Suppose I have service which does the following:
Receives input notification
Processes input notification which means:
some computing
storing in DB
some computring
generating it's own notification
Sends its own notification to multiple clients
What is the best practice in this case, should I granularly trace each operation like computing, storing in db etc with separate span or leave that for metrics (i.e. prometheus) and create single span for the whole notification processing?
It's somewhat up to you as to the granularity that's appropriate for your application, and also the volume of tracing data you're expecting to generate. An application handling a few requests per minute is going to have different needs than one handling 1000s of requests per second.
That said, I recommend creating spans when control flow enters or leaves your application (such as when your application starts processing a request or message from an external system, and when your application calls out to an external dependency, such as HTTP requests, sending notifications, or writing/reading from the database), and using logs/tags for everything that's internal to your application.
Related
Is there any way to set a limit on the number of requests for the azure web app (asp.net web API)?
I have an endpoint that runs for a long time and I like to avoid multiple triggers while the application is processing one request.
Thank you
This would have to be a custom implementation which can be done in a few ways
1. Leverage a Queue
This involves separating the background process into a separate execution flow by using a queue. So, your code would be split up into two parts
API Endpoint that receives the request and inserts a message into a queue
Separate Method (or Service) that listens on the queue and processes messages one by one
The second method could either be in the same Web App or could be separated into a Function App. The queue could be in Azure Service Bus, which your Web App or Function would be listening in on.
This approach has the added benefits of durability since if the web app or function were to crash and you would like to ensure that requests are all processed, the message would be processed again in order if not completed on the queue.
2. Distributed Lock
This approach is simpler but lacks durability. Here you would simply use an in-memory queue to process requests but ensure only one is being processed at a time but having the method acquire a lock which the following requests would wait for before being processed.
You could leverage blob storage leases as a option for distributed locks.
I am implementing a REST API that internally places a message on a message queue and receives a message as a response on a different topic.
How could API implementation handle publishing and consuming different messages and responds to the client?
What if it never receives a message?
How does the service handle this time-out scenario?
Example
I am implementing a REST API to process an order. The implementation internally publishes a series of messages to verify the payment, update inventory, and prepare shipping info. Finally, it sends the response back to the client.
Queues are too low-level abstraction to implement your requirements directly. Look at an orchestration solution like temporal.io that makes programming such async systems trivial.
Disclaimer: I'm one of the founders of the Temporal open source project.
How could API implementation handle publishing and consuming different messages and responds to the client?
Even though messaging systems can be used in RPC like fashion:
there is a request topic/queue and a reply topic/queue
with a request identifier in the messages' header/metadata
this type of communication kills the promise of the messaging system: decouple components in time and space.
Back to your example. If ServiceA receives the request then it publishes a message to topicA and returns with an 202 Accepted status code to indicate that the request is received but not yet processed completely. In the response you can indicate an url on which the consumer of ServiceA's API can retrieve the latest status of its previously issued request.
What if it never receives a message?
In that case the request related data remains in the same state as it was at the time of the message publishing.
How does the service handle this time-out scenario?
You can create scheduled jobs to clean-up never finished/got stuck requests. Based on your business requirements you can simple delete them or transfer them to manual processing by the customer service.
Order placement use case
Rather than creating a customer-facing service which waits for all the processing to be done you can define several statuses/stages of the process:
Order requested
Payment verified
Items locked in inventory
...
Order placed
You can inform your customers about these status/stage changes via websocket, push notification, e-mail, etc.. The orchestration of this order placement flow can be achieved for example via the Saga pattern.
I come across many blog that say using rabbitmq improve the performance of microservices due to asynchronous nature of rabbitmq.
I don't understand in that case how the the http response is send to end user I am elaborating my question below more clearly.
user send a http request to microservice1(which is user facing service)
microservice1 send it to rabbitmq because it need some service from microservice2
microservice2 receive the request process it and send the response to rabbitmq
microservice1 receive the response from rabbitmq
NOW how this response is send to browser?
Does microservice1 waits untill it receive the response from rabbitmq?
