This MSDN page describes the need for some filters to return VFW_S_CANT_CUE from GetState() in the paused state if there's a possibility that the filter can't deliver while paused. That all seems clear enough. It seems if there's any doubt for a particular then it's probably better to return VFW_S_CANT_CUE to make sure that Pause() doesn't hang.
Delivering Samples
Are there any downsides to returning VFW_S_CANT_CUE though? Is resuming streaming from the paused state likely to perform poorly or lose sync if a mux or demux filter in the graph returns VFW_S_CANT_CUE?
I've inherited source code for several filters that sometimes return VFW_S_CANT_CUE for reasons that aren't clear to me (for example only returning VFW_S_CANT_CUE if no output samples have been delivered). I'm wondering if there any risks from always returning VFW_S_CANT_CUE.
Return of VFW_S_CANT_CUE disables synchronization with renderers during stopped/paused transition: Filter Graph Manager is not waiting for renderers to report that they are ready, which in case of video renderer means that it receives a banner frame and presents it (I suppose with sending EC_PAUSED notification). Disabled synchronization means that IMediaControl::Pause returns immediately and does not wait for banner frame, what live sources might prefer to do.
The only downside I can think of is that having Pause call completed you cannot be sure that video renderer presents valid frame and not blackness instead. I suppose the unclear reasoning behind VFW_S_CANT_CUE you are seeing is attempts of the developer to avoid deadlocks he stumbled on during debugging.
If filter returns VFW_S_CANT_CUE in GetState() method (i.e. LiveSource), Pause() method will not wait for samples to be queued. And because of this, stream time startd when filter graph is started.
Otherwise, filter graph will wait until several samples have been queued. And only after that stream time will be started (because after Pause(), Run() method called)
Related
Given an asynchronous IMFSourceReader connected to a synchronous only IMFTransform.
Then for the IMFSourceReaderCallback::OnReadSample() callback is it a good idea not to call IMFTransform::ProcessInput directly within OnReadSample, but instead push the produced sample onto another queue for another thread to call the transforms ProcessInput on?
Or would I just be replicating identical work source readers typically do internally? Or put another way does work within OnReadSample run the risk of blocking any further decoding work within the source reader that could have otherwise happened more asynchronously?
So I am suggesting something like:
WorkQueue transformInputs;
...
// Called back async
HRESULT OnReadSampleCallback(... IMFSample* sample)
{
// Push sample and return immediately
Push(transformInputs, sample);
}
// Different worker thread awoken for transformInputs queue samples
void OnTransformInputWork()
{
// Transform object is not async capable
transform->TransformInput(0, Pop(transformInputs), 0);
...
}
This is touched on, but not elaborated on here 'Implementing the Callback Interface':
https://learn.microsoft.com/en-us/windows/win32/medfound/using-the-source-reader-in-asynchronous-mode
Or is it completely dependent on whatever the source reader sets up internally and not easily determined?
It is not a good idea to perform a long blocking operation in IMFSourceReaderCallback::OnReadSample. Nothing is going to be fatal or serious but this is not the intended usage.
Taking into consideration your previous question about audio format conversion though, audio sample data conversion is fast enough to happen on such callback.
Also, it is not clear or documented (depends on actual implementation), ProcessInput is often instant and only references input data. ProcessOutput would be computationally expensive in this case. If you don't do ProcessOutput right there in the same callback you might run into situation where MFT is no longer accepting input, and so you'd have to implement a queue anyway.
With all this in mind you would just do the processing in the callback neglecting performance impact assuming your processing is not too heavy, or otherwise you would just start doing the queue otherwise.
I'm writing an app using RxAndroidBle library (which is great) but due to a requirement of the Device vendor we must be able to cancel all operations after the device has reached a certain state. Is there a way of cancelling any read/write op that has been sent to the RxBleConnection?
Any ideas?
Thanks!
The library uses an internal ConnectionOperationQueue which tries to remove operations that have not yet reached execution state. A concrete implementation would vary case-to-case but using RxJava it should be fairly easy to achieve:
// First create a completable that will fulfil appropriately
Completable deviceReachedACertainState = ...;
// Then use it for every operation that you want to cancel (unsubscribe) when a certain state happens
Observable<byte[]> cancellableRead = rxBleConnection.readCharacteristic(UUID).takeUntil(deviceReachedACertainState);
Keep in mind that operations that were already taken off the queue for execution will not be cancelled.
I am learning about F# agents (MailboxProcessor).
I am dealing with a rather unconventional problem.
I have one agent (dataSource) which is a source of streaming data. The data has to be processed by an array of agents (dataProcessor). We can consider dataProcessor as some sort of tracking device.
