[
https://issues.apache.org/jira/browse/SPARK-13525?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15316187#comment-15316187
]
menda venugopal commented on SPARK-13525:
-----------------------------------------
Hi,
We have problem with similar kind of issue where SPARKML doesn't work with
SPARKR with yarn-client mode. In local mode, it works. Spark core and Spark
SQLworks without problem with R in local mode or yarn client.
#####
> head(filter(df01, df01$C3 < 014001))
C0 C1 C2 C3 C4 C5 C6 C7
1 01001A008W-1 1 Lotissement Bellevue 01400 L'Abergement-Cl\xe9menciat CAD
46.134565 4.924122
2 01001A008W-2 2 Lotissement Bellevue 01400 L'Abergement-Cl\xe9menciat CAD
46.134633 4.924168
3 01001A008W-4 4 Lotissement Bellevue 01400 L'Abergement-Cl\xe9menciat CAD
46.134637 4.924353
4 01001A008W-5 5 Lotissement Bellevue 01400 L'Abergement-Cl\xe9menciat CAD
46.134518 4.924333
5 01001A008W-6 6 Lotissement Bellevue 01400 L'Abergement-Cl\xe9menciat CAD
46.134345 4.924229
6 01001A015D-1 1 Lotissement Les Charmilles 01400 L'Abergement-Cl\xe9menciat
C+O 46.151573 4.921591
> groupByCode_Postal <- groupBy(df01, df01$C3)
> code_Postal_G <- summarize(groupByCode_Postal, count = count(df01$C3))
> df <- createDataFrame(sqlContext, iris)
Warning messages:
1: In FUN(X[[5L]], ...) :
Use Sepal_Length instead of Sepal.Length as column name
2: In FUN(X[[5L]], ...) :
Use Sepal_Width instead of Sepal.Width as column name
3: In FUN(X[[5L]], ...) :
Use Petal_Length instead of Petal.Length as column name
4: In FUN(X[[5L]], ...) :
Use Petal_Width instead of Petal.Width as column name
> model <- glm(Sepal_Length ~ Sepal_Width + Species, data = df, family =
> "gaussian")
model <- glm(Sepal_Length ~ Sepal_Width + Species, data = df, family =
"gaussian")
16/05/27 12:33:59 WARN TaskSetManager: Lost task 0.0 in stage 5.0 (TID 5,
host-172-30-125-248.openstacklocal): java.net.SocketTimeoutException: Accept
timed out
at java.net.PlainSocketImpl.socketAccept(Native Method)
at java.net.AbstractPlainSocketImpl.accept(AbstractPlainSocketImpl.java:398)
at java.net.ServerSocket.implAccept(ServerSocket.java:530)
at java.net.ServerSocket.accept(ServerSocket.java:498)
#####
16/05/27 12:34:29 ERROR TaskSetManager: Task 0 in stage 5.0 failed 4 times;
aborting job
16/05/27 12:34:29 ERROR RBackendHandler: fitRModelFormula on
org.apache.spark.ml.api.r.SparkRWrappers failed
Error in invokeJava(isStatic = TRUE, className, methodName, ...) :
org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in
stage 5.0 failed 4 times, most recent failure: Lost task 0.3 in stage 5.0 (TID
8, host-172-30-125-248.openstacklocal): java.net.SocketTimeoutException: Accept
timed out
at java.net.PlainSocketImpl.socketAccept(Native Method)
at java.net.AbstractPlainSocketImpl.accept(AbstractPlainSocketImpl.java:398)
at java.net.ServerSocket.implAccept(ServerSocket.java:530)
at java.net.ServerSocket.accept(ServerSocket.java:498)
at org.apache.spark.api.r.RRDD$.createRWorker(RRDD.scala:426)
at org.apache.spark.api.r.BaseRRDD.compute(RRDD.scala:62)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:300)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:264)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:300)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:264)
at org.apache.spark.rdd.MapPartitionsRDD.compute(Ma
> SparkR: java.net.SocketTimeoutException: Accept timed out when running any
> dataframe function
> ---------------------------------------------------------------------------------------------
>
> Key: SPARK-13525
> URL: https://issues.apache.org/jira/browse/SPARK-13525
> Project: Spark
> Issue Type: Bug
> Components: SparkR
> Reporter: Shubhanshu Mishra
> Labels: sparkr
>
> I am following the code steps from this example:
> https://spark.apache.org/docs/1.6.0/sparkr.html
> There are multiple issues:
> 1. The head and summary and filter methods are not overridden by spark. Hence
> I need to call them using `SparkR::` namespace.
