Re: Write-through caching in Forte

Hello Mark,
Just one point more. May be you can add an Event Notifier to the lock
manager to send the new instance of Obj1 to the clients (here client2)
who use it in their cache.
Hope this helps.
Daniel Nguyen.
Mark S. Potts wrote:
>
Andrew
This is a mixture of a cache strategy and object locking. If I
understand what you have said I have some suggestions;
The cache should hold copies of the object and the object should be
returned to the client. The obect that is returned to the client should
be version stamped ( optimistic locking ).
A) Client1 request Obj1
B) Obj1 is instantiated from the persistent store
C) Obj1 is version stamped via a lock manager service.
D) Obj1 is placed in the cache and copy returned to Client1
Client1 can now work on Obj1
When Client2 selects Obj2 - the cache size being 1 - the Obj1 is
replaced with Obj2.
Obj2 is selected stamped and returned to the client as per the steps
above.
When Client 2 now selects Obj1, no longer in the cache, the same steps
need to be completed as above.
The cache now contains the same version of Obj1 as give to Client1.
Now the important part, becuase this is an optimistic locking strategy -
two clients can have different version of the same object, it is only
when the object is saved - returned to the persistent store, that the
version stamp need to be checked. Lets say Client 2 saves before client
1
A) Client2 initiates a save on Obj1
B) Obj1 checks the lock manger to see if anyone has saved a new version
of Obj1 since it was selected.
C) If there have been no saves of Obj1 since Obj1 was selected ie the
version of Obj1 selected does not conflict with the last version saved -
then save Obj1
D) Update the version stamp for Obj1 via the Lock manager
E) Update Obj1 in the cache.
When Client1 now tries to save the version of Obj1 a conflict will
result and an exception needs to be raised - and if necessary the new
version of Obj1, from the cache, returned to Client1.
The version control can be done more easily if you are prepared to do
the locking in the database - I do not recommend this for a number of
well documented reasons.
However if you choose this alternative instead of using a seperate Lock
manager you could simply time stamp the row in the database iether on
that table or a separate lock table and when saving the Obj1 check the
time stamp on the object against the time stamp on the row. If they are
the same save the object and update the time stamp to the current time (
granularity of time stamp determined by number of concurrent users and
usage patterns ). The time stamp on the row acts as the version stamp
for the object and is selected into the object as a private attribute at
time of selection.
Hope this is of some help.
Mark Potts
SCAFFOLDS Product Manager
Sage IT Partners
A) Client1 requests Obj1.
B) Obj1 is instantiated from a persistent store and placed in the cache
and a reference to Obj1 is
returned to Client1.
C) As part of the instantiation of Obj1 the object is version stamped
through a lock manager service.
C) Client1 modifies the state of Obj1 trough its reference.
D) Client2 requests Obj2.
E) Obj2 is de-serialized, placed in the cache, knocking out Obj1, and a
reference to Obj2 is returned to Client2.
F) Client2 requests Obj1. Since it is no longer in the cache, we either
need to de-serialize Obj1 from some persistent store, in which case we
now have two out of sync copies of Obj1, or we need to give Client2 the
reference to the Obj1 that Client1 has.
Faibishenko, Andrew wrote:
Has anyone out there been successful at implementing a cache which
maintains updateable objects.
Due to financial considerations, we cannot buy an off-the-shelf
framework.
What we are trying to build is some kind of object persistence
mechanism
and the cache would be a layer in that service.
Our big issue is maintaining consistency within the cache, for
multiple
clients performing updates to an object.
Example:
A) Client1 requests Obj1.
B) Obj1 is de-serialized, placed in the cache and a reference to Obj1
is
returned to Client1.
C) Client1 modifies the state of Obj1 trough its reference.
D) Client2 requests Obj2.
E) Obj2 is de-serialized, placed in the cache, knocking out Obj1, and
a
reference to Obj2 is returned to Client2.
F) Client2 requests Obj1. Since it is no longer in the cache, we
either
need to de-serialize Obj1 from some persistent store, in which case we
now have two out of sync copies of Obj1, or we need to give Client2
the
reference to the Obj1 that Client1 has.
Is this something we should ask Forte Consulting about?
-Andy
============================================
Andy Faibishenko (312)251-3267
Senior Consultant (800)462-6301
Metamor Technologies, Inc. [email protected]

