gt; scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:48)
>>> > at
>>> > scala.collection.TraversableOnce$class.to(TraversableOnce.scala:310)
>>> > at scala.collection.AbstractIterator.to(Itera
ion.TraversableOnce$class.to(TraversableOnce.
>> scala:310)
>> > at scala.collection.AbstractIterator.to(Iterator.scala:1336)
>> > at
>> > scala.collection.TraversableOnce$class.toBuffer(
>> TraversableOnce.scala:302)
>> > at scala.collection.AbstractIterator
Vector)] =>
> RDD[(String,
> > Vector)] = { rdd =>
> > if (rdd.isEmpty) rdd else {
> > // reduce to 2 dimensions
> > val pca = new PCA(2).fit(rdd.map(_._2))
> >
> > // Project vectors to the linear space spanned by the top 2 principa
ver if I remove the transform call, I can process everything correctly.
>
> Any help will be most welcome ..
>
> regards.
> - Debasish
>
>
>
> --
> View this message in context:
> http://apache-spark-user-list.1001560.n3.nabb
onents, keeping the label
rdd.map(p => (p._1, pca.transform(p._2)))
}
}
However if I remove the transform call, I can process everything correctly.
Any help will be most welcome ..
regards.
- Debasish
--
View this message in context:
http://apache-spark-user-list.1001560.n3.nabble.
Hello -
I am facing some issues with the following snippet of code that reads from
Kafka and creates DStream. I am using KafkaUtils.createDirectStream(..)
with Kafka 0.10.1 and Spark 2.0.1.
// get the data from kafka
val stream: DStream[ConsumerRecord[Array[Byte], (String, String)]] =
KafkaUtil
