Write-behind max speed?

Hi,
We are trying to test the speed of the write behind mechanism and we would be interested to know how other coherence users handle, for example, writing 1 million rows into the database.
At the moment, using jdbc batch inserts we can write approximately 30000 rows per minute, which means it would take about 30 minutes to save 1 million rows. Are there any other methods that other coherence user's use that can improve on this?
Many thanks,

user738616 wrote:
Hi,
This has nothing to do with Coherence as the implementation of CacheStore is outside of Coherence. Apart from JDBC Batch, you should try using PLSQL Bulk binds for such numbers.
Hope this helps!
Cheers,
NJHi NJ,
we actually measured PLSQL bulk binds against plain SQL (both with JDBC)... for anything which can be translated to plain inserts/updates, plain SQL is way faster (more than 10x).
You can only win with bulk binds when that statement which you send down actually does more complex logic and multiple statements so you actually win with optimizing away the roundtrips, too.
Best regards,
Robert

Similar Messages

TTL specified in put operation doesn't always work when using write-behind

I'm using a distributed cache with a write-behind cache store (see the config below). I found that when I do something like myCache.put(key, value, ttl), the entry survives the specified ttl. I tried doing the same with a distributed cache with a write-through cachestore and there everything does happen correctly.
Is this sort of operation not permitted in caches containing a write-behind cachestore? If not wouldn't it be better to throw an UnsupportedOperationException.
I created a small test to simulate this. I added values to the cache with a TTL of 1 to 10 seconds and found that the 10 second entries stayed in the cache.
Configuration used:
<?xml version="1.0"?>
<!DOCTYPE cache-config SYSTEM "cache-config.dtd">
<cache-config>
     <caching-scheme-mapping>
          <cache-mapping>
               <cache-name>TTL_TEST</cache-name>
               <scheme-name>testScheme</scheme-name>
          </cache-mapping>
     </caching-scheme-mapping>
     <caching-schemes>
          <distributed-scheme>
               <scheme-name>testScheme</scheme-name>
               <service-name>testService</service-name>
               <backing-map-scheme>
                    <read-write-backing-map-scheme>
                         <internal-cache-scheme>
                              <local-scheme>
                                   <service-name>testBackLocalService</service-name>
                              </local-scheme>
                         </internal-cache-scheme>
                         <cachestore-scheme>
                              <class-scheme>
                                   <scheme-name>testBackStore</scheme-name>
                                   <class-name>TTLTestServer$TestCacheStore</class-name>
                              </class-scheme>
                         </cachestore-scheme>
                         <write-delay>3s</write-delay>
                    </read-write-backing-map-scheme>
               </backing-map-scheme>
               <local-storage>true</local-storage>
               <autostart>true</autostart>
          </distributed-scheme>
     </caching-schemes>
</cache-config>Code of test:
import java.util.Collection;
import java.util.List;
import java.util.Map;
import java.util.concurrent.Callable;
import java.util.concurrent.ExecutionException;
import java.util.concurrent.ExecutorService;
import java.util.concurrent.Executors;
import java.util.concurrent.Future;
import org.joda.time.DateTime;
import org.joda.time.Duration;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import org.springframework.util.StopWatch;
import org.testng.annotations.BeforeClass;
import org.testng.annotations.Test;
import com.google.common.collect.Lists;
import com.tangosol.net.CacheFactory;
import com.tangosol.net.NamedCache;
import com.tangosol.net.cache.CacheStore;
@Test
public class TTLTestServer
     private static final int RETRIES = 5;
     private static final Logger logger = LoggerFactory.getLogger( TTLTestServer.class );
     private NamedCache m_cache;
      * List of Time-To-Lives in seconds to check
     private final List<Integer> m_listOfTTLs = Lists.newArrayList(1, 3, 5, 10);
      * Test is done in separate threads to speed up the test
     private final ExecutorService m_executorService = Executors.newCachedThreadPool();
     @BeforeClass
     public void setup()
          logger.info("Getting the cache");
          m_cache = CacheFactory.getCache("TTL_TEST");
     public static class TestCacheStore implements CacheStore
          public void erase(Object arg0)
          public void eraseAll(Collection arg0)
          public void store(Object arg0, Object arg1)
          public void storeAll(Map arg0)
          public Object load(Object arg0)
          {return null;}
          public Map loadAll(Collection arg0)
          {return null;}
     public void testTTL() throws InterruptedException, ExecutionException
          logger.info("Starting TTL test");
          List<Future<StopWatch>> futures = Lists.newArrayList();
          for (final Integer ttl : m_listOfTTLs)
               futures.add(m_executorService.submit(new Callable()
                    public Object call() throws Exception
                         StopWatch stopWatch= new StopWatch("TTL=" + ttl);
                         for (int retry = 0; retry < RETRIES; retry++)
                              logger.info("Adding a value in cache for TTL={} in try={}", ttl, retry+1);
                              stopWatch.start("Retry="+retry);
                              m_cache.put(ttl, null, ttl*1000);
                              waitUntilNotInCacheAnymore(ttl, retry);
                              stopWatch.stop();
                         return stopWatch;
                    private void waitUntilNotInCacheAnymore(final Integer ttl, final int currentTry) throws InterruptedException
                         DateTime startTime = new DateTime();
                         long maxMillisToWait = ttl*2*1000;     //wait max 2 times the time of the ttl
                         while(m_cache.containsKey(ttl) )
                              Duration timeTaken = new Duration(startTime, new DateTime());
                              if(timeTaken.getMillis() > maxMillisToWait)
                                   throw new RuntimeException("Already waiting " + timeTaken + " for ttl=" + ttl + " and retry=" + currentTry);
                              Thread.sleep(1000);
          logger.info("Waiting until all futures are finished");
          m_executorService.shutdown();
          logger.info("Getting results from futures");
          for (Future<StopWatch> future : futures)
               StopWatch sw = future.get();
               logger.info(sw.prettyPrint());
}Failure message:
FAILED: testTTL
java.util.concurrent.ExecutionException: java.lang.RuntimeException: Already waiting PT20.031S for ttl=10 and retry=0
     at java.util.concurrent.FutureTask$Sync.innerGet(Unknown Source)
     at java.util.concurrent.FutureTask.get(Unknown Source)
     at TTLTestServer.testTTL(TTLTestServer.java:159)
Caused by: java.lang.RuntimeException: Already waiting PT20.031S for ttl=10 and retry=0
     at TTLTestServer$1.waitUntilNotInCacheAnymore(TTLTestServer.java:139)
     at TTLTestServer$1.call(TTLTestServer.java:122)
     at java.util.concurrent.FutureTask$Sync.innerRun(Unknown Source)
     at java.util.concurrent.FutureTask.run(Unknown Source)
     at java.util.concurrent.ThreadPoolExecutor$Worker.runTask(Unknown Source)
     at java.util.concurrent.ThreadPoolExecutor$Worker.run(Unknown Source)
     at java.lang.Thread.run(Unknown Source)I'm using Coherence 3.4.2.
Best regards
Jan

