It works now by configuring the $JAVA_HOME explicitly. The JAVA_HOME is configured as $JAVA_HOME by default. Now I configure it to the complete path of my JDK explicitly.
A little strange here, the $JAVA_HOME is already set in the shell environment, why do I still need to configure it again explicitly... 2014-09-15 14:58 GMT+08:00 牛兆捷 <[email protected]>: > java -d64 version works well in the shell. > > 2014-09-15 11:59 GMT+08:00 牛兆捷 <[email protected]>: > >> I use hbase-0.98-5-hadoop2 and modify the default heap size of region >> server in hbase-env.sh as below (keep all the other parameters in the file >> default): >> >> export HBASE_REGIONSERVER_OPTS="-Xmn200m >> -XX:CMSInitiatingOccupancyFraction=70 -Xms1024m -Xmx8000m" >> >> The error occurs when I start hbase cluster: >> >> 10.1.255.246: Invalid maximum heap size: -Xmx8000m >> 10.1.255.246: The specified size exceeds the maximum representable size. >> 10.1.255.246: Could not create the Java virtual machine. >> >> The jvm I use is 64 bit : >> >> java version "1.6.0_39" >> Java(TM) SE Runtime Environment (build 1.6.0_39-b04) >> Java HotSpot(TM) 64-Bit Server VM (build 20.14-b01, mixed mode) >> >> Why 8G setting exceeds the maximum representable size. >> >> 2014-09-15 11:39 GMT+08:00 Nick Dimiduk <[email protected]>: >> >>> The scripts automate use of the tool PerformanceEvaluation that ships >>> with >>> HBase, so in that way it runs against a cluster directly. It depends on >>> having independent configuration directories set up for each test >>> config. There's probably too much custom-to-my-environment stuff in >>> there, >>> but I hope I included enough diffs that you can work it out in your >>> deployment. Let me know if you have any more questions. >>> >>> -n >>> >>> On Sunday, September 14, 2014, 牛兆捷 <[email protected]> wrote: >>> >>> > Hi, Nick >>> > >>> > Can your perf_blockcache performance testing script can be applied to >>> hbase >>> > cluster directly? >>> > If not, what kind of things should I take care? >>> > >>> > 2014-08-22 7:06 GMT+08:00 Nick Dimiduk <[email protected] >>> <javascript:;> >>> > >: >>> > >>> > > I'm familiar with Stack's work too, but thanks for pointing it out :) >>> > > >>> > > On Wed, Aug 20, 2014 at 8:19 PM, 牛兆捷 <[email protected] >>> <javascript:;>> >>> > wrote: >>> > > >>> > > > Hi Nick: >>> > > > >>> > > > Yes, I am interested in it. I will try first. >>> > > > >>> > > > Btw, this site (http://people.apache.org/~stack/bc/) also does the >>> > > similar >>> > > > performance evaluation. >>> > > > You can have a look if you are interested in. >>> > > > >>> > > > >>> > > > 2014-08-21 1:48 GMT+08:00 Nick Dimiduk <[email protected] >>> > <javascript:;>>: >>> > > > >>> > > > > Hi Zhaojie, >>> > > > > >>> > > > > I'm responsible for this particular bit of work. One thing to >>> note in >>> > > > these >>> > > > > experiments is that I did not control explicitly for OS caching. >>> I >>> > ran >>> > > > > "warmup" workloads before collecting measurements, but because >>> the >>> > > amount >>> > > > > of RAM on the machine is fixed, it's impact of OS cache is >>> different >>> > > with >>> > > > > different based on the amount of memory used by HBase. Another, >>> as >>> > Todd >>> > > > > pointed out on an earlier thread, is that my trend lines are >>> probably >>> > > > > optimistic/misleading. >>> > > > > >>> > > > > Something I was driving for was to understand how well the >>> different >>> > > > > implementations before as they're managing more and