Hello Mark,
Just one point more. May be you can add an Event Notifier to the lock
manager to send the new instance of Obj1 to the clients (here client2)
who use it in their cache.
Hope this helps.
Daniel Nguyen.
Mark S. Potts wrote:
>
Andrew
This is a mixture of a cache strategy and object locking. If I
understand what you have said I have some suggestions;
The cache should hold copies of the object and the object should be
returned to the client. The obect that is returned to the client should
be version stamped ( optimistic locking ).
A) Client1 request Obj1
B) Obj1 is instantiated from the persistent store
C) Obj1 is version stamped via a lock manager service.
D) Obj1 is placed in the cache and copy returned to Client1
Client1 can now work on Obj1
When Client2 selects Obj2 - the cache size being 1 - the Obj1 is
replaced with Obj2.
Obj2 is selected stamped and returned to the client as per the steps
above.
When Client 2 now selects Obj1, no longer in the cache, the same steps
need to be completed as above.
The cache now contains the same version of Obj1 as give to Client1.
Now the important part, becuase this is an optimistic locking strategy -
two clients can have different version of the same object, it is only
when the object is saved - returned to the persistent store, that the
version stamp need to be checked. Lets say Client 2 saves before client
1
A) Client2 initiates a save on Obj1
B) Obj1 checks the lock manger to see if anyone has saved a new version
of Obj1 since it was selected.
C) If there have been no saves of Obj1 since Obj1 was selected ie the
version of Obj1 selected does not conflict with the last version saved -
then save Obj1
D) Update the version stamp for Obj1 via the Lock manager
E) Update Obj1 in the cache.
When Client1 now tries to save the version of Obj1 a conflict will
result and an exception needs to be raised - and if necessary the new
version of Obj1, from the cache, returned to Client1.
The version control can be done more easily if you are prepared to do
the locking in the database - I do not recommend this for a number of
well documented reasons.
However if you choose this alternative instead of using a seperate Lock
manager you could simply time stamp the row in the database iether on
that table or a separate lock table and when saving the Obj1 check the
time stamp on the object against the time stamp on the row. If they are
the same save the object and update the time stamp to the current time (
granularity of time stamp determined by number of concurrent users and
usage patterns ). The time stamp on the row acts as the version stamp
for the object and is selected into the object as a private attribute at
time of selection.
Hope this is of some help.
Mark Potts
SCAFFOLDS Product Manager
Sage IT Partners
A) Client1 requests Obj1.
B) Obj1 is instantiated from a persistent store and placed in the cache
and a reference to Obj1 is
returned to Client1.
C) As part of the instantiation of Obj1 the object is version stamped
through a lock manager service.
C) Client1 modifies the state of Obj1 trough its reference.
D) Client2 requests Obj2.
E) Obj2 is de-serialized, placed in the cache, knocking out Obj1, and a
reference to Obj2 is returned to Client2.
F) Client2 requests Obj1. Since it is no longer in the cache, we either
need to de-serialize Obj1 from some persistent store, in which case we
now have two out of sync copies of Obj1, or we need to give Client2 the
reference to the Obj1 that Client1 has.
Faibishenko, Andrew wrote:
Has anyone out there been successful at implementing a cache which
maintains updateable objects.
Due to financial considerations, we cannot buy an off-the-shelf
framework.
What we are trying to build is some kind of object persistence
mechanism
and the cache would be a layer in that service.
Our big issue is maintaining consistency within the cache, for
multiple
clients performing updates to an object.
Example:
A) Client1 requests Obj1.
B) Obj1 is de-serialized, placed in the cache and a reference to Obj1
is
returned to Client1.
C) Client1 modifies the state of Obj1 trough its reference.
D) Client2 requests Obj2.
E) Obj2 is de-serialized, placed in the cache, knocking out Obj1, and
a
reference to Obj2 is returned to Client2.
F) Client2 requests Obj1. Since it is no longer in the cache, we
either
need to de-serialize Obj1 from some persistent store, in which case we
now have two out of sync copies of Obj1, or we need to give Client2
the
reference to the Obj1 that Client1 has.
Is this something we should ask Forte Consulting about?
-Andy
============================================
Andy Faibishenko (312)251-3267
Senior Consultant (800)462-6301
Metamor Technologies, Inc. [email protected]

