MSMQ ARCHITECTURE WITH DEDICATED PROCESSORS PER DATABASE - multithreading

I have a web application in ASP.NET MVC , C# and I have a specific use case that takes long time to process and users have to wait until the process is complete. I want to use MSMQ and relay the heavy work to dedicated MSMQ consumer/servicer. Our application has multiple clients and each client has their own SQL database. So let's say 100 clients make 100 separate SQL databases. The real challenge I have is to make the process faster using MSMQ but task of 1 client should not effect the performance of others. So I have 2 solutions:
Option-1: Unique MSMQ Private Queue per database so in my case it will be 100 queues and growing. 1 dedicated ASP.NET console application that listens to a dedicated MSMQ so in my case it will be 100 processors or console applications.
Option-2: 1 big MSMQ private queue for all databases
A: 1 dedicated MSMQ consumer per database so 100 processors
B: 1 MSMQ consumer that listens to the big MSMQ
I want to stick with Option-1 but I would want to know is this a feasible and enterprise type solution?

You actually have two questions
First, how do you allocate a resources affinity to a processor to SQL Server.
Select the database in Sql Management Studio, right click and follow this..
Clean your Database regularly
DBCC FREEPROCCACHE;
DBCC DROPCLEANBUFFERS;
MSMQ, turn on [journaling][2], but also consider another queuing process RabbitMQ etc, or write a simple one to enquque the jobs sample from here
public class MultiThreadQueue
{
BlockingCollection<string> _jobs = new BlockingCollection<string>();
public MultiThreadQueue(int numThreads)
{
for (int i = 0; i < numThreads; i++)
{
var thread = new Thread(OnHandlerStart)
{ IsBackground = true };//Mark 'false' if you want to prevent program exit until jobs finish
thread.Start();
}
}
public void Enqueue(string job)
{
if (!_jobs.IsAddingCompleted)
{
_jobs.Add(job);
}
}
public void Stop()
{
//This will cause '_jobs.GetConsumingEnumerable' to stop blocking and exit when it's empty
_jobs.CompleteAdding();
}
private void OnHandlerStart()
{
foreach (var job in _jobs.GetConsumingEnumerable(CancellationToken.None))
{
Console.WriteLine(job);
Thread.Sleep(10);
}
}
}
Hope this helps :)
The question has been reworded, he meant sometheng else when he said Processors.
Update added a consumer pattern with onPeek :
You really need to post some code!
Consider using the OnPeekCompleted method. If there is an error you can leave the message on the queue
If you have some kind of header which identifies the message you can switch to a different dedicated/thread.
private static void OnPeekCompleted(Object sourceQueue, PeekCompletedEventArgs asyncResult)
{
// Set up and connect to the queue.
MessageQueue mq = (MessageQueue)sourceQueue;
// gets a new transaction going
using (var txn = new MessageQueueTransaction())
{
try
{
// retrieve message and process
txn.Begin();
// End the asynchronous peek operation.
var message = mq.Receive(txn);
#if DEBUG
// Display message information on the screen.
if (message != null)
{
Console.WriteLine("{0}: {1}", message.Label, (string)message.Body);
}
#endif
// message will be removed on txn.Commit.
txn.Commit();
}
catch (Exception ex)
{
// If there is an error you can leave the message on the queue, don't remove message from queue
Console.WriteLine(ex.ToString());
txn.Abort();
}
}
// Restart the asynchronous peek operation.
mq.BeginPeek();
}
You can also use a service broker

