Write-through cache error kills coherence

Hi, we have a write through cache, and when there's an error writing to the cache store, the cache dies. The first error we see is:
3.5.3/465 | Coherence(3) - 2012-07-19 07:28:35.918/34.768 Oracle Coherence GE 3.5.3/465 <Info> (thread=DistributedWriteThroughWorker:3, member=1): (Wrapped: Failed to store key="1342708115777") java.lang.RuntimeException: Failed to store hibernate entity : This is normal, since there's a legitimate reason why this couldn't get stored, but after the stack trace, we see the below. So the 1st thing is the "Terminating DistributedCache", that just kills the node and makes it unusable, then there's the "unknown user type" message, as if it's trying to send something over the wire, but it can't; although this "WrapperException" is not one of our classes. Clearly we don't want the cache dying, we want to continue to use it, but any subsequent request to the cache fails. Any ideas?
     [java] ERROR | 07-19-2012 07:28:36.116 | [email protected] 3.5.3/465 | Coherence(3) - 2012-07-19 07:28:35.919/34.769 Oracle Coherence GE 3.5.3/465 <Error> (thread=DistributedWriteThroughWorker:3, member=1): Terminating DistributedCache due to unhandled exception: java.lang.IllegalArgumentException
     [java] ERROR | 07-19-2012 07:28:36.117 | [email protected] 3.5.3/465 | Coherence(3) - 2012-07-19 07:28:35.919/34.769 Oracle Coherence GE 3.5.3/465 <Error> (thread=DistributedWriteThroughWorker:3, member=1):
     [java] java.lang.IllegalArgumentException: unknown user type: com.tangosol.util.WrapperException
     [java]      at com.tangosol.io.pof.ConfigurablePofContext.getUserTypeIdentifier(ConfigurablePofContext.java:400)
     [java]      at com.tangosol.io.pof.ConfigurablePofContext.getUserTypeIdentifier(ConfigurablePofContext.java:389)
     [java]      at com.tangosol.io.pof.PofBufferWriter.writeObject(PofBufferWriter.java:1432)
     [java]      at com.tangosol.io.pof.ConfigurablePofContext.serialize(ConfigurablePofContext.java:338)
     [java]      at com.tangosol.coherence.component.util.daemon.queueProcessor.Service.writeObject(Service.CDB:4)
     [java]      at com.tangosol.coherence.component.net.Message.writeObject(Message.CDB:1)
     [java]      at com.tangosol.coherence.component.net.message.DistributedCacheResponse.write(DistributedCacheResponse.CDB:2)
     [java]      at com.tangosol.coherence.component.util.daemon.queueProcessor.packetProcessor.PacketPublisher.packetizeMessage(PacketPublisher.CDB:137)
     [java]      at com.tangosol.coherence.component.util.daemon.queueProcessor.packetProcessor.PacketPublisher$InQueue.add(PacketPublisher.CDB:8)
     [java]      at com.tangosol.coherence.component.util.daemon.queueProcessor.service.Grid.dispatchMessage(Grid.CDB:50)
     [java]      at com.tangosol.coherence.component.util.daemon.queueProcessor.service.Grid.post(Grid.CDB:53)
     [java]      at com.tangosol.coherence.component.util.daemon.queueProcessor.service.grid.DistributedCache.onPutRequest(DistributedCache.CDB:146)
     [java]      at com.tangosol.coherence.component.util.daemon.queueProcessor.service.grid.DistributedCache$PutRequest.run(DistributedCache.CDB:1)
     [java]      at com.tangosol.coherence.component.util.DaemonPool$WrapperTask.run(DaemonPool.CDB:1)
     [java]      at com.tangosol.coherence.component.util.DaemonPool$WrapperTask.run(DaemonPool.CDB:32)
     [java]      at com.tangosol.coherence.component.util.DaemonPool$Daemon.onNotify(DaemonPool.CDB:63)
     [java]      at com.tangosol.coherence.component.util.Daemon.run(Daemon.CDB:42)
     [java]      at java.lang.Thread.run(Thread.java:662)

Ok, figured it out. I failed to include the standard "coherence-pof-config.xml" when loading in our pof config file, and that's what caused the troubles. Including it solved the problem of the dying cache.

