I have a messaging system using Azure ServiceBus but I'm using Nimbus on top of that. I have an endpoint that sends a command to another endpoint and at one point the handler class on the other side picks it up, so it is all working fine.
When the operation takes time, roughly more than 20 second or so, the handler gets 'another' call with the same message. It looks like Nimbus is retrying the message that is already being handled by an other (even the same) instance of the handler, I don't see any exceptions being thrown and I could easily repro this with the following handler:
public class Synchronizer : IHandleCommand<RequestSynchronization>
{
public async Task Handle(RequestSynchronization synchronizeInfo)
{
Console.WriteLine("Received Synchronization");
await Task.Delay(TimeSpan.FromSeconds(30)); //Simulate long running process
Console.WriteLine("Got through first timeout");
await Task.Delay(TimeSpan.FromSeconds(30)); //Simulate another long running process
Console.WriteLine("Got through second timeout");
}
}
My question is: How do I disable this behavior? I am happy for the transaction take time as it is a heavy process that I have off-loaded from my website, which was the whole point of going with this architecture in the first place.
In other words, I was expecting the message to not to be picked up by another handler while one has picked it up and is processing it, unless there's an exception and the message goes back to the queue and eventually gets picked up for a retry.
Any ideas how to do this? Anything I'm missing?
By default, ASB/WSB will give you a message lock of 30 seconds. The idea is that you pop a BrokeredMessage off the head of the queue but have to either .Complete() or .Abandon() that message within the lock timeout.
If you don't do that, the service bus assumes that you've crashed or otherwise failed and it will return that message to the queue to be re-processed.
You have a couple of options:
1) Implement ILongRunningHandler on your handler. Nimbus will pay attention to the remaining lock time and automatically renew your message lock. Caution: The maximum message lock time supported by ASB/WSB is five minutes no matter how many times you renew so if your handler takes longer than that then you might want option #2.
public class Synchronizer : IHandleCommand<RequestSynchronization>, ILongRunningTask
{
public async Task Handle(RequestSynchronization synchronizeInfo)
{
Console.WriteLine("Received Synchronization");
await Task.Delay(TimeSpan.FromSeconds(30)); //Simulate long running process
Console.WriteLine("Got through first timeout");
await Task.Delay(TimeSpan.FromSeconds(30)); //Simulate another long running process
Console.WriteLine("Got through second timeout");
}
}
2) In your handler, call a Task.Run(() => SomeService(yourMessage)) and return. If you do this, be careful about lifetime scoping of dependencies if your handler takes any. If you need an IFoo, take a dependency on a Func> (or equivalent depending on your container) and resolve that within your handling task.
public class Synchronizer : IHandleCommand<RequestSynchronization>
{
private readonly Func<Owned<IFoo>> fooFunc;
public Synchronizer(Func<Owned<IFoo>> fooFunc)
{
_fooFunc = fooFunc;
}
public async Task Handle(RequestSynchronization synchronizeInfo)
{
// don't await!
Task.Run(() => {
using (var foo = _fooFunc())
{
Console.WriteLine("Received Synchronization");
await Task.Delay(TimeSpan.FromSeconds(30)); //Simulate long running process
Console.WriteLine("Got through first timeout");
await Task.Delay(TimeSpan.FromSeconds(30)); //Simulate another long running process
Console.WriteLine("Got through second timeout");
}
});
}
}
I think you are looking for the code here: http://www.uglybugger.org/software/post/support_for_long_running_handlers_in_nimbus
Related
At work we have some code in a Azure WebJob where we use Rabbit
The basic workflow is this
A message arrives on RabbitMQ Queue
We have a message handler for the incoming message
Within the message handler we start a top level (user) supervisor actor where we "ask" it to handle the message
The supervisor actor hierarchy is like this
And the relevant top level code is something like this (this is the WebJob code)
static void Main(string[] args)
{
try
{
//Bootstrap akka IoC resolver well ahead of any actor usages
new AutoFacDependencyResolver(ContainerOperations.Instance.Container, ContainerOperations.Instance.Container.Resolve<ActorSystem>());
var system = ContainerOperations.Instance.Container.Resolve<ActorSystem>();
var busQueueReader = ContainerOperations.Instance.Container.Resolve<IBusQueueReader>();
var dateTime = ContainerOperations.Instance.Container.Resolve<IDateTime>();
busQueueReader.AddHandler<ProgramCalculationMessage>("RabbitQueue", x =>
{
//This is code that gets called whenever we have a RabbitMQ message arrive
//This is code that gets called whenever we have a RabbitMQ message arrive
//This is code that gets called whenever we have a RabbitMQ message arrive
//This is code that gets called whenever we have a RabbitMQ message arrive
//This is code that gets called whenever we have a RabbitMQ message arrive
try
{
//SupervisorActor is a singleton
var supervisorActor = ContainerOperations.Instance.Container.ResolveNamed<IActorRef>("SupervisorActor");
var actorMessage = new SomeActorMessage();
var supervisorRunTask = runModelSupervisorActor.Ask(actorMessage, TimeSpan.FromMinutes(25));
//we want to wait this guy out
var supervisorRunResult = supervisorRunTask.GetAwaiter().GetResult();
switch (supervisorRunResult)
{
case CompletedEvent completed:
{
break;
}
case FailedEvent failed:
{
throw failed.Exception;
}
}
}
catch (Exception ex)
{
_log.Error(ex, "Error found in Webjob");
//throw it for the actual RabbitMqQueueReader Handler so message gets NACK
throw;
}
});
Thread.Sleep(Timeout.Infinite);
}
catch (Exception ex)
{
_log.Error(ex, "Error found");
throw;
}
}
And this is the relevant IOC code (we are using Autofac + Akka.NET DI for Autofac)
builder.RegisterType<SupervisorActor>();
_actorSystem = new Lazy<ActorSystem>(() =>
{
var akkaconf = ActorUtil.LoadConfig(_akkaConfigPath).WithFallback(ConfigurationFactory.Default());
return ActorSystem.Create("WebJobSystem", akkaconf);
});
builder.Register<ActorSystem>(cont => _actorSystem.Value);
builder.Register(cont =>
{
var system = cont.Resolve<ActorSystem>();
return system.ActorOf(system.DI().Props<SupervisorActor>(),"SupervisorActor");
})
.SingleInstance()
.Named<IActorRef>("SupervisorActor");
The problem
So the code is working fine and doing what we want it to, apart from the Akka.Net "ask" timeout shown above in the WebJob code.