Hi, still no luck. However, I noticed that setting the write-delay value of the write-behind store to 0s or 1s, solved the problem. It only starts to given me "the node has already been removed" excpetions once the write-delay value is 2s or higher.
You can find the coherence-cache-config.xml below:
<?xml version="1.0"?>
<!DOCTYPE cache-config SYSTEM "cache-config.dtd">
<cache-config>
     <caching-scheme-mapping>
          <cache-mapping>
               <cache-name>TTL_TEST</cache-name>
               <scheme-name>testScheme</scheme-name>
          </cache-mapping>
     </caching-scheme-mapping>
     <caching-schemes>
          <distributed-scheme>
               <scheme-name>testScheme</scheme-name>
               <service-name>testService</service-name>
               <backing-map-scheme>
                    <read-write-backing-map-scheme>
                         <internal-cache-scheme>
                              <local-scheme>
                                   <service-name>testBackLocalService</service-name>
                              </local-scheme>
                         </internal-cache-scheme>
                         <cachestore-scheme>
                              <class-scheme>
                                   <scheme-name>testBackStore</scheme-name>
                                   <class-name>TTLTestServer$TestCacheStore</class-name>
                              </class-scheme>
                         </cachestore-scheme>
                         <write-delay>2s</write-delay>
                    </read-write-backing-map-scheme>
               </backing-map-scheme>
               <local-storage>true</local-storage>
               <autostart>true</autostart>
          </distributed-scheme>
     </caching-schemes>
</cache-config>You can find the test program below:
import java.util.ArrayList;
import java.util.Collection;
import java.util.List;
import java.util.Map;
import org.joda.time.DateTime;
import org.joda.time.Duration;
import org.springframework.util.StopWatch;
import com.tangosol.net.CacheFactory;
import com.tangosol.net.NamedCache;
import com.tangosol.net.cache.CacheStore;
public class TTLTestServer
     private static final int RETRIES = 5;
     private NamedCache m_cache;
      * List of Time-To-Lives in seconds to check
     private final List<Integer> m_listOfTTLs = new ArrayList<Integer>();
      * @param args
      * @throws Exception
     public static void main( String[] args ) throws Exception
          new TTLTestServer().test();
      * Empty CacheStore
      * @author jbe
     public static class TestCacheStore implements CacheStore
          public void erase(Object arg0)
          @SuppressWarnings ( "unchecked" )
          public void eraseAll(Collection arg0)
          public void store(Object arg0, Object arg1)
          @SuppressWarnings ( "unchecked" )
          public void storeAll(Map arg0)
          public Object load(Object arg0)
          {return null;}
          @SuppressWarnings ( "unchecked" )
          public Map loadAll(Collection arg0)
          {return null;}
      * Sets up and executes the test setting values in a cache with a given time-to-live value and waiting for the value to disappear.
      * @throws Exception
     private void test() throws Exception
          System.out.println(new DateTime() + " - Setting up TTL test");
          m_cache = CacheFactory.getCache("TTL_TEST");
          m_listOfTTLs.add( 1 );
          m_listOfTTLs.add( 3 );
          m_listOfTTLs.add( 5 );
          m_listOfTTLs.add( 10);
          System.out.println(new DateTime() + " - Starting TTL test");
          for (final Integer ttl : m_listOfTTLs)
               StopWatch sw = doTest(ttl);
               System.out.println(sw.prettyPrint());
      * Adds a value to the cache with the time-to-live as given by the ttl parameter and waits until it's removed from the cache.
      * Repeats this {@link #RETRIES} times
      * @param ttl
      * @return
      * @throws Exception
     private StopWatch doTest(Integer ttl) throws Exception
          StopWatch stopWatch= new StopWatch("TTL=" + ttl);
          for (int retry = 0; retry < RETRIES; retry++)
               System.out.println(new DateTime() + " - Adding a value in cache for TTL=" + ttl + " in try= " + (retry+1));
               stopWatch.start("Retry="+retry);
               m_cache.put(ttl, null, ttl*1000);
               waitUntilNotInCacheAnymore(ttl, retry);
               stopWatch.stop();
          return stopWatch;
      * Wait until the value for the given ttl is not in the cache anymore
      * @param ttl
      * @param currentTry
      * @throws InterruptedException
     private void waitUntilNotInCacheAnymore(final Integer ttl, final int currentTry) throws InterruptedException
          DateTime startTime = new DateTime();
          long maxMillisToWait = ttl*2*1000;     //wait max 2 times the time of the ttl
          while(m_cache.containsKey(ttl) )