If yes then how it become aynchronous??
It's a good question. To answer, you have to imagine the server running one thread at a time. Making a request to a microservice via RestTemplate is a blocking request. The user clicks a button on the web page, which triggers your spring-boot method in microservice1. In that method, you make a request to microservice2, and the microservice1 does a blocking wait for the response.
That thread is busy waiting for microservice2 to complete the request. Threads are not expensive, but on a very busy server, they can be a limiting factor.
RabbitMQ allows microservice1 to queue up a message to microservice2, and then release the thread. Your receive message will be trigger by the system (spring-boot / RabbitMQ) when microservice2 processes the message and provides a response. That thread in the thread pool can be used to process other users' requests in the meantime. When the RabbitMQ response comes, the thread pool uses an unused thread to process the remainder of the request.
Effectively, you're making the server running microservice1 have more threads available more of the time. It only becomes a problem when the server is under heavy load.
Good question , lets discuss one by one
Synchronous behavior:
Client send HTTP or any request and waits for the response HTTP.
Asynchronous behavior:
Client sends the request, There's another thread that is waiting on the socket for the response. Once response arrives, the original sender is notified (usually, using a callback like structure).
Now we can talk about blocking vs nonblocking call
When you are using spring rest then each call will initiate new thread and waiting for response and block your network , while nonblocking call all call going via single thread and pushback will return response without blocking network.
Now come to your question
Using rabbitmq improve the performance of microservices due to
asynchronous nature of rabbitmq.
No , performance is depends on your TPS hit and rabbitmq not going to improve performance .
Messaging give you two different type of messaging model
Synchronous messaging
Asynchronous messaging
Using Messaging you will get loose coupling and fault tolerance .
If your application need blocking call like response is needed else cannot move use Rest
If you can work without getting response go ahaead with non blocking
If you want to design your app loose couple go with messaging.
In short above all are architecture style how you want to architect your application , performance depends on scalability .
You can combine your app with rest and messaging and non-blocking with messaging.
In your scenario microservice 1 could be rest blocking call give call other api using rest template or web client and or messaging queue and once get response will return rest json call to your web app.
I would take another look at your architecture. In general, with microservices - especially user-facing ones that must be essentially synchronous, it's an anti-pattern to have ServiceA have to make a call to ServiceB (which may, in turn, call ServiceC and so on...) to return a response. That condition indicates those services are tightly coupled which makes them fragile. For example: if ServiceB goes down or is overloaded in your example, ServiceA also goes offline due to no fault of its own. So, probably one or more of the following should occur:
Deploy the related services behind a facade that encloses the entire domain - let the client interact synchronously with the facade and let the facade handle talking to multiple services behind the scenes.
Use MQTT or AMQP to publish data as it gets added/changed in ServiceB and have ServiceA subscribe to pick up what it needs so that it can fulfill the user request without explicitly calling another service
Consider merging ServiceA and ServiceB into a single service that can handle requests without having to make external calls
You can also send the HTTP request from the client to the service, set the application-state to waiting or similar, and have the consuming application subscribe to a eventSuccess or eventFail integration message from the bus. The main point of this idea is that you let daisy-chained services (which, again, I don't like) take their turns and whichever service "finishes" the job publishes an integration event to let anyone who's listening know. You can even do things like pass webhook URI's with the initial request to have services call the app back directly on completion (or use SignalR, or gRPC, or...)
The way we use RabbitMQ is to integrate services in real-time so that each service always has the info it needs to be responsive all by itself. To use your example, in our world ServiceB publishes events when data changes. ServiceA only cares about, and subscribes to a small subset of those events (and typically only a field or two of the event data), but it knows within seconds (usually less) when B has changed and it has all the information it needs to respond to requests. Each service literally has no idea what other services exist, it just knows events that it cares about (and that conform to a contract) arrive from time-to-time and it needs to pay attention to them.
You could also use events and make the whole flow async. In this scenario microservice1 creates an event representing the user request and then return a requested created response immediately to the user. You can then notify the user later when the request is finished processing.
