I figured it out. Needed a block style map and a check for null. The case
class is just to name the transformed columns.
case class Component(name: String, loadTimeMs: Long)
avroFile.filter($"lazyComponents.components".isNotNull)
.explode($"lazyComponents.components")
{ case Row(lazyComponents: Seq[Row]) => lazyComponents
.map { x => val name = x.getString(0);
val loadTimeMs = if (x.isNullAt(1)) 0 else x.getLong(1);
Component(name, loadTimeMs) } }
.select('pageViewId, 'name, 'loadTimeMs).take(20).foreach(println)
On Thu, Jun 4, 2015 at 12:05 PM Tom Seddon <mr.tom.sed...@gmail.com> wrote:
> Hi,
>
> I've worked out how to use explode on my input avro dataset with the
> following structure
> root
> |-- pageViewId: string (nullable = false)
> |-- components: array (nullable = true)
> | |-- element: struct (containsNull = false)
> | | |-- name: string (nullable = false)
> | | |-- loadTimeMs: long (nullable = true)
>
>
> I'm trying to turn this into this layout with repeated pageViewIds for
> each row of my components:
> root
> |-- pageViewId: string (nullable = false)
> |-- name: string (nullable = false)
> |-- loadTimeMs: long (nullable = true)
>
> Explode words fine for the first 10 records using this bit of code, but my
> big problem is that loadTimeMs has nulls in it, which I think is causing
> the error. Any ideas how I can trap those nulls? Perhaps by converting to
> zeros and then I can deal with them later? I tried writing a udf which
> just takes the loadTimeMs column and swaps nulls for zeros, but this
> separates the struct and then I don't know how to use explode.
>
> avroFile.filter($"lazyComponents.components".isNotNull)
> .explode($"lazyComponents.components")
> { case Row(lazyComponents: Seq[Row]) => lazyComponents
> .map(x => x.getString(0) -> x.getLong(1))}
> .select('pageViewId, '_1, '_2)
> .take(10).foreach(println)
>
> 15/06/04 12:01:21 ERROR Executor: Exception in task 0.0 in stage 19.0 (TID
> 65)
> java.lang.RuntimeException: Failed to check null bit for primitive long
> value.
> at scala.sys.package$.error(package.scala:27)
> at
> org.apache.spark.sql.catalyst.expressions.GenericRow.getLong(rows.scala:87)
> at
> $line127.$read$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$anonfun$1$$anonfun$apply$1.apply(<console>:33)
> at
> $line127.$read$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$anonfun$1$$anonfun$apply$1.apply(<console>:33)
> at
> scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
> at
> scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
> at
> scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
> at scala.collection.mutable.WrappedArray.foreach(WrappedArray.scala:34)
> at scala.collection.TraversableLike$class.map(TraversableLike.scala:244)
> at scala.collection.AbstractTraversable.map(Traversable.scala:105)
> at
> $line127.$read$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$anonfun$1.apply(<console>:33)
> at
> $line127.$read$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$anonfun$1.apply(<console>:33)
> at scala.Function1$$anonfun$andThen$1.apply(Function1.scala:55)
> at
> org.apache.spark.sql.catalyst.expressions.UserDefinedGenerator.eval(generators.scala:89)
> at
> org.apache.spark.sql.execution.Generate$$anonfun$2$$anonfun$apply$1.apply(Generate.scala:71)
> at
> org.apache.spark.sql.execution.Generate$$anonfun$2$$anonfun$apply$1.apply(Generate.scala:70)
> at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371)
> at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
> at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
> at scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:308)
> at scala.collection.Iterator$class.foreach(Iterator.scala:727)
> at scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
> at scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:48)
> at
> scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:103)
> at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:47)
> at scala.collection.TraversableOnce$class.to(TraversableOnce.scala:273)
> at scala.collection.AbstractIterator.to(Iterator.scala:1157)
> at
> scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:265)
> at scala.collection.AbstractIterator.toBuffer(Iterator.scala:1157)
> at
> scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:252)
> at scala.collection.AbstractIterator.toArray(Iterator.scala:1157)
> at
> org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:122)
> at
> org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:122)
> at
> org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1498)
> at
> org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1498)
> at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:61)
> at org.apache.spark.scheduler.Task.run(Task.scala:64)
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:203)
> at
> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
> at
> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
> at java.lang.Thread.run(Thread.java:744)
>
>
>
