Right thanks, that worked.
My goal is to programmatically submit things to the yarn cluster. The
underlying framework we have is a set of property files that specify different
machines for dev, qe, prod. While it's definitely possible to have different
things deployed as the client etc/hadoop directory, I was just curious if the
only way is to have the different things setup as environment variables or if
there was a way to programmatically override particular configurations.
I looked at the Client.scala code and it seems like it creates a new
Configuration object that isn't accessible from the outside so most likely the
answer is no, which is a reasonable answer. I just have to figure out a
different deployment model for doing the different stages of the lifecycle.
Thanks,
Ron
On Thursday, April 3, 2014 6:29 AM, Tom Graves <tgraves...@yahoo.com> wrote:
You should just be making sure your HADOOP_CONF_DIR env variable is correct and
not setting yarn.resourcemanager.address in SparkConf. For Yarn/Hadoop you
need to point it to the configuration files for your cluster. Generally that
setting goes into yarn-site.xml. If just setting it doesn't work, make sure
$HADOOP_CONF_DIR is getting put into your classpath. I would also make sure
HADOOP_PREFIX is being set.
Tom
On Wednesday, April 2, 2014 10:10 PM, Ron Gonzalez <zlgonza...@yahoo.com> wrote:
Hi,
I have a small program but I cannot seem to make it connect to the right
properties of the cluster.
I have the SPARK_YARN_APP_JAR, SPARK_JAR and SPARK_HOME set properly.
If I run this scala file, I am seeing that this is never using the
yarn.resourcemanager.address property that I set on the SparkConf instance.
Any advice?
Thanks,
Ron
import org.apache.spark.SparkContext
import org.apache.spark.SparkContext._
import org.apache.spark.deploy.yarn.Client
import java.lang.System
import org.apache.spark.SparkConf
object SimpleApp {
def main(args: Array[String]) {
val logFile =
"/home/rgonzalez/app/spark-0.9.0-incubating-bin-hadoop2/README.md"
val conf = new SparkConf()
conf.set("yarn.resourcemanager.address", "localhost:8050")
val sc = new SparkContext("yarn-client", "Simple App", conf)
val logData = sc.textFile(logFile, 2).cache()
val numAs = logData.filter(line => line.contains("a")).count()
val numBs = logData.filter(line => line.contains("b")).count()
println("Lines with a: %s, Lines with b: %s".format(numAs, numBs))
}
}
