I have a fairly simple http client using libevent's evhttp functions. It works almost perfectly, but it eventually fails with 'too many open files'. The client reads data from multiple http sources (less than a dozen) every 15 seconds. The framework is as follows (actual code is much longer):
callbackHandler {
Process results
Free connection with evhttp_connection_free()
}
doCycle {
Create base using event_base_new()
For each http data source {
Create connection using evhttp_connection_base_new() with callbackHandler
Create request using evhttp_request_new()
Register connection and request using evhttp_make_request()
}
Start loop with event_base_dispatch()
}
main {
call doCycle() every 15 seconds
}
Since everything is created in the doCycle() function that returns after event_base_dispatch(), I'm assuming that the base, all of the related events, and all the sockets should be cleaned up after the last event completes or times out. What do I need to do to close/free the sockets? Attempting to use evhttp_request_free() in the callback results in a 'double free' error.
Related
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
Do I have a wrong understanding of "reactive" or is something wrong in my example?
I did a small code sample in Vertx: In a REST service I read data from mongodb and returning as JSON.
...........
Router router = Router.router(vertx);
router.route().handler(BodyHandler.create());
router.get("/gilders").handler(this::listAll);
vertx.createHttpServer().requestHandler(router::accept).listen(8080);
}
private void listAll(RoutingContext routingContext) {
mongoClient.find("gliders", new JsonObject(), results -> {
List<JsonObject> objects = results.result();
/* is this non blocking?!
mongoClient.find return immediately, but the rest client just
gets results, after mongo delivered all results
*/
List<Glider> gilder = objects.stream()
.map(res -> {
Glider g = new Glider();
g.setName(res.getString("name"));
g.setPrice(res.getString("price"));
return g;
})
.collect(Collectors.toList());
routingContext.response()
.putHeader("content-type", "application/json; charset=utf-8")
.end(Json.encodePrettily(gilder));
});
}
OK, its not blocking, I could compute something else meanwhile waiting for mongo.
But somehow I thought about "reactive" is that the REST client will get already the first chunks of the mongo results even mongo is still not ready finding all by that time (HTTP Streaming). But like this, the callback is just invoked, when mongo found all results.
Reactive is not the same as streaming. Reactive is a concept around data flows, your application will react to events, e.g.: data returned from mongoDB. You can now implement streaming on top of it by asking the mongo client to start pumping data asap as it arrives from the network. However in a blocking API you could do streaming by blocking the application for data and then pass it one by one to a consumer.
I have a ServiceWorker registered on my page and want to pass some data to it so it can be stored in an IndexedDB and used later for network requests (it's an access token).
Is the correct thing just to use network requests and catch them on the SW side using fetch, or is there something more clever?
Note for future readers wondering similar things to me:
Setting properties on the SW registration object, e.g. setting self.registration.foo to a function within the service worker and doing the following in the page:
navigator.serviceWorker.getRegistration().then(function(reg) { reg.foo; })
Results in TypeError: reg.foo is not a function. I presume this is something to do with the lifecycle of a ServiceWorker meaning you can't modify it and expect those modification to be accessible in the future, so any interface with a SW likely has to be postMessage style, so perhaps just using fetch is the best way to go...?
So it turns out that you can't actually call a method within a SW from your app (due to lifecycle issues), so you have to use a postMessage API to pass serialized JSON messages around (so no passing callbacks etc).
You can send a message to the controlling SW with the following app code:
navigator.serviceWorker.controller.postMessage({'hello': 'world'})
Combined with the following in the SW code:
self.addEventListener('message', function (evt) {
console.log('postMessage received', evt.data);
})
Which results in the following in my SW's console:
postMessage received Object {hello: "world"}
So by passing in a message (JS object) which indicates the function and arguments I want to call my event listener can receive it and call the right function in the SW. To return a result to the app code you will need to also pass a port of a MessageChannel in to the SW and then respond via postMessage, for example in the app you'd create and send over a MessageChannel with the data:
var messageChannel = new MessageChannel();
messageChannel.port1.onmessage = function(event) {
console.log(event.data);
};
// This sends the message data as well as transferring messageChannel.port2 to the service worker.
