Re: Spark 1.5.1 Dynamic Resource Allocation
Did you go through
http://spark.apache.org/docs/latest/job-scheduling.html#configuration-and-setup
for yarn, i guess you will have to copy the spark-1.5.1-yarn-shuffle.jar to
the classpath of all nodemanagers in your cluster.
Thanks
Best Regards
On Fri, Oct 30, 2015 at 7:41 PM, Tom Stewart <
stewartthom...@yahoo.com.invalid> wrote:
> 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.
>
>
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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-
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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.
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
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
