If a YARN container grows beyond its heap size setting, the map or reduce task will fail, with an error similar to the one below:
2015-02-06 11:58:15,461 WARN org.apache.hadoop.yarn.server.nodemanager.containermanager.monitor.ContainersMonitorImpl: Container [pid=10305,containerID=container_1423215865404_0002_01_000007] is running beyond physical memory limits.
Current usage: 42.1 GB of 42 GB physical memory used; 42.9 GB of 168 GB virtual memory used. Killing container.
Dump of the process-tree for container_1423215865404_0002_01_000007 :
|- PID PPID PGRPID SESSID CMD_NAME USER_MODE_TIME(MILLIS) SYSTEM_TIME(MILLIS) VMEM_USAGE(BYTES) RSSMEM_USAGE(PAGES) FULL_CMD_LINE
|- 10310 10305 10305 10305 (java) 1265097 48324 46100516864 11028122 /usr/java/default/bin/java -server -XX:OnOutOfMemoryError=kill %p -Xms40960m -Xmx40960m -XX:MaxPermSize=128m -Dspark.sql.shuffle.partitions=20 -Djava.io.tmpdir=/data/yarn/datanode/nm-local-dir/usercache/admin/appcache/application_1423215865404_0002/container_1423215865404_0002_01_000007/tmp org.apache.spark.executor.CoarseGrainedExecutorBackend akka.tcp://sparkDriver#marx-61:56138/user/CoarseGrainedScheduler 6 marx-62 5
|- 10305 28687 10305 10305 (bash) 0 0 9428992 318 /bin/bash -c /usr/java/default/bin/java -server -XX:OnOutOfMemoryError='kill %p' -Xms40960m -Xmx40960m -XX:MaxPermSize=128m -Dspark.sql.shuffle.partitions=20 -Djava.io.tmpdir=/data/yarn/datanode/nm-local-dir/usercache/admin/appcache/application_1423215865404_0002/container_1423215865404_0002_01_000007/tmp org.apache.spark.executor.CoarseGrainedExecutorBackend akka.tcp://sparkDriver#marx-61:56138/user/CoarseGrainedScheduler 6 marx-62 5 1> /opt/hadoop/logs/userlogs/application_1423215865404_0002/container_1423215865404_0002_01_000007/stdout 2> /opt/hadoop/logs/userlogs/application_1423215865404_0002/container_1423215865404_0002_01_000007/stderr
It is interesting to note that all stages complete, just when save as sequence file is called, it fails. The executor is not using up the heap space, wonder what else is eating it up?
Spark executor gets killed all the time and Spark keeps retrying the failed stage. For Spark on YARN, nodemanager would kill Spark executor if it used more memory than the configured size of "spark.executor.memory" + "spark.yarn.executor.memoryOverhead". Increase "spark.yarn.executor.memoryOverhead" to make sure it covers the executor off-heap memory usage.
Some issues:
https://issues.apache.org/jira/browse/SPARK-2398
https://issues.apache.org/jira/browse/SPARK-2468
You are actually running the container out of physical memory in this case:
Current usage: 42.1 GB of 42 GB physical memory used
The virtual memory isn't the bounding factor. You'll have to increase the heap size of the container or increase spark.yarn.executor.memoryOverhead to give some more space to the YARN container without increasing the executor heap size necessarily.
I faced exact same problem as OP, all stages succeeded and only at the time of saving and writing the results, the container would be killed.
If java heap memory is exceeded, you see OutOfMemory exceptions but a container being killed is related to everything except java heap memory, which can be either related to memoryOverhead or application master memory.
In my case increasing spark.yarn.executor.memoryOverhead or spark.yarn.driver.memoryOverhead didn't help because probably it was my application master (AM) getting out of memory. In yarn-client mode, the configuration to increase AM memory is spark.yarn.am.memory. For yarn-cluster mode, it is the driver memory. This is how it worked for me.
Here's a reference to the error I got:
Application application_1471843888557_0604 failed 2 times due to AM Container for appattempt_1471843888557_0604_000002 exited with exitCode: -104
For more detailed output, check application tracking page:http://master01.prod2.everstring.com:8088/cluster/app/application_1471843888557_0604Then, click on links to logs of each attempt.
