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From Nick Telford <>
Subject Re: SSD vs. HDD
Date Fri, 05 Nov 2010 15:48:50 GMT
If you're experiencing high I/O load and not getting any Java OutOfMemory
(OOM) errors, you should try to keep your heap size as low as possible as
this provides the OS filesystem cache with more memory, which will reduce
read I/O load significantly. I'm not familiar the performance of Windows
filesystems, but I imagine NTFS is somewhat on a par with what we're
familiar with in Linux.

The row cache will be useful in cases where you have a high read/write ratio
(more reads than writes) especially if most of those reads are confined to a
specific subset of data. The key cache will also improve read performance
(which will be your main I/O bottleneck) with much less of a memory impact,
so in your case I would recommend enabling it for as many keys as possible.

Riptano have a pretty decent explanation of tuning Cassandra that I highly
recommend you read:


Nick Telford

On 4 November 2010 22:20, Alaa Zubaidi <> wrote:

> Thanks for the advise...
> We are running on Windows, and I just added more memory to my system, 16G I
> will run the test again with 8G heap.
> The load is continues, however, the CPU usage is around 40% with max of
> 70%.
> As for cache, I am not using cache, because I am under the impression that
> cache in my case, where the data keeps changing very quickly in and out of
> cache, is not a good idea?
> Thanks
> On 11/4/2010 3:14 AM, Nick Telford wrote:
>> If you're bottle-necking on read I/O making proper use of Cassandras key
>> cache and row cache will improve things dramatically.
>> A little maths using the numbers you've provided tells me that you have
>> about 80GB of "hot" data (data valid in a 4 hour period). That's obviously
>> too much to directly cache, but you can probably cache some or all of the
>> row keys, depending on your column distribution among keys. This will
>> prevent reads from having to hit the indexes for the relevant sstables -
>> eliminating a seek per sstable.
>> If you have a subset of this data that is read more than the rest, the row
>> cache will help you out a lot too. Have a look at your access patterns and
>> see if it's worthwhile caching some rows.
>> If you make progress using the various caches, but don't have enough
>> memory,
>> I'd explore the costs of expanding the available memory compared to
>> switching to SSDs as I imagine it'd be cheaper and would last longer.
>> Finally, given your particular deletion pattern, it's probably worth
>> looking
>> at 0.7 and upgrading once it is released as stable. CASSANDRA-699[1] adds
>> support for TTL columns that automatically expire and get removed (during
>> compaction) without the need for a manual deletion mechanism. Failing
>> this,
>> since data older than 4 hours is no longer relevant, you should reduce
>> your
>> GCGraceSeconds>= 4 hours. This will ensure deleted data is removed faster,
>> keeping your sstables smaller and allowing the fs cache to operate more
>> effectively.
>> 1:
>> On 4 November 2010 08:18, Peter Schuller<
>> >wrote:
>>  I am having time out errors while reading.
>>>> I have 5 CFs but two CFs with high write/read.
>>>> The data is organized in time series rows, in CF1 the new rows are read
>>>> every 10 seconds and then the whole rows are deleted, While in CF2 the
>>> rows
>>>> are read in different time range slices and eventually deleted may be
>>> after
>>>> few hours.
>>> So the first thing to do is to confirm what the bottleneck is. If
>>> you're having timeouts on reads, and assuming your not doing reads of
>>> hot-in-cache data so fast that CPU is the bottleneck (and given that
>>> you ask about SSD), the hypothesis then is that you're disk bound due
>>> to seeking.
>>> Observe the node(s) and in particular use "iostat -x -k 1" (or an
>>> equivalent graph) and look at the %util and %avgqu-sz columns to
>>> confirm that you are indeed disk-bound. Unless you're doing large
>>> reads, you will likely see, on average, small reads in amounts that
>>> simply saturate underlying storage, %util at 100% and the avgu-sz will
>>> probably be approaching the level of concurrency of your read traffic.
>>> Now, assuming that is true, the question is why. So:
>>> (1) Are you continually saturating disk or just periodically?
>>> (2) If periodically, does the periods of saturation correlate with
>>> compaction being done by Cassandra (or for that matter something
>>> else)?
>>> (3) What is your data set size relative to system memory? What is your
>>> system memory and JVM heap size? (Relevant because it is important to
>>> look at how much memory the kernel will use for page caching.)
>>> As others have mentioned, the amount of reads done on disk for each
>>> read form the database (assuming data is not in cache) can be affected
>>> by how data is written (e.g., partial row writes etc). That is one
>>> thing that can be addressed, as is re-structuring data to allow
>>> reading more sequentially (if possible). That only helps along one
>>> dimension though - lessening, somewhat, the cost of cold reads. The
>>> gains may be limited and the real problem may be that you simply need
>>> more memory for caching and/or more IOPS from your storage (i.e., more
>>> disks, maybe SSD, etc).
>>> If on the other hand you're normally completely fine and you're just
>>> seeing periods of saturation associated with compaction, this may be
>>> mitigated by software improvements by possibly rate limiting reads
>>> and/or writes during compaction and avoiding buffer cache thrashing.
>>> There's a JIRA ticket for direct I/O
>>> ( I don't think
>>> there's a JIRA ticket for rate limiting, but I suspect, since you're
>>> doing time series data, that you're not storing very large values -
>>> and I would expect compaction to be CPU bound rather than being close
>>> to saturate disk.
>>> In either case, please do report back as it's interesting to figure
>>> out what kind of performance issues people are seeing.
>>> --
>>> / Peter Schuller
> --
> Alaa Zubaidi
> PDF Solutions, Inc.
> 333 West San Carlos Street, Suite 700
> San Jose, CA 95110  USA
> Tel: 408-283-5639 (or 408-280-7900 x5639)
> fax: 408-938-6479
> email:

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