               Duration timeTaken = new Duration(startTime, new DateTime());
               if(timeTaken.getMillis() > maxMillisToWait)
                    throw new RuntimeException("Already waiting " + timeTaken + " for ttl=" + ttl + " and retry=" + currentTry);
               Thread.sleep(1000);
}You can find the output below:
2009-12-03T11:50:04.584+01:00 - Setting up TTL test
2009-12-03 11:50:04.803/0.250 Oracle Coherence 3.5.2/463p2 <Info> (thread=main, member=n/a): Loaded operational configuration from resource "jar:file:/C:/Temp/coherence3.5.2/coherence-java-v3.5.2b463-p1_2/coherence/lib/coherence.jar!/tangosol-coherence.xml"
2009-12-03 11:50:04.803/0.250 Oracle Coherence 3.5.2/463p2 <Info> (thread=main, member=n/a): Loaded operational overrides from resource "jar:file:/C:/Temp/coherence3.5.2/coherence-java-v3.5.2b463-p1_2/coherence/lib/coherence.jar!/tangosol-coherence-override-dev.xml"
2009-12-03 11:50:04.803/0.250 Oracle Coherence 3.5.2/463p2 <D5> (thread=main, member=n/a): Optional configuration override "/tangosol-coherence-override.xml" is not specified
2009-12-03 11:50:04.803/0.250 Oracle Coherence 3.5.2/463p2 <D5> (thread=main, member=n/a): Optional configuration override "/custom-mbeans.xml" is not specified
Oracle Coherence Version 3.5.2/463p2
Grid Edition: Development mode
Copyright (c) 2000, 2009, Oracle and/or its affiliates. All rights reserved.
2009-12-03 11:50:04.943/0.390 Oracle Coherence GE 3.5.2/463p2 <Info> (thread=main, member=n/a): Loaded cache configuration from "file:/C:/jb/workspace3.5/TTLTest/target/classes/coherence-cache-config.xml"
2009-12-03 11:50:05.318/0.765 Oracle Coherence GE 3.5.2/463p2 <D5> (thread=Cluster, member=n/a): Service Cluster joined the cluster with senior service member n/a
2009-12-03 11:50:08.568/4.015 Oracle Coherence GE 3.5.2/463p2 <Info> (thread=Cluster, member=n/a): Created a new cluster "cluster:0xD3FB" with Member(Id=1, Timestamp=2009-12-03 11:50:05.193, Address=172.16.44.32:8088, MachineId=36896, Location=process:11848, Role=TTLTestServerTTLTestServer, Edition=Grid Edition, Mode=Development, CpuCount=2, SocketCount=2) UID=0xAC102C20000001255429380990201F98
2009-12-03 11:50:08.584/4.031 Oracle Coherence GE 3.5.2/463p2 <D5> (thread=Invocation:Management, member=1): Service Management joined the cluster with senior service member 1
2009-12-03 11:50:08.756/4.203 Oracle Coherence GE 3.5.2/463p2 <D5> (thread=DistributedCache:testService, member=1): Service testService joined the cluster with senior service member 1
2009-12-03T11:50:08.803+01:00 - Starting TTL test
2009-12-03T11:50:08.818+01:00 - Adding a value in cache for TTL=1 in try= 1
2009-12-03T11:50:09.818+01:00 - Adding a value in cache for TTL=1 in try= 2
Exception in thread "main" (Wrapped: Failed request execution for testService service on Member(Id=1, Timestamp=2009-12-03 11:50:05.193, Address=172.16.44.32:8088, MachineId=36896, Location=process:11848, Role=TTLTestServerTTLTestServer)) java.lang.IllegalStateException: the node has already been removed
     at com.tangosol.util.Base.ensureRuntimeException(Base.java:293)
     at com.tangosol.coherence.component.util.daemon.queueProcessor.service.Grid.tagException(Grid.CDB:36)
     at com.tangosol.coherence.component.util.daemon.queueProcessor.service.grid.DistributedCache.onContainsKeyRequest(DistributedCache.CDB:41)
     at com.tangosol.coherence.component.util.daemon.queueProcessor.service.grid.DistributedCache$ContainsKeyRequest.run(DistributedCache.CDB:1)
     at com.tangosol.coherence.component.net.message.requestMessage.DistributedCacheKeyRequest.onReceived(DistributedCacheKeyRequest.CDB:12)
     at com.tangosol.coherence.component.util.daemon.queueProcessor.service.Grid.onMessage(Grid.CDB:9)
     at com.tangosol.coherence.component.util.daemon.queueProcessor.service.Grid.onNotify(Grid.CDB:136)
     at com.tangosol.coherence.component.util.daemon.queueProcessor.service.grid.DistributedCache.onNotify(DistributedCache.CDB:3)
     at com.tangosol.coherence.component.util.Daemon.run(Daemon.CDB:42)
     at java.lang.Thread.run(Thread.java:619)
Caused by: java.lang.IllegalStateException: the node has already been removed
     at com.tangosol.util.AbstractSparseArray$Crawler.remove(AbstractSparseArray.java:1274)
     at com.tangosol.net.cache.OldCache.evict(OldCache.java:580)
     at com.tangosol.net.cache.OldCache.containsKey(OldCache.java:171)
     at com.tangosol.net.cache.ReadWriteBackingMap.containsKey(ReadWriteBackingMap.java:597)
     at com.tangosol.coherence.component.util.daemon.queueProcessor.service.grid.DistributedCache.onContainsKeyRequest(DistributedCache.CDB:25)
     ... 7 more
2009-12-03 11:50:10.834/6.281 Oracle Coherence GE 3.5.2/463p2 <D4> (thread=ShutdownHook, member=1): ShutdownHook: stopping cluster node
2009-12-03 11:50:10.834/6.281 Oracle Coherence GE 3.5.2/463p2 <D5> (thread=Cluster, member=1): Service Cluster left the clusterBest regards
Jan

