I have a console application for the purposes of using quartz scheduling with masstransit. The related code is located in Task StartAsync(CancellationToken cancellationToken) implementation of IHostedService.
It looks like the following code
await _bus.ScheduleRecurringSend(new Uri($"queue_uri"),
new CustomMessageSchedule(),
new CustomMessage(),
cancellationToken: cancellationToken);
var scheduler = await _schedulerFactory.GetScheduler(cancellationToken);
scheduler.JobFactory = new MassTransitJobFactory(_bus, _jobFactory);
await scheduler.Start(cancellationToken);
Scheduled messages are successfully published to the queue, but those are getting the same messageId. That causes some problems on a consumer side.
I've to make use of IFilter<SendContext<CustomMessage>> implementation, but it didn't work out. Maybe i wasn't using it right, but i'm not even sure if that's the right approach.
Any suggestions on the matter?
Scheduled messages are not modified by Quartz before being sent, other than including a few additional message headers. It might make sense to generate a new MessageId for recurring messages (since they're not a one-and-done scheduled message like regularly scheduled messages).
However, as of right now the behavior is as expected.
Related
I want to subscribe a Mono before return it, the consumer of the subscriber will do some work like write some info, the code looks like this:
Mono result = a remote call by WebClient;
result.subscribe(data->successLog(log,JSON.toJSONString(data)));
return result;
and now the problem comes:
that remote call by WebClient will triggered twice!
how to subscribe a Mono and do something before return it?
It is not :)
In Reactive Streams, everything is "lazy" by default: you're not "calling operations" (imperative), you're building a pipeline that will later be executed (where subscribe() is what triggers the execution).
If you need to log when Mono successfully resolves, you can use Mono#doOnNext:
return result.doOnNext(data -> successLog(log, JSON.toJSONString(data)));
This way it will log every time your Mono is resolved.
Why "every time"? Because, in Reactive Streams, it is absolutely valid to re-subscribe on the same Mono (e.g. for retrying).
Using a saga, given an event EventA, saga starts, it sends a command (or many).
How can we make sure that the command is sent successfully then actual logic in other micro-service did not throw, etc.
Let's have an example of email saga:
When a user register, we create a User Aggregate which publishes UserRegisteredEvent, a saga will be created and this saga is responsible to make sure that registration email is sent to user (email may contain a verification key, welcome message, etc).
Should we use :
commandGateway.sendAndWait with a try/catch -> does it scale?
commandGateway.send and use a deadline and use some kind of "fail event" like SendEmailFailedEvent -> requires to associate a "token" for commands so can associate the "associationProperty" with the correct saga
that sent SendRegistrationEmailCommand
commandGateway.send(...).handle(...) -> in handle can we reference eventGateway/commandGateway that were in MyEmailSaga?
If error we send an event? Or can we modify/call a method from the saga instance we had. If no error then other service have sent an event like "RegistrationEmailSentEvent" so saga will end.
use deadline because we just use "send" and do not handle the eventual error of the command which may have failed to be sent (other service is down, etc)
something else?
Or a combination of all?
How to handle errors below? (use deadline or .handle(...) or other)
Errors could be:
command has no handlers (no service up, etc)
command was handled but exception is raised in other service and no event is sent (no try/catch in other service)
command was handled, exception raised and caught, other service publish an event to notify it failed to send email (saga will receive event and do appropriate action depending on event type and data provided -> maybe email is wrong or does not exist so no need to retry)
other errors I missed?
#Saga
public class MyEmailSaga {
#Autowired
transient CommandGateway commandGateway;
#Autowired
transient EventGateway eventGateway;
#Autowired
transient SomeService someService;
String id;
SomeData state;
/** count retry times we send email so can apply logic on it */
int sendRetryCount;
#StartSaga
#SagaEventHandler(associationProperty = "id")
public void on(UserRegisteredEvent event) {
id = event.getApplicationId();
//state = event........
// what are the possibilities here?
// Can we use sendAndWait but it does not scale very well, right?
commandGateway.send(new SendRegistrationEmailCommand(...));
// Is deadline good since we do not handle the "send" of the command
}
// Use a #DeadlineHandler to retry ?
#DeadlineHandler(deadlineName = "retry_send_registration_email")
fun on() {
// resend command and re-schedule a deadline, etc
}
#EndSaga
#SagaEventHandler(associationProperty = "id")
public void on(RegistrationEmailSentEvent event) {
}
}
EDIT (after accepted answer):
Mainly two options (Sorry but kotlin code below):
First option
commandGateway.send(SendRegistrationEmailCommand(...))
