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.
Related
I'm looking for a solution that will have the backend publish an event to the frontend as soon as a modification is done on the server side. To be more concise I want to emit a new List of objects as soon as one item is modified.
I've tried implementing on a SpringBoot project, that uses Reactive Web, MongoDB which has a #Tailable cursor that publish an event as soon as the capped collection is modified. The problem is that the capped collection has some limitation and is not really compatible with what I want to do. The thing is I cannot update an existing element if the new one has a different size(as I understood this is illegal because you cannot make a rollback).
I honestly don't even know if it's doable, but maybe I'm lucky and I'll run into a rocket scientist right here that will prove otherwise.
Thanks in advance!!
*** EDIT:
Sorry for the vague question. Yes I'm more focused on the HOW, using the Spring Reactive framework.
When I had a similar need - to inform frontend that something is done on the backend side - I have used a message queue.
I have published a message to the queue from the backend and the frontend consumed the message.
But I am not sure if that is what you're looking for.
if you are using webflux with spring reactor, I think you can simply have a client request with content-type as 'text/event-stream' or 'application/stream+json' and You shall have API that can produce those content-type. This gives you SSE model without too much effort.
#GetMapping(value = "/stream", produces = {MediaType.TEXT_EVENT_STREAM_VALUE, MediaType.APPLICATION_STREAM_JSON_VALUE, MediaType.APPLICATION_JSON_UTF8_VALUE})
public Flux<Message> get(HttpServletRequest request) {
Just as an idea - maybe you need to use a web socket technology here:
The frontend side (I assume its a client side application that runs in a browser, written in react, angular or something like that) can establish a web-socket communication with the backend server.
When the process on backend finishes, the message from backend to frontend can be sent.
You can do emitting changes by hand. For example:
endpoint:
public final Sinks.Many<SimpleInfoEvent> infoEventSink = Sinks.many().multicast().onBackpressureBuffer();
#RequestMapping(path = "/sseApproach", produces = MediaType.TEXT_EVENT_STREAM_VALUE)
public Flux<ServerSentEvent<SimpleInfoEvent>> sse() {
return infoEventSink.asFlux()
.map(e -> ServerSentEvent.builder(e)
.id(counter.incrementAndGet() + "")
.event(e.getClass().getName())
.build());
}
Code anywhere for emitting data:
infoEventSink.tryEmitNext(new SimpleInfoEvent("any custom event"));
Watch out of threads and things like "subscribeOn", "publishOn", but basically (when not using any third party code), this should work good enough.
I'm setting up a new mqtt conection in my app but there is a problem when i would like to create the main connection of mqtt.
I'm using mqtt.js.
I've tried all what is done in MQTT documentation but nothing happens..
mqttFunction(){
var mqtt = require('mqtt');
var client = mqtt.connect([{host: 'localhost', port: '1883'},]);
client.subscribe('presence')
client.on('message', function (topic, message) {
console.log(message);
});
}
I expect the output of the mqtt broker to be 'ON' when i asked it to respond.
The error is: ERROR ReferenceError: process is not defined
The documentation you followed is intended for Node.js and various other back-end JavaScript frameworks. Even though it uses NPM, Ionic ultimately produces a front-end framework, and its applications run a bit differently.
For example, Ionic programs may not have a global process variable like Node.js. mqtt.js expects this variable, with code like:
if (commist.parse(process.argv.slice(2)) !== null){...}
You could declare a process object, and get past this particular error. Other obstacles could come up.
var process = {env : {NODE_ENV: 'production'}}
If there are still issues with that, you could try the instructions for browser usage, which point to a specially compiled version, like https://unpkg.com/mqtt#3.0.0/dist/mqtt.min.js. I have had less luck with mqtt.js in the browser, and you may want an alternative like web-mqtt-cient / Paho if more complex connections are involved.
I am trying to write the first time of my life test based on BDD style as the following:
final class SapRsSpec extends FeatureSpec
with Matchers
with GivenWhenThen {
feature("KAFKA") {
scenario("Technical user starts SAP RS") {
Given("Consumer client gets started")
When("KAFKA server is not active")
Then("message `Can not connect ot KAFKA` appears.")
}
}
}
I was trying to write as a technical user perspective.
Is it correct?
When clause should ideally describe the action and not the state, so writing
When("consumer client gets started")
instead of When("Kafka server is not active") is more idiomatic. Thinking of Given-When-Then as the Hoare triple might be helpful, where we first specify state before the action (Given), then the action that mutates the sate (When), and finally the expected sate after the action (Then).
feature and scenario clauses should ideally make sense on their own even when leaving out Given-When-Then body. Just stating feature("Kafka") seems too broad. The feature being specified is actually the relationship between Kafka and replication server, not just Kafka by itself.
Say the feature being specified is replication server's distribution to Kafka under two scenarios, when Kafka is up and when it is down, then we might refine the spec as follows:
feature("Replication server's distribution to Kafka") {
scenario("Replication server's distribution when Kafka is DOWN") {
Given("Kafka server is NOT active")
When("consumer client gets started")
Then("print message 'Failed to connect to Kafka'")
}
scenario("Replication server's distribution when Kafka is UP") {
Given("Kafka server is active")
When("consumer client gets started")
Then("print message 'Successfully connected to Kafka'")
}
}
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've created an actor to send messages to a chat server. However, the chat server only permits 5 connections per user. If I hammer my scala server I get error messages because my chat clients get disconnected.
So how can I configure akka so that my XmppSenderActors only use a maximum of 5 threads? I don't want to restrict the rest of the actor system, only this object (at the path /XmppSenderActor/).
I'm trying this config since I think it's the dispatcher I need to configure, but I'm not sure:
akka.actor.deployment {
/XmppSenderActor {
dispatcher = xmpp-dispatcher
}
xmpp-dispatcher {
fork-join-executor.parallelism-min = 2
fork-join-executor.parallelism-max = 3
}
}
This gives me an error though: akka.ConfigurationException: Dispatcher [xmpp-dispatcher] not configured for path akka://sangria-server/user/XmppSenderActor
I would probably try to configure a Router instead.
http://doc.akka.io/docs/akka/2.0/scala/routing.html
A dispatcher seems to deal with sending messages to the inbox rather than the actual number or Actor targets.
That configuration in particular could work for you:
akka.actor.deployment {
/router {
router = round-robin
nr-of-instances = 5
}
}
The nr-of-instances will create 5 childrens from the get going and therefore fill your needs.
You might need to find the right Router implementation though.