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]

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

How to limit Write-Behind batch

We have a scenario:
we use read-write-backing-map-scheme having write-delay 60s.
System insert a lot of data and then time comes to write data coherence find 40-50 k of unsaved record and pass them all to cachestore.
Due to data volume or database busyness cashstore may work some time several seconds for instance.
<read-write-backing-map-scheme>
<scheme-name>TicketDatabaseScheme</scheme-name>
<scheme-ref>DefaultDatabaseScheme</scheme-ref>

<cachestore-scheme>
<class-scheme>
<class-name>com.griddynamics.ticketon.app.dao.coherence.TicketCacheStore</class-name>
</class-scheme>
</cachestore-scheme>
</read-write-backing-map-scheme>
<read-write-backing-map-scheme>
<scheme-name>DefaultDatabaseScheme</scheme-name>
<internal-cache-scheme>
<local-scheme>
<scheme-ref>LocalScheme</scheme-ref>
</local-scheme>
</internal-cache-scheme>
<write-delay>60s</write-delay>
</read-write-backing-map-scheme>
In the case we experience "Terminating guarded execution" followed by service termination.
2010-02-11 09:26:52.223/511.457 Oracle Coherence GE 3.5.2/463 <Error> (thread=DistributedCache:TicketonCache, member=2): Terminating guarded execution (due to hard timeout 1924ms ago) of Daemon{Thread="Thread[WriteBehindThread:CacheStoreWrapper(com.griddynamics.ticketon.app.dao.coherence.TicketCacheStore),5,WriteBehindThread:CacheStoreWrapper(com.griddynamics.ticketon.app.dao.coherence.TicketCacheStore)]", State=Running}
2010-02-11 09:26:52.225/511.459 Oracle Coherence GE 3.5.2/463 <Error> (thread=Termination Thread, member=2): Write-behind thread timed out; stopping the cache service
2010-02-11 09:26:52.226/511.460 Oracle Coherence GE 3.5.2/463 <D5> (thread=DistributedCache:TicketonCache, member=2): Service TicketonCache left the cluster
INFO 09:26:52,227 [http--80-22$27432016 DaoCoherenceImpl] - PROFILE_doCreatetickets putAll 200 tickets time 3444 time per 10 objects 172
INFO 09:26:52,227 [http--80-22$27432016 DaoCoherenceImpl] - PROFILE event and 1000 tickets ctreated time 9668
Broadcast Message from root (msglog) on ip-10-226-137-172 Thu Feb 11 09:57:18...ets putAll 200 tickets time 3365 time per 10 objects 168
2010-02-11 09:26:52.228/511.462 Oracle Coherence GE 3.5.2/463 <Info> (thread=httTHE SYSTEM ip-10-226-137-172 IS BEING SHUT DOWN NOW ! ! !et
Log off now or risk your files being damagedence GE 3.5.2/463 <Info> (thread=http--80-27$25787595, member=2): Restarting Service: TicketonCache
INFO 09:26:52,229 [http--80-20$15974570 DaoCoherenceImpl] - PROFILE_doCreatetickets putAll 200 tickets time 3447 time per 10 objects 172
INFO 09:26:52,289 [http--80-22$26935588 BackingBeanSuper] - request HttpRequest[22]
[09:26:53.446] {http--80-35$24027494} java.lang.RuntimeException: Failed to start Service "TicketonCache" (ServiceState=SERVICE_STOPPED)
[09:26:53.446] {http--80-35$24027494} at com.tangosol.coherence.component.util.daemon.queueProcessor.Service.waitAcceptingClients(Service.CDB:12)
Resume: coherence crashes in case far from fatal.
Questions
1. May i limit the size of a batch passed to cachestore?
2. Is it possible to configure the timeout "(due to hard timeout 1924ms ago)"
3. Is it possible to handle like this some way to prevent self killing of coherence cluster.

