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From "sbarriba" <sbarr...@yahoo.co.uk>
Subject RE: JackRabbit Caching: BundleCache vs ItemManager vs CacheManager
Date Sun, 20 Jul 2008 12:13:41 GMT
Hi Stefan,
So the intention is that once the session is no longer used then the
ItemImpl instances are cleared up? That makes sense except that when
investigating the lock contention issues we found that the creation of
ItemImpl can become expensive as they queue up on DefaultISMLocking. 

When relying on sessions to cache some item data (with a shared session per
request model) via the ItemManager we found that this significantly reduced
contention as clients using sessions with some ItemImpls didn't hit
DefaultISMLocking. By choosing a suitable X request per 1 session ratio we
could spread the locking to increase throughput.

With a pooled session per view model (where each request exclusively has
access to one session) we found no benefit from the ItemManger cache due to
the Weak Referenced data being cleared up after each request.

Are the LocalItemStateManager and SharedItemStateManager intended to help
reduce the load on DefaultISMLocking?

Regards,
Shaun



-----Original Message-----
From: stefan.guggisberg@gmail.com [mailto:stefan.guggisberg@gmail.com] On
Behalf Of Stefan Guggisberg
Sent: 16 July 2008 13:25
To: users@jackrabbit.apache.org
Subject: Re: JackRabbit Caching: BundleCache vs ItemManager vs CacheManager

hi sean

On Tue, Jul 1, 2008 at 7:11 PM, sbarriba <sbarriba@yahoo.co.uk> wrote:
> Hi Marcel et al,
> 3 suggestions come to mind from this (perhaps for the develop list):
>
> 1) the ItemManager should be using Soft References rather than Weak
> References otherwise a PooledSessionInView pattern is not really effective
> as, pooled (but unused) sessions have their caches cleared immediately by
> the GC (using weak references).

ItemManager cashes ItemImpl instances. the 'cache' guarantees that there's
no more than 1 ItemImpl instance per item id and session. weak references
are ideal for this task. ItemManager is not meant to be a 'cache'
since ItemImpl
instance creation is IMO not performance critical. i remember that i once
experimented with soft references but they tended to fill the heap pretty
fast
since soft references are typically cleared only when you're near an
OOM error...

ItemState caches are a different matter. LocalItemStateManager and
SharedItemStateManager do cache ItemState instances for performance
reasons. please take a look at the javadoc which should explain
why they're using weak references internally instead of soft references:

http://jackrabbit.apache.org/api/1.4/org/apache/jackrabbit/core/state/ItemSt
ateReferenceCache.html

cheers
stefan

>
> 2) the CacheManager config needs to be externalised so it can be changed
> within the XML config, not programmatically.
>
> 3) its worth considering using a caching library (e.g. ehcahe) for the
> BundleCache at least? As a case study we've got multi-GB of binaries in
> BLOBs in the database and the BundleCache (at 100MB+)  spends 2 hours
after
> each restart filling /tmp. It would be great to use a caching library
which
> supported a persistent cache etc. Obviously externalBlobs helps here.
>
> Regards,
> Shaun
>
> -----Original Message-----
> From: Marcel Reutegger [mailto:marcel.reutegger@gmx.net]
> Sent: 01 July 2008 09:47
> To: users@jackrabbit.apache.org
> Subject: Re: JackRabbit Caching: BundleCache vs ItemManager vs
CacheManager
>
> Hi,
>
> sbarriba wrote:
>> ..        PersistenceManager Cache:
>>
>> o   The "bundleCacheSize" determines how many nodes the
PersistenceManager
>> will cache. As this determines the lifetime of the references to the
>> temporary BLOB cache if its not large enough BLOBs will be continually
> read
>> from the database (if using externalBlobs=false).
>>
>> o   Configurable in <PersistenceManager> XML block
>>
>> o   Default size 8MB
>>
>> o   This cache is shared by all sessions.
>>
>> o   Synchronised access using the ISMLocking stategy e.g. Default or
>> FineGrained
>
> correct, but there's additional synchronization in the persistence manager
> using
> conventional synchronized methods. e.g. see
> AbstractBundlePersistenceManager.load(NodeId)
>
>> ..        Session ItemManager Cache:
>>
>> o   Items are cached from the underlying persistence manager on a per
>> session basis.
>>
>> o   Limit cannot be set.
>
> not sure, but I think this cache is also managed (at least partially) by
the
>
> CacheManager.
>
>> o   Uses a ReferenceMap which can be emptied by the JVM GC as required
>
> that's the 'other part' that manages the cache ;)
>
> items that are still referenced in the application will force the
reference
> map
> to keep the respective ItemState instances (using weak references).
>
>> o   Synchronised access using the itemCache object
>>
>> ..        CacheManager Cache:
>>
>> o   Limit can only be set programmatically via the Workspace cacheManager
>>
>> o   http://wiki.apache.org/jackrabbit/CacheManager
>>
>> o   Defaults to 16MB
>>
>> o   Its not clear as yet how the CacheManager relates, if at all, to the
>> ItemManager cache
>
> this only happens indirectly. see above.
>
>> 2 questions:
>>
>> ..        What is the purpose of the CacheManager and which caches does
it
>> actually control?
>
> It controls *all* the caches that contain ItemState instances.
>
>> ..        For example, for a workspace with 100,000 nodes what is an
>> appropriate setting for the Cache Manager?
>
> I guess that depends on your JVM heap settings and the usage pattern. if
you
>
> have a lot of random reads over nearly all 100k nodes and performance is
> critical you may consider caching all of them. have a look a
> ItemState.calculateMemoryFootprint() for a formula on how the memory
> consumption
> is calculated.
>
> regards
>  marcel
>
>
>



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