.handle({ t, result ->
if (t != null) {
// send event (could be caught be the same saga eventually) or send command or both
}else{
// send event (could be caught be the same saga eventually) or send command or both
}
})
// If not use handle(...) then you can use thenApply as well
.thenApply { eventGateway.publish(SomeSuccessfulEvent(...)) }
.thenApply { commandGateway.send(SomeSuccessfulSendOnSuccessCommand) }
2nd option:
Use a deadline to make sure that saga do something if SendRegistrationEmailCommand failed and you did not receive any events on the failure (when you do not handle the command sent).
Can of course use deadline for other purposes.
When the SendRegistrationEmailCommand was received successfully, the receiver will publish an event so the saga will be notified and act on it.
Could be an RegistrationEmailSentEvent or RegistrationEmailSendFailedEvent.
Summary:
It seems that it is best to use handle() only if the command failed to be sent or receiver has thrown an unexpected exception, if so then publish an event for the saga to act on it.
In case of success, the receiver should publish the event, saga will listen for it (and eventually register a deadline just in case); Receiver may also send event to notify of error and do not throw, saga will also listen to this event.
ideally, you would use the asynchronous options to deal with errors. This would either be commandGateway.send(command) or commandGateway.send(command).thenApply(). If the failure are businesslogic related, then it may make sense to emit events on these failures. A plain gateway.send(command) then makes sense; the Saga can react on the events returned as a result. Otherwise, you will have to deal with the result of the command.
Whether you need to use sendAndWait or just send().then... depends on the activity you need to do when it fails. Unfortunately, when dealing with results asynchronously, you cannot safely modify the state of the Saga anymore. Axon may have persisted the state of the saga already, causing these changes to go lost. sendAndWait resolves that. Scalability is not often an issue, because different Sagas can be executed in parallel, depending on your processor configuration.
The Axon team is currently looking at possible APIs that would allow for safe asynchronous execution of logic in Sagas, while still keeping guarantees about thread safety and state persistence.
I would like to know, if the following Gherkin phrase correspond to BDD rules:
final class KafkaSpec extends BddSpec {
feature("Kafka distribution to SAP server via websocket") {
scenario("Kafka consumer does not receive messages from Kafka server") {
Given("Kafka server is NOT active")
When("consumer client get started")
val ex = SenderActor.run
Then("print message `Failed to connect to Kafka`")
ex.failed map { ex =>
assertThrows[ConnectException](ex)
}
}
scenario("Kafka consumer receives messages from Kafka server") {
Given("Kafka server is ACTIVE")
When("consumer client get started")
Then("print message `Successfully connected to Kafka`")
succeed
}
}
}
Do I use the right tense? Do I use the Given-When-Then correctly?
The Givens (contexts) are fine; we normally use either continuous present or past tense for those:
Given the kafka server is active <-- continuous present
Given the kafka server was started <-- past tense
For the Whens (events), it's better if you can use an active voice. Active voice starts with who did it. Who started the server? (I've corrected the English a bit here too.)
When the consumer client was started <-- passive voice
When our consumer starts their client <-- active voice
For the Thens (outcomes), I really like the word "should". It encourages people to question it; should it really happen? Now? In this release? Is there any context for which this shouldn't happen or something different should happen? Should it still happen, or has this scenario changed?
Then the consumer interface should print the message, `Successfully connected to Kafka`.
One other thing though: the detail in that last step feels a bit too much to me. If the message changed, you'd have to change it everywhere. Instead I keep that in the code (you can abstract the step out) and would say something like:
Then the interface should tell the consumer that the connection was successful.
This is something we usually call "declarative over imperative". It's also OK to have the passive voice here:
Then the consumer should be told that the connection was successful.
Using the word "should" also helps differentiate between the outcomes of one scenario and the givens of another; often these overlap with an outcome forming the context for another scenario:
Given Priscilla has an account
When she enters her username and password correctly
Then she should be on her home page.
Given Priscilla is on her home page...
I wrote more about tenses and language of BDD here, where you'll also find tons of other resources for new BDDers under the BDD category.