> 2. When I try to execute the following, I get errors:
> {code}
> $> $R_HOME/bin/R
> R version 3.2.3 (2015-12-10) -- "Wooden Christmas-Tree"
> Copyright (C) 2015 The R Foundation for Statistical Computing
> Platform: x86_64-pc-linux-gnu (64-bit)
> R is free software and comes with ABSOLUTELY NO WARRANTY.
> You are welcome to redistribute it under certain conditions.
> Type 'license()' or 'licence()' for distribution details.
> Natural language support but running in an English locale
> R is a collaborative project with many contributors.
> Type 'contributors()' for more information and
> 'citation()' on how to cite R or R packages in publications.
> Type 'demo()' for some demos, 'help()' for on-line help, or
> 'help.start()' for an HTML browser interface to help.
> Type 'q()' to quit R.
> Welcome at Fri Feb 26 16:19:35 2016
> Attaching package: ‘SparkR’
> The following objects are masked from ‘package:base’:
> colnames, colnames<-, drop, intersect, rank, rbind, sample, subset,
> summary, transform
> Launching java with spark-submit command
> /content/smishra8/SOFTWARE/spark/bin/spark-submit --driver-memory "50g"
> sparkr-shell /tmp/RtmpfBQRg6/backend_portc3bc16f09b1b
> > df <- createDataFrame(sqlContext, iris)
> Warning messages:
> 1: In FUN(X[[i]], ...) :
> Use Sepal_Length instead of Sepal.Length as column name
> 2: In FUN(X[[i]], ...) :
> Use Sepal_Width instead of Sepal.Width as column name
> 3: In FUN(X[[i]], ...) :
> Use Petal_Length instead of Petal.Length as column name
> 4: In FUN(X[[i]], ...) :
> Use Petal_Width instead of Petal.Width as column name
> > training <- filter(df, df$Species != "setosa")
> Error in filter(df, df$Species != "setosa") :
> no method for coercing this S4 class to a vector
> > training <- SparkR::filter(df, df$Species != "setosa")
> > model <- SparkR::glm(Species ~ Sepal_Length + Sepal_Width, data = training,
> > family = "binomial")
> 16/02/26 16:26:46 ERROR Executor: Exception in task 0.0 in stage 1.0 (TID 1)
> java.net.SocketTimeoutException: Accept timed out
> at java.net.PlainSocketImpl.socketAccept(Native Method)
> at
> java.net.AbstractPlainSocketImpl.accept(AbstractPlainSocketImpl.java:398)
> at java.net.ServerSocket.implAccept(ServerSocket.java:530)
> at java.net.ServerSocket.accept(ServerSocket.java:498)
> at org.apache.spark.api.r.RRDD$.createRWorker(RRDD.scala:431)
> at org.apache.spark.api.r.BaseRRDD.compute(RRDD.scala:62)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:313)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:277)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:313)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:277)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:313)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:277)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:313)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:277)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:313)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:277)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:313)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:277)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:313)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:277)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:313)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:277)
> at
> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:77)
> at
> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:45)
> at org.apache.spark.scheduler.Task.run(Task.scala:81)
> at
> org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)
> at
> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
> at
> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
> at java.lang.Thread.run(Thread.java:745)
> 16/02/26 16:26:46 ERROR TaskSetManager: Task 0 in stage 1.0 failed 1 times;
> aborting job
> 16/02/26 16:26:46 ERROR RBackendHandler: fitRModelFormula on
> org.apache.spark.ml.api.r.SparkRWrappers failed
> Error in invokeJava(isStatic = TRUE, className, methodName, ...) :
> org.apache.spark.SparkException: Job aborted due to stage failure: Task 0
> in stage 1.0 failed 1 times, most recent failure: Lost task 0.0 in stage 1.0
> (TID 1, localhost): java.net.SocketTimeoutException: Accept timed out
> at java.net.PlainSocketImpl.socketAccept(Native Method)
> at
