I'm using WireMock to mock some SOAP services. One of them goes through a negotiation cycle for requests before finally authenticating with NTLM. Currently WireMock is recording the first auth-fail response and then ignoring the rest as it has already recorded something for that URL/header combination.
Is there a way to either force it to record everything simply overwriting previous recordings or to provide it with a filter to ignore certain responses when recording?
Thanks for your help,
Joe
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I'm building a web application using Flutter, and I have some requests that take a lot of time in server side, and "hold" http resources until the requests are answered.
To deal with this I read you can do polling or other solutions (see here).
What is the correct design in flutter? How would you implement it in flutter?
I have some requests that take a lot of time, and "hold" http
resources until the requests are answered
Sounds like you are using a synchronous approach, where your program sits and wait until the responses comes back. Instead, try using an asynchronous approach, that will allow your program to continue to run while you wait for the responses to come back.
I know nothing about Dart or Flutter, but these should help get you started:
https://dart.dev/codelabs/async-await
https://medium.com/flutterdevs/exploring-asynchronous-programming-in-dart-flutter-25f341af32f
Update in response to OP's comment
If you have long-running tasks happening on the server, and you need to get the data back to a web-client then async HTTP requests are a legitimate architecture based on what you have explained so far.
If processing is blocked then maybe there's possibly an issue with you implementation. Alternatively there might be issues between the browser and client (issues with a proxy server, etc), so check for those types of issues.
If you need another option altogether then websockets might be worth looking into.
I've been exploring the use of Gatling for JMS testing as part of broader perf testing of our AUT. I've played with the example as found at https://gatling.io/docs/current/jms/, and have successfully had gatling create a queue on my test ActiveMQ server, and read the message.
However, actual AUT testing needs dictates that services in our app will create the msgs on our ActiveMQ server - and all I want in my Gatling code is make REST calls to our services that generate the messages, then the Gatling JMS code should pick up the messages, parse them as appropriate, and when I find a certain message, move on to the next bit of the test.
As per the gatling link above, "Currently, requestReply and send (fire and forget) requests are supported." Does this mean what I am trying to do is impossible? Does this mean I have to create the messages with Gatling, but not necessarily look for a reply?
If it is possible, I assume I could split the example I've been playing with into 2 separate exec actions - one to send, and one to receive? But how?
Thanks!
No, it's not possible at the moment (Gatling 3.3).
Assume the following scenario A web application serves up resources through a RESTful API. A number of clients consume this API. The goal is to keep the data on the clients synchronized with the web application (in both directions).
The easiest way to do this is to ask the API if any of the resources have changed since the client last synchronized with the API. This means that the client needs to ask the API for the appropriate resources accompanied by timestamp (to see if the data needs to be updated). This seems to me like the approach with the least overhead in terms of needless consumption of bandwidth.
However, I have the feeling that this approach has a few downsides in terms of design and responsibilities. For example, the API shouldn't have to deal with checking whether the resources are out of date. It seems that the only responsibility of the API should be to serve up the resources when asked without having to deal with the updating aspect. By following this second approach, the client would ask for a lot of data every time it wants to update its data to keep it synchronized with the web application. In other words, the client would check whether the data it got back is newer than the locally stored data. If this process takes place every few minutes, this might become a significant burden for the system.
Am I seeing this correctly or is there a middle road that I am overlooking?
This is a pretty common problem, and a RESTful approach can help you solve it. HTTP (the application protocol typically used to build RESTful services) supports a variety of techniques that can be used to keep API clients in sync with the data on the server side.
If the client receives a Last-Modified or E-Tag header in a HTTP response, it may use that information to make conditional GET calls in the future. This allows the server to quickly indicate with a 304 – Not Modified response that the client’s previously stored representation of the resource is still valid and accurate. This will allow the server (or even better, an intermediate proxy or cache server) to be as efficient as possible in how it responds to the client’s requests, potentially reducing costly round-trips to a back-end data store.
If a response contains a Last-Modified header and the client wishes to take advantage of the performance optimization available with it, they must include an If-Modified-Since directive in a subsequent GET call to the same URI, passing in the same timestamp value it received. This instructs the server to only GET the information from the authoritative back-end source if it knows it has changed since that time. Your server will have to be built to support this technique, of course.
A similar principle applies to E-Tag headers. An E-Tag is a simple hash code representing a specific state of the resource at a particular point in time. If the resource changes in any way, so does its E-Tag value. If the client sees an E-Tag in a response it should pass it in subsequent GET requests to the same URI, thereby allowing the server to quickly determine if the client has an up-to-date representation of the resource.
