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

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...

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)? Is this an unusual amount (only noticed recently)?

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/3.1.0.0-78/0/resource-types.xml
[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
Hadoop 3.1.1.3.1.0.0-78
Source code repository git@github.com:hortonworks/hadoop.git -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/3.1.0.0-78/hadoop/hadoop-common-3.1.1.3.1.0.0-78.jar

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

Namenode heap is mostly determined by the number of file blocks that are stored in HDFS. In particular, many small files or many files being written at once would cause a large heap.

The ResourceManager is not correlated with the namenode. It's heap would be determinate on the number of YARN jobs that are actively being tracked

In a cluster I've maintained, the namenode heap was 32G, and I think the ResourceManager was only 8GB