I'm creating a system where users can chain together nodes, and data flows from one node to the other. At the end is a node which constantly pulls from the node before it, and does something with that data (e.g. for audio data, plays it). If you're familiar with FL Studio's Patcher, that's essentially what I'm trying to do:
The trivial implementation, which is to just have an interface like this:
interface Node {
byte[] getData();
}
where each node stores the one before it would work fine, except that I want to be able to have 2 nodes both requesting data from a single node, like this:
The issue here is that the source node is pulled twice every "step" (the intended behavior would be for the same value to be sent to both nodes). If you had for example an audio signal in the source node, the intended behavior would be for 2 different effects to be done to it, then the signals combined, while what would actually happen is that the signal is chopped up and each effect gets a different part of the signal depending on which happens to be called first.
What is a good way to solve this?
Related
I am developing a simple DDD + Event sourcing based app for educational purposes.
In order to set event version before storing to event store I should query event store but my gut tells that this is wrong because it causes concurrency issues.
Am I missing something?
There are different answers to that, depending on what use case you are considering.
Generally, the event store is a dumb, domain agnostic appliance. It's superficially similar to a List abstraction -- it stores what you put in it, but it doesn't actually do any work to satisfy your domain constraints.
In use cases where your event stream is just a durable record of things that have happened (meaning: your domain model does not get a veto; recording the event doesn't depend on previously recorded events), then append semantics are fine, and depending on the kind of appliance you are using, you may not need to know what position in the stream you are writing to.
For instance: the API for GetEventStore understands ExpectedVersion.ANY to mean append these events to the end of the stream wherever it happens to be.
In cases where you do care about previous events (the domain model is expected to ensure an invariant based on its previous state), then you need to do something to ensure that you are appending the event to the same history that you have checked. The most common implementations of this communicate the expected position of the write cursor in the stream, so that the appliance can reject attempts to write to the wrong place (which protects you from concurrent modification).
This doesn't necessarily mean that you need to be query the event store to get the position. You are allowed to count the number of events in the stream when you load it, and to remember how many more events you've added, and therefore where the stream "should" be if you are still synchronized with it.
What we're doing here is analogous to a compare-and-swap operation: we get a representation of the original state of the stream, create a new representation, and then compare and swap the reference to the original to point instead to our changes
oldState = stream.get()
newState = domainLogic(oldState)
stream.compareAndSwap(oldState, newState)
But because a stream is a persistent data structure with append only semantics, we can use a simplified API that doesn't require duplicating the existing state.
events = stream.get()
changes = domainLogic(events)
stream.appendAt(count(events), changes)
If the API of your appliance doesn't allow you to specify a write position, then yes - there's the danger of a data race when some other writer changes the position of the stream between your query for the position and your attempt to write. Data obtained in a query is always stale; unless you hold a lock you can't be sure that the data hasn't changed at the source while you are reading your local copy.
I guess you shouldn't to think about event version.
If you talk about the place in the event stream, in general, there's no guaranteed way to determine it at the creation moment, only in processing time or in event-storage.
If it is exactly about event version (see http://cqrs.nu/Faq, How do I version/upgrade my events?), you have it hardcoded in your application. So, I mean next use case:
First, you have an app generating some events. Next, you update app and events are changed (you add some fields or change payload structure) but kept logical meaning. So, now you have old events in your ES, and new events, that differ significantly from old. And to distinguish one from another you use event version, eg 0 and 1.
I'm designing a large-scale project, and I think I see a way I could drastically improve performance by taking advantage of multiple cores. However, I have zero experience with multiprocessing, and I'm a little concerned that my ideas might not be good ones.
Idea
The program is a video game that procedurally generates massive amounts of content. Since there's far too much to generate all at once, the program instead tries to generate what it needs as or slightly before it needs it, and expends a large amount of effort trying to predict what it will need in the near future and how near that future is. The entire program, therefore, is built around a task scheduler, which gets passed function objects with bits of metadata attached to help determine what order they should be processed in and calls them in that order.
Motivation
It seems to be like it ought to be easy to make these functions execute concurrently in their own processes. But looking at the documentation for the multiprocessing modules makes me reconsider- there doesn't seem to be any simple way to share large data structures between threads. I can't help but imagine this is intentional.
