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From Yiming Sun <yiming....@gmail.com>
Subject Re: need some clarification on recommended memory size
Date Wed, 16 May 2012 12:35:49 GMT
Thanks Aaron.  The reason I raised the question about memory requirements
is because we are seeing some very low performance on cassandra read.

We are using cassandra as the backend for an IR repository, and granted the
size of each column is very small (OCRed text).  Each row represents a book
volume, and the columns of the row represent pages of the volume.  The
average size of a column text is 2-3KB, and each row has about 250 columns
(varies quite a bit from one volume to another).

The read rate that I have been seeing is about 3MB/sec, and that is reading
the raw bytes... using string serializer the rate is even lower, about
2.2MB/sec.   To retrieve each volume, a slice query is used via Hector that
specifies the row key (the volume), and a list of column keys (pages), and
the consistency level is set to ONE.  So I am a bit lost in trying to
figure out how to increase the performance.  Using JNA may help, but a blog
article seems to say it only increase 13%, which is not very significant
when the base performance is in single-digit MBs.

Do you have any suggestions?

Oh, another thing is you mentioned memory mapped files.  Our environment is
virtualized, and the disks are actually SAN through fiber channels, so I
don't know if that has impact on performance as well.  Would greatly
appreciate any help.  Thanks.

-- Y.

On Wed, May 16, 2012 at 5:48 AM, aaron morton <aaron@thelastpickle.com>wrote:

> The JVM will not swap out if you have JNA.jar in the path or you have
> disabled swap on the machine (the simplest thing to do).
>
> Cassandra uses memory mapped file access. If you have 16GB of ram, 8 will
> go to the JVM and the rest can be used by the os to cache files. (Plus the
> off heap stuff)
>
> Cheers
>
>   -----------------
> Aaron Morton
> Freelance Developer
> @aaronmorton
> http://www.thelastpickle.com
>
> On 16/05/2012, at 11:12 AM, Yiming Sun wrote:
>
> Thanks Tyler... so my understanding is, even if Cassandra doesn't do
> off-heap caching, by having a large-enough memory, it minimize the chance
> of swapping the java heap to a disk.  Is that correct?
>
> -- Y.
>
> On Tue, May 15, 2012 at 6:26 PM, Tyler Hobbs <tyler@datastax.com> wrote:
>
>> On Tue, May 15, 2012 at 3:19 PM, Yiming Sun <yiming.sun@gmail.com> wrote:
>>
>>> Hello,
>>>
>>> I was reading the Apache Cassandra 1.0 Documentation PDF dated May 10,
>>> 2012, and had some questions on what the recommended memory size is.
>>>
>>> Below is the snippet from the PDF.  Bullet 1 suggests to have 16-32GB of
>>> RAM, yet Bullet 2 suggests to limit Java heap size to no more than 8GB.  My
>>> understanding is that Cassandra is implemented purely in Java, so all
>>> memory it sees and uses is the JVM Heap.
>>>
>>
>> The main way that additional RAM helps is through the OS page cache,
>> which will store hot portions of SSTables in memory. Additionally,
>> Cassandra can now do off-heap caching.
>>
>>
>>
>>>   So can someone help me understand the discrepancy between 16-32GB of
>>> RAM and 8GB of heap?  Thanks.
>>>
>>> == snippet ==
>>> Memory
>>> The more memory a Cassandra node has, the better read performance. More
>>> RAM allows for larger cache sizes and
>>> reduces disk I/O for reads. More RAM also allows memory tables
>>> (memtables) to hold more recently written data. Larger
>>> memtables lead to a fewer number of SSTables being flushed to disk and
>>> fewer files to scan during a read. The ideal
>>> amount of RAM depends on the anticipated size of your hot data.
>>>
>>> • For dedicated hardware, a minimum of than 8GB of RAM is needed.
>>> DataStax recommends 16GB - 32GB.
>>>
>>> • Java heap space should be set to a maximum of 8GB or half of your
>>> total RAM, whichever is lower. (A greater
>>> heap size has more intense garbage collection periods.)
>>>
>>> • For a virtual environment use a minimum of 4GB, such as Amazon EC2
>>> Large instances. For production clusters
>>> with a healthy amount of traffic, 8GB is more common.
>>>
>>
>>
>>
>> --
>> Tyler Hobbs
>> DataStax <http://datastax.com/>
>>
>>
>
>

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