Related to this question, which explains the origins of the logic in the controller, I have a question about background jobs with Delayed Job before pushing it to github and re-deploying.
The model and scopes work, the logic in the controller(s) works, the conditionals in the e-mail text.erb files work, users are either readers or subscribers and can set their e-mail preferences on their "My Account" page: [Articles & Updates, Just Articles, No E-mail, etc.]. Delayed Job is set up and processing it all in the background, making the front end nice and fast as always, and Mandrill SMTP is receiving it all correctly and sending out the e-mails in a speedy manner.
The main logic block in article_controller does this to send the right e-mails to the right users:
if #article.update(article_params) && #article.status == 'published' && #article.created_at.today?
User.wantsarticles.editor.each do |user|
ArticleMailer.delay.send_article_full(#article, user)
end
User.wantsarticles.subscribers.each do |user|
ArticleMailer.delay.send_article_full(#article, user)
end
User.wantsarticles.readers.each do |user|
ArticleMailer.delay.send_article_teaser(#article, user)
end
format.html { redirect_to :action => 'admin', notice: 'Article was successfully updated.' }
format.json { render :show, status: :ok, location: #article }
else
format.html { redirect_to :action => 'admin', notice: 'Article was successfully updated.' }
format.json { render :show, status: :ok, location: #article }
end
Looking at the Rails and Delayed Jobs logs, though, with a test set of just a few users (5-10), when it cycles through the logic and decides three e-mails need to be sent out, Rails is doing three INSERT INTO the DJ table and DJ then does this for each one:
Job NewsitemMailer.send_article_full (id=21) RUNNING
Job NewsitemMailer.send_article_full (id=21) COMPLETED after 0.8950
And then when it has finished, it reports back with:
3 jobs processed at 0.9039 j/s, 0 failed
And in the Mandrill logs, each e-mail sent gets its own API "success/fail" entry.
So: is this correct/expected behaviour for Delayed Job? Should it be creating one job for each e-mail? Or processing them in a different way? Is this way of doing things going to break the server when we start doing X thousand instead of ten/three?
This looks like the default behaviour of delayed_job. You have ArticleMailer.delay call 3 times and it queues them all and hence you see 3 jobs processed.
I think, you should also look at handle_asynchronously feature of delayed_job.
Also, if you plan to process a huge number of emails, I would suggest you to explore other options such as Resque or sidekiq or beanstalkd
They are better than delayed_job when it comes to process a lot of jobs. delayed_job is simple and easy to setup, but can face performance issue in scale.
See this to get an overview.
Related
Because of an error which is now fixed, our customers did not received order confirmation emails for a while.
Is there a way to resent the order emails for some orders once?
We know about the plugin, but as this is supposed be needed only once, it's kind of overkill to install, test and deploy a plugin.
Are there some code snippets or a bin/console command to accomplish this task?
Or is it even possible via the admin panel?
It is possible to define a flowbuilder flow to send order confirmation mails, but you'd to define an appropriate trigger action of course, that you can trigger yourself for the affected orders. After the oneshot command you could disable/delete the flow.
Not sure if this fits your needs...
You could write your own CLI command to re-fire the event when an order is placed.
// $this->orderRepostitory - DI service `order.repository`
// $this->orderConverter - DI service `Shopware\Core\Checkout\Cart\Order\OrderConverter`
// $this->eventDispatcher - DI service `event_dispatcher`
$criteria = new Criteria();
$criteria
->addAssociation('orderCustomer.customer')
->addAssociation('orderCustomer.salutation')
->addAssociation('deliveries.shippingMethod')
->addAssociation('deliveries.shippingOrderAddress.country')
->addAssociation('deliveries.shippingOrderAddress.countryState')
->addAssociation('transactions.paymentMethod')
->addAssociation('lineItems.cover')
->addAssociation('currency')
->addAssociation('addresses.country')
->addAssociation('addresses.countryState')
->getAssociation('transactions')->addSorting(new FieldSorting('createdAt'));
// add a filter for the orders for which no mail has been sent before
$criteria->addFilter(new RangeFilter('createdAt', [RangeFilter::LTE => (new \DateTime('2020-01-01'))->format(\DATE_ATOM)]));
$orderEntities = $this->orderRepository->search($criteria, $context->getContext())->getEntities();
foreach ($orderEntities as $orderEntity) {
$salesChannelContext = $this->orderConverter->assembleSalesChannelContext($orderEntity, Context::createDefaultContext());
$event = new CheckoutOrderPlacedEvent(
$salesChannelContext->getContext(),
$orderEntity,
$salesChannelContext->getSalesChannel()->getId()
);
$this->eventDispatcher->dispatch($event);
}
This should in theory also trigger the mails to be sent once more. Obviously try this with a test order before mass dispatching events like this. Also you should probably do this in smaller batches to avoid memory issues.
