openstack team mailing list archive

["Mark Washenberger" <mark.washenberger@xxxxxxxxxxxxx>]

This is precisely my concern.

It must be brought up that with Rackspace Cloud Servers, nearly
all client codes routinely submit requests with a query parameter 
"cache-busting=<some random string>" just to get around problems with
cache invalidation. And woe to the client that does not.

I get the feeling that once trust like this is lost, a project has
a hard time regaining it. I'm not saying that we can avoid
inconsistency entirely. Rather, I believe we will have to embrace
some eventual-consistency models to enable the performance and
scale we will ultimately attain. But I just get the feeling that
generic caches are really only appropriate for write-once or at
least write-rarely data. So personally I would rule out external
caches entirely and try to be very judicious in selecting internal
caches as well.

"Joshua Harlow" <harlowja@xxxxxxxxxxxxx> said:

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> Just from experience.
> 
> They do a great job. But the killer thing about caching is how u do the cache
> invalidation.
> 
> Just caching stuff is easy-peasy, making sure it is invalidated on all servers in
> all conditions, not so easy...
> 
> On 3/22/12 4:26 PM, "Sandy Walsh" <sandy.walsh@xxxxxxxxxxxxx> wrote:
> 
> We're doing tests to find out where the bottlenecks are, caching is the
> most obvious solution, but there may be others. Tools like memcache do a
> really good job of sharing memory across servers so we don't have to
> reinvent the wheel or hit the db at all.
> 
> In addition to looking into caching technologies/approaches we're gluing
> together some tools for finding those bottlenecks. Our first step will
> be finding them, then squashing them ... however.
> 
> -S
> 
> On 03/22/2012 06:25 PM, Mark Washenberger wrote:
>> What problems are caching strategies supposed to solve?
>>
>> On the nova compute side, it seems like streamlining db access and
>> api-view tables would solve any performance problems caching would
>> address, while keeping the stale data management problem small.
>>
> 
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>