But he didn't say he had a "5120MB Available Node Size." He said he had
a 512MiB (i.e., half a GiB) of RAM per node.
On 8/15/19 7:50 AM, Prabhu Josephraj wrote:
YARN allocates based on the configuration
(yarn.nodemanager.resource.memory-mb) user has configured. It has
allocated
the AM Container of size 1536MB as it can fit in 5120MB Available Node
Size.
yarn.nodemanager.pmem-check-enabled will kill the container if the
physical memory usage of the container process is above
1536MB. MR ApplicationMaster for a pi job is light weight and it won't
require that much memory and so not got killed.
On Thu, Aug 15, 2019 at 4:02 PM . .
<writeme...@googlemail.com.invalid> wrote:
Correct:?? I set 'yarn.nodemanager.resource.memory-mb' ten times
the node physical memory (512MB) and I was able to successfully
execute a?? 'pi 1 10' mapreduce job.
Since default 'yarn.app.mapreduce.am.resource.mb' value is 1536MB
I expected the job to never start / be allocated and I have no
valid explanation.
On Wed, Aug 14, 2019 at 10:32 PM . . <writeme...@googlemail.com
<mailto:writeme...@googlemail.com>> wrote:
Correct:?? I set 'yarn.nodemanager.resource.memory-mb' ten
times the node physical memory (512MB) and I was able to
successfully execute a?? 'pi 1 10' mapreduce job.
Since default 'yarn.app.mapreduce.am.resource.mb' value is
1536MB I expected the job to never start / be allocated and I
have no valid explanation.
On Wed, Aug 14, 2019 at 8:31 PM Jeff Hubbs <jhubbsl...@att.net
<mailto:jhubbsl...@att.net>> wrote:
To make sure I understand...you've allocated /ten times/
your physical RAM for containers? If so, I think that's
your issue.
For reference, under Hadoop 3.x I didn't have a cluster
that would really do anything until its worker nodes had
at least 8GiB.
On 8/14/19 12:10 PM, . . wrote:
Hi all,
I installed a basic 3 nodes Hadoop 2.9.1 cluster and
playing with YARN settings.
The 3 nodes has following configuration:
1 cpu / 1 core?? / 512MB RAM
I wonder I was able to configure yarn-site.xml with
following settings (higher than node physical limits) and
successfully run a mapreduce 'pi 1 10' job
quote...
?? <property>
?? ?? ??
<name>yarn.resourcemanager.scheduler.class</name><value>org.apache.hadoop.yarn.server.resourcemanager.scheduler.fair.FairScheduler</value>
</property>
?? ?? <property>
?? ?? ?? ?? <name>yarn.nodemanager.resource.memory-mb</name>
?? ?? ?? ?? <value>5120</value>
?? ?? ?? ?? <description>Amount of physical memory, in
MB, that can be allocated for containers. If set to -1
and
yarn.nodemanager.resource.detect-hardware-capabilities is
true, it is automatically calculated. In other cases, the
default is 8192MB</description>
?? ?? </property>
?? ?? <property>
?? ?? ?? ?? <name>yarn.nodemanager.resource.cpu-vcores</name>
?? ?? ?? ?? <value>6</value>
?? ?? ?? ?? <description>Number of CPU cores that can be
allocated for containers.</description>
?? ?? </property>
...unquote
Can anyone provide an explanation please?
Should 'yarn.nodemanager.vmem-check-enabled' and
'yarn.nodemanager.pmem-check-enabled' properties (set to
'true' as default) check that my YARN settings are higher
than physical limits?
Which mapreduce 'pi' job settings can I use, to 'force'
containers to use more than node physical resources?
Many thanks in advance!
Guido
