I have a java program running in centos Box.
My -Xmx and -Xms set to 4000 Mb.
The program works fine.
But when i do free -m , the used memory is showing as 506 MB. As per my understanding , XMS memory should be reserved for JVM.Why does free command not showing the java used memory ?
I have also done jstat -gccapacity $(pidof java) and there NGCMN and NGCMX updated and have the same value ?
Any support would be helpful.
I'm running my program as java -Xms41000m -Xmx42000m -jar
Even when -Xmx and -Xms set to the same value, the space reserved for Java Heap is not immediately allocated in RAM.
Operating System typically allocates physical memory lazily, only on the first access to a virtual page. So, while unused part of Java Heap is not touched, it won't really consume memory.
You may use -XX:+AlwaysPreTouch option to forcibly touch all heap pages on JVM start.
Related
I am getting the error message such as stack overflow, heap memory error and similar messages after trying to use TestNG. And after installing TestNg the Eclipse feels heavy and became very slow to respond. and throwing this error message.
An error of such kind Stackoverflow and heap memory error occurs because of physical lack of resources such as lower Ram or slower processor. So the only solution to this was to allocate more memory to eclipse IDE. you can allocate more memory to eclipse by finding the eclipse.ini file in your directory where you have installed it. After finding the file, the file should be in notepad. open the file in notepad and edit the memory allocated. there are two things you need to change. Xms and XMX, which is minimum and max memory. I made mine from 256m to 512m for XMS and from 1024m to 2048M. But make sure you allocate only the memory which is spare. otherwise your PC might crash. Hope this helps.
My test execution shows "gc memory overhead exceeded" exception in linux cent os 7. I changed jmeter.bat's heap max size 6g and min size as 512m. I am not used any listeners, preprocessor, http header manager. Used regular expression extractor for 2 samplers and constant timer as common. I run my test in terminal and store result in jtl file. I run it for 250 users, rampup period as 1 and scheduler as 5400 seconds. But still issue persist..
System configuration:
Ram 8 GB
CPU octa core 3.12 GHz
Swap memory 16 GB
You say that you changed jmeter.bat, but the problem is on Linux, which doesn't use jmeter.bat. Unless it's a typo, try to change jmeter or jmeter.sh (whichever one you use to invoke JMeter).
Generally I would not recommend more than 2GB for moderate use, and 4GB for heavy use. For instance my settings are:
HEAP="-Xms4096m -Xmx4096m"
and I can run up to 300 concurrent users with a lot of samplers/heavy scripting even in GUI mode. Setting larger heap may cause larger pauses on GC, which can cause the exception you are getting.
After you start JMeter, run the following command to make sure the memory settings are indeed as you expect them to be:
ps -ef | grep JMeter
I actually changed Xmx in jmeter.bat file instead of jmeter.sh file since i used linux for this test. Jmeter.bat is supported in windows os and jmeter.sh is supported for Linux os. So that the above mentioned error occurred. Once I changed it in jmeter.sh file it works perfectly.
I'm calling a bunch of shell programs from a program running on the JVM. When I execute a program like imagemagick, does it use my system's RAM or does it use RAM allocated to the JVM ?
If you run (shell) executables, they will use their own memory space in system RAM.
If you instead load DLLs using for example JNI and execute that code, they will be loaded into Java's memory space.
As per my knowledge, JVM is a process virtual machine which means it does not emulate the entire existing computer architechture but emulates/mimics only the cpu of the host computer.
Now, my question is:
Why a .class java file needs to be executed inside virtual CPU(i.e. JVM) instead of being executed on actual CPU memory of the host computer?
For code to run on the actual CPU, it has to be in the instruction set of that CPU. Each CPU architecture has its own, distinct instruction set, so code written for one CPU won't run on another type of CPU.
The point of defining a Java Virtual Machine is so that the code will run on any type of computer, as long as it has a JVM interpreter.
The JVM instructions are not real CPU instructions but are for an abstract CPU.
Add to that some security proofs on the JVM byte code.
The JVM implementation's Just in Time compiler will translate abstract instructions to host CPU instructions on demand to achieve better performance.
