Had a quick question: I have an Variable<[Session]>, where Session:
class Session {
...
var rx_serverRequestable: Driver<SessionRequestable>
...
}
which emits a .next event every time the session has all the information it needs to be passed on to the backend and I want to be able to flatMapLatest on the array of sessions, and do something like:
let sessions: Variable<[Session]>
sessions
.flatMapLatest { sessions in sessions.map { $0. rx_serverRequestable } }
.flatMap { $0.requestFromServer() }
but I only want to request each session once. There are two ways I see that failing with my current implementation:
1. flatMapLatest gets a new array of sessions, potentially disposing a request from server thats still in progress
2. rx_serverRequestable gets called each time the session has all the information required to be loaded from server, so it will get called multiple times, each time the session loads in any new information. I only want the session to be requested the first time, should I be using something like .multicast or .replay(1)?
any pointers on solving the two issues, or switching up my approach?
This can be solved using the zip operator http://reactivex.io/documentation/operators/zip.html
Related
I want to delete all of a user's inserts in a collection when they stop watching a change stream from a React client. I'm using the Realm Web SDK for this.
Here's a summary of my code with what I want to do at the end of it:
import * as Realm from "realm-web";
const realmApp: Realm.App = new Realm.App({ id: realmAppId });
const credentials = Realm.Credentials.anonymous();
const user: Realm.User = await realmApp.logIn(credentials);
const mongodb = realmApp?.currentUser?.mongoClient("mongodb-atlas");
const users = mongodb?.db("users").collection("users");
const changeStream = users.watch();
for await (const change of changeStream) {
switch (change.operationType) {
case "insert": {
...
break;
}
case ...
}
}
// This pseudo-code shows what I want to do
changeStream.on("close", () => // delete all user's inserts)
changeStream.on("timeout", () => // delete all user's inserts)
changeStream.on("user closes app thus also closing stream", () => ... )
Realm Web SDK patterns seem rather different from the NodeJS ones and do not seem to include a method for closing a stream or for running a callback when it closes. In any case, they don't fit my use case.
These MongoDB Realm Web docs lead to more docs about Realm. Unless I'm missing it, both sets don't talk about how to monitor for closing and timing out of a change stream watcher instantiated from the Realm Web SDK, and how to do something when it happens.
I thought another way to do this would be in Realm's Triggers. But it doesn't seem likely from their docs.
Can this even be done from a front end client? Is there a way to do this on MongoDB itself in a "serverless" way?
If you want to delete the inserts specifically when a (client-)listener of a change-stream stops listening you have to implement some logic on client side. There is currently no way to get notified of such even within Mongodb Realm.
Sice a watcher could be closed because the app / browser is closed I would recommend against running the deletion logic on your client. Instead notify a server (or call a Mongodb Realm function / http endpoint) to make the deletions.
You can use the Beacon API to reliably send a request to trigger the delete, even when the window unloads.
Client side
const inserts = [];
for await (const change of changeStream) {
switch (change.operationType) {
case 'insert': inserts.push(change);
}
}
// This point is only reached if the generator returns / stream closes
navigator.sendBeacon('url/to/endpoint', JSON.stringify(inserts));
// Might also add a handler to catch users closing the app.
window.addEventListener('unload', sendBeacon);
Note that the unload event is not reliable MDN. But there are some alternatives which maybe be good enough for your use case.
Inside a realm function you could delete the documents.
That being said, maybe there is a better way to do what you want to achieve. Is it really the timeout of the change stream listener that has to trigger the delete or some other userevent?
I am designing a call manager with the help of RXSwift (ReactiveX) that continuously interacts with an API. The call manager comprises several objects that itself comprises an indicator (indicating status information loaded from the API) and control (requests to be sent to the API).
class CallManagerObjectA() {
var control = PublishSubject<String>()
var indicator = BehaviorSubject<String>(value: "string status")
}
Within the call manager, a scheduler regularly provides new values to the indicator observable:
<... API response ...>
indicator.onNext(newValue)
Somewhere else in a view controller, the indicator will be observed for a label:
indicator.subscribe(onNext: { label.stringValue = $0 })
Within the same view controller, the user can control the object status via GUI elements continuously:
control.onNext(commandValue)
Within the call manager, the control will be observed for an API call:
control.subscribe(onNext: { (command) in
// API request call
})
So far so good, this is working very well with reactive patterns.
Now, I am looking for a good solution to handle errors, if the call manager recognizes errors during the API interaction and show these errors to the user in the view controller. I was immediately thinking of something like this:
// Call manager recognizes the error
control.onError(error)
...
// Call manager ignores errors for the subscriber
control.retry().ignoreErrors().subscribe(onNext: { (command) in
// API request call
})
...
// View controller shows the errors
indicator.subscribe(onNext: { label.stringValue = $0 })
control.subscribe(onError: { print("error", $0) })
This however ends up in an infinite loop.
I fear that I have a fundamental understanding issue with reactive programming, or I miss something very important, but I am not able to understand how the handle errors in this reactive pattern environment.
