Thanks Jia, Wenchen for your reply.
I've change my code like belows :
val sparkPlan =
sqlContext.sparkSession.sessionState.planner.plan(pipeLinePlan).next()
sparkPlan.execute().mapPartitions { itr =>
itr.map { internalRow =>
val encoder = RowEncoder(schema)
encoder.fromRow(internalRow)
}
}
but this time, i've got this exception :
---
java.lang.RuntimeException: Error while decoding:
java.lang.UnsupportedOperationException: Cannot evaluate expression:
getcolumnbyordinal(0, StringType)
createexternalrow(getcolumnbyordinal(0, StringType).toString,
getcolumnbyordinal(1, StringType).toString, getcolumnbyordinal(2,
IntegerType), getcolumnbyordinal(3, IntegerType), getcolumnbyordinal(4,
StringType).toString, getcolumnbyordinal(5, LongType),
StructField(product,StringType,true),
StructField(category,StringType,true),
StructField(revenue,IntegerType,true),
StructField(item_count,IntegerType,true),
StructField(product_category,StringType,true),
StructField(tot_revenue,LongType,true))
at
org.apache.spark.sql.catalyst.encoders.ExpressionEncoder.fromRow(ExpressionEncoder.scala:305)
at
com.zetaris.lightning.sql.vpipeline.PipeLineRelation$$anonfun$3$$anonfun$apply$1.apply(PipeLineRelation.scala:53)
at
com.zetaris.lightning.sql.vpipeline.PipeLineRelation$$anonfun$3$$anonfun$apply$1.apply(PipeLineRelation.scala:43)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:370)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:370)
at
org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown
Source)
at
org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at
org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$11$$anon$1.hasNext(WholeStageCodegenExec.scala:619)
at
org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:255)
at
org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:247)
at
org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:836)
at
org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:836)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
at org.apache.spark.scheduler.Task.run(Task.scala:121)
at
org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:402)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:408)
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:748)
---
Any idea about this error?
Thanks
Jason
On Mon, 30 Nov 2020 at 19:34, Jia, Ke A <ke.a....@intel.com> wrote:
> The fromRow method is removed in spark3.0. And the new API is :
>
> val encoder = RowEncoder(schema)
>
> val row = encoder.createDeserializer().apply(internalRow)
>
>
>
> Thanks,
>
> Jia Ke
>
>
>
> *From:* Wenchen Fan <cloud0...@gmail.com>
> *Sent:* Friday, November 27, 2020 9:32 PM
> *To:* Jason Jun <jaes...@gmail.com>
> *Cc:* Spark dev list <dev@spark.apache.org>
> *Subject:* Re: How to convert InternalRow to Row.
>
>
>
> InternalRow is an internal/developer API that might change overtime.
> Right now, the way to convert it to Row is to use `RowEncoder`, but you
> need to know the data schema:
>
> val encoder = RowEncoder(schema)
>
> val row = encoder.fromRow(internalRow)
>
>
>
> On Fri, Nov 27, 2020 at 6:16 AM Jason Jun <jaes...@gmail.com> wrote:
>
> Hi dev,
>
>
>
> i'm working on generating custom pipeline on the fly, which means I
> generate SparkPlan along with each node in my pipeline.
>
>
>
> So, my pipeline end up with PipeLineRelation extending BaseRelation like:
>
>
>
> case class PipeLineRelation(schema: StructType, pipeLinePlan:
> LogicalPlan)(@transient override val sqlContext: SQLContext) extends
> BaseRelation with PrunedFilteredScan {
> override def needConversion: Boolean = true
> override def unhandledFilters(filters: Array[Filter]): Array[Filter] =
> filters
>
> override def buildScan(requiredColumns: Array[String], filters:
> Array[Filter]): RDD[Row] = {
> ...
> val sparkPlan =
> sqlContext.sparkSession.sessionState.planner.plan(pipeLinePlan).next()
> *sparkPlan.execute().mapPartitions* { itr =>
> itr.map { internalRow =>
> val values = prunedColumnWithIndex.map { case (index, columnType)
> =>
> internalRow.get(index, columnType)
> }
> * Row.fromSeq(values) // Line 46*
> }
> }
> }
> }
>
>
>
> I'm getting InternalRow by executing subsequent Spark Plan, and converting
> it into Row using Row.fromSeq(). i saw values at Line 46 are what i exactly
> want :
>
> ------
>
> values = {Object[5]@14277}
> 0 = {UTF8String@14280} "Thin"
> 1 = {UTF8String@14281} "Cell phone"
> 2 = {Integer@14282} 6000
> 3 = {Integer@14283} 2
> 4 = {Integer@14284} 12000
>
>
>
> but execution of Line 46 ended up with this error :
>
> ------
>
> Job aborted due to stage failure: Task 0 in stage 0.0 failed 1 times, most
> recent failure: Lost task 0.0 in stage 0.0 (TID 0, localhost, executor
> driver): java.lang.IllegalArgumentException: The value (2) of the type
> (java.lang.Integer) cannot be converted to the string type
> at
> org.apache.spark.sql.catalyst.CatalystTypeConverters$StringConverter$.toCatalystImpl(CatalystTypeConverters.scala:290)
> at
> org.apache.spark.sql.catalyst.CatalystTypeConverters$StringConverter$.toCatalystImpl(CatalystTypeConverters.scala:285)
> at
> org.apache.spark.sql.catalyst.CatalystTypeConverters$CatalystTypeConverter.toCatalyst(CatalystTypeConverters.scala:103)
> at
> org.apache.spark.sql.catalyst.CatalystTypeConverters$$anonfun$createToCatalystConverter$2.apply(CatalystTypeConverters.scala:396)
> at
> org.apache.spark.sql.execution.RDDConversions$$anonfun$rowToRowRdd$1$$anonfun$apply$3.apply(ExistingRDD.scala:60)
> at
> org.apache.spark.sql.execution.RDDConversions$$anonfun$rowToRowRdd$1$$anonfun$apply$3.apply(ExistingRDD.scala:57)
> at scala.collection.Iterator$$anon$11.next(Iterator.scala:370)
> at
> org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown
> Source)
> at
> org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
> at
> org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$11$$anon$1.hasNext(WholeStageCodegenExec.scala:619)
> at
> org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:255)
> at
> org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:247)
> at
> org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:836)
> at
> org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:836)
> at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
> at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
> at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
> at org.apache.spark.scheduler.Task.run(Task.scala:121)
> at
> org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:402)
> at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:408)
> at
> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
> at
> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
> at java.lang.Thread.run(Thread.java:748)
>
> ----
>
>
>
> Is it existing bug? otherwise how do I convert InternalRow to Row?
>
>
>
> Thanks in advance.
>
> Jason
>
>
