Are there any best practices that dictate the maximum time between an asynchronous call and its corresponding response.
Basically I have a process that takes a long time to run (eg: 5 minutes). Option 1: I could expose the process as an asynchronous call. In which case the user calls my service and then at some later time, I respond with a process status.
Option 2
The other way I could implement it is to setup the system such that there is a one-way operation on my web-service that begins the process and immediately returns an id for the process. I could then mandate that the consumer provide a one-way operation, that I can call and report back when the process is done.
The first option is easier as I dont have to mandate anything from the caller. The second seems better as I can report back at anytime (5 minutes to years later).
As I have complete control over the caller and its an internally available service, I am leaning towards option 2.
So I am wondering if there are any time limits imposed on async calls (can they span days? if not what is the best practice). Is option 2 a standard pattern employed?
References would be extremely useful.
Option #2 is better as it's more event driven.
However, there exists an Option #3. Client issues request to server. Server queues request and responds with the id. Client checks back every so often, passing the request id, to see if it's completed.
This way you don't have to depend on the client being available when the request is completed.
I'd probably mix options #2 and #3 and let the client choose if they want an event fired on their side or if they just want to check back later.
UPDATE
Rajah has asked about the maximum time between async request and response. For a WEB application, this is typically measured in seconds. Most servers have timeout values that are typically defaulted in the 30 second range. Personally, I think this is too long.
Consider that an Async call requires the communications channel between the client and server to be open for the duration. How many of those channels can a single server handle? More to the point, how many channels will you have to maintain as requests are made? This can become quite outrageous even if you do control both ends.
Whatever is hosting your services is going to determine the maximum amount of time to keep a request open. Again, every server I've seen measures this in seconds.
Related
I have a function doWork(id) that I'm offloading to some worker servers using AWS SQS. This function can get called very frequently but I'd like to throttle the function so that for a given id, the work is don't no more than once per second.
Is it possible with AWS / are there any services that feature this functionality?
EDIT: Some clarification.
doWork(id) does some expensive work on a record in a database. This work needs to continuously update whenever the user interacts with the record. Thus, I call doWork(id) whenever the user called a method that edits the record. However, the user may edit the record many times very quickly (I'm building a text editor so every character is an edit). Rather than doWork(id) a unnecessary amount of times, I'd like to throttle that work so it happens at most once per second.
Because this work is expensive, I enqueue a message in SQS and have a set of "worker" servers that dequeue tasks and run them.
My goal here is to somehow maintain the stateless horizontal scalability of my servers while throttling doWork(id). To make matters a little more complicated, I don't want to throttle the doWork function itself -- I want to throttle the work for each individual record identified by the id passed to doWork.
You could use a Redis instance on ElastiCache and configure your workers to use a distributed rate limiter for keys based on id. There are also many packages for different languages based on this kind of idea that might be ready to run on your workers.
That's interesting. You want to delay the work in case they hit another key within a given time period. If they don't hit another key in that time period, you then want to do the work. You might also want to do it after x seconds even if they continue typing (Auto Save).
The problem is that each keypress sends a message to the queue. When a worker receives the message, they have no idea whether another key has been pressed since the message was sent, and there's no way to look in the queue for other matching messages.
Amazon SQS does have the ability to delay a message, which means it will not be available for receiving for a given period, but this alone can't solve the problem because the worker doesn't know what else has happened.
Bottom line: A traditional queue is not a suitable mechanism for this use-case. You need something akin to a database/cache that can update a "last modified" timestamp each time that a key is pressed. Once that timestamp is more than x seconds old, you should queue the worker.
My django rest app accepts request to scrape multiple pages for prices & compare them (which takes time ~5 seconds) then returns a list of the prices from each page as a json object.
I want to update the user with the current operation, for example if I scrape 3 pages I want to update the interface like this :
Searching 1/3
Searching 2/3
Searching 3/3
How can I do this?
I am using Angular 2 for my front end but this shouldn't make a big difference as it's a backend issue.
This isn't the only way, but this is how I do this in Django.
