I have a very small 2 node Hadoop-HBase cluster. I am executing MapReduce jobs on it. I use Hadoop-2.5.2. I have 32GB(nodes have 64GB memory each) free for MapReduce in each node with the configuration in yarn site as follows
<property>
<name>yarn.nodemanager.resource.memory-mb</name>
<value>32768</value>
</property>
<property>
<name>yarn.nodemanager.resource.cpu-vcores</name>
<value>15</value>
</property>
My resource requirements are 2GB for each mapper/reducer that gets executed. I have configured this in the mapred-site.xml Given these configurations, with a total of about 64GB in memory and 30 vcores, I see about 31 mappers or 31 reducers getting executed in parallel.
While all this is fine, there is one part that I am trying to figure out. The number of mappers or reducers executing in parallel, is not the same on both nodes, one of the nodes has higher number of tasks than the other. Why does this happen? Can this be controlled? If so, how?
I suppose YARN does not see this as resources of a node rather resources of a cluster and spawns the tasks wherever it can in the cluster. Is this understanding correct? If not, what is the correct explanation to the said behaviour during a MR execution?
I have a simple job workflow which executes a mapreduce job as a shell action. After submitting the job, its status becomes Running and it stays there but never ends. The mapreduce cluster shows that there are two jobs running, one belongs to the shell application launcher and one for the actual mapreduce job. However the one for the mapreduce job is shown as UNASSIGNED and the progress is zero (which means it has been started yet).
Interestingly when I kill the oozie job, the mapreduce job actually starts running and completes successfully. It looks like the shell launcher is blocking it.
p.s. It is a simple workflow and there is no start or end date that may cause it wait.
Please consider the below case as per you memory resource
Number of container are dependent on the number of blocksize. if you have 2 GB data of 512 mb block size, Yarn creates 4 maps and 1 reduce. While running the mapreduce we should follow some rules to submit the mapreduce job.(this should be applicable for small cluster)
You should configure the below property asper you RAM DISK and CORES.
<property>
<description>The minimum allocation for every container request at the RM,
in MBs. Memory requests lower than this won't take effect,
and the specified value will get allocated at minimum.</description>
<name>yarn.scheduler.minimum-allocation-mb</name>
<value>512</value>
</property>
<property>
<description>The maximum allocation for every container request at the RM,
in MBs. Memory requests higher than this won't take effect,
and will get capped to this value.</description>
<name>yarn.scheduler.maximum-allocation-mb</name>
<value>2048</value>
</property>
<property>
<name>yarn.nodemanager.resource.memory-mb</name>
<value>2048</value>
</property>
And set the Java heap size as per the Memory Resources. Once ensured with the above property in yarn-site.xml according the mapreduce will succeed efficiently.
When a job stuck in "UNASSIGNED" state, it usually means resource manager(RM) can't allocate container to the job.
Check the capacity configure for the user and queue. Giving them more capacity should help.
With Hadoop 2.7 and capacity scheduler, specifically, the following properties need to be examined:
yarn.scheduler.capacity.<queue-path>.capacity
yarn.scheduler.capacity.<queue-path>.user-limit-factor
yarn.scheduler.capacity.maximum-applications
/ yarn.scheduler.capacity.<queue-path>.maximum-applications
yarn.scheduler.capacity.maximum-am-resource-percent
/ yarn.scheduler.capacity.<queue-path>.maximum-am-resource-percent
see more details on those properties at
Hadoop: Capacity Scheduler - Queue Properties
I have a Hadoop cluster running on 2 nodes(master and slave) each of which have
126GB RAM and 32 CPU.
when I run my cluster i am only able to see 8 GB memory per node. How do I increase this? What would be the optimal memory to be allocated per node and how to do it?
This blog post will give you a ton of help; http://hortonworks.com/blog/how-to-plan-and-configure-yarn-in-hdp-2-0/
You might have to tell Hadoop what parameters to use when launching the JVMs or else it will use your Java implementation default values.
In your mapred-site.xml, you can add this mapred.child.java.opts field to specify the memory size to use for the JVMs.
<property>
<name>mapred.child.java.opts</name>
<value>-Xmx16000m</value>
</property>
Where 16000 is the number of MB you want to allocate to each JVM.
I hope it helps!
Source
I am running some map reduce tasks on hadoop. The mapper is used to generate data and hence does not depend upon the hdfs block placement. To test my system I am using 2 nodes and one master node. I am doing my testing on hadoop-2.0 with yarn.