Thank you Mark you are vary helpfull
Did you mean that (lower) by "bundle strategy"?
<cachestore-scheme>
<class-scheme>
<class-name>com.griddynamics.ticketon.app.dao.coherence.TicketCacheStore</class-name>
</class-scheme>
<operation-bundling>
<bundle-config>
<operation-name>store</operation-name>
<preferred-size>5000</preferred-size>
<auto-adjust>true</auto-adjust>
</bundle-config>
</operation-bundling>
</cachestore-scheme>
And if yes is it looks sense?
I mean by this, "send records to TicketCacheStore by 5000 per call " am i right?
I dropped delay to 10s and set factor to 0.5
Not coherece send me 5-20k records and cachestore handle whis successfuly.
But! By diferent means it may work longer sometimes, some lock in database for instance.
I want to find durable solution for the case, not only lower a chance i meet one.
Issuing heartbeat from cachestore looks best for me now.
I find that default guardian timeout is 65s and it is not looks as good idea to make it higher.

How do I change the background pic thats behind my speed dial thumbnails

i put a pic in the browser window behind my speed dial thumbnails and i cannot remember how to change it.

Figured it out - When looking at your speed dial screen, do a right click, off to the side of a dialer, and choose '''''edit group''''', then select '''''additional style options''''', from there you can select an image background or colour.

How to force write-behind store on cache node shutdown?

Hi,
I built a small pilot project based on Coherence and now I test it for failover. I found replication issues with Distributed cache in the following scenario:
- start cache node 1 (JVM instance 1);
- connect Extend client to it and get 1 object from cache (only 1 object in the cache - loaded by CacheStore from DB);
- change the object and put it back (I use EntryProcessor for this);
- start cache node 2 (JVM instance 2);
- stop cache instance 1 (write-behind store wasn't invoked yet: write-delay = 2m);
- load/change the same object on node 2; all changes done on node 1 are lost.
My expectation was that cache will replicate its data between nodes when new member joins cache cluster. The backup count = 1 by default, right?
What should I do in order to prevent such behavior? Is it possible to force write-behind store on cache node shutdown event?
Thanks, Denis.
My cache-config, just in case:
<cache-config>
<caching-scheme-mapping>
<cache-mapping>
<cache-name>AccountCache</cache-name>
<scheme-name>account-distributed</scheme-name>
</cache-mapping>
</caching-scheme-mapping>
<caching-schemes>
<distributed-scheme>
<scheme-name>account-distributed</scheme-name>
<service-name>DistributedCache</service-name>
<serializer>
<class-name>com.tangosol.io.pof.ConfigurablePofContext</class-name>
<init-params>
<init-param>
<param-type>String</param-type>
<param-value>account-pof-config.xml</param-value>
</init-param>
</init-params>
</serializer>
<backing-map-scheme>
<read-write-backing-map-scheme>
<scheme-name>AccountDatabaseScheme</scheme-name>
<internal-cache-scheme>
<local-scheme>