Things you'll need
Asynchronous worker procecess
This allows you to do work outside the context of the request-response cycle. The most common are either django-rq or Celery. I'd recommend django-rq for its simplicity, especially if all you're implementing is a progress indicator.
Caching layer (optional)
While you can use the database for persistence in this case, temporary cache key-value stores make more sense here as the progress information is ephemeral. The Memcached backend is built into Django, however I'd recommend switching to Redis as it's more fully featured, super fast, and since it's behind Django's caching abstraction, does not add complexity. (It's also a requirement for using the django-rq worker processes above)
Implementation
Overview
Basically, we're going to send a request to the server to start the async worker, and poll a different progress-indicator endpoint which gives the current status of that worker's progress until it's finished (or failed).
Server side
Refactor the function you'd like to track the progress of into an async task function (using the #job decorator in the case of django-rq)
The initial POST endpoint should first generate a random unique ID to identify the request (possibly with uuid). Then, pass the POST data along with this unique ID to the async function (in django-rq this would look something like function_name.delay(payload, unique_id)). Since this is an async call, the interpreter does not wait for the task to finish and moves on immediately. Return a HttpResponse with a JSON payload that includes the unique ID.
Back in the async function, we need to set the progress using cache. At the very top of the function, we should add a cache.set(unique_id, 0) to show that there is zero progress so far. Using your own math implementation, as the progress approaches 100% completion, change this value to be closer to 1. If for some reason the operation fails, you can set this to -1.
Create a new endpoint to be polled by the browser to check the progress. This looks for a unique_id query parameter and uses this to look up the progress with cache.get(unique_id). Return a JSON object back with the progress amount.
Client side
After sending the POST request for the action and receiving a response, that response should include the unique_id. Immediately start polling the progress endpoint at a regular interval, setting the unique_id as a query parameter. The interval could be something like 1 second using setInterval(), with logic to prevent sending a new request if there is still a pending request.
When the progress received equals to 1 (or -1 for failures), you know the process is finished and you can stop polling
That's it! It's a bit of work just to get progress indicators, but once you've done it once it's much easier to re-use the pattern in other projects.
Another way to do this which I have not explored is via Webhooks / Channels. In this way, polling is not required, and the server simply sends the messages to the client directly.
I have heard that there is a limit for a server for the requests number it can process.
So if the requests from the client are large than the number people will queue the requests.
So I have two problems:
1 When
How to decide if it is necessary to queue the requests? How to measure the largest number?
2 How
If the queue is unavoidable, so where should be the queue done?
For a J2EE application using spring web mvc as the framework, I want to know if the queue should be put in the Controller or the Model or the DAO?
3 Is there a idea which can avoid the queue but keeping providing the service?
First you have to establish your limit at the server actually is. Its likely that its a limit on the frequency of messages, ie. maybe your limited to sending 10 requests a second. If thats the case then your would need to keep a count of how many messages you've sent out in a second, then before you send out a request check to see if you will breach this limit, if this is true then you must make the thread wait until the second is up. If not your free to send the request. This thread would be reading from a queue of outbound messages.
If the server limit is determined in an other way, i.e. dynamically based on its current load, which sounds like it might be in your case, there must be a continuous feed of request limits which you must process to determine the current limit. Once you have this limit you can process the requests in the same way as mentioned in the first paragraph.
As for where to put the queue and the associated logic, i'd put it in the controller.
I don't think there is a way to avoid the queue, you are forced to throttle your requests an therefore you must queue your outbound requests internally so that they are not lost, and will be processed at some point in the future.
Imagine 3 system components:
1. External ecommerce web service to process credit card transactions
2. Local Database to store processing results
3. Local UI (or win service) to perform payment processing of the customer order document
The external web service is obviously not transactional, so how to guarantee:
1. results to be eventually persisted to database when received from web service even in case the database is not accessible at that moment(network issue, db timeout)
2. prevent clients from processing the customer order while payment initiated by other client but results not successfully persisted to database yet(and waiting in some kind of recovery queue)
The aim is to do processing having non transactional system components and guarantee the transaction won't be repeated by other process in case of failure.