Related

Event Hub Consumer in Service Fabric

I'm trying to get a service fabric to consistently pull messages from an azure event hub. I seem to have everything wired up but notice that my consumer just stops pulling events.
I have a hub with a couple thousand events I've pushed to it. Configured the hub with 1 partition and have my service fabric service with also only 1 partition to ease debugging.
Service starts, creates the EventHubClient, from there uses it to create a PartitionReceiver. The receiver is passed to an "EventLoop" that enters an "infinite" while that calls receiver.ReceiveAsync. The code for the EventLoop is below.
What I am observing is the first time through the loop I almost always get 1 message. Second time through I get somewhere between 103 and 200ish messages. After that, I get no messages. Also seems like if I restart the service, I get the same messages again - but that's because when I restart the service I'm having it start back at the beginning of the stream.
Would expect this to keep running until my 2000 messages were consumed and then it would wait for me (polling ocassionally).
Is there something specific I need to do with the Azure.Messaging.EventHubs 5.3.0 package to make it keep pulling events?
//Here is how I am creating the EventHubClient:
var connectionString = "something secret";
var connectionStringBuilder = new EventHubsConnectionStringBuilder(connectionString)
{
EntityPath = "NameOfMyEventHub"
};
try
{
m_eventHubClient = EventHubClient.Create(connectionStringBuilder);
}
//Here is how I am getting the partition receiver
var receiver = m_eventHubClient.CreateReceiver("$Default", m_partitionId, EventPosition.FromStart());
//The event loop which the receiver is passed to
private async Task EventLoop(PartitionReceiver receiver)
{
m_started = true;
while (m_keepRunning)
{
var events = await receiver.ReceiveAsync(m_options.BatchSize, TimeSpan.FromSeconds(5));
if (events != null) //First 2/3 times events aren't null. After that, always null and I know there are more in the partition/
{
var eventsArray = events as EventData[] ?? events.ToArray();
m_state.NumProcessedSinceLastSave += eventsArray.Count();
foreach (var evt in eventsArray)
{
//Process the event
await m_options.Processor.ProcessMessageAsync(evt, null);
string lastOffset = evt.SystemProperties.Offset;
if (m_state.NumProcessedSinceLastSave >= m_options.BatchSize)
{
m_state.Offset = lastOffset;
m_state.NumProcessedSinceLastSave = 0;
await m_state.SaveAsync();
}
}
}
}
m_started = false;
}
**EDIT, a question was asked on the number of partitions. The event hub has a single partition and the SF service also has a single one.
Intending to use service fabric state to keep track of my offset into the hub, but that's not the concern for now.
Partition listeners are created for each partition. I get the partitions like this:
public async Task StartAsync()
{
// slice the pie according to distribution
// this partition can get one or more assigned Event Hub Partition ids
string[] eventHubPartitionIds = (await m_eventHubClient.GetRuntimeInformationAsync()).PartitionIds;
string[] resolvedEventHubPartitionIds = m_options.ResolveAssignedEventHubPartitions(eventHubPartitionIds);
foreach (var resolvedPartition in resolvedEventHubPartitionIds)
{
var partitionReceiver = new EventHubListenerPartitionReceiver(m_eventHubClient, resolvedPartition, m_options);
await partitionReceiver.StartAsync();
m_partitionReceivers.Add(partitionReceiver);
}
}
When the partitionListener.StartAsync is called, it actually creates the PartitionListener, like this (it's actually a bit more than this, but the branch taken is this one:
m_eventHubClient.CreateReceiver(m_options.EventHubConsumerGroupName, m_partitionId, EventPosition.FromStart());
Thanks for any tips.
Will
How many partition do you have? I can't see in your code how you make sure you read all partitions in the default consumer group.
Any specific reason why you are using PartitionReceiver instead of using an EventProcessorHost?
To me, SF seems like a perfect fit for using the event processor host. I see there is already a SF integrated solution that uses stateful services for checkpointing.