Diagnostics: Container [pid=89920,containerID=container_e59_1471843888557_0604_02_000001] is running beyond physical memory limits.
Current usage: 14.0 GB of 14 GB physical memory used; 16.0 GB of 29.4 GB virtual memory used. Killing container.
Related
I have a 6 node cluster - 5 DN and 1 NN. All have 32 GB RAM. All slaves have 8.7 TB HDD. DN has 1.1 TB HDD. Here is the link to my core-site.xml , hdfs-site.xml , yarn-site.xml.
After running an MR job, i checked my RAM Usage which is mentioned below:
Namenode
free -g
total used free shared buff/cache available
Mem: 31 7 15 0 8 22
Swap: 31 0 31
Datanode :
Slave1 :
free -g
total used free shared buff/cache available
Mem: 31 6 6 0 18 24
Swap: 31 3 28
Slave2:
total used free shared buff/cache available
Mem: 31 2 4 0 24 28
Swap: 31 1 30
Likewise, other slaves have similar RAM usage. Even if a single job is submitted, the other submitted jobs enter into ACCEPTED state and wait for the first job to finish and then they start.
Here is the output of ps command of the JAR that I submnitted to execute the MR job:
/opt/jdk1.8.0_77//bin/java -Dproc_jar -Xmx1000m
-Dhadoop.log.dir=/home/hduser/hadoop/logs -Dyarn.log.dir=/home/hduser/hadoop/logs
-Dhadoop.log.file=yarn.log -Dyarn.log.file=yarn.log
-Dyarn.home.dir= -Dyarn.id.str= -Dhadoop.root.logger=INFO,console
-Dyarn.root.logger=INFO,console -Dyarn.policy.file=hadoop-policy.xml
-Dhadoop.log.dir=/home/hduser/hadoop/logs -Dyarn.log.dir=/home/hduser/hadoop/logs
-Dhadoop.log.file=yarn.log -Dyarn.log.file=yarn.log
-Dyarn.home.dir=/home/hduser/hadoop -Dhadoop.home.dir=/home/hduser/hadoop
-Dhadoop.root.logger=INFO,console -Dyarn.root.logger=INFO,console
-classpath --classpath of jars
org.apache.hadoop.util.RunJar abc.jar abc.mydriver2 /raw_data /mr_output/02
Is there any settings that I can change/add to allow multiple jobs to run simultaneously and speed up current data processing ? I am using hadoop 2.5.2. The cluster is in PROD environment and I can not take it down for updating hadoop version.
EDIT 1 : I started a new MR job with 362 GB of data and still the RAM usage is around 8 GB and 22 GB of RAM is free. Here is my job submission command -
nohup yarn jar abc.jar def.mydriver1 /raw_data /mr_output/01 &
Here is some more information :
18/11/22 14:09:07 INFO input.FileInputFormat: Total input paths to process : 130363
18/11/22 14:09:10 INFO mapreduce.JobSubmitter: number of splits:130372
Is there some additional memory parameters that we can use to submit the job to have efficient memory usage ?
I believe you can edit the mapred-default.xml
The Params you are looking for are
mapreduce.job.running.map.limit
mapreduce.job.running.reduce.limit
0 (Probably what it is set too at the moment) means UNLIMITED.
Looking at your Memory 32G/Machine seems too small.
What CPU/Cores are you having ? I would expect Quad CPU/16 Cores Minimum. Per Machine.
Based on your yarn-site.xml your yarn.scheduler.minimum-allocation-mb setting of 10240 is too high. This effectively means you only have at best 18 vcores available. This might be the right setting for a cluster where you have tons of memory but for 32GB it's way too large. Drop it to 1 or 2GB.
Remember, HDFS block sizes are what each mapper typically consumes. So 1-2GB of memory for 128MB of data sounds more reasonable. The added benefit is you could have up to 180 vcores available which will process jobs 10x faster than 18 vcores.
To give you an idea of how a 4 node 32 core 128GB RAM per node cluster is setup:
For Tez: Divide RAM/CORES = Max TEZ Container size
So in my case: 128/32 = 4GB
TEZ:
YARN:
When I start H2o on a cdh cluster I get the following error. I downloaded everything formt he wbesite and followed the tutorial. The command I ran was
hadoop jar h2odriver.jar -nodes 2 -mapperXmx 1g -output hdfsOutputDirName
It shows that containers are not being used. It's not clear what settings these would be on hadoop. I have given all settings memory. It's the 0.0 for memory that doesnt make sense, and why are the containers not using memory. Is the cluster even running now?
