Can you try with
val encoder = RowEncoder(schema).resolveAndBind()
...
On Mon, Nov 30, 2020 at 5:07 PM Jason Jun <jaes...@gmail.com> wrote:
> 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
>>
>>