There is something very uncomfortable that I find with hadoop. I have configured it to run 8 maps tasks. Unfortunately hadoop is launching all the 8 map tasks on one node, and the other node is almost ideal. There are 4 reducers, and it does not balance these reducers too. It really results in a poor performance when that happens.
I have these properties set in mapred-site.xml in both the job tracker and task tracker
<property>
<name>mapreduce.tasktracker.map.tasks.maximum</name>
<value>2</value>
</property>
<property>
<name>mapreduce.tasktracker.reduce.tasks.maximum</name>
<value>2</value>
</property>
Can some one explain if this problem can be solved or why does such problem exists with hadoop?
Don't think of mappers/reducers as one to one with servers. What it sounds like is happening is your system knows that the load is so low their is no need to launch reducers across the cluster. It is trying to avoid the network overhead of transfering files from master to the slave nodes.
Think of the number of mappers and reducers as how many concurrent threads you will allow your cluster to run. This is important when determing how much memory to allocate for each mapper/reducer.
To force an even distrubtion you could try allocating enough memory for each mapper/reducer to make it require a whole node. For example, 4 nodes, 8 mappers. Force each mapper to have 50% of the ram on each node. Not sure if this will work as expected, but really Hadoop load balancing itself is something good in theory, but might not seem that way for small data situations.
In Hadoop v1, I have assigned each 7 mapper and reducer slot with size of 1GB, my mappers & reducers runs fine. My machine has 8G memory, 8 processor.
Now with YARN, when run the same application on the same machine, I got container error.
By default, I have this settings:
<property>
<name>yarn.scheduler.minimum-allocation-mb</name>
<value>1024</value>
</property>
<property>
<name>yarn.scheduler.maximum-allocation-mb</name>
<value>8192</value>
</property>
<property>
<name>yarn.nodemanager.resource.memory-mb</name>
<value>8192</value>
</property>
It gave me error:
Container [pid=28920,containerID=container_1389136889967_0001_01_000121] is running beyond virtual memory limits. Current usage: 1.2 GB of 1 GB physical memory used; 2.2 GB of 2.1 GB virtual memory used. Killing container.
I then tried to set memory limit in mapred-site.xml:
<property>
<name>mapreduce.map.memory.mb</name>
<value>4096</value>
</property>
<property>
<name>mapreduce.reduce.memory.mb</name>
<value>4096</value>
</property>
But still getting error:
Container [pid=26783,containerID=container_1389136889967_0009_01_000002] is running beyond physical memory limits. Current usage: 4.2 GB of 4 GB physical memory used; 5.2 GB of 8.4 GB virtual memory used. Killing container.
I'm confused why the the map task need this much memory. In my understanding, 1GB of memory is enough for my map/reduce task. Why as I assign more memory to container, the task use more? Is it because each task gets more splits? I feel it's more efficient to decrease the size of container a little bit and create more containers, so that more tasks are running in parallel. The problem is how can I make sure each container won't be assigned more splits than it can handle?
You should also properly configure the maximum memory allocations for MapReduce. From this HortonWorks tutorial:
[...]
Each machine in our cluster has 48 GB of RAM. Some of this RAM should be >reserved for Operating System usage. On each node, we’ll assign 40 GB RAM for >YARN to use and keep 8 GB for the Operating System
For our example cluster, we have the minimum RAM for a Container
(yarn.scheduler.minimum-allocation-mb) = 2 GB. We’ll thus assign 4 GB
for Map task Containers, and 8 GB for Reduce tasks Containers.
In mapred-site.xml:
mapreduce.map.memory.mb: 4096
mapreduce.reduce.memory.mb: 8192
Each Container will run JVMs for the Map and Reduce tasks. The JVM
heap size should be set to lower than the Map and Reduce memory
defined above, so that they are within the bounds of the Container
memory allocated by YARN.
In mapred-site.xml:
mapreduce.map.java.opts: -Xmx3072m
mapreduce.reduce.java.opts: -Xmx6144m
The above settings configure the upper limit of the physical RAM that
Map and Reduce tasks will use.
To sum it up:
In YARN, you should use the mapreduce configs, not the mapred ones. EDIT: This comment is not applicable anymore now that you've edited your question.
What you are configuring is actually how much you want to request, not what is the max to allocate.
The max limits are configured with the java.opts settings listed above.
Finally, you may want to check this other SO question that describes a similar problem (and solution).
There is a check placed at Yarn level for Virtual and Physical memory usage ratio.
Issue is not only that VM doesn't have sufficient physical memory. But it is because Virtual memory usage is more than expected for given physical memory.