How to read Azure Service Bus messages from Multiple Queues with one worker

I have three queues and one worker that I want monitoring the three queues (or only two of them)
One queue is qPirate
One queue is qShips
One queue is qPassengers
The idea is that workers will either be looking at all 3 of them, 2 of them, or one of them, and doing different things depending on what the message says.
The key though is that say a message is failing because ship1 is offline, all queues in qships will refresh, workers that are looking at that and other queues will get hung up slightly from it as they will try to process the messages for that queue while only looking at the other queues a little bit, while the other workers that are looking at the other 2 queues and skipping qships will continue to process through messages without holdup or delays.
public static void GotMessage([ServiceBusTrigger("%LookAtAllQueuesintheservicebus%")] BrokeredMessage message)
{
var handler = new MessageHandler();
var manager = new MessageManager(
handler,
"PirateShips"
);
manager.ProcessMessageViaHandler(message);
}
Looking around online I'm guessing this isn't something that's possible, but it seems like it would be? Thanks in advance either way!
Edit1: I'll add the Job Host as well to attempt to clarify things a bit
JobHostConfiguration config = new JobHostConfiguration()
{
DashboardConnectionString = "DefaultEndpointsProtocol=https;AccountName=PiratesAreUs;AccountKey=Yarr",
StorageConnectionString = "DefaultEndpointsProtocol=https;AccountName=PiratesAreUs;AccountKey=Yarr",
NameResolver = new QueueNameResolver()
};
ServiceBusConfiguration serviceBusConfig = new ServiceBusConfiguration()
{
ConnectionString = "Endpoint=AllPirateQueuesLocatedHere;SharedAccessKeyName=PiratesAreUs;SharedAccessKey=Yarr"
};
serviceBusConfig.MessageOptions.AutoComplete = false;
serviceBusConfig.MessageOptions.AutoRenewTimeout = TimeSpan.FromMinutes(1);
serviceBusConfig.MessageOptions.MaxConcurrentCalls = 1;
config.UseServiceBus(serviceBusConfig);
JobHost host = new JobHost(config);
host.RunAndBlock();
Also the QueueNameResolverClass is simply
public class QueueNameResolver : INameResolver
{
public string Resolve(string name)
{
return name;
}
}
I don't appear to have anyway to have the NameResolver be multiple queues, while I can say that I want the jobhost to look at a certain ServiceBus, I don't know how to tell it to look at all the queues within the ServiceBus.
In other words, I want multiple servicebustriggers on this worker so that if a message gets sent to qpirate1 and qships1 which are both located in service bus AllPirateQueuesHere, the worker can pick up the message in qpirate1, process it, then pick up the message in qships1 and process it.
Figured out the answer... This is possible and its simpler than I thought I'm not sure why I didn't connect the dots but I'm still curious why there isn't more documentation about this. Apparently it's simply make a function per queue you want a worker to look at multiple queues. So if you had three queues you'd want something like the below (you can handle each message differently).
public static void GotMessage1([ServiceBusTrigger("%qPirate1%")] BrokeredMessage message)
{
var handler = new MessageHandler();
var manager = new MessageManager(
handler,
"Pirates"
);
manager.ProcessMessageViaHandler(message);
}
public static void GotMessage2([ServiceBusTrigger("%qShip1%")] BrokeredMessage message)
{
var handler = new MessageHandler();
var manager = new MessageManager(
handler,
"Ships"
);
manager.ProcessMessageViaHandler(message);
}
public static void GotBooty([ServiceBusTrigger("%qBooty%")] BrokeredMessage message)
{
var handler = new MessageHandler();
var manager = new MessageManager(
handler,
"Booty"
);
manager.ProcessMessageViaHandler(message);
}

