[
https://issues.apache.org/jira/browse/SPARK-2468?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14201844#comment-14201844
]
zzc commented on SPARK-2468:
----------------------------
By the way, My test code:
val mapR = textFile.map(line => {
......
((value(1) + "_" + date.toString(), url), (flow, 1))
}).reduceByKey((pair1, pair2) => {
(pair1._1 + pair2._1, pair1._2 + pair2._2)
}, 100)
mapR.persist(StorageLevel.MEMORY_AND_DISK_SER)
val mapR1 = mapR.groupBy(_._1._1)
.mapValues(pairs => {
pairs.toList.sortBy(_._2._1).reverse
})
.flatMap(values => {
values._2
})
.map(values => {
values._1._1 + "\t" + values._1._2 + "\t" +
values._2._1.toString() + "\t" + values._2._2.toString()
})
.saveAsTextFile(outputPath + "_1/")
val mapR2 = mapR.groupBy(_._1._1)
.mapValues(pairs => {
pairs.toList.sortBy(_._2._2).reverse
})
.flatMap(values => {
values._2
})
.map(values => {
values._1._1 + "\t" + values._1._2 + "\t" +
values._2._1.toString() + "\t" + values._2._2.toString()
})
.saveAsTextFile(outputPath + "_2/")
> Netty-based block server / client module
> ----------------------------------------
>
> Key: SPARK-2468
> URL: https://issues.apache.org/jira/browse/SPARK-2468
> Project: Spark
> Issue Type: Improvement
> Components: Shuffle, Spark Core
> Reporter: Reynold Xin
> Assignee: Reynold Xin
> Priority: Critical
> Fix For: 1.2.0
>
>
> Right now shuffle send goes through the block manager. This is inefficient
> because it requires loading a block from disk into a kernel buffer, then into
> a user space buffer, and then back to a kernel send buffer before it reaches
> the NIC. It does multiple copies of the data and context switching between
> kernel/user. It also creates unnecessary buffer in the JVM that increases GC
> Instead, we should use FileChannel.transferTo, which handles this in the
> kernel space with zero-copy. See
> http://www.ibm.com/developerworks/library/j-zerocopy/
> One potential solution is to use Netty. Spark already has a Netty based
> network module implemented (org.apache.spark.network.netty). However, it
> lacks some functionality and is turned off by default.
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