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.
Related
The question is simple: is it possible to have multiple failure handlers for one route?
router.route(HttpMethod.POST, "/test")
.handler(LoggerHandler.create())
.handler(ResponseTimeHandler.create())
.failureHandler(MyCustomFailureHandler1.create())
.failureHandler(MyCustomFailureHandler2.create());
I'm currently using vert.x version 4.0.2, and I can see that internally, every failure handler I create is added to a failureHandlers list, but when an error is thrown, the only failure handler executing is the first one specified.
From the first failure handler (MyCustomFailureHandler1.create()), you have to call RoutingContext#next()
Documentation of RoutingContext#next() states: "If next is not called for a handler then the handler should make sure it ends the response or no response will be sent".
See TestCase here: testMultipleSetFailureHandler
Using mirth version 3.8.1. I've set up an alert for a channel's errors. When errors come from the destination transformer (which is Javascript), the alert is able to access the {messageId} variable and pull the correct id. However, when an error originates in the Javascript-based database writer, the alert just returns '{messageId}' instead of the value.
I tried a bunch of things...
The global map is accessible from the alert, but putting a message id in there would get overwritten by another processing thread.
Other destination types - http sender, tcp sender, channel writer, and even a non-javascript-based database writer destination all work.
I even stripped the database writer code down to just:
var dbConn;
dbConn = DatabaseConnectionFactory.createDatabaseConnection('com.mysql.cj.jdbc.Driver','jdbc:mysql://host:port/dbname','','');
Do I just have to raise specific exceptions within the db writer code and raise alerts when those exceptions are hit, and send the message id in the error string?
You stumbled across a bug. I opened an issue and a fix.
If not for another bug that also neglects to provide the messageId, you should be able to use alerts.sendAlert('Custom Error Message'). alerts is an instance of AlertSender from the User API that mirth creates for you. I created a fix for that as well.
The only workaround I know of at this time to manually send an alert that includes the messageId is to call the EventController directly. The caveat is that this is technically not supported as part of a public API and usage could break in future versions without notice.
com.mirth.connect.server.controllers.ControllerFactory
.getFactory()
.createEventController()
.dispatchEvent(new com.mirth.connect.donkey.server.event.ErrorEvent(
connectorMessage.getChannelId(),
connectorMessage.getMetaDataId(),
connectorMessage.getMessageId(),
com.mirth.connect.donkey.model.event.ErrorEventType.USER_DEFINED_TRANSFORMER,
connectorMessage.getConnectorName(),
null, /* connectorType */
'A TEST ERROR MESSAGE',
null /* throwable */
)
);
This will work as written from a filter, transformer, Javascript Writer, or Database Writer in javascript mode. In other contexts, connectorMessage won't be defined and you'll have to provide some of those values in a different way. If you don't need the messageId and don't want to throw an exception, just use alerts.sendAlert(errorMessage) since that doesn't require calling unsupported internal classes.
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'm having some issues with the newest version of sails.js (0.11.0). It stated in github that plain socket.io code will be accepted and ran in sails.js; however, I am simply trying to emit a message from a client when they click on something like so:
$('#myBtn').on('click', function(){
io.socket.emit('message', {
message: {
subject: subject
},
sender: id
});
});
I end up getting an "Uncaught TypeError: undefined is not a function" on the line of io.socket.emit() aka emit is not a function of io.socket.
Here are some references that I have looked at:
https://github.com/balderdashy/sails/issues/2397
http://www.tysoncadenhead.com/blog/getting-started-with-socket-io#.VQCFjvnF9tU
I have a feeling with the updated version of sails, instead of emitting a message I should be doing something along the lines of:
io.socket.post('/user/message', data, function(data, jwres) {
});
Something concerns me with the following answer here:
Sending session specific messages with socket.io and sails.js
It states "class rooms" are being deprecated along with publishCreate, publishDestroy, introduce, and obituary.
So do I follow a Pub/Sub paradigm, re-write my more "socket-io-ish" code to utilize sails Blueprints & Pub/Sub, or continue in my socket-io fashion?
Is there another way of emitting a message from client using sails?
You are correct in that the recommended way of communicating with the server via sockets is to use the RESTful socket client methods. The benefit is that you can use the regular Sails routing and controller/action architecture for socket communication instead of having to support a whole other layer of subscription and event-handling on the backend. This is one of the main reasons why #mikermcneil originally created Sails. Two things to note:
You can use req.isSocket in your controller action to determine whether the request is coming from a socket, and
You can get the raw, underlying socket.io instance on the client with io.socket._raw, which will have the emit method. But again, this is not the recommended practice.
I have a jobque mechanism in ZF.
The jobque simlpy stores the the function call (Class, Method and params) and later executes it as CLI daemon. The daemon works, however at places the application looks for information from the request object, and when called from the CLI these places fail, or get no info.
I would like to store the original request object together with the job and when the job is processed set the request object back as if the job was done by the originall request, somethin along the line of the following pseudo code:
$ser_request = serialize(Zend_Controller_Front::getInstance ()->getRequest ());
-->save to db
-->retrive from db
$ZCF= new Zend_Controller_Front;
$ZCF::getInstance ()->setRequest (unserialize($ser_request))
The aim is to store and replay the jobs later withouth having to change the rest of the application.
Any suggestions how to do that?
I am not sure if this works, but here's an idea. Try to implement _sleep and _wakeup magic methods for the request object. Haven't tried it out, but maybe it's at least a starting solution.