How to do Async in Azure WebJob function

I have an async method that gets api data from a server. When I run this code on my local machine, in a console app, it performs at high speed, pushing through a few hundred http calls in the async function per minute. When I put the same code to be triggered from an Azure WebJob queue message however, it seems to operate synchronously and my numbers crawl - I'm sure I am missing something simple in my approach - any assistance appreciated.
(1) .. WebJob function that listens for a message on queue and kicks off the api get process on message received:
public class Functions
{
// This function will get triggered/executed when a new message is written
// on an Azure Queue called queue.
public static async Task ProcessQueueMessage ([QueueTrigger("myqueue")] string message, TextWriter log)
{
var getAPIData = new GetData();
getAPIData.DoIt(message).Wait();
log.WriteLine("*** done: " + message);
}
}
(2) the class that outside azure works in async mode at speed...
class GetData
{
// wrapper that is called by the message function trigger
public async Task DoIt(string MessageFile)
{
await CallAPI(MessageFile);
}
public async Task<string> CallAPI(string MessageFile)
{
/// create a list of sample APIs to call...
var apiCallList = new List<string>();
apiCallList.Add("localhost/?q=1");
apiCallList.Add("localhost/?q=2");
apiCallList.Add("localhost/?q=3");
apiCallList.Add("localhost/?q=4");
apiCallList.Add("localhost/?q=5");
// setup httpclient
HttpClient client =
new HttpClient() { MaxResponseContentBufferSize = 10000000 };
var timeout = new TimeSpan(0, 5, 0); // 5 min timeout
client.Timeout = timeout;
// create a list of http api get Task...
IEnumerable<Task<string>> allResults = apiCallList.Select(str => ProcessURLPageAsync(str, client));
// wait for them all to complete, then move on...
await Task.WhenAll(allResults);
return allResults.ToString();
}
async Task<string> ProcessURLPageAsync(string APIAddressString, HttpClient client)
{
string page = "";
HttpResponseMessage resX;
try
{
// set the address to call
Uri URL = new Uri(APIAddressString);
// execute the call
resX = await client.GetAsync(URL);
page = await resX.Content.ReadAsStringAsync();
string rslt = page;
// do something with the api response data
}
catch (Exception ex)
{
// log error
}
return page;
}
}
First because your triggered function is async, you should use await rather than .Wait(). Wait will block the current thread.
public static async Task ProcessQueueMessage([QueueTrigger("myqueue")] string message, TextWriter log)
{
var getAPIData = new GetData();
await getAPIData.DoIt(message);
log.WriteLine("*** done: " + message);
}
Anyway you'll be able to find usefull information from the documentation
Parallel execution
If you have multiple functions listening on different queues, the SDK will call them in parallel when messages are received simultaneously.
The same is true when multiple messages are received for a single queue. By default, the SDK gets a batch of 16 queue messages at a time and executes the function that processes them in parallel. The batch size is configurable. When the number being processed gets down to half of the batch size, the SDK gets another batch and starts processing those messages. Therefore the maximum number of concurrent messages being processed per function is one and a half times the batch size. This limit applies separately to each function that has a QueueTrigger attribute.
Here is a sample code to configure the batch size:
var config = new JobHostConfiguration();
config.Queues.BatchSize = 50;
var host = new JobHost(config);
host.RunAndBlock();
However, it is not always a good option to have too many threads running at the same time and could lead to bad performance.
Another option is to scale out your webjob:
Multiple instances
if your web app runs on multiple instances, a continuous WebJob runs on each machine, and each machine will wait for triggers and attempt to run functions. The WebJobs SDK queue trigger automatically prevents a function from processing a queue message multiple times; functions do not have to be written to be idempotent. However, if you want to ensure that only one instance of a function runs even when there are multiple instances of the host web app, you can use the Singleton attribute.
Have a read of this Webjobs SDK documentation - the behaviour you should expect is that your process will run and process one message at a time, but will scale up if more instances are created (of your app service). If you had multiple queues, they will trigger in parallel.
In order to improve the performance, see the configurations settings section in the link I sent you, which refers to the number of messages that can be triggered in a batch.
If you want to process multiple messages in parallel though, and don't want to rely on instance scaling, then you need to use threading instead (async isn't about multi-threaded parallelism, but making more efficient use of the thread you're using). So your queue trigger function should read the message from the queue, the create a thread and "fire and forget" that thread, and then return from the trigger function. This will mark the message as processed, and allow the next message on the queue to be processed, even though in theory you're still processing the earlier one. Note you will need to include your own logic for error handling and ensuring that the data wont get lost if your thread throws an exception or can't process the message (eg. put it on a poison queue).
The other option is to not use the [queuetrigger] attribute, and use the Azure storage queues sdk API functions directly to connect and process the messages per your requirements.

How to parallelize an azure worker role?

