Re: Spark 1.5.1 Dynamic Resource Allocation
https://issues.apache.org/jira/browse/SPARK-10790 Changed to add minExecutors < initialExecutors < maxExecutors and that works. spark-shell --conf spark.dynamicAllocation.enabled=true --conf spark.shuffle.service.enabled=true --conf spark.dynamicAllocation.minExecutors=2 --conf spark.dynamicAllocation.initialExecutors=4 --conf spark.dynamicAllocation.maxExecutors=12 --conf spark.dynamicAllocation.sustainedSchedulerBacklogTimeout=120 --conf spark.dynamicAllocation.schedulerBacklogTimeout=300 --conf spark.dynamicAllocation.executorIdleTimeout=60 --executor-memory 512m --master yarn-client --queue default -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/Spark-1-5-1-Dynamic-Resource-Allocation-tp25275p25277.html Sent from the Apache Spark User List mailing list archive at Nabble.com. - To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
Spark 1.5.1 Dynamic Resource Allocation
(apologies if this re-posts, having challenges with the various web front
ends to this mailing list)
I am running the following command on a Hadoop cluster to launch Spark shell
with DRA:
spark-shell --conf spark.dynamicAllocation.enabled=true --conf
spark.shuffle.service.enabled=true --conf
spark.dynamicAllocation.minExecutors=4 --conf
spark.dynamicAllocation.maxExecutors=12 --conf
spark.dynamicAllocation.sustainedSchedulerBacklogTimeout=120 --conf
spark.dynamicAllocation.schedulerBacklogTimeout=300 --conf
spark.dynamicAllocation.executorIdleTimeout=60 --executor-memory 512m
--master yarn-client --queue default
This is the code I'm running within the Spark Shell - just demo stuff from
teh web site.
import org.apache.spark.mllib.clustering.KMeans
import org.apache.spark.mllib.linalg.Vectors
// Load and parse the data
val data = sc.textFile("hdfs://ns/public/sample/kmeans_data.txt")
val parsedData = data.map(s => Vectors.dense(s.split('
').map(_.toDouble))).cache()
// Cluster the data into two classes using KMeans
val numClusters = 2
val numIterations = 20
val clusters = KMeans.train(parsedData, numClusters, numIterations)
This works fine on Spark 1.4.1 but is failing on Spark 1.5.1. Did something
change that I need to do differently for DRA on 1.5.1?
This is the error I am getting:
15/10/29 21:44:19 WARN YarnScheduler: Initial job has not accepted any
resources; check your cluster UI to ensure that workers are registered and
have sufficient resources
15/10/29 21:44:34 WARN YarnScheduler: Initial job has not accepted any
resources; check your cluster UI to ensure that workers are registered and
have sufficient resources
15/10/29 21:44:49 WARN YarnScheduler: Initial job has not accepted any
resources; check your cluster UI to ensure that workers are registered and
have sufficient resources
That happens to be the same error you get if you haven't followed the steps
to enable DRA, however I have done those and as I said if I just flip to
Spark 1.4.1 on the same cluster it works with my YARN config.