more memory. >>> I'd >>> > > like >>> > > > > to get some insight into how we might be able to take advantage >>> of >>> > > 100's >>> > > > or >>> > > > > even 1000's of GB of memory when the time comes. That's part of >>> why >>> > > > there's >>> > > > > so many variables. >>> > > > > >>> > > > > I scripted out the running of the tests, all of my >>> configurations are >>> > > > > available in the associated github repo [0], and all of the data >>> > points >>> > > > are >>> > > > > available as a csv. If you're interested in experimenting >>> yourself, >>> > > > please >>> > > > > let me know how I can help. >>> > > > > >>> > > > > Cheers, >>> > > > > Nick >>> > > > > >>> > > > > [0]: https://github.com/ndimiduk/perf_blockcache >>> > > > > >>> > > > > >>> > > > > On Wed, Aug 20, 2014 at 6:00 AM, 牛兆捷 <[email protected] >>> > <javascript:;>> wrote: >>> > > > > >>> > > > > > the complete blog link is: >>> > > > > > http://zh.hortonworks.com/blog/blockcache-showdown-hbase/ >>> > > > > > >>> > > > > > >>> > > > > > 2014-08-20 11:41 GMT+08:00 牛兆捷 <[email protected] >>> <javascript:;> >>> > >: >>> > > > > > >>> > > > > > > Hi all: >>> > > > > > > >>> > > > > > > I saw some interesting results from Hortonworks blog (block >>> cache >>> > > > > > > < >>> > > > > > >>> > > > > >>> > > > >>> > > >>> > >>> http://zh.hortonworks.com/wp-content/uploads/2014/03/perfeval_blockcache_v2.pdf >>> > > > > > > >>> > > > > > > ). >>> > > > > > > >>> > > > > > > In this result, the ratio of memory footprint to database >>> size is >>> > > > held >>> > > > > > > fixed while >>> > > > > > > the absolute values are increased. >>> > > > > > > >>> > > > > > > In my mind, the performance should becomes worse for larger >>> ratio >>> > > as >>> > > > > the >>> > > > > > > increase >>> > > > > > > of the absolute value. For example BucketCache#(tmpfs), the >>> > > > difference >>> > > > > > > between ratio (DB"1.5":"RAM"1.0) and ratio (DB"4.5":"RAM"1.0) >>> > > becomes >>> > > > > > > larger as the increase of memory. >>> > > > > > > Actually, the result of ratio ( DB"1.5":"RAM"1.0) increase >>> > > linearly, >>> > > > > and >>> > > > > > > the result of ratio (DB"1.5":"RAM"1.0) exponentially. >>> > > > > > > >>> > > > > > > However, for BucketCache#(heap) and LruBlockCache, the >>> result is >>> > > out >>> > > > of >>> > > > > > my >>> > > > > > > expectation. >>> > > > > > > The curves of ratio (DB"1.5":"RAM"1.0) and ratio >>> > (DB"4.5":"RAM"1.0) >>> > > > > both >>> > > > > > > increase exponentially, but the relative differences as the >>> > > increase >>> > > > of >>> > > > > > > memory are not consistent. >>> > > > > > > Take LruBlockCache as an example, the difference of ratio >>> > > > > > > (DB"1.5":"RAM"1.0) and ratio (DB"4.5":"RAM"1.0) becomes >>> smaller >>> > > from >>> > > > 20 >>> > > > > > GB >>> > > > > > > to 50 GB, but becomes larger from 50 GB to 60 GB. >>> > > > > > > >>> > > > > > > How to analysis the cause of this result, any ideas? >>> > > > > > > >>> > > > > > > -- >>> > > > > > > *Regards,* >>> > > > > > > *Zhaojie* >>> > > > > > > >>> > > > > > > >>> > > > > > >>> > > > > > >>> > > > > > -- >>> > > > > > *Regards,* >>> > > > > > *Zhaojie* >>> > > > > > >>> > > > > >>> > > > >>> > > > >>> > > > >>> > > > -- >>> > > > *Regards,* >>> > > > *Zhaojie* >>> > > > >>> > > >>> > >>> > >>> > >>> > -- >>> > *Regards,* >>> > *Zhaojie* >>> > >>> >> >> >> >> -- >> *Regards,* >> *Zhaojie* >> >> > > > -- > *Regards,* > *Zhaojie* > > -- *Regards,* *Zhaojie*
