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Difference between NameNode heap usage and ResourceManager heap usage (trying to find NameNode heap usage cause)?


Difference between NameNode heap usage and ResourceManager heap usage (trying to find NameNode heap usage cause)?

Rising Star

What is the difference between NameNode heap usage and ResourceManager heap usage? I am trying to find heavy NameNode heap usage cause.

In the ambari dashboard, I see... enter image description here

when running some sqoop jobs. Not sure what is causing the NN usage to be so high here (not a lot of experience with hadoop admin stuff)?

Furthermore the sqoop jobs appear to be frozen after 100% completion of the mapreduce task for abnormal amount of time than usual, eg. seeing...

[2020-01-31 14:00:55,193]  INFO mapreduce.JobSubmitter: number of splits:12
[2020-01-31 14:00:55,402]  INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1579648183118_1085[2020-01-31 14:00:55,402]  INFO mapreduce.JobSubmitter: Executing with tokens: []
[2020-01-31 14:00:55,687]  INFO conf.Configuration: found resource resource-types.xml at file:/etc/hadoop/[2020-01-31 14:00:55,784]  INFO impl.YarnClientImpl: Submitted application application_1579648183118_1085[2020-01-31 14:00:55,837]  mapreduce.Job: The url to track the job: http://hw001.ucera.local:8088/proxy/application_1579648183118_1085/
[2020-01-31 14:00:55,837]  mapreduce.Job: Running job: job_1579648183118_1085[2020-01-31 14:01:02,964]  mapreduce.Job: Job job_1579648183118_1085 running in uber mode : false[2020-01-31 14:01:02,965]  mapreduce.Job:  map 0% reduce 0%
[2020-01-31 14:01:18,178]  mapreduce.Job:  map 8% reduce 0%
[2020-01-31 14:02:21,552]  mapreduce.Job:  map 17% reduce 0%
[2020-01-31 14:04:55,239]  mapreduce.Job:  map 25% reduce 0%
[2020-01-31 14:05:36,417]  mapreduce.Job:  map 33% reduce 0%
[2020-01-31 14:05:37,424]  mapreduce.Job:  map 42% reduce 0%
[2020-01-31 14:05:40,440]  mapreduce.Job:  map 50% reduce 0%
[2020-01-31 14:05:41,444]  mapreduce.Job:  map 58% reduce 0%
[2020-01-31 14:05:44,455]  mapreduce.Job:  map 67% reduce 0%
[2020-01-31 14:05:52,484]  mapreduce.Job:  map 75% reduce 0%
[2020-01-31 14:05:56,499]  mapreduce.Job:  map 83% reduce 0%
[2020-01-31 14:05:59,528]  mapreduce.Job:  map 92% reduce 0%
[2020-01-31 14:06:00,534] INFO mapreduce.Job:  map 100% reduce 0%

<...after some time longer than usual...>

[2020-01-31 14:10:05,446] INFO mapreduce.Job: Job job_1579648183118_1085 completed successfully

My hadoop version

[airflow@airflowetl root]$ hadoop version
Source code repository -r e4f82af51faec922b4804d0232a637422ec29e64
Compiled by jenkins on 2018-12-06T12:26Z
Compiled with protoc 2.5.0
From source with checksum eab9fa2a6aa38c6362c66d8df75774
This command was run using /usr/hdp/

Anyone with more hadoop experience know what could be going on here? Any debugging advice?


Re: Difference between NameNode heap usage and ResourceManager heap usage (trying to find NameNode heap usage cause)?

Super Mentor


The NameNode Java heap size depends on many factors such as the load on the cluster, the numbers of files, and the numbers of blocks.
NameNode loads the FSImage/block details ..etc information in memory. The heap size depends on many factors, such as the number of files, the number of blocks, and the load on the system

On the other hand for Resource Manager, In order to find out  the suitable value for the Resource Manager heap we can enable the GC logging for and Resource Manager and Similarly for the NodeManager daemons by adding some of the Java Options to enable GC logging for monitoring purpose '-XX:+PrintGCDateStamps -XX:+PrintGCDetails -Xloggc:$PATH/gc_%t_%p.log' to YARN_RESOURCEMANAGER_OPTS and YARN_NODEMANAGER_OPTS in etc/hadoop/

Some other factory that can affect ResourceManager heap memory may be like some properties values "yarn.resourcemanager.max-completed-applications" is a parameter which tells the RM to keep the number of completed jobs kept in RM memory and shown in RM GUI. By default it is 10000. So if a user customizes such properties then accordingly the RM heap will need to be adjusted.


Regarding MapReduce job execution, You might want to tune some of the settings like following:

"" The amount of physical memory that your YARN map process can use.
"mapreduce.reduce.memory.mb" The amount of physical memory that your YARN reduce process can use.
"" Used to configure the heap size for the map JVM process. Should be 80% of
"" Used to configure the heap size for the reduce JVM process. Should be 80% of mapreduce.reduce.memory.mb.
"" Sets the memory requested for the application master container to the value in MB. Defaults to 1536.

For more detail on this please refer to : "Using Resources With MapReduce" section in the following link:

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