----- YARN cluster metrics -----
Number of YARN worker nodes: 3
----- Nodes -----
Node: http://data-node-3:8042 Rack: /default, RUNNING, 1 containers used, 1.0 / 6.0 GB used, 1 / 4 vcores used
Node: http://data-node-1:8042 Rack: /default, RUNNING, 0 containers used, 0.0 / 6.0 GB used, 0 / 4 vcores used
Node: http://data-node-2:8042 Rack: /default, RUNNING, 0 containers used, 0.0 / 6.0 GB used, 0 / 4 vcores used
----- Queues -----
Queue name: root.default
Queue state: RUNNING
Current capacity: 0.00
Capacity: 0.00
Maximum capacity: -1.00
Application count: 0
Queue 'root.default' approximate utilization: 0.0 / 0.0 GB used, 0 / 0 vcores used
----------------------------------------------------------------------
WARNING: Job memory request (2.2 GB) exceeds queue available memory capacity (0.0 GB)
WARNING: Job virtual cores request (2) exceeds queue available virtual cores capacity (0)
----------------------------------------------------------------------
For YARN users, logs command is 'yarn logs -applicationId application_1462681033282_0008'
You should setup your default queue to have available resources to run 2nodes cluster.
See warnings:
WARNING: Job memory request (2.2 GB) exceeds queue available memory capacity (0.0 GB)
you ask 1GB per node (+overhead) but there is no available resources in the YARN queue
WARNING: Job virtual cores request (2) exceeds queue available virtual cores capacity (0)
you ask for 2 virtual cores but no cores are available in your default queue
Please check YARN documentation - for example setup of capacity scheduler and max available resources:
https://hadoop.apache.org/docs/r2.4.1/hadoop-yarn/hadoop-yarn-site/CapacityScheduler.html
I made the following changes in Cloudera Manager yarn configuration
Setting Value
yarn.scheduler.maximum-allocation-vcores 8
yarn.nodemanager.resource.cpu-vcores 4
yarn.nodemanager.resource.cpu-vcores 4
yarn.scheduler.maximum-allocation-mb 16 GB
I want to ask. I can say for example I have 10 MB memory on each node after I activate start-all.sh process. So, I run the namenode, datanode, secondary namenode, dll. But after I've done the hadoop mapreduce job, why the memory for example decrease to 5 MB for example. Whereas, the hadoop mapreduce job has done.
How can it back to the 10 MB free memory? Thanks all....
Maybe you can try the linux clear memory command :
echo 3 > /proc/sys/vm/drop_caches
I want to ask about hadoop YARN. For example before I start the daemon I have free memory "X" MB, so after I do start-all.sh to start the daemon, my free memory become "Y" MB. So I want to know in particular what service or something that consume my node memory?
Thanks all....
start-all.sh script starts the HDFS daemons: NameNode and DataNode, and YARN daemons: ResourceManager and Nodemanager. Enter the "jps" command, this will list all the running java processes. These daemons consume your CPU Memory.
I am running a micro instace in EC2 with 592 MB available RAM
Jenkins was crashing due to Out Of Memory build errors while running UPDATE on big SQL Table in backend.
Disk utilisation is 83% with 6 GB out of 8GB EBS volume used ..
sudo du -hsx * | sort -rh | head -10
/
2.7G opt
1.5G var
1.2G usr
I found only 6 MB was free with command - "free -m " with these services running -
(i) LAMPP
(ii) Jenkins
(iii) Mysql 5.6
I stopped LAMPP and that created 70 MB free space
Then , I closed Jenkins, it created 320 MB free space
Closing MySQL 5.6 brings it up to 390 MB free space ..
So, 200MB RAM is still getting used with none of my services running.
Is 200MB RAM minimum required for an Ubuntu micro Instance running on Amazon EC2 ?
Nope, i believe it can run till its 100% used.
If a task that requires a large memory than what is available, the task is killed.
To free up more memory space, you can run this from your terminal
sudo apt-get autoremove