<eviction-policy>LRU</eviction-policy>
<high-units>0</high-units>
<expiry-delay>30m</expiry-delay>
</local-scheme>
</internal-cache-scheme>
<cachestore-scheme>
<class-scheme>
<class-name>com.roox.bss.cache.store.AccountCacheStore</class-name>
<init-params>
<init-param>
<param-type>java.lang.String</param-type>
<param-value>dburl_</param-value>
</init-param>
<init-param>
<param-type>java.lang.String</param-type>
<param-value>user</param-value>
</init-param>
<init-param>
<param-type>java.lang.String</param-type>
<param-value>password</param-value>
</init-param>
</init-params>
</class-scheme>
</cachestore-scheme>
<write-delay>2m</write-delay>
<write-batch-factor>.5</write-batch-factor>
</read-write-backing-map-scheme>
</backing-map-scheme>
</distributed-scheme>
<proxy-scheme>
<service-name>ExtendTcpProxyService</service-name>
<thread-count>10</thread-count>
<acceptor-config>
<tcp-acceptor>
<local-address>
<address>localhost</address>
<port>9098</port>
<reuse-address>true</reuse-address>
<reusable>true</reusable>
</local-address>
</tcp-acceptor>
<serializer>
<class-name>com.tangosol.io.pof.ConfigurablePofContext</class-name>
<init-params>
<init-param>
<param-type>String</param-type>
<param-value>account-pof-config.xml</param-value>
</init-param>
</init-params>
</serializer>
</acceptor-config>
<autostart>true</autostart>
</proxy-scheme>
</caching-schemes>
</cache-config>

solved with autostart=true

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.

Write-Behind batch behavior in EP partition level transactions

Hi,
We use EntryProcessors to perform updates on multiple entities stored in the same cache partition. According to the documentation, Coherence handles all the updates in a "sandbox" and then commits them atomically to the cache backing map.
The question is, when using write-behind, does Coherence guarantee that all entries updated in the same "partition level transaction" will be present in the same "storeAll" operation?
Again, according to the documentation, the write-behind thread behavior is the following:
The thread waits for a queued entry to become ripe.
When an entry becomes ripe, the thread dequeues all ripe and soft-ripe entries in the queue.
The thread then writes all ripe and soft-ripe entries either via store() (if there is only the single ripe entry) or storeAll() (if there are multiple ripe/soft-ripe entries).
The thread then repeats (1).
If all entries updated in the same partition level transaction become ripe or soft-ripe at the same instant they will all be present in the storeAll operation. If they do not become ripe/soft-ripe in the same instant, they may not be all present.
So, it all depends on the behavior of the commit of the partition level transaction, if all entries get the same update timestamp, they will all become ripe at the same time.
Does anyone know what is the behavior we can expect regarding this issue?
Thanks.

Hi,
That comment is still correct for 12.1 and 3.7.1.10.
I've checked Coherence APIs and the ReadWriteBackingMap behavior, and although partition level transactions are atomic, the updated entries will be added one by one to the write behind queue. In each added entry coherence uses current time to calculate when each entry will become ripe, so, there is no guarantee that all entries in the same partition level transaction will become ripe at the same time.
This leads me to another question.
We have a use case where we want to split a large entity we are storing in coherence into several smaller fragments. We use EntryProcessors and partition level transactions to guarantee atomicity in operations that need to update more than one fragment of the same entity. This guarantees that all fragments of the same entity are fully consistent. The cached fragments are then persisted into database using write-behind.
The problem now is how to guarantee that all fragments are fully consistent in the database. If we just relly on coherence write-behind mecanism we will have eventual consistency in DB, but in case of multi-server failure the entity may become inconsistent in database, which is a risk we wouldnt like to take.
Is there any other option/pattern that would allow us to either store all updates done on the entity or no update at all?
Probably if in the EntryProcessor we identify which entities were updated and if we place them in another persistency queue as a whole, we will be able to achieve this, but this is a kind of tricky workaround that we wouldnt like to use.
Thanks.

Write-Behind, Expiration, and SQL Exceptions.

Hi Chaps,
If a cache with write-behind enabled has problems writing to the DB I understand that Coherence will re-queue the objects and write them when the DB is available.
The problem I have is that (after a DB failure) I don't see them being written - I can see these items in the cache but not in the DB, even several hours after the outage. (Items that were added to the cache after the outage are being written).
Is there anything the cachestore methods (specifically store() ) need to do with regards to exceptions to ensure that these items are re-qeueued?
Next question is: I was also wondering how is this managed with regards to expiry?
We have our own expiry routine which removes items from the cache that are older than 24 hours (this was from before we could expire objects by specifying the timeout in the put() method call, which I am intending to switch to).
If an item has not been written to the DB due to an outage and is then expired (by our own routine or by Coherence) is it then lost forever, or will it remain in the queue? (seeing as the queue holds references I am guessing not but though I'd check).
Thanks,
Randal.

