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From 宿荣全 (JIRA) <j...@apache.org>
Subject [jira] [Comment Edited] (SPARK-4817) [streaming]Print the specified number of data and handle all of the elements in RDD
Date Thu, 11 Dec 2014 05:57:13 GMT

    [ https://issues.apache.org/jira/browse/SPARK-4817?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14242177#comment-14242177
] 

宿荣全 edited comment on SPARK-4817 at 12/11/14 5:57 AM:
------------------------------------------------------

[~srowen]
Always call foreachRDD, and operate on all of the RDD, and then call take on the RDD to get
a few elements to print. It can achieve the effect, but it is more complicated.
for example:
```
1.val dstream = stream.map->filter->..foreachRDD(rdd => {
      val array = rdd.collect
      var result = Array[(String,String)]()
      result = if (array.size > 5) array.take(5) else array.take(array.size)
      result foreach println
    })
2.val dstream = stream.map->filter->foreachRDD(rdd => {
      val rddarray = ssc.sparkContext.runJob(rdd, (iter: Iterator[(String, String)]) =>
iter.toArray)
      val array = Array.concat(rddarray: _*)
      var result = Array[(String,String)]()
      result = if (array.size > 5) array.take(5) else array.take(array.size)
      result foreach println
    })
```
This two samples can achieve the effect. From the design perspective streaming direct manipulation
of the RDD is not a good design.and I think the method 'foreachRDD'  is generally not used
in coding. 
Generally when streaming register action by  through the following 6 methods. Those methods
all called method 'foreachRDD'.
1.DStream.foreach
2.DStream.saveAsObjectFiles
3.DStream.saveAsTextFiles
4.PairDStreamFunctions.saveAsHadoopFiles
5.PairDStreamFunctions.saveAsNewAPIHadoopFiles


was (Author: surq):
[~srowen]
Always call foreachRDD, and operate on all of the RDD, and then call take on the RDD to get
a few elements to print.It can achieve the effect, but it is more complicated.
for example:
1.val dstream = stream.map->filter->..foreachRDD(rdd => {
      val array = rdd.collect
      var result = Array[(String,String)]()
      result = if (array.size > 5) array.take(5) else array.take(array.size)
      result foreach println
    })
2.val dstream = stream.map->filter->foreachRDD(rdd => {
      val rddarray = ssc.sparkContext.runJob(rdd, (iter: Iterator[(String, String)]) =>
iter.toArray)
      val array = Array.concat(rddarray: _*)
      var result = Array[(String,String)]()
      result = if (array.size > 5) array.take(5) else array.take(array.size)
      result foreach println
    })
this two samples can achieve the effect. From the design perspective streaming direct manipulation
of the RDD is not a good design.and I thank the method 'foreachRDD'  is generally not used
in coding. 
Generally when streaming register action by  through the following 6 methods.Those methods
all called method 'foreachRDD'.
1.DStream.foreach
2.DStream.saveAsObjectFiles
3.DStream.saveAsTextFiles
4.PairDStreamFunctions.saveAsHadoopFiles
5.PairDStreamFunctions.saveAsNewAPIHadoopFiles

> [streaming]Print the specified number of data and handle all of the elements in RDD
> -----------------------------------------------------------------------------------
>
>                 Key: SPARK-4817
>                 URL: https://issues.apache.org/jira/browse/SPARK-4817
>             Project: Spark
>          Issue Type: New Feature
>          Components: Streaming
>            Reporter: 宿荣全
>            Priority: Minor
>
> Dstream.print function:Print 10 elements and handle 11 elements.
> A new function based on Dstream.print function is presented:
> the new function:
> Print the specified number of data and handle all of the elements in RDD.
> there is a work scene:
> val dstream = stream.map->filter->mapPartitions->print
> the data after filter need update database in mapPartitions,but don't need print each
data,only need to print the top 20 for view the data processing.



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