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Posted to issues@spark.apache.org by "Lars Albertsson (JIRA)" <ji...@apache.org> on 2014/11/20 11:39:33 UTC
[jira] [Commented] (SPARK-2579) Reading from S3 returns an
inconsistent number of items with Spark 0.9.1
[ https://issues.apache.org/jira/browse/SPARK-2579?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14219212#comment-14219212 ]
Lars Albertsson commented on SPARK-2579:
----------------------------------------
Is this problem solved by enabling the consistent view feature in S3?
http://aws.amazon.com/blogs/aws/emr-consistent-file-system/
> Reading from S3 returns an inconsistent number of items with Spark 0.9.1
> ------------------------------------------------------------------------
>
> Key: SPARK-2579
> URL: https://issues.apache.org/jira/browse/SPARK-2579
> Project: Spark
> Issue Type: Bug
> Components: Input/Output
> Affects Versions: 0.9.1
> Reporter: Eemil Lagerspetz
> Priority: Critical
> Labels: hdfs, read, s3, skipping
>
> I have created a random matrix of 1M rows with 10K items on each row, semicolon-separated. While reading it with Spark 0.9.1 and doing a count, I consistently get less than 1M rows, and a different number every time at that ( !! ). Example below:
> head -n 1 tool-generate-random-matrix*log
> ==> tool-generate-random-matrix-999158.log <==
> Row item counts: 999158
> ==> tool-generate-random-matrix.log <==
> Row item counts: 997163
> The data is split into 1000 partitions. When I download it using s3cmd sync, and run the following AWK on it, I get the correct number of rows in each partition (1000x1000 = 1M). What is up?
> {code:title=checkrows.sh|borderStyle=solid}
> for k in part-0*
> do
> echo $k
> awk -F ";" '
> NF != 10000 {
> print "Wrong number of items:",NF
> }
> END {
> if (NR != 1000) {
> print "Wrong number of rows:",NR
> }
> }' "$k"
> done
> {code}
> The matrix generation and counting code is below:
> {code:title=Matrix.scala|borderStyle=solid}
> package fi.helsinki.cs.nodes.matrix
> import java.util.Random
> import org.apache.spark._
> import org.apache.spark.SparkContext._
> import scala.collection.mutable.ListBuffer
> import org.apache.spark.rdd.RDD
> import org.apache.spark.storage.StorageLevel._
> object GenerateRandomMatrix {
> def NewGeMatrix(rSeed: Int, rdd: RDD[Int], features: Int) = {
> rdd.mapPartitions(part => part.map(xarr => {
> val rdm = new Random(rSeed + xarr)
> val arr = new Array[Double](features)
> for (i <- 0 until features)
> arr(i) = rdm.nextDouble()
> new Row(xarr, arr)
> }))
> }
> case class Row(id: Int, elements: Array[Double]) {}
> def rowFromText(line: String) = {
> val idarr = line.split(" ")
> val arr = idarr(1).split(";")
> // -1 to fix saved matrix indexing error
> new Row(idarr(0).toInt-1, arr.map(_.toDouble))
> }
> def main(args: Array[String]) {
> val master = args(0)
> val tasks = args(1).toInt
> val savePath = args(2)
> val read = args.contains("read")
>
> val datapoints = 1000000
> val features = 10000
> val sc = new SparkContext(master, "RandomMatrix")
> if (read) {
> val randomMatrix: RDD[Row] = sc.textFile(savePath, tasks).map(rowFromText).persist(MEMORY_AND_DISK)
> println("Row item counts: "+ randomMatrix.count)
> } else {
> val rdd = sc.parallelize(0 until datapoints, tasks)
> val bcSeed = sc.broadcast(128)
> /* Generating a matrix of random Doubles */
> val randomMatrix = NewGeMatrix(bcSeed.value, rdd, features).persist(MEMORY_AND_DISK)
> randomMatrix.map(row => row.id + " " + row.elements.mkString(";")).saveAsTextFile(savePath)
> }
>
> sc.stop
> }
> }
> {code}
> I run this with:
> appassembler/bin/tool-generate-random-matrix master 1000 s3n://keys@path/to/data 1>matrix.log 2>matrix.err
> Reading from HDFS gives the right count and right number of items on each row. However, I had to run with the full path with the server name, just /matrix does not work (it thinks I want file://):
> p="hdfs://ec2-54-188-6-77.us-west-2.compute.amazonaws.com:9000/matrix"
> appassembler/bin/tool-generate-random-matrix $( cat /root/spark-ec2/cluster-url ) 1000 "$p" read 1>readmatrix.log 2>readmatrix.err
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