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.

Similar Messages

Can you limit write-behind batch sizes to a set number in Coherence 3.5?

I'm currently running Coherence 3.5.3p9 on Windows.
The cache store is set up to use the write-behind scheme via the read-write-backing-map-scheme tag.
Batching is enabled with a write-delay of 5s.
My understanding is that essentially anything that was newly inserted into the cache more than 5 seconds ago becomes eligable for storage.
Our application goes through a bit of a peak-trough cycle. Sometimes very little data is inserted all at once and sometimes a lot is. This results in quite varying batch sizes and big batch sizes do cause issues on our db from time to time.
I can decrease the write-delay to 1s in the hope that this will in turn decrease the batch sizes, but is there a way to set a specific number e.g. I only ever want to write 20 in a batch?

Hi,
you can just break down that big batch into smaller batches (DB transactions) yourself, and you can also decide that you don't want to write more at the moment.
If you throw an exception Coherence will retry whatever is in the parameter map passed to storeAll and parameter collection to eraseAll. But it does not have to be the full list, it is expected that you remove those entries/elements which you have successfully persisted.
This way you can control the rate of writing yourself. Also, since the write-behind thread does not block event processing, therefore you are sort of safe to do longer waits in those methods if you want to somewhat space out resource transactions without returning from storeAll.
To answer your question:
You can either
- do a physical transaction of 20 elements, remove those 20 elements from the map, then optionally wait and then continue with more elements from the map as long as there are any (this gives you the chance of controlling the rate of transactions).
- send a physical transaction of at most 20 elements to the database, remove those 20 elements from the map and then throw a dummy exception (in which case Coherence requeues the rest... take care, after this they are considered freshly changed entries).
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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>
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<caching-schemes>
<distributed-scheme>
<scheme-name>account-distributed</scheme-name>
<service-name>DistributedCache</service-name>
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<class-name>com.tangosol.io.pof.ConfigurablePofContext</class-name>
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<init-param>
<param-type>String</param-type>
<param-value>account-pof-config.xml</param-value>
</init-param>
</init-params>
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<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>
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<cachestore-scheme>
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<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>
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<init-params>
<init-param>
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<param-value>account-pof-config.xml</param-value>
</init-param>
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</serializer>
</acceptor-config>
<autostart>true</autostart>
</proxy-scheme>
</caching-schemes>
</cache-config>

solved with autostart=true

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 batch behaviour confirmation

Scenario as follows. Cache is configured with a write behind delay of 10 seconds. An entry is inserted into the cache. For some reason, the db is slow and when the cache store attempts to flush this entry to disk it takes 1 minute. During this period, that same entry is updated in the cache twice, but those updates are > 10 seconds apart).
Do those last 2 updates get aggregated into one store operation to the cache store? Or will the cache store be asked to store both versions of the entry because the updates were greater than the batch delay apart?
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Hi anonymous user628574,
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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"
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Thanks!
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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!
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- sleeping the current thread - makes it slower than one-thread.
- looping blank while Thread.activeCount() > x - doesnt work at all
- suspending resuming threads based on the activeCount - doesnt work too
Code:
import java.util.*;
import java.io.*;
public class ThreadedSort extends Thread{
private ArrayList<Integer> _list;
     public ThreadedSort()
          _list = new ArrayList<Integer>();
     public ThreadedSort(ArrayList<Integer> List)
          _list = List;
     public void run()
          threadQuickSort();
public void threadQuickSort()
          try{
if (_list.size() < 2)
return;
Integer pivot = new Integer(_list.get(_list.size()-1));
          ArrayList<Integer> left = new ArrayList<Integer>();
          ArrayList<Integer> right = new ArrayList<Integer>();
//ListIterator iter = arr.listIterator();
          // *** optimize here
for (int i = 0; i < _list.size()-1; i++) {
               Integer next = (Integer)_list.get(i);
               if (next <= pivot)
                    left.add(next);
               else
                    right.add(next);
          ThreadedSort LeftThread = new ThreadedSort(left);
          ThreadedSort RightThread = new ThreadedSort(right);
//          System.out.println(Thread.currentThread().getName()+" --> "+Thread.activeCount());
//          Thread.sleep(4000);
          LeftThread.start();
          RightThread.start();
//          System.out.println(Thread.currentThread().getName()+" --> "+Thread.activeCount());
          LeftThread.join();
          RightThread.join();
          _list.clear();
          list.addAll(LeftThread.list);
          _list.add(pivot);
          list.addAll(RightThread.list);
          }catch (InterruptedException ie) { ie.printStackTrace(); }
          return;
public static void main(String[] args) { ///////////////////////////////////////////
          Random rand = new Random();
          ArrayList arr1 = new ArrayList();
          long before_threadedQuickSort, after_threadedQuickSort;
          try{
          FileWriter fout = new FileWriter("out.txt");
          BufferedWriter bw = new BufferedWriter(fout);
          before_populate = System.currentTimeMillis();
          for (int i = 0; i< 2000; i++){//***
               arr1.add(rand.nextInt(2000000));
          after_populate = System.currentTimeMillis();
          ThreadedSort sorter3 = new ThreadedSort(new ArrayList(arr1));
          before_threadedQuickSort = System.currentTimeMillis();
          sorter3.start();
          sorter3.join();
          after_threadedQuickSort = System.currentTimeMillis();
          bw.write("time for threadedQuickSort: " + (after_threadedQuickSort - before_threadedQuickSort)); bw.newLine();
          bw.newLine();
          arr1.clear();
          bw.close();
          }catch (Exception IOException){};
          System.out.printf("\n\n");
} //end of main
} //end of class

So you dont have to go thru the code:
Quicksort:
with one thread only I start the sorting by choosing a pivot from the initial list and then generating a left list (all values less then pivot) and a right list (all values greater then pivot).
then i recursively call quicksort with the left and right lists.
with one thread that works fine.
threadedQuickSort:
each thread object has a variable _list that keeps its list.
from the initial thread, i do generate again two lists (left and right) but then i construct two threadable objects with those lists and start them, creating for each list a new thread.
so each thread will create two new threads.
for a initial list with up to 2000 rows, that yields to the correct result although much slower than the one-threaded version. maximum number of threads ever used was around 500~600.
for a list with 20000 rows, my machine chokes at around 2600 threads. (OutOfMemory exception)
My Processor is a 8 core (each core can run up to two threads) giving me a total of 16 actual threads i can work with.
So the question is how to limit the threads that are created to 16. There is no method in the Thread class where you can specify the max number of threads in a threadgroup.
@ jverd:
I looked at ThreadPoolExecutor, but it looks kinda complicated. I see that the idea is creating a queue of jobs to be executed (which would be the different sublists (left and right lists), and then using the specified number of threads to work with the queued jobs, but im failing to understand how it works. Can you point to a simple example somewhere showing how to declare a ThreadPoolExecutor object and work with it. thanks

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