// The service worker can then use the transferred port to reply via postMessage(), which
// will in turn trigger the onmessage handler on messageChannel.port1.
// See https://html.spec.whatwg.org/multipage/workers.html#dom-worker-postmessage
navigator.serviceWorker.controller.postMessage(message, [messageChannel.port2]);
and then you can respond via it in your Service Worker within the message handler:
evt.ports[0].postMessage({'hello': 'world'});
To pass data to your service worker, the above mentioned is a good way. But in case, if someone is still having a hard time implementing that, there is an other hack around for that,
1 - append your data to get parameter while you load service-worker (for eg., from sw.js -> sw.js?a=x&b=y&c=z)
2- Now in service worker, fetch those data using self.self.location.search.
Note, this will be beneficial only if the data you pass do not change for a particular client very often, other wise it will keep changing the loading url of service worker for that particular client and every time the client reloads or revisits, new service worker is installed.
I Implemented a HttpListener to process SoapRequests. This works fine but I can't find a soloution for the problem, that some soap-requests take too much time, resulting in timeouts on client side.
How do I let the requesting client know, that his request is not a timeout?
I thought about sending "dummy"-information while the request gets processsed, but the HttpListener only seems to send the data when you Close the response-object, and this can be done only once, so this is not the right thing to do I suppose.
Soloution:
Thread alliveWorker = new Thread(() =>
{
try
{
while (context.Response.OutputStream.CanWrite)
{
context.Response.OutputStream.WriteByte((byte) ' ');
context.Response.OutputStream.Flush();
Thread.Sleep(5000);
}
}
finally
{
}
});
alliveWorker.Start();
doWork();
alliveWorker.Interrupt();
createTheRealResponse();
Sending dummy information is not a bad idea.
I think you need to call the Flush() method on the HttpListenerResponse's OutputStream property after writing the dummy data. You must also enable SendChunked property:
Try sending a dummy space at regular interval:
response.SendChunked = true;
response.OutputStream.WriteByte((byte)' ');
response.OutputStream.Flush();
I see two options - increase timeouts on client side or extend protocol with operation status requests from client for long running operations.
If you are using .net 4.5, take a look at the HttpListenerTimeoutManager Class, you can use this class as a base to implement custom timeout behaviour.
I'm looking for an example, how to implement a longpoling mechanism in java. I would love to use a stateless EJB.
I know that something like that would work:
#WebService(serviceName="mywebservice")
#Stateless
public class MyWebService {
#WebMethod
public String longPoll() {
short ct = 0;
while(someCondition == false && ct < 60) {
sleep(1000); // 1 sec
ct++;
}
if (someCondition)
return "got value";
else
return "";
}
}
Unfortunately i know that this does'nt scale. Can i return in the webmethod without finishing the response and finish it somewhere else?
JAX-WS provides support for invoking Web services using an asynchronous client invocation and supports both a callback and polling model. Have a look at:
Asynchronous Web Service Invocation with JAX-WS 2.0
Using the JAX-WS asynchronous programming model
In particular, the Polling Example
The thing you're trying to implement is called server push.
Each webserver/appserver has a pool of threads, say 10 threads for processing web requests, if all those threads will go into 'sleep' no other web request will be serviced until one of those 'sleeps' exists. Some solution is to increase number of those threads but then you'll eat more memory and more operating system resources (each thread costs). So yes, your implementation of 'server push' isn't scalable.
Solutions:
your web application can send a http request every (say) 5 secs, to check if your 'someCondition' changed, and then get the data
AFAIK, Tomcat (so JBoss too) already has some 'connector' for supporting such requests, so Thread.sleep() or semaphores won't be needed
use latest web server implementing Servlet API 3, it also has support for such long-running HTTP requests
read more: Online tutorials for implementing comets (server push)