Similar Messages

  • Write-through caching in Forte

    Has anyone out there been successful at implementing a cache which
    maintains updateable objects.
    Due to financial considerations, we cannot buy an off-the-shelf
    framework.
    What we are trying to build is some kind of object persistence mechanism
    and the cache would be a layer in that service.
    Our big issue is maintaining consistency within the cache, for multiple
    clients performing updates to an object.
    Example:
    A) Client1 requests Obj1.
    B) Obj1 is de-serialized, placed in the cache and a reference to Obj1 is
    returned to Client1.
    C) Client1 modifies the state of Obj1 trough its reference.
    D) Client2 requests Obj2.
    E) Obj2 is de-serialized, placed in the cache, knocking out Obj1, and a
    reference to Obj2 is returned to Client2.
    F) Client2 requests Obj1. Since it is no longer in the cache, we either
    need to de-serialize Obj1 from some persistent store, in which case we
    now have two out of sync copies of Obj1, or we need to give Client2 the
    reference to the Obj1 that Client1 has.
    Is this something we should ask Forte Consulting about?
    -Andy
    ============================================
    Andy Faibishenko (312)251-3267
    Senior Consultant (800)462-6301
    Metamor Technologies, Inc. [email protected]

    Andrew
    This is a mixture of a cache strategy and object locking. If I
    understand what you have said I have some suggestions;
    The cache should hold copies of the object and the object should be
    returned to the client. The obect that is returned to the client should
    be version stamped ( optimistic locking ).
    A) Client1 request Obj1
    B) Obj1 is instantiated from the persistent store
    C) Obj1 is version stamped via a lock manager service.
    D) Obj1 is placed in the cache and copy returned to Client1
    Client1 can now work on Obj1
    When Client2 selects Obj2 - the cache size being 1 - the Obj1 is
    replaced with Obj2.
    Obj2 is selected stamped and returned to the client as per the steps
    above.
    When Client 2 now selects Obj1, no longer in the cache, the same steps
    need to be completed as above.
    The cache now contains the same version of Obj1 as give to Client1.
    Now the important part, becuase this is an optimistic locking strategy -
    two clients can have different version of the same object, it is only
    when the object is saved - returned to the persistent store, that the
    version stamp need to be checked. Lets say Client 2 saves before client
    1
    A) Client2 initiates a save on Obj1
    B) Obj1 checks the lock manger to see if anyone has saved a new version
    of Obj1 since it was selected.
    C) If there have been no saves of Obj1 since Obj1 was selected ie the
    version of Obj1 selected does not conflict with the last version saved -
    then save Obj1
    D) Update the version stamp for Obj1 via the Lock manager
    E) Update Obj1 in the cache.
    When Client1 now tries to save the version of Obj1 a conflict will
    result and an exception needs to be raised - and if necessary the new
    version of Obj1, from the cache, returned to Client1.
    The version control can be done more easily if you are prepared to do
    the locking in the database - I do not recommend this for a number of
    well documented reasons.
    However if you choose this alternative instead of using a seperate Lock
    manager you could simply time stamp the row in the database iether on
    that table or a separate lock table and when saving the Obj1 check the
    time stamp on the object against the time stamp on the row. If they are
    the same save the object and update the time stamp to the current time (
    granularity of time stamp determined by number of concurrent users and
    usage patterns ). The time stamp on the row acts as the version stamp
    for the object and is selected into the object as a private attribute at
    time of selection.
    Hope this is of some help.
    Mark Potts
    SCAFFOLDS Product Manager
    Sage IT Partners
    A) Client1 requests Obj1.
    B) Obj1 is instantiated from a persistent store and placed in the cache
    and a reference to Obj1 is
    returned to Client1.
    C) As part of the instantiation of Obj1 the object is version stamped
    through a lock manager service.
    C) Client1 modifies the state of Obj1 trough its reference.
    D) Client2 requests Obj2.
    E) Obj2 is de-serialized, placed in the cache, knocking out Obj1, and a
    reference to Obj2 is returned to Client2.
    F) Client2 requests Obj1. Since it is no longer in the cache, we either
    need to de-serialize Obj1 from some persistent store, in which case we
    now have two out of sync copies of Obj1, or we need to give Client2 the
    reference to the Obj1 that Client1 has.
    Faibishenko, Andrew wrote:
    Has anyone out there been successful at implementing a cache which
    maintains updateable objects.
    Due to financial considerations, we cannot buy an off-the-shelf
    framework.
    What we are trying to build is some kind of object persistence
    mechanism
    and the cache would be a layer in that service.
    Our big issue is maintaining consistency within the cache, for
    multiple
    clients performing updates to an object.
    Example:
    A) Client1 requests Obj1.
    B) Obj1 is de-serialized, placed in the cache and a reference to Obj1
    is
    returned to Client1.
    C) Client1 modifies the state of Obj1 trough its reference.
    D) Client2 requests Obj2.
    E) Obj2 is de-serialized, placed in the cache, knocking out Obj1, and
    a
    reference to Obj2 is returned to Client2.
    F) Client2 requests Obj1. Since it is no longer in the cache, we
    either
    need to de-serialize Obj1 from some persistent store, in which case we
    now have two out of sync copies of Obj1, or we need to give Client2
    the
    reference to the Obj1 that Client1 has.
    Is this something we should ask Forte Consulting about?
    -Andy
    ============================================
    Andy Faibishenko (312)251-3267
    Senior Consultant (800)462-6301
    Metamor Technologies, Inc. [email protected]

