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From Mohit Anchlia <mohitanch...@gmail.com>
Subject Re: ParNew and caching
Date Fri, 18 Nov 2011 17:31:05 GMT
On Fri, Nov 18, 2011 at 7:47 AM, Sylvain Lebresne <sylvain@datastax.com> wrote:
> On Fri, Nov 18, 2011 at 4:23 PM, Mohit Anchlia <mohitanchlia@gmail.com> wrote:
>> On Fri, Nov 18, 2011 at 6:39 AM, Sylvain Lebresne <sylvain@datastax.com> wrote:
>>> On Fri, Nov 18, 2011 at 1:53 AM, Todd Burruss <bburruss@expedia.com> wrote:
>>>> I'm using cassandra 1.0.  Been doing some testing on using cass's cache.
>>>>  When I turn it on (using the CLI) I see ParNew jump from 3-4ms to
>>>> 200-300ms.  This really screws with response times, which jump from ~25-30ms
>>>> to 1300+ms.  I've increase new gen and that helps, but still this is
>>>> suprising to me, especially since 1.0 defaults to the
>>>> SerializingCacheProvider – off heap.
>>>> The interesting tid bit is that I have wide rows.  70k+ columns per row,
~50
>>>> bytes per column value.  The cache only must be about 400 rows to catch
all
>>>> the data per node and JMX is reporting 100% cache hits.  Nodetool ring
>>>> reports < 2gb per node, my heap is 6gb and total RAM is 16gb.
>>>> Thoughts?
>>>
>>> You're problem is the mix of wide rows and the serializing cache.
>>> What happens with the serializing cache is that our data is stored
>>> out of the heap. But that means that for each read to a row, we
>>> 'deserialize' the row for the out-of-heap memory into the heap to
>>> return it. The thing is, when we do that, we do the full row each
>>> time. In other word, for each query we deserialize 70k+ columns
>>> even if to return only one. I'm willing to bet this is what is killing
>>> your response time. If you want to cache wide rows, I really
>>> suggest you're using the ConcurrentLinkedHashCacheProvider
>>> instead.
>>
>> What happens when using ConcurrentLinkedHashCache? What is the
>> implementation like and why is it better?
>
> With ConcurrentLinkedHashCache, the cache is in the heap. So there
> is no deserialization/copy during gets, so having wide rows is not a
> problem. Outside of the fact that if you're enabling cache on a column
> family with wide rows, you have to keep in mind that we always keep
> full rows in cache.
>

Wouldn't it move the problem to GC pauses from not being able to clean
up old generation? I am using these rows in concurrenthashmap will get
migrated to old gen.
>>
>>>
>>> I'll also note that this explain the ParNew times too. Deserializing
>>> all those columns from off-heap creates lots of short-lived object,
>>> and since you deserialize 70k+ on each query, that's quite some
>>> pressure on the new gen. Note that the serializing cache is
>>> actually minimizing the use of old gen, because that is the one
>>> that is the one that can create huge GC pauses with big heap,
>>> but it actually put more pressure on the new gen. This is by
>>> design and because new gen is much less of a problem than
>>> old gen.
>>
>> In this scenario would it help if Young generation space is increased?
>
> That's a hard one to answer because GC tuning is a bit of a black
> art, when testing and benchmarking is often key. Having a bigger
> young generation means having young collection kicked less often
> but on the other side it reduces the size for the old generation.
> But again, I don't think the problem is really the GC here, at least not
> primarily.
>
> --
> Sylvain
>
>>
>>>
>>> --
>>> Sylvain
>>>
>>
>

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