Questions
So I suppose the fundamental questions I need to know the answers to are thus:
Is there any practical way to allow multiple threads to access the same list/dict/etc... for both reading and writing at the same time? Can I just launch multiple instances of my star generator, give it access to the dict that holds all the stars, and have new objects appear to just pop into existence in the dict from the perspective of other threads (that is, I wouldn't have to explicitly grab the star from the process that made it; I'd just pull it out of the dict as if the main thread had put it there itself).
If not, is there any practical way to allow multiple threads to read the same data structure at the same time, but feed their resultant data back to a main thread to be rolled into that same data structure safely?
Would this design work even if I ensured that no two concurrent functions tried to access the same data structure at the same time, either for reading or for writing?
Can data structures be inherently shared between processes at all, or do I always explicitly have to send data from one process to another as I would with processes communicating over a TCP stream? I know there are objects that abstract away that sort of thing, but I'm asking if it can be done away with entirely; have the object each thread is looking at actually be the same block of memory.
How flexible are the objects that the modules provide to abstract away the communication between processes? Can I use them as a drop-in replacement for data structures used in existing code and not notice any differences? If I do such a thing, would it cause an unmanageable amount of overhead?
Sorry for my naivete, but I don't have a formal computer science education (at least, not yet) and I've never worked with concurrent systems before. Is the idea I'm trying to implement here even remotely practical, or would any solution that allows me to transparently execute arbitrary functions concurrently cause so much overhead that I'd be better off doing everything in one thread?
Example
For maximum clarity, here's an example of how I imagine the system would work:
The UI module has been instructed by the player to move the view over to a certain area of space. It informs the content management module of this, and asks it to make sure that all of the stars the player can currently click on are fully generated and ready to be clicked on.
The content management module checks and sees that a couple of the stars the UI is saying the player could potentially try to interact with have not, in fact, had the details that would show upon click generated yet. It produces a number of Task objects containing the methods of those stars that, when called, will generate the necessary data. It also adds some metadata to these task objects, assuming (possibly based on further information collected from the UI module) that it will be 0.1 seconds before the player tries to click anything, and that stars whose icons are closest to the cursor have the greatest chance of being clicked on and should therefore be requested for a time slightly sooner than the stars further from the cursor. It then adds these objects to the scheduler queue.
The scheduler quickly sorts its queue by how soon each task needs to be done, then pops the first task object off the queue, makes a new process from the function it contains, and then thinks no more about that process, instead just popping another task off the queue and stuffing it into a process too, then the next one, then the next one...
Meanwhile, the new process executes, stores the data it generates on the star object it is a method of, and terminates when it gets to the return statement.
The UI then registers that the player has indeed clicked on a star now, and looks up the data it needs to display on the star object whose representative sprite has been clicked. If the data is there, it displays it; if it isn't, the UI displays a message asking the player to wait and continues repeatedly trying to access the necessary attributes of the star object until it succeeds.
Even though your problem seems very complicated, there is a very easy solution. You can hide away all the complicated stuff of sharing you objects across processes using a proxy.
The basic idea is that you create some manager that manages all your objects that should be shared across processes. This manager then creates its own process where it waits that some other process instructs it to change the object. But enough said. It looks like this:
import multiprocessing as m
manager = m.Manager()
starsdict = manager.dict()
process = Process(target=yourfunction, args=(starsdict,))
process.run()
The object stored in starsdict is not the real dict. instead it sends all changes and requests, you do with it, to its manager. This is called a "proxy", it has almost exactly the same API as the object it mimics. These proxies are pickleable, so you can pass as arguments to functions in new processes (like shown above) or send them through queues.
You can read more about this in the documentation.
I don't know how proxies react if two processes are accessing them simultaneously. Since they're made for parallelism I guess they should be safe, even though I heard they're not. It would be best if you test this yourself or look for it in the documentation.
How should I stream the output from one program to an undefined number of programs in such a fashion that the data isn't buffered anywhere and that the application where the stream originates from doesn't block even if there's nothing reading the stream, but the programs reading the stream do block if there's no output from the first-mentioned program?