Jon,
I have a question related to this...If you remember a few weeks back, I stumbled upon the problem of the "version-persistent" map for the versioned-backing-map-scheme does not accept putAll operations. The workaround until you guys implement it, was to override the putAll method of the cacheStore and throw and unsupported operation exception (to force individual puts).
Well, although this workaround works, I am getting tons and tons of:
2006-04-06 17:18:27.347 Tangosol Coherence 3.1/339 <Warning> (thread=WriteBehindThread:MyCacheStore, member=1): The CacheStore "MyCacheStore@46b9979b" does not support storeAll().
2006-04-06 17:18:27.348 Tangosol Coherence 3.1/339 <Error> (thread=WriteBehindThread:MyCacheStore, member=1): Failed to store keys="[16, 18, 21, 26, 5, 13, 14, 25, 17, 15, 23, 19, 2, 6, 9, 7]":
java.lang.UnsupportedOperationException
at ...MyCacheStore.storeAll(MyCacheStore.java:126)
at com.tangosol.net.cache.ReadWriteBackingMap$CacheStoreWrapper.storeAll(ReadWriteBackingMap.java:3820)
at com.tangosol.net.cache.ReadWriteBackingMap$WriteThread.run(ReadWriteBackingMap.java:3538)
at com.tangosol.util.Daemon$1.run(Daemon.java:63)
2006-04-06 17:18:27.349 Tangosol Coherence 3.1/339 <Warning> (thread=WriteBehindThread:MyCacheStore, member=1): Requeued store for key="16"
2006-04-06 17:18:27.349 Tangosol Coherence 3.1/339 <Warning> (thread=WriteBehindThread:MyCacheStore, member=1): Requeued store for key="18"
2006-04-06 17:18:27.350 Tangosol Coherence 3.1/339 <Warning> (thread=WriteBehindThread:MyCacheStore, member=1): Requeued store for key="21"
2006-04-06 17:18:27.351 Tangosol Coherence 3.1/339 <Warning> (thread=WriteBehindThread:MyCacheStore, member=1): Requeued store for key="26"
the first OperationNotSupported is expected, but I'm not sure what the requeued warnings are all about. These are not failures to the DB...it is something else. (mind you that this happens when trying to load a lot of data into the map.)
1- Is this requeuing related or the same as in failed DB stores?
2- Is it possible to "lose" stores if I don't configure the write-requeue-threshold with very, very high values? I must ensure I don't lose anything.
In a related note, in some circumstances, I need to ensure that the "write queue" is flushed or cleared. For example, I may want to force a flush of all pending stores (and wait/block until that's done).
I have looked into it and I don't seem to know how to do it. I can read the write-queue length, but I believe that this is not very accurate...since my tests seem to indicate that the write-behind thread may take the entries to store off the write-queue and then deal with them in parallel (which means that there are still entries althought the write-queue size is 0). Also, there are some calls from the cache store that, at first, seem to give some access to the write thread (potentially allowing me to contact the thread to tell him to flush or discard any pending stores)...but I believe that all of the functions are protected...but there may be other ways..
I guess my second batch of questions are:
1- How can I effectively force a flush (or clear) of the pending stores. Such that there is no single store pending in any queue (visible or invisible to the programmer).
2- What is the role of re-queuing in these situations? where is the queue sitting, the thread? the cache store? who's responsible of retrying that, and when?...I would like to flush those entries too.
A quick explanation of the operation of the write thread would also be very appreciated.
Thanks!
Josep M.

Write behind exception and recovery

Hi all,
I am working on write behind part in equity trading system. I know that cache store operation will eventually be thrown away if no of retry exceed write-requeue-threshold. However, this is not acceptable as DB must sync with caches at least at day end. For some more complicated caches, we use cache store implementation and Hiberate for simple cache. I am thinking to capture the sql statements that failed during the day and finally at day end, manually fix issues (egDB issue or others) then have them executed.
Questions:
1. Is this a good approach for handling the scenario? If yes, any way I can capture the statements and write to file for running in SQL plus for example in case of Hiberate?
2. Is there any out of box mechanism in Coherence for recovering write-behind queues in case of WHOLE cluster fail (not node fail).
Henry

922963 wrote:
Hi all,
I am working on write behind part in equity trading system. I know that cache store operation will eventually be thrown away if no of retry exceed write-requeue-threshold. However, this is not acceptable as DB must sync with caches at least at day end. For some more complicated caches, we use cache store implementation and Hiberate for simple cache. I am thinking to capture the sql statements that failed during the day and finally at day end, manually fix issues (egDB issue or others) then have them executed.
Questions:
1. Is this a good approach for handling the scenario? If yes, any way I can capture the statements and write to file for running in SQL plus for example in case of Hiberate?Hi Henry,
There are a few caveats you need to care about but in general it is possible.
Not necessarily SQLs but serialized entries would probably be simpler to work with when you try to restore them.
Also, you have to be aware that Coherence may fail to write an entry to the DB but at retry it may try to write a new entry. If it succeeds, you have to be able to figure that out that the earlier failure must not be reexecuted.
In effect, you should have per-entry versioning in the database and you should check versions of the entity in the database upon writing both from the cache store and also from your end-of-day retry logic.
2. Is there any out of box mechanism in Coherence for recovering write-behind queues in case of WHOLE cluster fail (not node fail).
No, nothing like that comes out-of-the-box, if you lost a partition, you lost your write-behind-enqueued entries, too. You could log your failed writes to disk though as you indicated above.
Best regards,
Robert

Write-behind queue resilience

For our project, our coherence cache is the trusted store. We plan on using a DB backing store, so that should we need to shutdown the cache cluster, or lose it for reasons outside our control we have a copy to reload from. We can't afford to lose any data from the cache or lose any writes through to the backing store.
Ideally, we would like to make use of write-behind caching for the obvious performance benefits. However, I've got some concerns with this strategy that I wasn't able to find the answer to in the user guide.
1. Can we configure the size of the write behind queue? If events are coming in faster than we can write to the DB, then the queue will grow. At some point the queue will exhaust system resources I assume.
2. If the primary is lost before the store operation is performed, will the backup partition assume responsibility?
3. Is there a safe way I can shutdown cache nodes? I.e. once we initiate the shutdown, the cache node will not accept any more puts, but will wait for the write behind queue to flush.