I have got a Worker Role running in azure.
This worker processes a queue in which there are a large number of integers. For each integer I have to do processings quite long (from 1 second to 10 minutes according to the integer).
As this is quite time consuming, I would like to do these processings in parallel. Unfortunately, my parallelization seems to not be efficient when I test with a queue of 400 integers.
Here is my implementation :
public class WorkerRole : RoleEntryPoint {
private readonly CancellationTokenSource cancellationTokenSource = new CancellationTokenSource();
private readonly ManualResetEvent runCompleteEvent = new ManualResetEvent(false);
private readonly Manager _manager = Manager.Instance;
private static readonly LogManager logger = LogManager.Instance;
public override void Run() {
logger.Info("Worker is running");
try {
this.RunAsync(this.cancellationTokenSource.Token).Wait();
}
catch (Exception e) {
logger.Error(e, 0, "Error Run Worker: " + e);
}
finally {
this.runCompleteEvent.Set();
}
}
public override bool OnStart() {
bool result = base.OnStart();
logger.Info("Worker has been started");
return result;
}
public override void OnStop() {
logger.Info("Worker is stopping");
this.cancellationTokenSource.Cancel();
this.runCompleteEvent.WaitOne();
base.OnStop();
logger.Info("Worker has stopped");
}
private async Task RunAsync(CancellationToken cancellationToken) {
while (!cancellationToken.IsCancellationRequested) {
try {
_manager.ProcessQueue();
}
catch (Exception e) {
logger.Error(e, 0, "Error RunAsync Worker: " + e);
}
}
await Task.Delay(1000, cancellationToken);
}
}
}
And the implementation of the ProcessQueue:
public void ProcessQueue() {
try {
_queue.FetchAttributes();
int? cachedMessageCount = _queue.ApproximateMessageCount;
if (cachedMessageCount != null && cachedMessageCount > 0) {
var listEntries = new List<CloudQueueMessage>();
listEntries.AddRange(_queue.GetMessages(MAX_ENTRIES));
Parallel.ForEach(listEntries, ProcessEntry);
}
}
catch (Exception e) {
logger.Error(e, 0, "Error ProcessQueue: " + e);
}
}
And ProcessEntry
private void ProcessEntry(CloudQueueMessage entry) {
try {
int id = Convert.ToInt32(entry.AsString);
Service.GetData(id);
_queue.DeleteMessage(entry);
}
catch (Exception e) {
_queueError.AddMessage(entry);
_queue.DeleteMessage(entry);
logger.Error(e, 0, "Error ProcessEntry: " + e);
}
}
In the ProcessQueue function, I try with different values of MAX_ENTRIES: first =20 and then =2.
It seems to be slower with MAX_ENTRIES=20, but whatever the value of MAX_ENTRIES is, it seems quite slow.
My VM is a A2 medium.
I really don't know if I do the parallelization correctly ; maybe the problem comes from the worker itself (which may be it is hard to have this in parallel).
You haven't mentioned which Azure Messaging Queuing technology you are using, however for tasks where I want to process multiple messages in parallel I tend to use the Message Pump Pattern on Service Bus Queues and Subscriptions, leveraging the OnMessage() method available on both Service Bus Queue and Subscription Clients:
QueueClient OnMessage() - https://msdn.microsoft.com/en-us/library/microsoft.servicebus.messaging.queueclient.onmessage.aspx
SubscriptionClient OnMessage() - https://msdn.microsoft.com/en-us/library/microsoft.servicebus.messaging.subscriptionclient.onmessage.aspx
An overview of how this stuff works :-) - http://fabriccontroller.net/blog/posts/introducing-the-event-driven-message-programming-model-for-the-windows-azure-service-bus/
From MSDN:
When calling OnMessage(), the client starts an internal message pump
that constantly polls the queue or subscription. This message pump
consists of an infinite loop that issues a Receive() call. If the call
times out, it issues the next Receive() call.
This pattern allows you to use a delegate (or anonymous function in my preferred case) that handles the receipt of the Brokered Message instance on a separate thread on the WaWorkerHost process. In fact, to increase the level of throughput, you can specify the number of threads that the Message Pump should provide, thereby allowing you to receive and process 2, 4, 8 messages from the queue in parallel. You can additionally tell the Message Pump to automagically mark the message as complete when the delegate has successfully finished processing the message. Both the thread count and AutoComplete instructions are passed in the OnMessageOptions parameter on the overloaded method.
public override void Run()
{
var onMessageOptions = new OnMessageOptions()
{
AutoComplete = true, // Message-Pump will call Complete on messages after the callback has completed processing.
MaxConcurrentCalls = 2 // Max number of threads the Message-Pump can spawn to process messages.
};
sbQueueClient.OnMessage((brokeredMessage) =>
{
// Process the Brokered Message Instance here
}, onMessageOptions);
RunAsync(_cancellationTokenSource.Token).Wait();
}
You can still leverage the RunAsync() method to perform additional tasks on the main Worker Role thread if required.
Finally, I would also recommend that you look at scaling your Worker Role instances out to a minimum of 2 (for fault tolerance and redundancy) to increase your overall throughput. From what I have seen with multiple production deployments of this pattern, OnMessage() performs perfectly when multiple Worker Role Instances are running.
A few things to consider here:
Are your individual tasks CPU intensive? If so, parallelism may not help. However, if they are mostly waiting on data processing tasks to be processed by other resources, parallelizing is a good idea.
If parallelizing is a good idea, consider not using Parallel.ForEach for queue processing. Parallel.Foreach has two issues that prevent you from being very optimal:
The code will wait until all kicked off threads finish processing before moving on. So, if you have 5 threads that need 10 seconds each and 1 thread that needs 10 minutes, the overall processing time for Parallel.Foreach will be 10 minutes.
Even though you are assuming that all of the threads will start processing at the same time, Parallel.Foreach does not work this way. It looks at number of cores on your server and other parameters and generally only kicks off number of threads it thinks it can handle, without knowing too much about what's in those threads. So, if you have a lot of non-CPU bound threads that /can/ be kicked off at the same time without causing CPU over-utilization, default behaviour will not likely run them optimally.
How to do this optimally:
I am sure there are a ton of solutions out there, but for reference, the way we've architected it in CloudMonix (that must kick off hundreds of independent threads and complete them as fast as possible) is by using ThreadPool.QueueUserWorkItem and manually keeping track number of threads that are running.
Basically, we use a Thread-safe collection to keep track of running threads that are started by ThreadPool.QueueUserWorkItem. Once threads complete, remove them from that collection. The queue-monitoring loop is indendent of executing logic in that collection. Queue-monitoring logic gets messages from the queue if the processing collection is not full up to the limit that you find most optimal. If there is space in the collection, it tries to pickup more messages from the queue, adds them to the collection and kick-start them via ThreadPool.QueueUserWorkItem. When processing completes, it kicks off a delegate that cleans up thread from the collection.
Hope this helps and makes sense

