i think you will see a slow down because of large values in your columns. make sure you take a look at MemtableThroughputInMB in your config. if you are writing 1MB of data per row, then you'll probably want to increase this quite a bit so you are not constantly creating sstables. can't recall, did you see compaction mgr reporting a lot of pending compactions? maybe try to "chunk" your data into multiple columns or multiple rows.
i too see slowness that exhibits in the same manner as you guys have described. i'm still trying to track it down as well.
On 05/06/2010 10:56 AM, Ran Tavory wrote:Jonathan, I think it's the case of large values in the columns. The problematic CF is a key-value store, so it has only one column per row, however the value of that column can be large. It's a java serialized object (uncompressed) which, may be 100s of bytes, maybe even a few megs. This CF also suffers from zero cache hits since each time a read is for a unique key.
I ran stress.py and I see much better results (reads are < 1ms) so I assume my cluster is healthy, so I need to fix the app. Would 1meg bytes object explain a 30ms (sometimes even more) read latency? The boxes aren't fancy, not sure exactly what hardware we have there but it's "commodity"...
On Thu, May 6, 2010 at 5:22 PM, Jonathan Ellis <email@example.com> wrote:
columns, not CFs.
put another way, how wide are the rows in the slow CF?
On Wed, May 5, 2010 at 11:30 PM, Ran Tavory <firstname.lastname@example.org> wrote:
> I have a few CFs but the one I'm seeing slowness in, which is the one with
> plenty of cache misses has only one column per key.
> Latency varies b/w 10m and 60ms but I'd say average is 30ms.
> On Thu, May 6, 2010 at 4:25 AM, Jonathan Ellis <email@example.com> wrote:
>> How many columns are in the rows you are reading from?
>> 30ms is quite high, so I suspect you have relatively large rows, in
>> which case decreasing the column index threshold may help.
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