I've been trying to Google around for a while now, but all I find is methods where the program does block if nothing is reading the stream.
How should I stream the output from one program to an undefined number of programs in such a fashion that the data isn't buffered anywhere and that the application where the stream originates from doesn't block even if there's nothing reading the stream
Your requirements as stated can not possibly be satisfied without some form of a buffer.
Most straightforward option is to write the output to the file and let consumers read that file.
Another option is to have a ring-buffer in a form of a memory mapped file. As the capacity of a ring-buffer is normally fixed there needs to be a policy for dealing with slow consumers. Options are: block the producer; terminate the slow consumer; let the slow consumer somehow recover when it missed data.
Many years ago I wrote something like what you describe for an audio stream processing app (http://hewgill.com/nwr/). It's on github as splitter.cpp and has a small man page.
The splitter program currently does not support dynamically changing the set of output programs. The output programs are fixed when the command is started.
Without knowing exactly what sort of data you are talking about (how large is the data, what format is it, etc, etc) it is hard to come up with a concrete answer. Let's say for example you want a "ticker-tape" application that sends out information for share purchases on the stock exchange, you could quite easily have a server that accepts a socket from each application, starts a thread and sends the relevant data as it appears from the recoder at the stock market. I'm not aware of any "multiplexer" that exists today (but Greg's one may be a starting point). If you use (for example) XML to package the data, you could send the second half of a packet, and the client code would detect that it's not complete, so throws it away.
If, on the other hand, you are sending out high detail live update weather maps for the whole country, the data is probably large enough that you don't want to wait for a full new one to arrive, so you need some sort of lock'n'load protocol that sets the current updated map, and then sends that one out until (say) 1 minute later you have a new one. Again, it's not that complex to write some code to do this, but it's quite a different set of code to the "ticker tape" solution above, because the packet of data is larger, and getting "half a packet" is quite wasteful and completely useless.
If you are streaming live video from the 2016 Olympics in Brazil, then you probably want a further diffferent solution, as timing is everything with video, and you need the client to buffer, pick up key-frames, throw away "stale" frames, etc, etc, and the server will have to be different.
I have a fairly involved download process I want to perform in a background thread. There are some natural dependencies between steps in this process. For example, I need to complete the downloads of both Table A and Table B before setting the relationships between them (I'm using Core Data).
I thought first of putting each dependent step in its own NSOperation, then creating a dependency between the two operations (i.e. download the two tables in one operation, then set the relationship between them in the next, dependent operation). However, each NSOperation requires it's own NSManagedContext, so this is no good. I don't want to save the background context until both tables have been downloaded and their relationships set.
I've therefore concluded this should all occur inside one NSOperation, and that I should use notifications or some other mechanism to call the dependent method when all the conditions for running it have been met.
I'm an iOS beginner, however, so before I venture down this path, I wouldn't mind advice on whether I've reached the right conclusion.
Given your validation requirements, I think it will be easiest inside of one operation, although this could turn into a bit of a hairball as far as code structure goes.
You'll essentially want to make two wire fetches to get the entire dataset you require, then combine the data and parse it at one time into Core Data.
If you're going to use the asynchronous API's this essentially means structuring a class that waits for both operations to complete and then launches another NSOperation or block which does the parse and relationship construction.
Imagine this order of events:
User performs some action (button tap, etc.)
Selector for that action fires two network requests
When both requests have finished (they both notify a common delegate) launch the parse operation
Might look something like this in code:
- (IBAction)someAction:(id)sender {
//fire both network requests
request1.delegate = aDelegate;
request2.delegate = aDelegate;
}
//later, inside the implementation of aDelegate
- (void)requestDidComplete... {
if (request1Finished && request2Finished) {
NSOperation *parse = //init with fetched data
//launch on queue etc.
}
}
There's two major pitfalls that this solution is prone to:
It keeps the entire data set around in memory until both requests are finished
You will have to constantly switch on the specific request that's calling your delegate (for error handling, success, etc.)
Basically, you're implementing operation dependencies on your own, although there might not be a good way around that because of the structure of NSURLConnection.