Note : This is happening on Centos/RHEL 6 due to its aggressive allocation of virtual memory.
It can be resolved either by :
Disable virtual memory usage check by setting
yarn.nodemanager.vmem-check-enabled to false;
Increase VM:PM ratio by setting yarn.nodemanager.vmem-pmem-ratio to some higher value.
References :
https://issues.apache.org/jira/browse/HADOOP-11364
http://blog.cloudera.com/blog/2014/04/apache-hadoop-yarn-avoiding-6-time-consuming-gotchas/
Add following property in yarn-site.xml
<property>
<name>yarn.nodemanager.vmem-check-enabled</name>
<value>false</value>
<description>Whether virtual memory limits will be enforced for containers</description>
</property>
<property>
<name>yarn.nodemanager.vmem-pmem-ratio</name>
<value>4</value>
<description>Ratio between virtual memory to physical memory when setting memory limits for containers</description>
</property>
I had a really similar issue using HIVE in EMR. None of the extant solutions worked for me -- ie, none of the mapreduce configurations worked for me; and neither did setting yarn.nodemanager.vmem-check-enabled to false.
However, what ended up working was setting tez.am.resource.memory.mb, for example:
hive -hiveconf tez.am.resource.memory.mb=4096
Another setting to consider tweaking is yarn.app.mapreduce.am.resource.mb
I can't comment on the accepted answer, due to low reputation. However, I would like to add, this behavior is by design. The NodeManager is killing your container. It sounds like you are trying to use hadoop streaming which is running as a child process of the map-reduce task. The NodeManager monitors the entire process tree of the task and if it eats up more memory than the maximum set in mapreduce.map.memory.mb or mapreduce.reduce.memory.mb respectively, we would expect the Nodemanager to kill the task, otherwise your task is stealing memory belonging to other containers, which you don't want.
While working with spark in EMR I was having the same problem and setting maximizeResourceAllocation=true did the trick; hope it helps someone. You have to set it when you create the cluster. From the EMR docs:
aws emr create-cluster --release-label emr-5.4.0 --applications Name=Spark \
--instance-type m3.xlarge --instance-count 2 --service-role EMR_DefaultRole --ec2-attributes InstanceProfile=EMR_EC2_DefaultRole --configurations https://s3.amazonaws.com/mybucket/myfolder/myConfig.json
Where myConfig.json should say:
[
{
"Classification": "spark",
"Properties": {
"maximizeResourceAllocation": "true"
}
}
]
We also faced this issue recently. If the issue is related to mapper memory, couple of things I would like to suggest that needs to be checked are.
Check if combiner is enabled or not? If yes, then it means that reduce logic has to be run on all the records (output of mapper). This happens in memory. Based on your application you need to check if enabling combiner helps or not. Trade off is between the network transfer bytes and time taken/memory/CPU for the reduce logic on 'X' number of records.
If you feel that combiner is not much of value, just disable it.
If you need combiner and 'X' is a huge number (say millions of records) then considering changing your split logic (For default input formats use less block size, normally 1 block size = 1 split) to map less number of records to a single mapper.
Number of records getting processed in a single mapper. Remember that all these records need to be sorted in memory (output of mapper is sorted). Consider setting mapreduce.task.io.sort.mb (default is 200MB) to a higher value if needed. mapred-configs.xml
If any of the above didn't help, try to run the mapper logic as a standalone application and profile the application using a Profiler (like JProfiler) and see where the memory getting used. This can give you very good insights.
Running yarn on Windows Linux subsystem with Ubunto OS, error "running beyond virtual memory limits, Killing container"
I resolved it by disabling virtual memory check in the file yarn-site.xml
<property> <name>yarn.nodemanager.vmem-check-enabled</name> <value>false</value> </property>
I am practicing Hadoop programs (version hadoop3). Via virtual box I have installed Linux OS. We allocate very limited memory at time of installation of Linux.
By setting the following memory limit properties in mapred-site.xml and restarting your HDFS and YARN then my program worked.
<property>
<name>mapreduce.map.memory.mb</name>
<value>4096</value>
</property>
<property>
<name>mapreduce.reduce.memory.mb</name>
<value>4096</value>
</property>
I haven't personally checked, but hadoop-yarn-container-virtual-memory-understanding-and-solving-container-is-running-beyond-virtual-memory-limits-errors sounds very reasonable
I solved the issue by changing yarn.nodemanager.vmem-pmem-ratio to a higher value , and I would agree that:
Another less recommended solution is to disable the virtual memory check by setting yarn.nodemanager.vmem-check-enabled to false.