Based on the code you have shown, you have a big misunderstanding, not just with how to handle Errors, but with how to program reactively in general. Try watching this video "Reactive Programming: Why It Matters"
To answer your specific question, there are two misunderstandings here:
When you call control.onError(_:) it will be the last call you will be able to make on control. Once it emits an error it will stop working.
The retry() operator asks its source to "try again on Error". If it's source is determinate, then it will just do the exact same thing it did before and emit the exact same output (i.e., the same error it emitted last time.) In the case of a PublishSubject, it doesn't know why onError was called. So the best it can do is just emit the error again.
Honestly, I consider this a bug in the API because subscribing to a publish subject that emitted an error at some point in the past should just do nothing. But then, you wouldn't be asking why you got an infinite loop. Instead you would be asking why your control stopped emitting events.
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 want to refresh data every 15 seconds from an API using Reactive Cocoa 4. Since more than one subscriber can ask for this data at the same time, I want to have multiple subscribers to share one source of data.
My current approach is to have one Signal and share it to every instance that asks for the data. This Signal should start refreshing as soon as the first Signal is subscribed and end after the last has disposed.
SignalProducer<String, NoError> { observer, disposable in
self.disposable = self.repeatTimer.observeNext { _ in
NSLog("start network request")
observer.sendNext("result")
}
}.on(disposed: {
NSLog("disposed")
}).startWithSignal { signal, disposable1 in
self.updateSignal = signal
}
}
return (updateSignal, disposable!)
So for the first request I create and store the updateSignal and each following request will get that signal.
My first question: How can I know when the last subscriber disposed its signal? So when can I stop the requests?
My second question: I store the disposable from my repeatin network request in self.disposable which I also return to the subscriber. If the subscriber only disposes its Signal (which he got from Signal.observeNext()) the inner loop, where I log "start network request" is running endless. Do I really need to stop that Signal myself even when the outer Signal gets disposed?
Is there any nicer way or pattern for shared repeating requests?
Use the global timer function to perform work at specified intervals.
You could do something like this:
self.disposable =
timer(SomeTimeInterval onScheduler:QueueScheduler.mainQueueScheduler)
.startWithNext { _ in
//start network request here
}
But it's better if you chain your network request producer and observe the results, like this:
self.disposable =
timer(SomeTimeInterval onScheduler:QueueScheduler.mainQueueScheduler)
.flatMap(.Latest, transform { _ in
return self.networkRequestSignalProducer()
})
.start({ event in
//monitor the result of the network request
})
Note that you may not want to use the main queue like I did in this example, depending on how you've implemented your network requests.
If you want to avoid dealing with disposables, you can add a .takeUntil before .flatMap and terminate the timer with a signal
Each Sails.js model has the method publishAdd(). This notifies every listener, when a new record was added to a associated model.
This notification does not contain the newly created record. So I have to start another request from the client side to get the new record.
Is there a possibility, that Sails.js sends the new record with the notification, so I can reduce my request count?
Solution
I realized the accepted answer like that:
https://gist.github.com/openscript/7016c5fd8c5053b5e3a3
There's no way to get this record using the default publishAdd method. However, you can override that method and do the child record lookup in your implementation.
You can override publishAdd on a per-model basis by adding a publishAdd method to that model class, or override it for all models by adding the method to the config/models.js file.
I would start by copying the default publishAdd() method and then tweaking as necessary.
I know this is old, but I just had to solve this again, and didn't like the idea of dropping in duplicate code so if someone is looking for alternative, the trick is to update the model of the newly created record with an afterCreate: method.
Say you have a Game that you want to your Players to subscribe to. Games have notifications, a collection of text alerts that you only want players in the game to receive. To do this, subscribe to Game on the client by requesting it. Here I'm getting a particular game by calling game/gameId, then building my page based on what notifications and players are already on the model:
io.socket.get('/game/'+gameId, function(resData, jwres) {
let players = resData.players;
let notifications = resData.notifications;
$.each(players, function (k,v) {
if(v.id!=playerId){
addPartyMember(v);
}
});
$.each(notifications, function (k,v) {
addNotification(v.text);
});
});
Subscribed to game will only give the id's, as we know, but when I add a notification, I have both the Game Id and the notification record, so I can add the following to the Notification model:
afterCreate: function (newlyCreatedRecord, cb) {
Game.publishAdd(newlyCreatedRecord.game,'notifications',newlyCreatedRecord);
cb();}
Since my original socket.get subscribes to a particular game, I can publish only to those subscriber by using Game.publishAdd(). Now back on the client side, listen for the data coming back:
io.socket.on('game', function (event) {
if (event.attribute == 'notifications') {
addNotification(event.added.text);
}
});
The incoming records will look something like this:
{"id":"59fdd1439aee4e031e61f91f",
"verb":"addedTo",
"attribute" :"notifications",
"addedId":"59fef31ba264a60e2a88e5c1",
"added":{"game":"59fdd1439aee4e031e61f91f",
"text":"some messages",
"createdAt":"2017-11-05T11:16:43.488Z",
"updatedAt":"2017-11-05T11:16:43.488Z",
"id":"59fef31ba264a60e2a88e5c1"}}