Annoyingly this seems to work fine if I try and run the webjob locally. Where I can simulate a "ask" timeout by providing a new supervisorActor that simply doesn't EVER respond with a message back to the "Sender".
This works perfectly running on my machine, but when we run this code in Azure, we DO NOT see a Timeout for the "ask" even though one of our workflow runs exceeded the "ask" timeout by a mile.
I just don't know what could be causing this behavior, does anyone have any ideas?
Could there be some Azure specific config value for the WebJob that I need to set.
The answer to this was to use the async rabbit handlers which apparently came out in V5.0 of the C# rabbit client. The offical docs still show the sync usage (sadly).
This article is quite good : https://gigi.nullneuron.net/gigilabs/asynchronous-rabbitmq-consumers-in-net/
Once we did this, all was good
I am developing a scheduled job to send message to Message queue using Quartz.net. The Execute method of IJob is not async. so I can't use async Task. But I want to call a method with await keyword.
Please find below my code. Not sure whether I am doing correct. Can anyone please help me with this?
private async Task PublishToQueue(ChangeDetected changeDetected)
{
_logProvider.Info("Publish to Queue started");
try
{
await _busControl.Publish(changeDetected);
_logProvider.Info($"ChangeDetected message published to RabbitMq. Message");
}
catch (Exception ex)
{
_logProvider.Error("Error publishing message to queue: ", ex);
throw;
}
}
public class ChangedNotificatonJob : IJob
{
public void Execute(IJobExecutionContext context)
{
//Publish message to queue
Policy
.Handle<Exception>()
.RetryAsync(3, (exception, count) =>
{
//Do something for each retry
})
.ExecuteAsync(async () =>
{
await PublishToQueue(message);
});
}
}
Is this correct way? I have used .GetAwaiter();
Policy
.Handle<Exception>()
.RetryAsync(_configReader.RetryLimit, (exception, count) =>
{
//Do something for each retry
})
.ExecuteAsync(async () =>
{
await PublishToQueue(message);
}).GetAwaiter()
Polly's .ExecuteAsync() returns a Task. With any Task, you can just call .Wait() on it (or other blocking methods) to block synchronously until it completes, or throws an exception.
As you have observed, since IJob.Execute(...) isn't async, you can't use await, so you have no choice but to block synchronously on the task, if you want to discover the success-or-otherwise of publishing before IJob.Execute(...) returns.
.Wait() will cause any exception from the task to be rethrown, wrapped in an AggregateException. This will occur if all Polly-orchestrated retries fail.
You'll need to decide what to do with that exception:
If you want the caller to handle it, rethrow it or don't catch it and let it cascade outside the Quartz job.
If you want to handle it before returning from IJob.Execute(...), you'll need a try {} catch {} around the whole .ExecuteAsync(...).Wait(). Or consider Polly's .ExecuteAndCaptureAsync(...) syntax: it avoids you having to provide that outer try-catch, by instead placing the final outcome of the execution into a PolicyResult instance. See the Polly doco.
There is a further alternative if your only intention is to log somewhere that message publishing failed, and you don't care whether that logging happens before IJob.Execute(...) returns or not. In that case, instead of using .Wait(), you could chain a continuation task on to ExecuteAsync() using .ContinueWith(...), and handle any logging in there. We adopt this approach, and capture failed message publishing to a special 'message hospital' - capturing enough information so that we can choose whether to republish that message again later, if appropriate. Whether this approach is valuable depends on how important it is to you never to lose a message.
EDIT: GetAwaiter() is irrelevant. It won't magically let you start using await inside a non-async method.
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.
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
I am playing around with Tasks, Async and await in my WorkerRole (RoleEntryPoint).
I had some unexplained recycles and i have found out now that if something is running to long in a await call, the role recycles. To reproduce it, just do a await Task.Delay(60000) in the Run method.
Anyone who can explain to me why?
The Run method must block. From the docs:
If you do override the Run method, your code should block indefinitely. If the Run method returns, the role is automatically recycled by raising the Stopping event and calling the OnStop method so that your shutdown sequences may be executed before the role is taken offline.
A simple solution is to just do this:
public override void Run()
{
RunAsync().Wait();
}
public async Task RunAsync()
{
while (true)
{
await Task.Delay(60000);
}
}
Alternatively, you can use AsyncContext from my AsyncEx library:
public override void Run()
{
AsyncContext.Run(async () =>
{
while (true)
{
await Task.Delay(60000);
}
});
}
Whichever option you choose, Run should not be async. It's kind of like Main for a Console app (see my blog for why async Main is not allowed).
I would recommend a lower value for Task.Delay like 1000 (ms). I suspect that the worker role cannot respond quickly enough to the health check. The role is then considered unresponsive and restarted.
Make sure the Run method never returns with something like this
while (true)
{
Thread.Sleep(1000);
}
Or with Task.Delay in your case.