I'm currently working on a personal project: creating a library for realtime audio synthesis in Flash. In short: tools to connect wavegenarators, filters, mixers, etc with eachother and supply the soundcard with raw (realtime) data. Something like max/msp or Reaktor.
I already have some working stuff, but I'm wondering if the basic setup that I wrote is right. I don't want to run into problems later on that force me to change the core of my app (although that can always happen).
Basically, what I do now is start at the end of the chain, at the place where the (raw) sounddata goes 'out' (to the soundcard). To do that, I need to write chunks of bytes (ByteArrays) to an object, and to get that chunk I ask whatever module is connected to my 'Sound Out' module to give me his chunk. That module does the same request to the module that's connected to his input, and that keeps happening until the start of the chain is reached.
Is this the right approach? I can imagine running into problems if there's a feedbackloop, or if there's another module with no output: if i were to connect a spectrumanalyzer somewhere, that would be a dead end in the chain (a module with no outputs, just an input). In my current setup, such a module wouldnt work because i only start calculating from the sound-output module.
Has anyone experience with programming something like this? I'd be very interested in some thoughts about the right approach. (For clarity: i'm not looking for specific Flash-implementations, and that's why i didnt tag this question under flash or actionscript)
I did a similar thing a while back, and I used the same approach as you do - start at the virtual line out, and trace the signal back to the top. I did this per sample though, not per buffer; if I were to write the same application today, I might choose per-buffer instead though, because I suspect it would perform better.
The spectrometer was designed as an insert module, that is, it would only work if both its input and its output were connected, and it would pass its input to the output unchanged.
To handle feedback, I had a special helper module that introduced a 1-sample delay and would only fetch its input once per cycle.
Also, I think doing all your internal processing with floats, and thus arrays of floats as the buffers, would be a lot easier than byte arrays, and it would save you the extra effort of converting between integers and floats all the time.
In later versions you may have different packet rates in different parts of your network.
One example would be if you extend it to transfer data to or from disk. Another example
would be that low data rate control variables such as one controlling echo-delay may, later, become a part of your network. You probably don't want to process control variables with the same frequency that you process audio packets, but they are still 'real time' and part of the function network. They may for example need smoothing to avoid sudden transitions.
As long as you are calling all your functions at the same rate, and all the functions are essentially taking constant-time, your pull-the-data approach will work fine. There will
be little to choose between pulling data and pushing. Pulling is somewhat more natural for playing audio, pushing is somewhat more natural for recording, but either works and ends up making the same calls to the underlying audio processing functions.
For the spectrometer you've got
the issue of multiple sinks for
data, but it is not a problem.
Introduce a dummy link to it from
the real sink. The dummy link can
cause a request for data that is not
honoured. As long as the dummy link knows
it is a dummy and does not care about
the lack of data, everything will be
OK. This is a standard technique for reducing multiple sinks or sources to a single one.
With this kind of network you do not want to do the same calculation twice in one complete update. For example if you mix a high-passed and low-passed version of a signal you do not want to evaluate the original signal twice. You must do something like record a timer tick value with each buffer, and stop propagation of pulls when you see the current tick value is already present. This same mechanism will also protect you against feedback loops in evaluation.
So, those two issues of concern to you are easily addressed within your current framework.
Rate matching where there are different packet rates in different parts of the network is where the problems with the current approach will start. If you are writing audio to disk then for efficiency you'll want to write large chunks infrequently. You don't want to be blocking your servicing of the more frequent small audio input and output processing packets during those writes. A single rate pulling or pushing strategy on its own won't be enough.
Just accept that at some point you may need a more sophisticated way of updating than a single rate network. When that happens you'll need threads for the different rates that are running, or you'll write your own simple scheduler, possibly as simple as calling less frequently evaluated functions one time in n, to make the rates match. You don't need to plan ahead for this. Your audio functions are almost certainly already delegating responsibility for ensuring their input buffers are ready to other functions, and it will only be those other functions that need to change, not the audio functions themselves.
The one thing I would advise at this stage is to be careful to centralise audio buffer
allocation, noticing that buffers are like fenceposts. They don't belong to an audio
function, they lie between the audio functions. Centralising the buffer allocation will make it easy to retrospectively modify the update strategy for different rates in different parts of the network.