Data may flow in faster than the speed with which the dataProcessor may be able to process its input.
It is OK to have some delay. However, I have to ensure that the agent stays on top of its work and does not get piled under obsolete observations
I am exploring ways to deal with this problem.
The first idea is to implement a stack (LIFO) in dataSource. dataSource would send over the latest observation available when dataProcessor becomes available to receive and process the data. This solution may work but it may get complicated as dataProcessor may need to be blocked and re-activated; and communicate its status to dataSource, leading to a two way communication problem. This problem may boil down to a blocking queue in the consumer-producer problem but I am not sure..
The second idea is to have dataProcessor taking care of message sorting. In this architecture, dataSource will simply post updates in dataProcessor's queue. dataProcessor will use Scanto fetch the latest data available in his queue. This may be the way to go. However, I am not sure if in the current design of MailboxProcessorit is possible to clear a queue of messages, deleting the older obsolete ones. Furthermore, here, it is written that:
Unfortunately, the TryScan function in the current version of F# is
broken in two ways. Firstly, the whole point is to specify a timeout
but the implementation does not actually honor it. Specifically,
irrelevant messages reset the timer. Secondly, as with the other Scan
function, the message queue is examined under a lock that prevents any
other threads from posting for the duration of the scan, which can be
an arbitrarily long time. Consequently, the TryScan function itself
tends to lock-up concurrent systems and can even introduce deadlocks
because the caller's code is evaluated inside the lock (e.g. posting
from the function argument to Scan or TryScan can deadlock the agent
when the code under the lock blocks waiting to acquire the lock it is
already under).
Having the latest observation bounced back may be a problem.
The author of this post, #Jon Harrop, suggests that
I managed to architect around it and the resulting architecture was actually better. In essence, I eagerly Receive all messages and filter using my own local queue.
This idea is surely worth exploring but, before starting to play around with code, I would welcome some inputs on how I could structure my solution.
Thank you.
Sounds like you might need a destructive scan version of the mailbox processor, I implemented this with TPL Dataflow in a blog series that you might be interested in.
My blog is currently down for maintenance but I can point you to the posts in markdown format.
Part1
Part2
Part3
You can also check out the code on github
I also wrote about the issues with scan in my lurking horror post
Hope that helps...
tl;dr I would try this: take Mailbox implementation from FSharp.Actor or Zach Bray's blog post, replace ConcurrentQueue by ConcurrentStack (plus add some bounded capacity logic) and use this changed agent as a dispatcher to pass messages from dataSource to an army of dataProcessors implemented as ordinary MBPs or Actors.
tl;dr2 If workers are a scarce and slow resource and we need to process a message that is the latest at the moment when a worker is ready, then it all boils down to an agent with a stack instead of a queue (with some bounded capacity logic) plus a BlockingQueue of workers. Dispatcher dequeues a ready worker, then pops a message from the stack and sends this message to the worker. After the job is done the worker enqueues itself to the queue when becomes ready (e.g. before let! msg = inbox.Receive()). Dispatcher consumer thread then blocks until any worker is ready, while producer thread keeps the bounded stack updated. (bounded stack could be done with an array + offset + size inside a lock, below is too complex one)
Details
MailBoxProcessor is designed to have only one consumer. This is even commented in the source code of MBP here (search for the word 'DRAGONS' :) )
If you post your data to MBP then only one thread could take it from internal queue or stack.
In you particular use case I would use ConcurrentStack directly or better wrapped into BlockingCollection:
It will allow many concurrent consumers
It is very fast and thread safe
BlockingCollection has BoundedCapacity property that allows you to limit the size of a collection. It throws on Add, but you could catch it or use TryAdd. If A is a main stack and B is a standby, then TryAdd to A, on false Add to B and swap the two with Interlocked.Exchange, then process needed messages in A, clear it, make a new standby - or use three stacks if processing A could be longer than B could become full again; in this way you do not block and do not lose any messages, but could discard unneeded ones is a controlled way.
BlockingCollection has methods like AddToAny/TakeFromAny, which work on an arrays of BlockingCollections. This could help, e.g.:
dataSource produces messages to a BlockingCollection with ConcurrentStack implementation (BCCS)
another thread consumes messages from BCCS and sends them to an array of processing BCCSs. You said that there is a lot of data. You may sacrifice one thread to be blocking and dispatching your messages indefinitely
each processing agent has its own BCCS or implemented as an Agent/Actor/MBP to which the dispatcher posts messages. In your case you need to send a message to only one processorAgent, so you may store processing agents in a circular buffer to always dispatch a message to least recently used processor.