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sparkR 1.5.1 batch yarn-client mode failing on daemon.R not found
(apologies if this re-posts, having challenges with the various web front ends to this mailing list) I have the following script in a file named test.R: library(SparkR) sc <- sparkR.init(master="yarn-client") sqlContext <- sparkRSQL.init(sc) df <- createDataFrame(sqlContext, faithful) showDF(df) sparkR.stop() q(save="no") If I submit this with "sparkR test.R" or "R CMD BATCH test.R" or "Rscript test.R" it fails with this error: 15/10/29 08:08:49 INFO r.BufferedStreamThread: Fatal error: cannot open file '/mnt/hdfs9/yarn/nm-local-dir/usercache/hadoop/appcache/application_1446058618330_0171/container_e805_1446058618330_0171_01_05/sparkr/SparkR/worker/daemon.R': No such file or directory 15/10/29 08:08:59 ERROR executor.Executor: Exception in task 0.0 in stage 1.0 (TID 1) java.net.SocketTimeoutException: Accept timed out However, if I launch just an interactive sparkR shell and cut/paste those commands - it runs fine. It also runs fine on the same Hadoop cluster with Spark 1.4.1. And, it runs fine from batch mode if I just use sparkR.init() and not sparkR.init(master="yarn-client") -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/sparkR-1-5-1-batch-yarn-client-mode-failing-on-daemon-R-not-found-tp25274.html Sent from the Apache Spark User List mailing list archive at Nabble.com. - To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
Spark 1.5.1 Dynamic Resource Allocation
I am running the following command on a Hadoop cluster to launch Spark shell
with DRA:
spark-shell --conf spark.dynamicAllocation.enabled=true --conf
spark.shuffle.service.enabled=true --conf
spark.dynamicAllocation.minExecutors=4 --conf
spark.dynamicAllocation.maxExecutors=12 --conf
spark.dynamicAllocation.sustainedSchedulerBacklogTimeout=120 --conf
spark.dynamicAllocation.schedulerBacklogTimeout=300 --conf
spark.dynamicAllocation.executorIdleTimeout=60 --executor-memory 512m
--master yarn-client --queue default
This is the code I'm running within the Spark Shell - just demo stuff from
teh web site.
import org.apache.spark.mllib.clustering.KMeans
import org.apache.spark.mllib.linalg.Vectors
// Load and parse the data
val data = sc.textFile("hdfs://ns/public/sample/kmeans_data.txt")
val parsedData = data.map(s => Vectors.dense(s.split('
').map(_.toDouble))).cache()
// Cluster the data into two classes using KMeans
val numClusters = 2
val numIterations = 20
val clusters = KMeans.train(parsedData, numClusters, numIterations)
This works fine on Spark 1.4.1 but is failing on Spark 1.5.1. Did something
change that I need to do differently for DRA on 1.5.1?
This is the error I am getting:
15/10/29 21:44:19 WARN YarnScheduler: Initial job has not accepted any
resources; check your cluster UI to ensure that workers are registered and
have sufficient resources
15/10/29 21:44:34 WARN YarnScheduler: Initial job has not accepted any
resources; check your cluster UI to ensure that workers are registered and
have sufficient resources
15/10/29 21:44:49 WARN YarnScheduler: Initial job has not accepted any
resources; check your cluster UI to ensure that workers are registered and
have sufficient resources
That happens to be the same error you get if you haven't followed the steps
to enable DRA, however I have done those and as I said if I just flip to
Spark 1.4.1 on the same cluster it works with my YARN config.
--
View this message in context:
http://apache-spark-user-list.1001560.n3.nabble.com/Spark-1-5-1-Dynamic-Resource-Allocation-tp25231.html
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For additional commands, e-mail: user-h...@spark.apache.org
sparkR 1.5.1 batch yarn-client mode failing on daemon.R not found
I have the following script in a file named test.R: library(SparkR) sc <- sparkR.init(master="yarn-client") sqlContext <- sparkRSQL.init(sc) df <- createDataFrame(sqlContext, faithful) showDF(df) sparkR.stop() q(save="no") If I submit this with "sparkR test.R" or "R CMD BATCH test.R" or "Rscript test.R" it fails with this error: 15/10/29 08:08:49 INFO r.BufferedStreamThread: Fatal error: cannot open file '/mnt/hdfs9/yarn/nm-local-dir/usercache/hadoop/appcache/application_1446058618330_0171/container_e805_1446058618330_0171_01_05/sparkr/SparkR/worker/daemon.R': No such file or directory 15/10/29 08:08:59 ERROR executor.Executor: Exception in task 0.0 in stage 1.0 (TID 1) java.net.SocketTimeoutException: Accept timed out However, if I launch just an interactive sparkR shell and cut/paste those commands - it runs fine. It also runs fine on the same Hadoop cluster with Spark 1.4.1. And, it runs fine from batch mode if I just use sparkR.init() and not sparkR.init(master="yarn-client") -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/sparkR-1-5-1-batch-yarn-client-mode-failing-on-daemon-R-not-found-tp25230.html Sent from the Apache Spark User List mailing list archive at Nabble.com. - To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