I am building a bot for for Facebook Messenger using Microsoft Bot Framework. I am planning to use CosmosDB for State Management and also as my backend data store. (I am not stuck to CosmosBD and can use any other store if needed)
I need to send daily/weekly proactive messages(push notifications) to users based on their time preference. I will capturing their time preference when they first interact with the bot.
What is the best way to deliver these notifications?
As I will be storing these preferences in CosmosDB, I am thinking using ComosDB trigger of creating an Azure Function and schedule it based on the user time preference. This Azure function will make a call to my webhook which will deliver these messages. If requried, I will change Function schedule when a user changes his/her preference.
My questions are:
Is this a good approach?
Are there any other alternatives (Notifications Hub?)
I should be able to set specific times for notifications (like at the top of the hour or something like that), does it make sense to schedule an Azure Function to run at these hours rather than creating a function based on user preference (I can actually combine these two approaches too)
Thank you in advance.
First, I don't think there's any "right" answer to be given here; it's going to depend a lot on your domain's specific needs. Scale is going to play a major factor in the design of this. Will you have 100 users? 10000 users? 1mil users? I'm going to assume you want to design for maximum scale up front, but it could be overkill.
First, based on what you've described, I don't think a CosmosDB trigger is necessarily the solution to your problem because that's only going to fire when the preference data is created/updated. I assume that, from that point forward, your function needs to continuously fire at the time slot they've opted into, correct?
So let's pretend you let people choose from the 24hrs in the day. A naïve approach would be to simply use a scheduled trigger that fires up every hour, queries the CosmosDB for all the documents where the preference is set to that particular hour and then begins sending out notifications from there. The problem is how you scale from there and deal with issues of idempotency in the face of failures.
First off, a timer trigger only ever spins up one instance. If you were to just go query the CosmosDB documents and start processing them one by one in the scope of that single trigger, you'd hit a ceiling relatively quickly on how many notifications you can scale to. Instead what you'd want to do is use that timer trigger to fan out the notifications to as many "worker" function instances as possible. The timer trigger can act as the orchestrator in the sense that it can own the query against the CosmosDB and then turn each document result it finds for that particular notification time window into a message that it places on a queue to be processed by a separate function which will scale out on its own.
There are actually a couple ways you can accomplish this with Azure Functions, it really depends on how early an adopter of technology you are comfortable with being.
The first is what I would call the "manual" way which would be done by simply using the existing Azure Storage Queue extension by taking an IAsyncCollector<YourNotificationWorkerMessage> as a parameter to the timer function that's bound to the worker queue and then pumping out the messages through that. Then you write a second companion function which uses a QueueTrigger, bind it to that same queue, and it will take care of processing each message. This second function is where you get the scaling, enabling process all of the queued messages as quickly as possible based on whatever scaling parameters you choose to configure. This is the "simplest" approach
The second approach would be to adopt the newer Durable Functions extension. With that model, you don't have to directly think about creating a worker queue. You simply kick off a new instance of your orchestrator function from the timer function and the orchestrator fans out the work by invoking N "concurrent" calls to an action for each notification. Now, it happens to distribute those calls using queues under the covers, but that's an implementation detail that you need no longer maintain yourself. Additionally, if the work of delivering the notification requires more involved work and/or retry logic, you might actually consider using a sub-orchestration instead of a simple action. Finally, another added benefit of this approach, is that you can "fan back in" to your main orchestrator function once all the notifications are delivered to do some follow up work... even if that's simply some kind of event logging that the notification cycle has completed for this hour.
Now, the challenge with either of these approach is actually dealing with failure in initially fetching the candidates for notification from CosmosDB, paging through the results and making sure you actually fan all of them out in an idempotent manner. You need to deal with possible hiccups as you page and you need to deal with the fact that your whole function could be torn down and you might have to restart. Perhaps on the initial run of the 8AM notifications you got through page 273 out of 371 pages and then you got hit with a complete network connectivity fail or the VM your function was running on suffered a power failure. You could resume, but you'd need to know that you left off on page 273 and that you actually processed the 27th record out of that page and start from there. Otherwise, you risk sending double notifications to your users. Maybe that's something you can accept, maybe it's not. Maybe you're ok with the 27 notifications on that page being duplicated as long as the first 272 pages aren't. Again, this is something you need to decide for your domain, but if you want to avoid this issue your orchestrator function will need to track its progress to ensure that it doesn't send out dupes. Again I would say Durable Functions has a leg up here as it comes with the ability to configure retries. Maintaining the state of a particular run is left up to the author in either approach though.
