Hi,
Earlier my code was like follwing but slow due to repartition. I want top K of
each window in a stream.
val counts = keyAndValues.map(x =>
math.round(x._3.toDouble)).countByValueAndWindow(Seconds(4), Seconds(4))val
topCounts = counts.repartition(1).map(_.swap).transform(rdd =>
rdd.sortByKey(false)).map(_.swap).mapPartitions(rdd => rdd.take(10))
so I thought to use dstream.transform(rdd=>rdd.top()) but this return Array
rather than rdd. I have to perform further steps on topCounts dstream.
[ERROR] found : Array[(Long, Long)][ERROR] required:
org.apache.spark.rdd.RDD[?][ERROR] val topCounts = counts.transform(rdd =>
rdd.top(10))
Regards,Laeeq
On Friday, March 13, 2015 1:47 PM, Sean Owen <so...@cloudera.com> wrote:
Hm, aren't you able to use the SparkContext here? DStream operations
happen on the driver. So you can parallelize() the result?
take() won't work as it's not the same as top()
On Fri, Mar 13, 2015 at 11:23 AM, Akhil Das <ak...@sigmoidanalytics.com> wrote:
> Like this?
>
> dtream.repartition(1).mapPartitions(it => it.take(5))
>
>
>
> Thanks
> Best Regards
>
> On Fri, Mar 13, 2015 at 4:11 PM, Laeeq Ahmed <laeeqsp...@yahoo.com.invalid>
> wrote:
>>
>> Hi,
>>
>> I normally use dstream.transform whenever I need to use methods which are
>> available in RDD API but not in streaming API. e.g. dstream.transform(x =>
>> x.sortByKey(true))
>>
>> But there are other RDD methods which return types other than RDD. e.g.
>> dstream.transform(x => x.top(5)) top here returns Array.
>>
>> In the second scenario, how can i return RDD rather than array, so that i
>> can perform further steps on dstream.
>>
>> Regards,
>> Laeeq
>
>
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