I recommend the book Designing Event-Driven Systems written by Ben Stopford.
I asked a similar question to Chris Richardson (www.microservices.io). The result was:
Option 1
You use something like websockets, so the microservice1 can send the response, when it's done.
Option 2
microservice1 responds immediately (OK - request accepted). The client pulls from the server repeatedly until the state changed. Important is that microservice1 stores some state about the request (ie. initial state "accepted", so the client can show the spinner) which is modified, when you finally receive the response (ie. update state to "complete").
I have created a REST API - in a few words, my client hits a particular URL and she gets back a JSON response.
Internally, quite a complicated process starts when the URL is hit, and there are various services involved as a microservice architecture is being used.
I was observing some performance bottlenecks and decided to switch to a message queue system. The idea is that now, once the user hits the URL, a request is published on internal message queue waiting for it to be consumed. This consumer will process and publish back on a queue and this will happen quite a few times until finally, the same node servicing the user will receive back the processed response to be delivered to the user.
An asynchronous "fire-and-forget" pattern is now being used. But my question is, how can the node servicing a particular person remember who it was servicing once the processed result arrives back and without blocking (i.e. it can handle several requests until the response is received)? If it makes any difference, my stack looks a little like this: TomCat, Spring, Kubernetes and RabbitMQ.
In summary, how can the request node (whose job is to push items on the queue) maintain an open connection with the client who requested a JSON response (i.e. client is waiting for JSON response) and receive back the data of the correct client?
You have few different scenarios according to how much control you have on the client.
If the client behaviour cannot be changed, you will have to keep the session open until the request has not been fully processed. This can be achieved employing a pool of workers (futures/coroutines, threads or processes) where each worker keeps the session open for a given request.
This method has few drawbacks and I would keep it as last resort. Firstly, you will only be able to serve a limited amount of concurrent requests proportional to your pool size. Lastly as your processing is behind a queue, your front-end won't be able to estimate how long it will take for a task to complete. This means you will have to deal with long lasting sessions which are prone to fail (what if the user gives up?).
If the client behaviour can be changed, the most common approach is to use a fully asynchronous flow. When the client initiates a request, it is placed within the queue and a Task Identifier is returned. The client can use the given TaskId to poll for status updates. Each time the client requests updates about a task you simply check if it was completed and you respond accordingly. A common pattern when a task is still in progress is to let the front-end return to the client the estimated amount of time before trying again. This allows your server to control how frequently clients are polling. If your architecture supports it, you can go the extra mile and provide information about the progress as well.
Example response when task is in progress:
{"status": "in_progress",
"retry_after_seconds": 30,
"progress": "30%"}
A more complex yet elegant solution would consist in using HTTP callbacks. In short, when the client makes a request for a new task it provides a tuple (URL, Method) the server can use to signal the processing is done. It then waits for the server to send the signal to the given URL. You can see a better explanation here. In most of the cases this solution is overkill. Yet I think it's worth to mention it.
One option would be to use DeferredResult provided by spring but that means you need to maintain some pool of threads in request serving node and max no. of active threads will decide the throughput of your system. For more details on how to implement DeferredResult refer this link https://www.baeldung.com/spring-deferred-result
I'm new in the world of actor modeling and I am in love with the idea. But does some pattern exists for processing a batch of messages simply for bulk storage in a safe manner?
I'm afraid if I read 400 messages of expected 500 and put them in a list, if the system closes, I don't want to lose those 400 messages from the (persisted)
mailbox. In a service bus world you could ask for a batch of messages and only when processed, commit all of them. Thank you.
You may want to combine your actor system with some service bus/reliable queues, like RabbitMQ or Azure Service Bus, at use actor system only for message processing.
From within Akka.NET itself, you have persistence extension, which can be used for storing actor state in persistent backend of your choice. It also contains a dedicated kind of an actor, AtLeastOnceDeliveryActor that may be used to resend messages until they will be confirmed.
you can extend split and aggregate in your ESB to do it, I made something similar with mule ESB from long time.