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    Edited by: SKR on Feb 16, 2012 3:14 PM
    Edited by: SKR on Feb 16, 2012 3:15 PM

    SKR wrote:
    Jonathan.Knight wrote:
    Hi Sairam
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    Robert

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         > and ensures that there are no conflicts. The second
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         >
         > For this reason, write-through and cache transactions
         > are not a supported combination.
         This is not true for a cache transaction that updaets a single cache entry, right?
         >
         > For single-cache-entry updates, CacheStore operations
         > are fully fault-tolerant in that the cache and
         > database are guaranteed to be consistent during any
         > server failure (including failures during partial
         > updates). While the mechanisms for fault-tolerance
         > vary, this is true for both write-through and
         > write-behind caches.
         For the write-thru case, I believe Database and cache are atomically updated.
         > Coherence does not support two-phase CacheStore
         > operations across multiple CacheStore instances. In
         > other words, if two cache entries are updated,
         > triggering calls to CacheStore modules sitting on
         > separate servers, it is possible for one database
         > update to succeed and for the other to fail.
         But once we have multiple CacheStore modules, then once one atomic write-thru put succeeds that means database is already updated for that specific put. There is no way to roll back the database update (although we can roll back the cache update). Therefore, you may end up in partial commits in such situations where multiple cache entries are updated across different CacheStore modules.
         If I use write-behind CacheStore modules, I can roll back entirely and avoid partial commits? Since writes are not immediately propagated to the database? So in essence, write-behind cache stores are no different than local transactions... Is my understanding correct?

  • Write-through limitation and putAll

    Please find the quote below from developer guide, particularly this one In other words, if two cache entries are updated, triggering calls to CacheStore modules sitting on separate cache servers, it is possible for one database update to succeed and for the other to fail.If a putAll is called on a cache, will it result in one CacheStore.storeAll or many storeAll triggered from different coherence nodes/servers? (assume a distributed topology coherence 3.7.1)
    Will the store transaction failure lead to putAll transaction failure?
    Are there any patterns that shows how this coherence works with typical databases?
    14.7.2 Write-Through LimitationsCoherence does not support two-phase CacheStore operations across multiple CacheStore instances. In other words, if two cache entries are updated, triggering calls to CacheStore modules sitting on separate cache servers, it is possible for one database update to succeed and for the other to fail. In this case, it may be preferable to use a cache-aside architecture (updating the cache and database as two separate components of a single transaction) with the application server transaction manager. In many cases it is possible to design the database schema to prevent logical commit failures (but obviously not server failures). Write-behind caching avoids this issue as "puts" are not affected by database behavior (as the underlying issues have been addressed earlier in the design process).

