We're developing warehouse app for picking items which sends requests to a web service on every item scan, e. g. to update the quantity scanned in DB. From the log files I saw thet every now and then the connection on android scanners is lost and that leads to item quantity not being updated or in worst case an app crash.
What would be the best way to handle such connection failures so that I can ensure that the call to web method was successul before continuing code execution? Should I define some variable which accepts response from the web method and repeat the call until success is returned? Or is there some smarter way?
Related
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.
We have a BizTalk application which sends XML files to external applications by using a web-service.
BizTalk calls the web-services method by passing XML file and destination application URL as parameters.
If the external applications are not able to receive the XML, or if there is no response received from the web-service back to BizTalk the message gets suspended in BizTalk.
Presently for this situation we manually go to BizTalk admin and resume each suspended message.
Our clients want this process to be automated all, they want an dashboard which shows list of message details and a button, on its click all the suspended messages have to be resumed.
If you are doing this within an orchestration and catching the connection error, just add a delay shape configured to 5 hours. Or set a retry interval to 300 minutes and multiple retries on the send port if that makes sense. You can do this using the rule engine as well.
Why not implement an asynchronous pattern?
You make it so, so that the orchestration sends the file out via a send shape while initializing a certain correlation set.
You then put a listen shape with at one end:
- the receive (following the initialized correlation set)
- a delay shape set to 5 hours.
When you receive the message, your orchestration can handle it gracefully.
When you don't, the delay shape will kick in and you handle accordingly.
Benefit to this solution in comparison to the solution of 40Alpha will be that your orchestration will only 'wake up' from a dehydrated state if the timeout kicks in OR when the response is received. In the example of 40Alpha, the orchestration would wake up a lot of times, consuming extra resources.
You may want to look a product like BizTalk 360. It has those sort of monitoring and command built into it. I'm not sure it works with BizTalk 2006R2 though, but you should be thinking about moving off that platform anyway as it is going out of Microsoft support.
I am trying to build a web service on top of hbase, so the code looks roughly like:
#GET
#Path("/blabla")
#Override
public List<String> getEvents($$$params$$$) {
......
//calling hbase query the events
......
}
When Hbase service is down, the hbase Java API keeps retrying to connect to Hbase region server util eventually it times out and throws a RT Exception:
NoServerForRegionException: Unable to find region for event,,99999999999999 after 10 tries.
The logic has no problem, my issue here is that the HttpClient times out way before hbase times out the retries. Then my web service API consumer gets no response, ugly.
Question -
What's the best practice here if you have server's timeout potentially longer than the http connection itself? How to have the web service respond to client gracefully in this case?
set the cashing for you scan object to some reasonable value. another thing, since you are using a web service to show the results to your users, i am assuming that you must be showing only a few rows(or records) at a time. you can use Hbase PageFilter so that you get only a specified no of rows each time and don't have to wait to get all the rows in one shot.
I've got a django site that is producing a csv download. The content of the csv is dictated by user defined parameters. It's possible that users will set parameters that require significant thinking time on the server. I need a way of sustaining the http connection so the browser doesn't kick up an error message. I heard that it's possible to send intermittent http headers to do this. Can anyone point me in the right direction to set this up on a django site?
(unfortunatly I'm stuck with the possibility of slow reports - improving my sql won't mitigate this)
Don't do it online. Trigger an offline task, use a bit of Javascript to repeatedly call a view that checks if the task has finished, and redirect to the finished file when it's ready.
Instead of blocking the user and it's browser for 20 minutes (which is not a good idea) do the time-consuming task in the background. When the task will finish and generate the result simply notify the user so that he/she will just need to download the ready result.
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.