> java.net.AbstractPlainSocketImpl.accept(AbstractPlainSocketImpl.java:398)
> at java.net.ServerSocket.implAccept(ServerSocket.java:530)
> at java.net.ServerSocket.accept(ServerSocket.java:498)
> at org.apache.spark.api.r.RRDD$.createRWorker(RRDD.scala:431)
> at org.apache.spark.api.r.BaseRRDD.compute(RRDD.scala:62)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:313)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:277)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:313)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:277)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> >
> {code}
> Even, when I try to run the head command on the dataframe, I get similar
> error:
> {code}
> > SparkR::head(df)
> 16/02/26 16:32:05 ERROR Executor: Exception in task 0.0 in stage 3.0 (TID 2)
> java.net.SocketTimeoutException: Accept timed out
> at java.net.PlainSocketImpl.socketAccept(Native Method)
> at
> java.net.AbstractPlainSocketImpl.accept(AbstractPlainSocketImpl.java:398)
> at java.net.ServerSocket.implAccept(ServerSocket.java:530)
> at java.net.ServerSocket.accept(ServerSocket.java:498)
> at org.apache.spark.api.r.RRDD$.createRWorker(RRDD.scala:431)
> at org.apache.spark.api.r.BaseRRDD.compute(RRDD.scala:62)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:313)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:277)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:313)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:277)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:313)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:277)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:313)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:277)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:313)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:277)
> at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:69)
> at org.apache.spark.scheduler.Task.run(Task.scala:81)
> at
> org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)
> at
> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
> at
> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
> at java.lang.Thread.run(Thread.java:745)
> 16/02/26 16:32:05 ERROR TaskSetManager: Task 0 in stage 3.0 failed 1 times;
> aborting job
> 16/02/26 16:32:05 ERROR RBackendHandler: dfToCols on
> org.apache.spark.sql.api.r.SQLUtils failed
> Error in invokeJava(isStatic = TRUE, className, methodName, ...) :
> org.apache.spark.SparkException: Job aborted due to stage failure: Task 0
> in stage 3.0 failed 1 times, most recent failure: Lost task 0.0 in stage 3.0
> (TID 2, localhost): java.net.SocketTimeoutException: Accept timed out
> at java.net.PlainSocketImpl.socketAccept(Native Method)
> at
> java.net.AbstractPlainSocketImpl.accept(AbstractPlainSocketImpl.java:398)
> at java.net.ServerSocket.implAccept(ServerSocket.java:530)
> at java.net.ServerSocket.accept(ServerSocket.java:498)
> at org.apache.spark.api.r.RRDD$.createRWorker(RRDD.scala:431)
> at org.apache.spark.api.r.BaseRRDD.compute(RRDD.scala:62)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:313)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:277)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:313)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:277)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> {code}
> I have a .Rprofile file in my directory which looks like the following:
> {code:title=.Rprofile|borderStyle=solid}
> # Sample Rprofile.site file
> # Things you might want to change
> .First <- function(){
> cat("\nWelcome at", date(), "\n")
> SPARK_HOME <- "/content/user/SOFTWARE/spark"
> .libPaths(c(file.path(SPARK_HOME, "R", "lib"), .libPaths()))
> library(SparkR)
> sc <<- sparkR.init(master="local[20]", appName="Model SparkR",
> sparkHome=SPARK_HOME,
> sparkEnvir=list(spark.local.dir="./tmp",
> spark.executor.memory="50g",
> spark.driver.maxResultSize="50g",
> spark.driver.memory="50g"))
> sqlContext <<- sparkRSQL.init(sc)
> }
> .Last <- function(){
> cat("\nGoodbye at ", date(), "\n")
> }
> {code}
> I am using the master branch of Spark since the following commit:
> {code}
> commit 35316cb0b744bef9bcb390411ddc321167f953be
> Author: Yu ISHIKAWA <yuu.ishik...@gmail.com>
> Date: Thu Feb 25 13:29:10 2016 -0800
> {code}
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