I'd like to know, what is a proper way to implement my own cold source (publisher) using the Mutiny library.
Let's say there is huge file parser that should return lines as Multi<String> items according to the Subscriber's consumption rate.
New lines should be read only after previous were processed to optimize memory usage, while buffering a couple of hundred items to eliminate consumer idling.
I know about the Multi.createFrom.emitter() factory method, but using it I can't see a convenient way to implement the backpressure.
Does Mutiny have a idiomatic way to create cold sources that produce next items only after requested by the downstream, or in this case I supposed to implement my own Publisher using the Java Reactive Streams API and then wrap it in Multi?
You can use Multi.createFrom().generator(...).
The function is called for every request. But you can pass a "state" to remember where you are, typically an Iterator.
This is the opposite of the emitter approach (which does not check for requests but has a backpressure strategy attached to it).
If you need more fine-grain back-pressure support, you would need to implement a Publisher.
Related
Let's say I have a stage controller and I want to write a method to move the stage. I want to be able to have the method either return after the stage has physically completed the stage move, or has started the stage move. For any kind of external control of hardware, I typically write async methods with a Task return. This way, users can await on the completion of the task, e.g. await the stage to finish it's move, or just call the move method, and await the returned task at a later point if necessary.
Is this the right approach for controller external hardware? Should these kind of methods be written synchronously with with separate methods used to determine operation completed? People I talk to seem to have an issue with using async methods; mostly because they feel it is too indeterminate for them for hardware control.
Is this the right approach for controller external hardware? Should these kind of methods be written synchronously with with separate methods used to determine operation completed?
I hesitate to use async for any kind of system that is driven by external forces. One that I've seen a lot is people try to use tasks to represent "the user pressed this button". And your example reminds me of that, but with external hardware in place of a person.
The problem with these kinds of approaches is twofold. First, it restricts you to a very linear logic flow. Second, it doesn't easily provide results other than success/fail. What if the hardware does something other than what was instructed? How easy is it to do logic that tries to do A but then times out waiting for state A' to be reached so it tries to do B?
Bear in mind that tasks must be completed. While it's possible to handle this using something like task cancellation (or hardware-specific exceptions), that can considerably complicate the logic code. Particularly when you consider timeouts, retries, and fallback logic.
So, I generally avoid using tasks for modeling that kind of domain. Something like an observable may be a better fit, or even just a Channel of state updates. Both of those permit the hardware to "push" its state and allows the logic code to respond appropriately, usually with a state machine of its own.
I have a program that reads 173 (c) data structures from a memory map that need to be converted to Go. The value of the type is stored as a string in those structures. The structures are received 60 times per second.
I'm now using reflection (FieldByName) to get a reference to the go struct field and set the received data. But because there a many fields (173) and they get updated a lot this ads a lot of overhead and that function call is the slowest part of my program (jay go prof!).
What is the best way to make this faster? As far as I can see I have three options:
cache the reflect.Value's in a map and make a function that receives data, use a template struct tied to the cache map, fill that struct and return a copy of that template-struct
go generate all the setters and a giant switch statement for each received field
Just code all the different setters
What would be the "best" option? Is there an option I'm overlooking?
With #1, to be concurrency-safe you'd need a pool of those "template-struct" or at least a mutex protecting it. That adds some overhead and can be tricky to debug.
#3 is a nightmare to maintain.
I would go with #2. The running code will be fast, concurrency-safe and easy to debug.
Once your tool is setup, a change in your struct only requires running a command line to update the setters.
Normally seek commands are executed on a filter graph, get called on the renderers in the graph and calls are passed upstream by filters until a filter that can handle the seek does the actual seek operation.
Could an individual filter seek the upstream filters connected to one or more of its input pins in the same way without it affecting the downstream portion of the graph in unexpected ways? I wouldn't expect that there wouldn't be any graph state changes caused by calling IMediaSeeking.SetPositions upstream.
I'm assuming that all upstream filters are connected to the rest of the graph via this filter only.