    gs100 wrote:
    Thanks for the input, I have further questions based on these suggestions.
    1. Let us say one of the putAll fails we would know that it has failed due to underlying one or more store/storeAll. And even if we rollback the coherence transaction, the store/storeAll that succeeded would not be rolled back automatically, is that correct? If true, this means that it would leave the underlying DB/store in the inconsistent state with that of in-memory cache?I guess that is one of the reasons why the transaction framework does not support cache stores... also, write-behind would coalesce updates which would have funny consequences with regards to the transactional context...
    2. How do we get the custom implementation of putAll, that you suggested to handle specific errors? any pointers on this would be helpful.I guess it is not going to be posted, the Coherence team may or may not add something which is a bit more deterministic with regards to error.
    A few aspects of Coherence behaviour (a.k.a pitfalls) which you need to be aware of to be able to implement your own solution:
    Exceptions propagating back to the client can happen in:
    - entry-processor (not for putAll specifically)
    - result serialization code (not for putAll specifically, but for processAll/aggregate for example)
    - deserialization code (indexes/filter-based backing map listeners/cache stores lead to deserialization even for putAll)
    - triggers (intentionally, too)
    - cache stores
    There is no place where you could catch any exceptions from inside the NamedCache call, so they will come out.
    Coherence may execute the operation on one thread per partition or one thread per multiple partitions, but never on multiple threads per partition. This means there may be multiple exceptions even from a single storage node, but only at most one exception would be generated per partition (starting with 3.5).
    If you send multiple partitions with the same NamedCache call, you can lose exceptions as you wouldn't know if an exception would have or wouldn't have happened with a partition if it was sent alone instead of together with another on the same node.
    As you need to be able to return all exceptions from your method call, you have to produce and catch all of them and collect them otherwise you would lose all but one. To produce and catch all exceptions you have to produce all exceptions independently, i.e. different partitions must be operated on independently.
    To send an operation to a single partition only, you can separate the operations to different partitions by separating the keysets for different partitions with key-based operations, or applying a PartitionedFilter for filter-based operations.
    It is up to you where and how you iterate through the partitions. You can do it on the caller, you can do it on storage node from an Invocable sent via an InvocationService (in this case you can be either optimistic with ownership or chase a partition).
    3. Because we are thinking putAll that coherence implemented is most optimized (parallelism). I am not sure how the custom implementation can be as optimal (hope we don't end up calling one by one).You cannot implement it as optimally as Coherence itself does as it interleaves operations (Messages) to independent partitions/nodes (does not have to wait for the return message) from a single thread without waiting for the responses from individual nodes/partitions.
    You can either parallelize operations to multiple threads, or do the iteration on the single thread at the cost of higher latency.
    Best regards,
    Robert

  • Thread pool configuration for write-behind cache store operation?

    Hi,
    Does Coherence have a thread pool configuration for the Coherence CacheStore operation?
    Or the CacheStore implementation needs to do that?
    We're using write-behind and want to use multiple threads to speed up the store operation (storeAll()...)
    Thanks in advance for your help.

    user621063 wrote:
    Hi,
    Does Coherence have a thread pool configuration for the Coherence CacheStore operation?
    Or the CacheStore implementation needs to do that?
    We're using write-behind and want to use multiple threads to speed up the store operation (storeAll()...)
    Thanks in advance for your help.Hi,
    read/write-through operations are carried out on the worker thread (so if you configured a thread-pool for the service the same thread-pool will be used for the cache-store operation).
    for write-behind/read-ahead operations, there is a single dedicated thread per cache above whatever thread-pool is configured, except for remove operations which are synchronous and still carried out on the worker thread (see above).
    All above is of course per storage node.
    Best regards,
    Robert

  • Write through and CacheStore

    Hi,
         I'm running a near cache implementation, with the front being a local cache and the back being a distributed cache. The distributed cache has a local cache and a read-write-backing-map-scheme for persisting each cache to disk every t minutes (for backup purposes - we still use a cache in memory).
         I have a few questions about the Write through capabilities and CacheStore so as to better understand what's going on here:
         1. We only need to store the data to disk for backup in case of complete cluster failure (for example, all n machines in our cluster go down). Right now my implementation of the CacheStore has one line which reads "return null" for the following methods:
         load(..)
         loadAll(..)
         Is there a more efficient/effective way to write to disk and ignore reads if item does not exist in distributed map rather than extending CacheStore and returning null for all methods noted above?
         My reading and writing to disk occurs using the ExternalizableHelper class, thx for this nice work.
         2. How are CacheStore's instantiated initially? Since we want each cache (say we have two different caches here for simplicity) backed up to file every t minutes, do we have to have a separate CacheStore object for each cache? What is the best practice to attach a cachestore to a particular cache?
         For example, I start two Tangosol instances, one on machineA and one on machineB, both storing data as per my configuration. The 2 caches being used are "cacheA" and "cacheB". So when I start the two Tangosol instances, I have to instantiate CacheStore twice so that I can separately write "cacheA" and "cacheB" to their own separate files.
         3. When backup to disk occurs, is there any removing of items from the distributed cache?
         4. I'm not completely sure on the write delay here. What if an item in the cache is just added once, and no updates occur on it (ie. just one put, and 0+ gets). After a specified amount of time, will this be written to disk, or does an update on this object in the cache have to occur before this item can be added to the write queue and eventually written to disk? Once an item is added for the first time, does this trigger the update time for this object to be the first write time?
         Thanks,
         - Noah

