1. I have configured a FAIR_TEST queue and set the Ordering to FAIR
2. Have added "fair-scheduler.xml" in HADOOP_CONF_DIR default path (/usr/hdp/126.96.36.199-78/hadoop/conf) and have set minResources and maxResources to 4 GB and 8 GB respectively.
3. Changed the Scheduler Class in Ambari to fair scheduler class and added a parameter "yarn.scheduler.fair.allocation.file" to point to the above XML file.
While re-starting the YARN affected components in Ambari, I am getting the below error:
Can you please let me know what's going wrong and how to fix this issue.
2019-12-19 09:48:17,762 INFO service.AbstractService (AbstractService.java:noteFailure(267)) - Service NodeManager failed in state INITED java.lang.RuntimeException: java.lang.RuntimeException: class org.apache.hadoop.yarn.server.resourcemanager.scheduler.fair.FairScheduler not org.apache.hadoop.yarn.server.nodemanager.ContainerExecutor at org.apache.hadoop.conf.Configuration.getClass(Configuration.java:2628) at org.apache.hadoop.yarn.server.nodemanager.NodeManager.createContainerExecutor(NodeManager.java:347) at org.apache.hadoop.yarn.server.nodemanager.NodeManager.serviceInit(NodeManager.java:389) at org.apache.hadoop.service.AbstractService.init(AbstractService.java:164) at org.apache.hadoop.yarn.server.nodemanager.NodeManager.initAndStartNodeManager(NodeManager.java:933) at org.apache.hadoop.yarn.server.nodemanager.NodeManager.main(NodeManager.java:1013) Caused by: java.lang.RuntimeException: class org.apache.hadoop.yarn.server.resourcemanager.scheduler.fair.FairScheduler not org.apache.hadoop.yarn.server.nodemanager.ContainerExecutor at org.apache.hadoop.conf.Configuration.getClass(Configuration.java:2622)
I successfully configured the fair scheduler on the below HDP version
Default capacity scheduler after deployment of HDP
Pre-emption enabled before the change to fair-scheduler
Grabbed the template fair-scheduler.xml fair-scheduler here I then changed a few values for testing purposes but ensured the is valid using the XML using XML Validator I then copied the fair-scheduler.xml to the $HADOOP_CONF directory and changed the user & permission
# cd /usr/hdp/188.8.131.52-78/hadoop/conf
# chown hdfs:hadoop fair-scheduler.xml
# chmod 644 fair-scheduler.xml
Changed the Scheduler class in the yarn-site.xml see the attached screenshot.
For my testing I didn't add the below properties, you will notice that above that despite disabling the pre-emption in the Ambari UI the fair schedule shows it's enabled [True] and my queues ain't showing I need to check my fair-scheduler.xml attached is the template I used
I have made some progress on this issue. I have modified the fair-scheduler.xml and have set both "maxAMShare" and "queueMaxAMShareDefault" to 0.8 and weight to default value (1.0).
The result: One spark job is running fine. However, I am getting the same error as before on the exceeding of maximum AM resources limit, when I try to run the next job.
The modified fair-scheduler.xml is given below. Please provide your inputs on how to fix this particular issue.
Also, one interesting observation is that, even though the YARN Scheduling mode is showing as "Fair", the Spark Scheduling mode is still showing as "FIFO". Can I set it to "Fair" as well through the program? Since I am setting spark.master as "YARN", I believe the Fair scheduling mode will take precedence over the Spark scheduling mode. Please correct me if I am wrong.
Without any intervention, newly submitted jobs go into a default pool, but jobs’ pools can be set by adding the spark.scheduler.pool “local property” to the SparkContext in the thread that’s submitting them. This is done as follows:
// Assuming sc is your SparkContext variable to pick the FAIR
Thanks for your inputs. If I understand correctly, there will be 2 fair-scheduler.xml files? One for YARN kept in $HADOOP_CONF_DIR and one more in $SPARK_HOME?
For fair-scheduler.xml belonging to Spark, how to configure the parameter in Ambari?
Also, the queueMaxAMShareDefault or maxAMShare value - earlier it was 0.5 only - but since it was not launching the jobs due to the AM resource exceeded error, I did set it to 0.8 - I will try setting it to 0.1 and will check it.
I went through your email again and tried out all the options that you have mentioned. But, I am still facing the same issue, while running the second job.
Please let me know if anything else needs to be set, or it is a pure memory related issue and kindly suggest on fixing this issue.
Here are the relevant screenshots:
Screenshot 1: YARN Fair Scheduler XML file (I tried setting maxAMShare to 0.1 - but the first spark job didn't start at all - so I had to bump it to 0.5)
Screenshot 2: Spark Fair Scheduler XML file (this is placed in $SPARK_HOME/conf directory, i.e /usr/hdp/184.108.40.206-78/spark2/conf)
Screenshot 3: Spark Configuration Parameters set through the pyspark program
Screenshot 4: YARN Cluster Information (Total number of VCORES is 6 and Total amount of memory present in 2 node cluster is 15.3 GB)
Note: Since this is a flask application, it will launch 2 jobs I believe, one to open the port at 5000 and another to accept the inputs. The whole idea behind this exercise is to test how many number of spark sessions can run at parallel in a single Spark Context.
Screenshot 5: This shows the percentage usage of queue as well as cluster by the first job. As we can see, there is sufficient space in both Cluster as well as Queue. But, for some reason, the second job never gets the required amount of resources. I know this could be because the fair-scheduler's maximum allocation is set to 3GB. Can you please let me know how to bump up this value. I am also curious here - even though the maxResources in fair-scheduler.xml file is set to 8 GB, the fair scheduler's maximum allocation is set to 3 GB only. Is it because of the value of maxAMShare?
Also, I am supplying both driver and executor memory to 512 MB only. How is my job occupying 3 GB of space?
Screenshot 6: This screenshot shows that the job 2 never gets the required amount of resources.