Hi,
It looks like you are using com.oracle.coherence.handson.DBCacheStore.
http://download.oracle.com/docs/cd/E13924_01/coh.340/e14135/cohjdev.htm
If so, then I think that you could simply change the following method in the DBCacheStore ...
public Connection getConnection()
        if (m_con == null || m_con.isClosed())
            configureConnection();
        return m_con;
        }Thanks,
Tom

Cache write-behind complete check

Is there a surefire way to check a cache that has a store persisting objects to the database and write-behind set to 2 seconds, has persisted all objects put into the cache?
We have tried using JMX, querying the Cache's QueueSize and waiting until it reaches 0. It turns out that when putting objects into the write-behind cache, the write-behind queue is not necessarily non-zero immediately after the put(s). e.g. QueueSize may be 0, even if objects still need to be persisted.
For our nightly integration tests we need to clear out the cache, but want to make sure we do not call NamedCache.clear() on a cache that still has objects that need to be persisted.
Any ideas?

Hi Rob,
The problem may actually be the timeliness of updates to the QueueSize JMX attribute as we're using the MBeanConnector to obtain information on our cache members (and providers). Assuming that objects actually make it to the write-behind queue during the cache put call and certain tests need to be sure these objects are persisted, instead of doing the accounting approach discussed previously, I found a forum thread on ReadWriteBackingMap flush calls.
To get access to the ReadWriteBackingMap.flush() I created a small test today using a subclass of ReadWriteBackingMap that registers the backingmap with our CashStore implementation:
    protected void configureCacheStore(CacheStore store, boolean readOnly) {
        super.configureCacheStore(store, readOnly);
        if (store instanceof CacheLoaderWriterProvider) {
            ((CacheLoaderWriterProvider)store).registerBackingMap(this);
    }Our cachstore (CacheLoaderWriterProvider) in turn exposes a call to the registered map's flush method as a JMX operation.
Whenever we need to be sure the write-behind queue is empty during our tests, we'll call this JMX operation.
Best Regards,
Marcel.

Can a db slowdown with write-behind cause a slowdown in cache operations?

If we have a coherence cluster, and one cache configured with write-behind is having trouble writing to the db (ie, it's slow), and we keep adding objects to the cache that exceed the ability of the db to consume them; will flow-control kick in and cause the writes to the cache to block/slow-down? Ie, the classic producer-consumer problem, where we are adding objects to the cache, faster than the cachestore can consume them.
What happens in this case? Will flow-control kick in and block writes to the cache? Will an internal buffer just keep growing? Are there any knobs to tweak this behavior (eg, in the case of spikes, where temporarily the producer is producing faster than the consumer can consume for a brief period of time, but then things go back to normal)?

user9222505 wrote:
I believe we discovered that the same thread pool is used for all requests to the cache, including gets, puts and calls into the cachestore. So if the writes are slow within the cachestore, then it uses up all of the threads and slows everything down.Hi,
This is not really correct.
If a cache in a service is configured to use write-behind then a separate thread for that service is started, which deals with write-behind store and storeAll operations.
The remove operations need to be handled synchronously to avoid corruption of the data-set in the scenario of reading a entry from the cache immediately after removing it (if it were not synchronously deleted from the backing storage, then reading it back could give an incorrect non-null value). Therefore remove operations are handled synchronously on the service / worker thread, and not delayed on the write-behind thread.
Gets are also synchronously handled, so they again are served on the service / worker thread.
So if the puts are slow and wait too much, that may delay other puts but should not contend with other threads. If the puts are computation intensive, then obviously they hinder other threads because of consumption of the same CPU resource, and not simply because they execute.
Best regards,
Robert

Handling Database failure in Write-Behind

Hi,
In the link mentioned below it is mentioned that "The application is somewhat insulated from database failures: the Write-Behind feature can be configured in such a way that a write failure will result in the object being re-queued for write"
http://coherence.oracle.com/display/COH35UG/Read-Through,+Write-Through,+Write-Behind+and+Refresh-Ahead+Caching
I wanted know how write behind can be configured so as to insulate it from database failures. How can it be configured so that in case of db failure object is re-queded for write
Thanks,
Sudhir

Requeuing can be enabled by setting the write-requeue-threshold to the maximum number of expected entries
that will exist in the queue when it is time to write the data to the database.
A complete example can be found here: http://middlewaremagic.com/weblogic/?p=5954
Look for the cache configuration section.

Write-behind max speed?

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