I have a load test where three sets of users create something and a different set of users perform some actions on them.
What is the recommended way to co-ordinate this behaviour in Gatling?
I'm currently using an object which contains a LinkedBlockingQueue which the "producers" put the ID and consumers take, see below.
However, it causes the test to hang after ~20s (targeting 1tps).
I've also tried using poll with a timeout, but instead of hanging the poll almost always fails (after 30s) or causes a hang if the timeout is larger (1m+).
This seems to be because all the threads are blocked waiting for something from the queue so isn't compatible with the way Gatling tests run (i.e. not 1 thread per user). Is there a non-blocking way to wait in the Gatling DSL?
Producer.scala
// ...
scenario("Produce stuff")
.exec(/* HTTP call which extracts an ID*/)
.exec(session => Queue.ids.put(session("my-id").as[String])
// ...
Consumer.scala
// ...
scenario("Consume stuff")
.exec(session => session.set("my-id", Queue.ids.take()))
.exec(/* HTTP call which users ID*/)
// ...
Queue.scala
object Queue {
val ids = new LinkedBlockingQueue[String]()
}
As an alternative I've tried to use the application functionality but it seems a harder problem to ensure that each user picks a unique item from the app.
Acknowledging this is all a hack, my current solution in Consumer.scala is:
doIf(_ => Queue.ids.size() < MIN_COUNT)(
pause(30) // wait for 30s if queue is initially too small
)
.doWhile(_ => Queue.ids.size() >= MIN_COUNT)(
exec(session => session.set("my-id", Queue.ids.take()))
.exec(...)
.pause(30)
)
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
Is there any way to intercept all Hubot triggers/response globally? The interception should be able to inspect, modify, forward, or reject Hubot response before being sent.
Some goals I would like to achieve:
Throttle all message sent by Hubot (from all plugins/scripts) to prevent flooding.
Apply some kind of ACL (Access Control List) to limit who can use a command.
etc.
I cannot find it in the official Hubot documentation. Am I missing some things?
For controlling access to listeners, check out listener middleware: https://hubot.github.com/docs/scripting/#listener-middleware
https://hubot.github.com/docs/patterns/#restricting-access-to-commands
For rate limiting command execution, check out hubot-rate-limit: https://github.com/michaelansel/hubot-rate-limit
For controlling responses, keep an eye on the response middleware PR: https://github.com/github/hubot/pull/1021
this is a simple middleware I wrote to log messages that are directed at the robot. it can easily be modified to do something else depending on the user name or room name or whatever.
module.exports = (robot) ->
robot.listenerMiddleware (context, next, done) ->
#create a regex with the robots name in it
robotName = new RegExp("#{context.listener.robot.name}", "i")
#only log messages meant for the robot
if robotName.test("#{context.response.message.text}")
#only log messages once with the "everything" listener context
if context.listener.regex.source is /(.+)/i.source
console.log "User: #{context.response.message.user.name} asked me to \"#{context.response.message.text}\" in Channel: #{context.response.message.room}"
#your code goes here
next()
this thing will allow you to rate limit
So I'm using the Paypal PHP SDK on Github, http://paypal.github.io/PayPal-PHP-SDK/ . Some strange behavior I've noticed which I'm not sure what's going on.
So let's say I create a billing plan, but don't touch it after creation, so that the state is simple CREATED. Everything is good, I can retrieve it from the list of plans. However, the moment I change the state to ACTIVE via a patch, I can see that it is in fact active, but only just once. Any subsequent attempts to see the list of plans no longer shows that plan. What's going on? I'm literally copy pasting the example source they give.
Edit - just to expand, I know the plan still exists, because I can subscribe users to it. Weirdly the paypal page where you click ok to subscribe is extremely non verbose... doesn't even say what the price is, just to approve paying my store. And yet the Agreement object that is returned by PayPal, which includes the approval url, has all this info. Weird.
If you are using the PayPal-PHP-SDK, you could assign more parameters to Plan::all() method.
As shown in the List Plan sample code, you could pass parameter 'status' as :
try {
// Get the list of all plans
// You can modify different params to change the return list.
// The explanation about each pagination information could be found here
// at https://developer.paypal.com/webapps/developer/docs/api/#list-plans
$params = array('page_size' => '20', 'page' => '98', 'status' => 'ACTIVE');
$planList = Plan::all($params, $apiContext);
} catch (Exception $ex) {
ResultPrinter::printError("List of Plans", "Plan", null, $params, $ex);
exit(1);
}
As in the case, you could change the status, and page along with page_size. This will help you get the active list of plans.
Actually, by default the list plan status is defaulted to CREATED.