Obviously the filter would need to be prepared to handle the resulting BeginFlush, EndFlush and NewSegment calls coming from upstream appropriately and distinguish samples that arrived before and after the seek operation. It would also need to set new sample times on its output samples so that the output samples had consistent sample presentation times. Any other issues?
It is perfectly feasible to do what you require. I used this approach to build video and audio mixer filters for a video editor. A full description of the code is available from the BBC White Papers 129 and 138 available from http://www.bbc.co.uk/rd
A rather ancient version of the code can be found on www.SourceForge.net if you search for AAFEditPack. The code is written in Delphi using DSPack to get access to the DirectShow headers. I did this because it makes it easier to handle com object lifetimes - by implementing smart pointers by default. It should be fairly straightforward to transfer the ideas to a C++ implementation if that is what you use.
The filters keep lists of the sub-graphs (a section of a graph but running in the same FilterGraph as the mixers). The filters implement a custom version of TBCPosPassThru which knows about the output pins of the sub-graph for each media clip. It handles passing on the seek commands to get each clip ready for replay when its point in the timeline is reached. The mixers handle the BeginFlush, EndFlush, NewSegment and EndOfStream calls for each sub-graph so they are kept happy. The editor uses only one FilterGraph that houses both video and audio graphs. Seeking commands are make by the graph on both the video and audio renderers and these commands are passed upstream to the mixers which implement them.
Sub-graphs that are not currently active are blocked by the mixer holding references to the samples they have delivered. This does not cause any problems for the FilterGraph because, as Roman R says, downstream filters only care about getting a consecutive stream of sample and do not know about what happens upstream.
Some key points you need to make sure of to avoid wasted debugging time are:
Your decoder filters need to be able to queue to the exact media frame or audio time. Not as easy to do as you might expect, especially with compressed formats such as mpeg2, which was designed for transmission and has no frame index in the files. If you do not do this, the filter may wait indefinitely to get a NewSegment call with the correct media times.
Your sub graphs need to present a NewSegment time equal to the value you asked for in your seek command before delivering samples. Some decoders may seek to the nearest key frame, which is a bit unhelpful and some are a bit arbitrary about the timings of their NewSegment and the following samples.
The start and stop times of each clip need to be within the duration of the file. Its probably not a good idea to police this in the DirectShow filter because you would probably want to construct a timeline without needing to run the filter first. I did this in the component that manages the FilterGraph.
If you want to add sections from the same source file consecutively in the timeline, and have effects that span the transition, you need to have two instances of the sub-graph for that file and if you have more than one transition for the same source file, your list needs to alternate the graphs for successive clips. This is because each sub graph should only play monotonically: calling lots of SetPosition calls would waste cpu cycles and would not work well with compressed files.
The filter's output pins define the entire seeking behaviour of the graph. The output sample time stamps (IMediaSample.SetTime) are implemented by the filter so you need to get them correct without any missing time stamps. and you can also set the MediaTime (IMediaSample.SetMediaTime) values if you like, although you have to be careful to get them correct or the graph may drop samples or stall.
Good luck with your development. If you need any more information please contact me through StackOverflow or DTSMedia.co.uk
I'll try to keep the case as general as possible here: I'm writing a C++/CX application for Windows Phone 8.1 that manages a state, which is being changed in reaction to input coming, in turns, from different sources (e.g. app UI or network). I want to utilize an approach with a program loop that will, for each source, wait for input from it and then modify the state accordingly. The problem I'm having is that I could not find a good way to mirror the behavior of the await mechanism in C++/CX. Tasks seem to be the way to handle asynchronous data processing in C++/CX, but as far as I understand they are used for waiting for results of a well-defined operation, whereas I need to wait for an asynchronous event to happen and then act appropriately depending on the type of the event.
Is there an appropriate language construct, or a way to utilize tasks, to be used in this case?
Should I make use of basic multi-threading mechanisms, like semaphores, instead?
Alternatively, should I abandon this approach and handle state changes with events, securing the state from being otherwise modified?
Thanks in advance.
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.