BlackBerry - App freezes when background thread executing

I have a BlackBerry App that sends data over a web service when a button has it state set to ON. When the button is ON a timer is started which is running continuously in the background at fixed intervals. The method for HttpConnection is called as follows:
if(C0NNECTION_EXTENSION==null)
{
Dialog.alert("Check internet connection and try again");
return;
}
else
{
confirmation=PostMsgToServer(encryptedMsg);
}
The method PostMsgToServer is as follows:
public static String PostMsgToServer(String encryptedGpsMsg) {
//httpURL= "https://prerel.track24c4i.com/track24prerel/service/spi/post?access_id="+DeviceBoardPassword+"&IMEI="+IMEI+"&hex_data="+encryptedGpsMsg+"&device_type=3";
httpURL= "https://t24.track24c4i.com/track24c4i/service/spi/post?access_id="+DeviceBoardPassword+"&IMEI="+IMEI+"&hex_data="+encryptedGpsMsg+"&device_type=3";
//httpURL= "http://track24.unit1.overwatch/track24/service/spi/post?access_id="+DeviceBoardPassword+"&IMEI="+IMEI+"&hex_data="+encryptedGpsMsg+"&device_type=3";
try {
String C0NNECTION_EXTENSION = checkInternetConnection();
if(C0NNECTION_EXTENSION==null)
{
Dialog.alert("Check internet connection and try again");
return null;
}
else
{
httpURL=httpURL+C0NNECTION_EXTENSION+";ConnectionTimeout=120000";
//Dialog.alert(httpURL);
HttpConnection httpConn;
httpConn = (HttpConnection) Connector.open(httpURL);
httpConn.setRequestMethod(HttpConnection.POST);
DataOutputStream _outStream = new DataOutputStream(httpConn.openDataOutputStream());
byte[] request_body = httpURL.getBytes();
for (int i = 0; i < request_body.length; i++) {
_outStream.writeByte(request_body[i]);
}
DataInputStream _inputStream = new DataInputStream(
httpConn.openInputStream());
StringBuffer _responseMessage = new StringBuffer();
int ch;
while ((ch = _inputStream.read()) != -1) {
_responseMessage.append((char) ch);
}
String res = (_responseMessage.toString());
responce = res.trim();
httpConn.close();
}
}catch (Exception e) {
//Dialog.alert("Connection Time out");
}
return responce;
}
My Question: The App freezes whenever the method is called, i.e. whenever the timer has to execute and send data to the web service the App freezes - at times for a few seconds and at times for a considerable amount of time applying to the user as if the handset has hanged. Can this be solved? Kindly help!!
You are running your networking operation on the Event Thread - i.e. the same Thread that processes your application's Ui interactions. Networking is a blocking operation so effectively this is stopping your UI operation. Doing this on the Event Thread is not recommended and to be honest, I'm surprised it is not causing your application to be terminated, as this is often what the OS will do, if it thinks the application has blocked the Event Thread.
The way to solve this is start your network processing using a separate Thread. This is generally the easy part, the difficult part is
blocking the User from doing anything else while waiting for the
response (assuming you need to do this)
updating the User Interface with the results of your networking
processing
I think the second of these issues are discussed in this Thread:
adding-field-from-a-nonui-thread-throws-exception-in-blackberry
Since it appears you are trying to do this update at regular intervals in the background, I don't think the first is an issue, - for completeness, you can search SO for answers including this one:
blackberry-please-wait-screen-with-time-out
There is more information regarding the Event Thread here:
Event Thread

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