    Hi Noah,
         1. No, load() and loadAll() returning null is the most effective way of implementing this.
         2. You can pass the cache name as a constructor parameter - see Parameter Macros in the Coherence User Guide.
         3. No, nothing is removed from the cache
         4. Writes are only triggered by put()'s.
         For more information please take a look at this forum post: <a href = "http://www.tangosol.net/forums/thread.jspa?threadID=445&tstart=0">What is Read-Through/Write-Through/Write-Behind Caching? </a>
         Regards,
         Dimitri

  • What triggers a write-through/write-behind of entry processor changes?

    What triggers a write-through/write-behind when a cache entry is modified by a custom entry processor (subclass of AbstractProcessor)? Is it simply the call to Entry.setValue() that triggers this or are entry modifications detected in some other way?
    The reason I am asking is that in our Coherence cluster it looks like some entry modifications through entry processors have not triggered a write-behind, and I see no logical pattern as to which ones have and which ones haven't except that some specific entries are just never written to our database. We see from our logs that our implementation of the CacheStore.store() method is not called in these cases, and we also see that the cache entry has been modified successfully.
    We are using Coherence 3.3 on a three machine cluster with 8 nodes on each machine, accessed from clients through a TCP extend proxy.
    Regards,
    Mikael Carlstedt
    mBlox Inc
    Edited by: user3849225 on 16-Sep-2010 04:57

    Hi Mikael
    Calling setEntry() will result in a call to the CacheStore.store() method unless the value you are setting is the same as the existing entry value. If you are using writebehind then storeAll() will be called instead of store() if there are multiple entries waiting to be stored. Writebehind will also coelesce entries so that only the last entry for a given key will be stored.
    What patch level are you using?
    Paul
    Edited by: pmackin on Sep 17, 2010 12:08 AM

  • Map listeners and write-through strategy.

    Hi.
    Write-through strategy seems to be synchronious operations and if it fails, no data should appear in cache. Logically this means, that no events will be produced if the persisting fails (that's what we exactly need). But could not find any mention about this in documentation. Can anyone verify this?
    Thanks, Anton.

    If you are talking about throwing an exception in your CacheStore code,
    it will happen before anything occurs in the internal cache managed by
    Coherence and no events will be generated (that would have been generated
    under normal cases where the operation succeeded.)

  • Write behind cache, DB down, when should the system stop taking new data in

    Hello:
    We are trying to use Coherence for our custom ESB, which is brokering payloads of various size between consumer and provider applications.
    Before Coherence, stopping our DB meant organization-wide outage for critically important business services.
    Since we have at least 40G of RAM in production environment, we believe that our app
    can use Coherence write-behind option for tolerating at least several hours worth of DB outage.
    We are currently using a near cache backed by distributed cache in write-behind mode.
    9 business service JVMs (storage enabled=false) use 30 storage enabled JVMs.
    IMPORTANT: We need to create an automated alerting facility determining when
    amount of unsaved data reaches critical level since DB goes down. This alert should help us decide when our application stops accepting inbound traffic.
    It is hard to use QueueSize parameter for that because our payload memory footprint can vary from 1KB to 3MB.
    We do not expire any entries in order to enable support queries against the cache during DB outage.
    Our experiments with trying various flavors of overflow-scheme resulted in OutOfMemoryError, therefore
    we decided to implement RAM-only cache as a first step.
    <near-scheme>
    <scheme-name>message_payload_scheme</scheme-name>
    <front-scheme>
    <local-scheme>
    <scheme-ref>limited_entities_front_scheme</scheme-ref>
    <high-units>100</high-units>
    </local-scheme>
    </front-scheme>
    <back-scheme>
    <distributed-scheme>
    <backing-map-scheme>
    <read-write-backing-map-scheme>
    <internal-cache-scheme>
    <local-scheme>
    <scheme-ref>limited_bytes_scheme</scheme-ref>
    <high-units>199229440</high-units>
    </local-scheme>
    </internal-cache-scheme>
    <cachestore-scheme>
    <class-scheme>
    <class-name>com.comp.MessagePayloadStore</class-name>
    </class-scheme>
    </cachestore-scheme>
    <read-only>false</read-only>
    <write-delay-seconds>3</write-delay-seconds>
    <write-requeue-threshold>2147483646</write-requeue-threshold>
    </read-write-backing-map-scheme>
    </backing-map-scheme>
    <autostart>true</autostart>
    </distributed-scheme>
    </back-scheme>
    </near-scheme>
    <local-scheme>
    <scheme-name>limited_entities_front_scheme</scheme-name>
    <eviction-policy>LRU</eviction-policy>
    <unit-calculator>FIXED</unit-calculator>
    </local-scheme>
    <local-scheme>
    <scheme-name>limited_bytes_scheme</scheme-name>
    <eviction-policy>HYBRID</eviction-policy>
    <unit-calculator>BINARY</unit-calculator>
    </local-scheme>