(please look at it in the context of post sell payment processing, where multiple operators might attempt manual payment processing; not web checkout application)
Ask the payment processor whether they can detect duplicate transactions based on an order ID you supply. Then if you are unable to store the response due to a database failure, you can safely resubmit the request without fear of double-charging (at least one PSP I've used returned the same response/auth code in this scenario, along with a flag to say that this was a duplicate).
Alternatively, just set a flag on your order immediately before attempting payment, and don't attempt payment if the flag was already set. If an error then occurs during payment, you can investigate and fix the data at your leisure.
I'd be reluctant to go down the route of trying to automatically cancel the order and resubmitting, as this just gets confusing (e.g. what if cancelling fails - should you retry or not?). Best to keep the logic simple so when something goes wrong you know exactly where you stand.
In any system like this, you need robust error handling and error reporting. This is doubly true when it comes to dealing with payments, where you absolutely do not want to accidentaly take someone's money and not deliver the goods.
Because you're outsourcing your payment handling to a 3rd party, you're ultimately very reliant on the gateway having robust error handling and reporting systems.
In general then, you hand off control to the payment gateway and start a task that waits for a response from the gateway, which is either 'payment accepted' or 'payment declined'. When you get that response you move onto the next step in your process and everything is good.
When you don't get a response at all (time out), or the response is invalid, then how you proceed very much depends on the payment gateway:
If the gateway supports it send a 'cancel payment' style request. If the payment cancels successfully then you probably want to send the user to a 'sorry, please try again' style page.
If the gateway doesn't support canceling, or you have no communications to the gateway then you will need to manually (in person, such as telephone) contact the 3rd party to discover what went wrong and how to proceed. To aid this you need to dump as much detail as you have to error logs, such as date/time, customer id, transaction value, product ids etc.
Once you're back on your site (and payment is accepted) then you're much more in control of errors, but in brief if you cant complete the order, then you should either dump the details to disk (such as csv file for manual handling) or contact the gateway to cancel the payment.
Its also worth having a system in place to track errors as they occur, and if an excessive number occur then consider what should happen. If its a high traffic site for example you may want to temporarily prevent further customers from placing orders whilst the issue is investigated.
Distributed messaging.
When your payment gateway returns submit a message to a durable queue that guarantees a handler will eventually get it and process it. The handler would update the database. Should failure occur at that point the handler can leave the message in the queue or repost it to the queue, or post an alternate message.
Should something occur later that invalidates the transaction, another message could be queued to "undo" the change.
There's a fair amount of buzz lately about eventual consistency and distribute messaging. NServiceBus is the new component hotness. I suggest looking into this, I know we are.
Company A has async pooling based webservice for notifications. Company B checks for notifications. Every time when it reads new notifications A deletes them from the system. Thus subsequent read requests return only new notifications. There is also requirement for the client B to interrupt the connection if there is no response within 30 sec.
This causes one potential problem: Due to unexpected slowness it is possible for A get the request deleted a notification and send the response back while B is already interrupted the connection. Under this scenario notification gets lost. Now one can argue that the core problem lies within operation realm (the HTTP response must be delivered withing 20 sec ) still on practice it is not always feasible.
How to design B (the client) to avoid this problem?
One way I can see is to do not delete the notifications by A and make B be aware of its state, so that it knows starting from what ID it needs to process notifications, but that presumes that ID will be sequential. Which is controlled by A. Even if B defines its own sequence A still has to be altered to return it back.
Are there any other approaches?
Thanks!
Web services in general are unreliable enough that it's rarely a good idea to make a "read" request serve double-duty as a "delete" request, especially without the client's knowledge. There is just too much risk of a connection dropping or timing out. There is no way to get around this only by modifying the client, because it's the server that is at fault here - the way it's designed is fundamentally unsuited for a web service.
I think you're on the right track with the incrementing IDs idea. The client knows (or can be modified to know) which notifications it's received, so if it can supply the ID of the last message it's received when it polls for notifications, the server should be able to respond based on that ID.
It really seems like Company A's webservice should be synchronous instead of asynchronous. If that is not possible, it may be a good idea to send a "ACK"-like response to a new Company A webservice that indicates a specific notification was received (by Company B) and can be deleted.