I use pro-active dialog extensively with botframwork and messenger without any issue. During your facebook approval process you simply need to inform them you will be sending notifications trough messenger with your bot. Usually if you use it to inform your user and stay away from promotional content you should be fine.
I also use azure function to trigger the pro-active dialog from a custom controller endpoint.
Bellow sample code for azure function:
public static void Run(TimerInfo notificationTrigger, TraceWriter log)
{
try
{
//Serialize request object
string timerInfo = JsonConvert.SerializeObject(notificationTrigger);
//Create a request for bot service with security token
HttpRequestMessage hrm = new HttpRequestMessage()
{
Method = HttpMethod.Post,
RequestUri = new Uri(NotificationEndPointUrl),
Content = new StringContent(timerInfo, Encoding.UTF8, "application/json")
};
hrm.Headers.Add("Authorization", NotificationApiKey);
log.Info(JsonConvert.SerializeObject(hrm));
//Call service
using (var client = new HttpClient())
{
Task task = client.SendAsync(hrm).ContinueWith((taskResponse) =>
{
HttpResponseMessage result = taskResponse.Result;
var jsonString = result.Content.ReadAsStringAsync();
jsonString.Wait();
if (result.StatusCode != System.Net.HttpStatusCode.OK)
{
//Throw what ever problem as an exception with details
throw new Exception($"AzureFunction - ERRROR - HTTP {result.StatusCode}");
}
});
task.Wait();
}
}
catch (Exception ex)
{
//TODO log
}
}
Bellow sample code for starting the pro-active dialog:
public static async Task Resume<T, R>(string resumptionCookie) where T : IDialog<R>, new()
{
//Deserialize reference to conversation
ConversationReference conversationReference = JsonConvert.DeserializeObject<ConversationReference>(resumptionCookie);
//Generate message from bot to user
var message = conversationReference.GetPostToBotMessage();
var builder = new ContainerBuilder();
using (var scope = DialogModule.BeginLifetimeScope(Conversation.Container, message))
{
//From a cold start the service is not yet authenticated with dev bot azure services
//We thus must trust endpoint url.
if (!MicrosoftAppCredentials.IsTrustedServiceUrl(message.ServiceUrl))
{
MicrosoftAppCredentials.TrustServiceUrl(message.ServiceUrl, DateTime.MaxValue);
}
var botData = scope.Resolve<IBotData>();
await botData.LoadAsync(CancellationToken.None);
//This is our dialog stack
var task = scope.Resolve<IDialogTask>();
T dialog = scope.Resolve<T>(); //Resolve the dialog using autofac
try
{
task.Call(dialog.Void<R, IMessageActivity>(), null);
await task.PollAsync(CancellationToken.None);
}
catch (Exception ex)
{
//TODO log
}
finally
{
//flush dialog stack
await botData.FlushAsync(CancellationToken.None);
}
}
}
Your dialog needs to be registered in autofac.
Your resumptionCookie needs to be saved in your db.
You might want to check FB policy regarding proactive messages
There’s a 24h limit but it might not be totally screwed in your case
https://developers.facebook.com/docs/messenger-platform/policy/policy-overview#standard_messaging
I would like to set a callback to be fired if a produced records fail. Initially, I would just like to log the failed record.
The Confluent Kafka python library provides a mechanism for adding a callback:
produce(topic[, value][, key][, partition][, on_delivery][, timestamp])
...
on_delivery(err,msg) (func) – Delivery report callback to call (from poll() or flush()) on successful or failed delivery
How can I achieve similar behaviour with kafka-python kafka.KafkaProducer#send() without having to use the deprecated SimpleClient using kafka.SimpleClient#send_produce_request()
Although it isn't documented, this is relatively straightforward. Whenever you send a message, you immediately get a Future back. You can append callbacks/errback's to that Future:
F = producer.send(topic=topic, value=message, key=key)
F.add_callback(callback, message=message, **kwargs_to_pass_to_callback_method)
F.add_errback(erback, message=message, **kwargs_to_pass_to_errback_method)
Relevant source code here:
https://github.com/dpkp/kafka-python/blob/1937ce59b4706b44091bb536a9b810ae657c3225/kafka/future.py#L48-L64
We really should document this, I filed https://github.com/dpkp/kafka-python/issues/1256 to track it.