JVM actually conversts the java bytecode to the instruction set applicable to that particular CPU.Every CPU do not have similar instruction set.
So .class file is generated as it can run on any CPU. JVM does the task of converting onto machin code applicable to it.
I would like to know the relation between the mapreduce.map.memory.mb and mapred.map.child.java.opts parameters.
Is mapreduce.map.memory.mb > mapred.map.child.java.opts?
mapreduce.map.memory.mb is the upper memory limit that Hadoop allows to be allocated to a mapper, in megabytes. The default is 512.
If this limit is exceeded, Hadoop will kill the mapper with an error like this:
Container[pid=container_1406552545451_0009_01_000002,containerID=container_234132_0001_01_000001]
is running beyond physical memory limits. Current usage: 569.1 MB of
512 MB physical memory used; 970.1 MB of 1.0 GB virtual memory used.
Killing container.
Hadoop mapper is a java process and each Java process has its own heap memory maximum allocation settings configured via mapred.map.child.java.opts (or mapreduce.map.java.opts in Hadoop 2+).
If the mapper process runs out of heap memory, the mapper throws a java out of memory exceptions:
Error: java.lang.RuntimeException: java.lang.OutOfMemoryError
Thus, the Hadoop and the Java settings are related. The Hadoop setting is more of a resource enforcement/controlling one and the Java is more of a resource configuration one.
The Java heap settings should be smaller than the Hadoop container memory limit because we need reserve memory for Java code. Usually, it is recommended to reserve 20% memory for code. So if settings are correct, Java-based Hadoop tasks should never get killed by Hadoop so you should never see the "Killing container" error like above.
If you experience Java out of memory errors, you have to increase both memory settings.
The following properties let you specify options to be passed to the JVMs running your tasks. These can be used with -Xmx to control heap available.
Hadoop 0.x, 1.x (deprecated) Hadoop 2.x
------------------------------- --------------------------
mapred.child.java.opts
mapred.map.child.java.opts mapreduce.map.java.opts
mapred.reduce.child.java.opts mapreduce.reduce.java.opts
Note there is no direct Hadoop 2 equivalent for the first of these; the advice in the source code is to use the other two. mapred.child.java.opts is still supported (but is overridden by the other two more-specific settings if present).
Complementary to these, the following let you limit total memory (possibly virtual) available for your tasks - including heap, stack and class definitions:
Hadoop 0.x, 1.x (deprecated) Hadoop 2.x
------------------------------- --------------------------
mapred.job.map.memory.mb mapreduce.map.memory.mb
mapred.job.reduce.memory.mb mapreduce.reduce.memory.mb
I suggest setting -Xmx to 75% of the memory.mb values.
In a YARN cluster, jobs must not use more memory than the server-side config yarn.scheduler.maximum-allocation-mb or they will be killed.
To check the defaults and precedence of these, see JobConf and MRJobConfig in the Hadoop source code.
Troubleshooting
Remember that your mapred-site.xml may provide defaults for these settings. This can be confusing - e.g. if your job sets mapred.child.java.opts programmatically, this would have no effect if mapred-site.xml sets mapreduce.map.java.opts or mapreduce.reduce.java.opts. You would need to set those properties in your job instead, to override the mapred-site.xml. Check your job's configuration page (search for 'xmx') to see what values have been applied and where they have come from.
ApplicationMaster memory
In a YARN cluster, you can use the following two properties to control the amount of memory available to your ApplicationMaster (to hold details of input splits, status of tasks, etc):
Hadoop 0.x, 1.x Hadoop 2.x
------------------------------- --------------------------
yarn.app.mapreduce.am.command-opts
yarn.app.mapreduce.am.resource.mb
Again, you could set -Xmx (in the former) to 75% of the resource.mb value.
Other configurations
There are many other configurations relating to memory limits, some of them deprecated - see the JobConf class. One useful one:
Hadoop 0.x, 1.x (deprecated) Hadoop 2.x
------------------------------- --------------------------
mapred.job.reduce.total.mem.bytes mapreduce.reduce.memory.totalbytes
Set this to a low value (10) to force shuffle to happen on disk in the event that you hit an OutOfMemoryError at MapOutputCopier.shuffleInMemory.