { ... }}
This is my test code:
{{def x(): Unit = \{ val df1 =
sparkSession.readStream.format(myV2Source).load() val df2 = df1
.withColumn("epoch", (round(col("epoch") / (30 * 1000)) *
30).cast(TimestampType)) .withWatermark("epoch", "1 milliseconds")
.groupBy(col("epoch"), col("id")).count() val streamingQuery = df2 .writeStream
.format("console") .trigger(Trigger.ProcessingTime("10 seconds"))
.outputMode(OutputMode.Append()) .start() streamingQuery.awaitTermination() } }}
I get the following exception:
{{Caused by: java.lang.AssertionError: assertion failed: No plan for
EventTimeWatermark epoch#201: timestamp, interval 1 milliseconds +- Project
[cast((round((cast(epoch#320L as double) / 30000.0), 0) * 30.0) as timestamp)
AS epoch#201, id#367L] +- DataSourceV2Relation [epoch#320L, id#367L],
com.akamai.csi.datacatalog.spark.connectors.endor.v2.reader.stream.EndorDataSourceStreamReader@173b1b7c
at scala.Predef$.assert(Predef.scala:170) at
org.apache.spark.sql.catalyst.planning.QueryPlanner.plan(QueryPlanner.scala:93)
at
org.apache.spark.sql.catalyst.planning.QueryPlanner$$anonfun$2$$anonfun$apply$2.apply(QueryPlanner.scala:78)
at
org.apache.spark.sql.catalyst.planning.QueryPlanner$$anonfun$2$$anonfun$apply$2.apply(QueryPlanner.scala:75)
at
scala.collection.TraversableOnce$$anonfun$foldLeft$1.apply(TraversableOnce.scala:157)
at
scala.collection.TraversableOnce$$anonfun$foldLeft$1.apply(TraversableOnce.scala:157)
at scala.collection.Iterator$class.foreach(Iterator.scala:893) at
scala.collection.AbstractIterator.foreach(Iterator.scala:1336) }}
Note that in the logical plan I got *DataSourceV2Relation* and not
*StreamingDataSourceV2Relation*although I use streaming.
> 'No plan for EventTimeWatermark' error while using structured streaming with
> column pruning
> -------------------------------------------------------------------------------------------
>
> Key: SPARK-27564
> URL: https://issues.apache.org/jira/browse/SPARK-27564
> Project: Spark
> Issue Type: Bug
> Components: Structured Streaming
> Affects Versions: 2.3.1
> Reporter: Kineret
> Priority: Major
>
> I get 'No plan for EventTimeWatermark' error while doing a query with columns
> pruning using structured streaming with a custom data source that implements
> Spark datasource v2.
> My data source implementation that handles the schemas includes the following:
> {{class MyDataSourceReader extends DataSourceReader with
> SupportsPushDownRequiredColumns \{ var schema: StructType = createSchema()
> override def readSchema(): StructType = schema override def
> pruneColumns(requiredSchema: StructType) = { this.schema = requiredSchema } }}
> and then:
> {{class MyDataSourceReaderStream extends MyDataSourceReader { ... }}
> This is my test code:
> {{def x(): Unit = \{ val df1 =
> sparkSession.readStream.format(myV2Source).load() val df2 = df1
> .withColumn("epoch", (round(col("epoch") / (30 * 1000)) *
> 30).cast(TimestampType)) .withWatermark("epoch", "1 milliseconds")
> .groupBy(col("epoch"), col("id")).count() val streamingQuery = df2
> .writeStream .format("console") .trigger(Trigger.ProcessingTime("10
> seconds")) .outputMode(OutputMode.Append()) .start()
> streamingQuery.awaitTermination() } }}
> I get the following exception:
> {{Caused by: java.lang.AssertionError: assertion failed: No plan for
> EventTimeWatermark epoch#201: timestamp, interval 1 milliseconds +- Project
> [cast((round((cast(epoch#320L as double) / 30000.0), 0) * 30.0) as timestamp)
> AS epoch#201, id#367L] +- DataSourceV2Relation [epoch#320L, id#367L],
> com.akamai.csi.datacatalog.spark.connectors.endor.v2.reader.stream.EndorDataSourceStreamReader@173b1b7c
> at scala.Predef$.assert(Predef.scala:170) at
> org.apache.spark.sql.catalyst.planning.QueryPlanner.plan(QueryPlanner.scala:93)
> at
> org.apache.spark.sql.catalyst.planning.QueryPlanner$$anonfun$2$$anonfun$apply$2.apply(QueryPlanner.scala:78)
> at
> org.apache.spark.sql.catalyst.planning.QueryPlanner$$anonfun$2$$anonfun$apply$2.apply(QueryPlanner.scala:75)
> at
> scala.collection.TraversableOnce$$anonfun$foldLeft$1.apply(TraversableOnce.scala:157)
> at
> scala.collection.TraversableOnce$$anonfun$foldLeft$1.apply(TraversableOnce.scala:157)
> at scala.collection.Iterator$class.foreach(Iterator.scala:893) at
> scala.collection.AbstractIterator.foreach(Iterator.scala:1336) }}
> Note that in the logical plan I got *DataSourceV2Relation* and not
> *StreamingDataSourceV2Relation*although I use streaming.
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