    Good info ... I feel like I need to restate my original question along with a couple of new questions caused by the discussion above.
    Q1. Does Coherence evict 'dirty', or 'queued', or 'unsaved' objects for cache configuration provided above?
    The answer should be 'NO', otherwise Coherence is unsafe to use as a system of record,
    it should not just drop unsaved information on the floor.
    Q2. What happens to the front tier of the near+partitioned write behind cache described above when amount of unsaved data exceeds max cache capacity defined via high-units?
    I would expect that map.put starts throwing exceptions: cache storage is full, so it should not accept more data
    Q3. How can I determine a moment when amount of dirty data in bytes(!), not in objects, hits 85% of
    max allowed cache capasity configured in bytes (using high-units param and BINARY calculator).
    'DirtyUnits' counter can probably be built with some lower-level Coherence API. Can we use
    this API?
    Please, understand, that we purchased Coherence for reliability, for making our
    system independent from short DB outages, for keeping our business services up
    and running when DBA need some time for admin operations like rebuilding an index.
    Performance benefits are secondary and are not as obvious for our system which
    uses primary keys only and has a well-tuned co-located Oracle back-end.
    We simply cannot put Coherence to production unless we prove that Coherence
    can reliably hold the data and give us information about approaching crisis
    (the cache full of unsaved data).
    If possible, forward this message to Cameron Purdy,
    who was presenting Coherence to our team several moths ago.
    Thanks,
    Vasili Smaliak
    Applications Architect, Enterprise App Integration
    GMAC ResCap
    [email protected]

  • Write-Behind Caching and Re-entrant Calls

    Support Team -
         The Coherence User Guide states that:
         "The CacheStore implementation must not call back into the hosting cache service. This includes OR/M solutions that may internally reference Coherence cache services. Note that calling into another cache service instance is allowed, though care should be taken to avoid deeply nested calls (as each call will "consume" a cache service thread and could result in deadlock if a cache service threadpool is exhausted)."
         I have Load-tested a use case wherein I have two caches: ABCache and BACache. ABCache is accessed by the application for write operation, BACache is accessed by the application for read operation. ABCache is a write-behind cache whose CacheStore populates BACache by reversing key and value of each cache entry stored in the ABCache.
         The solution worked under load with no issues.
         But can I use it? Or is it too dangerous?
         My write-behind thread-count setting is left at default (0). The documentation states that
         "If zero, all relevant tasks are performed on the service thread."
         What does this mean? Can I re-enter the caching service if my thread-count is zero?
         Thank you,
         Denis.

    Dimitri -
         I am not sure I fully understand your answer:
         1. "Your test worked because write-behing backing map invokes CacheStore methods asynchronously, on a write-behind thread." In my configuration, I have default value for thread-count, which is zero. According to the documentation, that means that CacheStore methods would be executed by the service thread and not by the write-behind thread. Do I understand this correctly?
         2. "If will fail if CacheStore method will need to be invoked synchronously on a service thread." I am not sure what is the purpose of the "service thread". In which scenarios the "CacheStore method will need to be invoked synchronously on a service thread"?
         Thank you,
         Denis.

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