Finally, you should probably look at the long polling technique to reduce the number of repeated GET requests issued by your clients to the server. In essence, the trick is to issue very long GET requests to the server to watch for server data changes. The GET will not return a response until either the data has changed or the very long timeout fires. If the latter, the client just re-issues the same long-lived request to watch for changes again. See also topics like Comet and Web Sockets which are similar in approach.
I unsuccessfully searched Google for a good definition and understanding of streaming data and its characteristics. My questions are:
What is streaming data?
How can it be detected?
Correction:
"How can it be detected" is not an appropriate question. Instead my question is:
How is it different from buffered data and other data transfer mechanisms?
It depends in what context you mean but basically streaming data is analagous to asynchronous data. Take the Web as an example. The Web (or HTTP specifically) is (basically) a request-response mechanism in that a client makes a request and receives a response (typically a Web page of some kind).
HTTP doesn't natively support the ability for servers to push content to clients. There are a number of ways this can be faked, including:
Polling: forcing the client to make repeated requests, typically inconspicuously (as far as the client is concerned);
Long-lived connections: this is where the client makes a normal HTTP request but instead of returning immediately the server hangs on to the request until there's something to send back. When the request times out or a response is sent th eclient sends another request. In this way you can fake server push;
Plug-ins: Java applets, Flash, Silverlight and others can be used to achieve this.
Anything where the server effectively sends data to the client (rather than the client asking for it)--regardless of the mechanism and whether or not the client is polling for that data--can be characterised as streaming data.
With non-HTTP transports (eg vanilla TCP) server push is typically easier (but can still run afoul of firewalls and th elike). An example of this might be a sharetrading application that receives market information from a provider. That's streaming data.
How do you detect it? Bit of a vague question. I'm not really sure what you're getting at.
When you say streaming data I think of the following, although I'm not sure if this is what you're getting at. To me it's playing a video/audio file while it's downloading. That's what happens when you go to YouTube and watch a video and it starts playing even though you haven't downloaded the whole video yet. But you can see the video downloading - I'm sure you're familiar with the seek bar filling up as the file downloads. It doesn't necessarily have to be a video or audio file but that's the most common.
If there is a REST resource that I want to monitor for changes or modifications from other clients, what is the best (and most RESTful) way of doing so?
One idea I've had for doing so is by providing specific resources that will keep the connection open rather than returning immediately if the resource does not (yet) exist. For example, given the resource:
/game/17/playerToMove
a "GET" on this resource might tell me that it's my opponent's turn to move. Rather than continually polling this resource to find out when it's my turn to move, I might note the move number (say 5) and attempt to retrieve the next move:
/game/17/move/5
In a "normal" REST model, it seems a GET request for this URL would return a 404 (not found) error. However, if instead, the server kept the connection open until my opponent played his move, i.e.:
PUT /game/17/move/5
then the server could return the contents that my opponent PUT into that resource. This would both provide me with the data I need, as well as a sort of notification for when my opponent has moved without requiring polling.
Is this sort of scheme RESTful? Or does it violate some sort of REST principle?
Your proposed solution sounds like long polling, which could work really well.
You would request /game/17/move/5 and the server will not send any data, until move 5 has been completed. If the connection drops, or you get a time-out, you simply reconnect until you get a valid response.
The benefit of this is it's very quick - as soon as the server has new data, the client will get it. It's also resilient to dropped connections, and works if the client is disconnected for a while (you could request /game/17/move/5 an hour after it's been moved and get the data instantly, then move onto move/6/ and so on)
The issue with long polling is each "poll" ties up a server thread, which quickly breaks servers like Apache (as it runs out of worker-threads, so can't accept other requests). You need a specialised web-server to serve the long-polling requests.. The Python module twisted (an "an event-driven networking engine") is great for this, but it's more work than regular polling..
In answer to your comment about Jetty/Tomcat, I don't have any experience with Java, but it seems they both use a similar pool-of-worker-threads system to Apache, so it will have that same problem. I did find this post which seems to address exactly this problem (for Tomcat)
I'd suggest a 404, if your intended client is a web browser, as keeping the connection open can actively block browser requests in the client to the same domain. It's up to the client how often to poll.
2021 Edit: The answer above was in 2009, for context.
Today, I would suggest using a WebSocket interface with push notifications.
Alternatively, in the above suggestion, I might suggest holding the connection for 500-1000ms and check twice at the server before returning the 404, to reduce the overhead of creating multiple connections at the client.
I found this article proposing a new HTTP header, "When-Modified-After", that essentially does the same thing--the server waits and keeps the connection open until the resource is modified.
I prefer a version-based approach rather than a timestamp-based approach, since it's less prone to race conditions and gives you a little more information about what it is you're retrieving. Any thoughts to this approach?