Something like this:
(data stream produces 'T)
|
[dispatcher's BCSC]
|
(a dispatcher thread consumes 'T and pushes to processors, manages capacity of BCCS and LRU queue)
| |
[processor1's BCCS/Actor/MBP] ... [processorN's BCCS/Actor/MBP]
| |
(process) (process)
Instead of ConcurrentStack, you may want to read about heap data structure. If you need your latest messages by some property of messages, e.g. timestamp, rather than by the order in which they arrive to the stack (e.g. if there could be delays in transit and arrival order <> creation order), you can get the latest message by using heap.
If you still need Agents semantics/API, you could read several sources in addition to Dave's links, and somehow adopt implementation to multiple concurrent consumers:
An interesting article by Zach Bray on efficient Actors implementation. There you do need to replace (under the comment // Might want to schedule this call on another thread.) the line execute true by a line async { execute true } |> Async.Start or similar, because otherwise producing thread will be consuming thread - not good for a single fast producer. However, for a dispatcher like described above this is exactly what needed.
FSharp.Actor (aka Fakka) development branch and FSharp MPB source code (first link above) here could be very useful for implementation details. FSharp.Actors library has been in a freeze for several months but there is some activity in dev branch.
Should not miss discussion about Fakka in Google Groups in this context.
I have a somewhat similar use case and for the last two days I have researched everything I could find on the F# Agents/Actors. This answer is a kind of TODO for myself to try these ideas, of which half were born during writing it.
The simplest solution is to greedily eat all messages in the inbox when one arrives and discard all but the most recent. Easily done using TryReceive:
let rec readLatestLoop oldMsg =
async { let! newMsg = inbox.TryReceive 0
match newMsg with
| None -> oldMsg
| Some newMsg -> return! readLatestLoop newMsg }
let readLatest() =
async { let! msg = inbox.Receive()
return! readLatestLoop msg }
When faced with the same problem I architected a more sophisticated and efficient solution I called cancellable streaming and described in in an F# Journal article here. The idea is to start processing messages and then cancel that processing if they are superceded. This significantly improves concurrency if significant processing is being done.
I am working on an application which requires video frame capture from different frame-grabber cards. I am using directshow ISampleGrabberCB::SampleCB callback to receive pointer to the new frame. Now I want to know when exactly this callback gets called ? Is it guaranteed that every time frame-grabber receives a new frame it will automatically get called ?
I was trying for 120Hz signals with various resolutions but I this callback is only getting called 50-55 times. So there is a possibility that my frame-grabber is not able to capture at that rate (although theoretically its capable). I want to find out whether this callback is bottleneck or framegrabber card.
Thank You
SampleCB is immediately called called from streaming thread, you have one call for every frame. While in callback, you block further streaming, that is you need to return control from your callback in order to resume (in particular, if your callback is "slow", it can reduce effective FPS).
and by unresponsive I mean that after the first three successful connections, the fourth connection is initiated and nothing happens, no crashes, no delegate functions called, no data is sent out (according to wireshark)... it just sits there?!
I've been beating my head against this for a day and half...
iOS 4.3.3
latest xCode, happens the same way on a real device as in the simulator.
I've read all the NSURLConnection posts in the Developer Forums... I'm at a loss.
From my application delegate, I kick off an async NSURLConnection according to Apple docs, using the App Delegate as the delegate for the NSURLConnection.
From my applicationDidFinishLaunching... I trigger the initial two queries which successfully return XML that I then pass off to a OperationQueue to be parsed.
I can even loop, repeating these queries with no issues, repeated them 10 times and worked just fine.
The next series of five queries are triggered via user input. The first query runs successfully and returns the correct response, then the next query is created and when used to create a NSURLConnection (just like all the others), just sits there.?!
The normal delegate calls I see on all the other queries are never seen.
Nothing goes over the wire according to Wireshark?
I've reordered the queries and regardless of the query, after the first one the next one fails (fails as in does nothing, no errors or aborts, just sits there)
It's obviously in my code, but I am blind to it.
So what other tools can I use to debug the async NSURLConnection... how can I tell what it's doing? if at all.
Any suggestions for debugging a NSURLConnection or other ways accomplish doing the same thing a NSURLConnection does??
Thanks for any help you can offer...
OK tracked it down...
I was watching the stack dump in each thread as I was about to kick off each NSURLConnection, the first three were all in the main thread as expected... the fourth one ended up in a new thread?! In one of my NSOperation thread?!?!
As it turns out I inadvertently added logic(?) that started one my NSURLConnection in the last NSOperation call to didFinishParsing: so the NSURLConnection was async started and then the NSOperation terminated... >.<
So I'll move the NSURLConnection out of the didFinishParsing and it should stay in the main loop and I should be good!