I have a hadoop cluster 2.7.4 version. Due to some reason, I have to restart my cluster. I need job IDs of those jobs that were executed on cluster before cluster reboot. Command mapred -list provide currently running of waiting jobs details only
You can see a list of all jobs on the Yarn Resource Manager Web UI.
In your browser go to http://ResourceManagerIPAdress:8088/
This is how the history looks on the Yarn cluster I am currently testing on (and I restarted the services several times):
See more info here
I have configured Spark jobserver to run on YARN.
I am able to send spark jobs to YARN but even after the job finishes it does not quit on YARN
For eg:
I tried to make a simple spark context.
The context is reflecting in jobserver but YARN is still running the process and is not quieting I have to manually kill the tasks.
Yarn Job
Spark Context
Job server reflects the contexts but as soon as I try to run any task in it Job server give me an error
{
"status": "ERROR",
"result": "context test-context2 not found"
}
My Spark UI is also not very helpful
I have been trying to run a pig job with multiple steps on Amazon EMR. Here are the details of my environment:
Number of nodes: 20
AMI Version: 3.1.0
Hadoop Distribution: 2.4.0
The pig script has multiple steps and it spawns a long-running map reduce job that has both a map phase and reduce phase. After running for sometime (sometimes an hour, sometimes three or four), the job is killed. The information on the resource manager for the job is:
Kill job received from hadoop (auth:SIMPLE) at
Job received Kill while in RUNNING state.
Obviously, I did not kill it :)
My question is: how do I go about trying to identify what exactly happened? How do I diagnose the issue? Which log files to look at (what to grep for)? Any help on even where the appropriate log files would be greatly helpful. I am new to YARN/Hadoop 2.0
There can be number of reasons. Enable debugging on your cluster and see in the stderr logs for more information.
aws emr create-cluster --name "Test cluster" --ami-version 3.9 --log-uri s3://mybucket/logs/ \
--enable-debugging --applications Name=Hue Name=Hive Name=Pig
More details here:
http://docs.aws.amazon.com/ElasticMapReduce/latest/DeveloperGuide/emr-plan-debugging.html
I am running Spark 1.1.0, HDP 2.1, on a kerberized cluster. I can successfully run spark-submit using --master yarn-client and the results are properly written to HDFS, however, the job doesn't show up on the Hadoop All Applications page. I want to run spark-submit using --master yarn-cluster but I continue to get this error:
appDiagnostics: Application application_1417686359838_0012 failed 2 times due to AM Container
for appattempt_1417686359838_0012_000002 exited with exitCode: -1000 due to: File does not
exist: hdfs://<HOST>/user/<username>/.sparkStaging/application_<numbers>_<more numbers>/spark-assembly-1.1.0-hadoop2.4.0.jar
.Failing this attempt.. Failing the application.
I've provisioned my account with access to the cluster. I've configured yarn-site.xml. I've cleared .sparkStaging. I've tried including --jars [path to my spark assembly in spark/lib]. I've found this question that is very similar, yet unanswered. I can't tell if this is a 2.1 issue, spark 1.1.0, kerberized cluster, configurations, or what. Any help would be much appreciated.
This is probably because you left sparkConf.setMaster("local[n]") in the code.
I am trying to understand how spark runs on YARN cluster/client. I have the following question in my mind.
Is it necessary that spark is installed on all the nodes in yarn cluster? I think it should because worker nodes in cluster execute a task and should be able to decode the code(spark APIs) in spark application sent to cluster by the driver?
It says in the documentation "Ensure that HADOOP_CONF_DIR or YARN_CONF_DIR points to the directory which contains the (client side) configuration files for the Hadoop cluster". Why does client node have to install Hadoop when it is sending the job to cluster?
Adding to other answers.
Is it necessary that spark is installed on all the nodes in the yarn
cluster?
No, If the spark job is scheduling in YARN(either client or cluster mode). Spark installation is needed in many nodes only for standalone mode.
These are the visualizations of spark app deployment modes.
Spark Standalone Cluster
In cluster mode driver will be sitting in one of the Spark Worker node whereas in client mode it will be within the machine which launched the job.
YARN cluster mode
YARN client mode
This table offers a concise list of differences between these modes:
pics source
It says in the documentation "Ensure that HADOOP_CONF_DIR or YARN_CONF_DIR points to the directory which contains the (client-side)
configuration files for the Hadoop cluster". Why does the client node have
to install Hadoop when it is sending the job to cluster?
Hadoop installation is not mandatory but configurations(not all) are!. We can call them Gateway nodes. It's for two main reasons.
The configuration contained in HADOOP_CONF_DIR directory will be distributed to the YARN cluster so that all containers used by the application use the same configuration.
In YARN mode the ResourceManager’s address is picked up from the
Hadoop configuration(yarn-default.xml). Thus, the --master parameter is yarn.
Update: (2017-01-04)
Spark 2.0+ no longer requires a fat assembly jar for production
deployment. source
We are running spark jobs on YARN (we use HDP 2.2).
We don't have spark installed on the cluster. We only added the Spark assembly jar to the HDFS.
For example to run the Pi example:
./bin/spark-submit \
--verbose \
--class org.apache.spark.examples.SparkPi \
--master yarn-cluster \
--conf spark.yarn.jar=hdfs://master:8020/spark/spark-assembly-1.3.1-hadoop2.6.0.jar \
--num-executors 2 \
--driver-memory 512m \
--executor-memory 512m \
--executor-cores 4 \
hdfs://master:8020/spark/spark-examples-1.3.1-hadoop2.6.0.jar 100
--conf spark.yarn.jar=hdfs://master:8020/spark/spark-assembly-1.3.1-hadoop2.6.0.jar - This config tell the yarn from were to take the spark assembly. If you don't use it, it will upload the jar from were you run spark-submit.
About your second question: The client node doesn't not need Hadoop installed. It only needs the configuration files. You can copy the directory from your cluster to your client.
1 - Spark if following s slave/master architecture. So on your cluster, you have to install a spark master and N spark slaves. You can run spark in a standalone mode. But using Yarn architecture will give you some benefits.
There is a very good explanation of it here : http://blog.cloudera.com/blog/2014/05/apache-spark-resource-management-and-yarn-app-models/
2- It is necessary if you want to use Yarn or HDFS for example, but as i said before you can run it in standalone mode.
Let me try to cut glues and make it short for impatient.
6 components: 1. client, 2. driver, 3. executors, 4. application master, 5. workers, and 6. resource manager; 2 deploy modes; and 2 resource (cluster) management.
Here's the relation:
Client
Nothing special, is the one submitting spark app.
Worker, executors
Nothing special, one worker holds one or more executors.
Master, & resource (cluster) manager
(no matter client or cluster mode)
in yarn, resource manager and master sit in two different nodes;
in standalone, resource manager == master, same process in the same node.
Driver
in client mode, sits with client
in yarn - cluster mode, sits with master (in this case, client process exits after submission of app)
in standalone - cluster mode, sits with one worker
Voilà!