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    A) Client1 requests Obj1.
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    -Andy
    ============================================
    Andy Faibishenko (312)251-3267
    Senior Consultant (800)462-6301
    Metamor Technologies, Inc. [email protected]

    Hello Mark,
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    >
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    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
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    Hope this is of some help.
    Mark Potts
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    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
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    Due to financial considerations, we cannot buy an off-the-shelf
    framework.
    What we are trying to build is some kind of object persistence
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    Our big issue is maintaining consistency within the cache, for
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    Example:
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    B) Obj1 is de-serialized, placed in the cache and a reference to Obj1
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    C) Client1 modifies the state of Obj1 trough its reference.
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    E) Obj2 is de-serialized, placed in the cache, knocking out Obj1, and
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    F) Client2 requests Obj1. Since it is no longer in the cache, we
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    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]

  • Write-through caching in Forte

    Has anyone out there been successful at implementing a cache which
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    ============================================
    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
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    Obj2 is selected stamped and returned to the client as per the steps
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    When Client 2 now selects Obj1, no longer in the cache, the same steps
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    The cache now contains the same version of Obj1 as give to Client1.
    Now the important part, becuase this is an optimistic locking strategy -
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    when the object is saved - returned to the persistent store, that the
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    When Client1 now tries to save the version of Obj1 a conflict will
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    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
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    Example:
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    B) Obj1 is de-serialized, placed in the cache and a reference to Obj1
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    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
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    now have two out of sync copies of Obj1, or we need to give Client2
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    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 3:15 PM

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    Robert

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    http://wiki.tangosol.com/display/COH33UG/Read-Through,+Write-Through,+Refresh-Ahead+and+Write-Behind+Caching
    For applications where write-behind would be used, the partitioned (distributed) cache is almost always a far better option. Is there a reason to not use this?
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    Oracle

  • Transactional Caches and Write Through

    I've been trying to implement the use of multiple caches, each with write through, all within a transaction.
         The CacheFactory.commitTransactionCollection(..) method only seems to work correctly if the first transactionMap throws an exception in the database code.
         If the second transactionMap throws exceptions, the caches do not appear to rollback correctly.
         I can wrap the whole operation in a JDBC transaction that rolls back the database correctly but the caches are not all rolled back because they are committed one by one?
         For example, I write to two transaction maps, each one created from separate caches. When commiting the transaction maps, the second transaction map causes a database exception. It appears the first transaction map has already committed its objects and doesn't roll back.
         Is it possible to use Coherence with multiple transaction maps and get all the caches and databases rolled back?
         I've also been trying to look at using coherence-tx.rar as described in the forums within WebLogic but I'm getting @@@@@ Failed to commit: javax.transaction.SystemException: Could not contact coordinator at null+SMARTPC:7001+null+t3+
         (SMARTPC being my pc name)
         Has anybody else had this problem? Bonus points for describing how to fix it!
         Mike

    The transaction support in Coherence is for Local     > Transactions. Basically, what this means is that the
         > first phase of the commit ("prepare") acquires locks
         > and ensures that there are no conflicts. The second
         > phase ("commit") does nothing but push data out to
         > the caches.
         This means that once prepare succeeds (all locks acquired), commit will try to copy local data into the base map. If there is a failure on any put, rollback will undo any changes made. All locks are cleared at the end.
         > The problem is that when you are using a
         > CacheStore module, the exception is occurring during
         > the second phase.
         If you start using a CacheStore module, then database update has to be part of the atomic procedure.
         >
         > 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?

  • Should OS/FileSystem caching be write-through?

    I have a question. I use Ubuntu. Should I mount my filesystem (which holds BDB's content) with "-o sync" option? That is, should my file system cache be write-through?
    I have this question because, if I turn on the logging feature in Berkeley DB but let the file system cache be write-back, I don't exactly know if the log is properly flushed to the disk or not.

    Thanks George. I agree that mature applications would be better off mounting their filesystem with "-o sync" option.
    But here is a thing: I ran an example test case where I inserted 10 million key-value pairs with logging enabled, and saw that the average response time per insertion was 10 milli seconds, and I did the same experiment with logging disabled and saw that it too took 10 milliseconds per insertion on an average.
    For the experiment with logging enabled, I create the environment with DB_INIT_LOG and DB_INIT_TXN flags but don't surround the insertion requests with txn_begin() and txn->commit(). I guess this way of doing insertions is called autocommit. I am hoping I am doing this experiment right.
    Thanks for the pointers about set_flags() and DB_TXN_NOSYNC, I am going to look them up.

  • 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

  • Drive Cache Errors in Logs

    I noticed that the same two entries in /var/log/errors are flooding my logs and I would like to know if this is an issue I can resolve or a red flag for bigger things...
    Jun 2 11:18:12 ghost kernel: sd 2:2:0:0: [sda] Asking for cache data failed
    Jun 2 11:18:12 ghost kernel: sd 2:2:0:0: [sda] Assuming drive cache: write through
    Does anyone know whats causing the entries above and how I can resolve this?

    is your drive new or old, you can ignore this message since the kernel thinks that the drive does not support caching. if your drive supports caching but the kernel is not recognizing it, you can change it yourself by using hdparm/sdparm

  • 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
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