In Trio, if you want to write some data to a TCP socket then the obvious choice is send_all:
my_stream = await trio.open_tcp_stream("localhost", 1234)
await my_stream.send_all(b"some data")
Note that this both sends that data over the socket and waits for it to be written. But what if you just want to queue up the data to be sent, but not wait for it to be written (at least, you don't want to wait in the same coroutine)?
In asyncio this is straightforward because the two parts are separate functions: write() and drain(). For example:
writer.write(data)
await writer.drain()
So of course if you just want to write the data and not wait for it you can just call write() without awaiting drain(). Is there equivalent functionality in Trio? I know this two-function design is controversial because it makes it hard to properly apply backpressure, but in my application I need them separated.
For now I've worked around it by creating a dedicated writer coroutine for each connection and having a memory channel to send data to that coroutine, but it's quite a lot of faff compared to choosing between calling one function or two, and it seems a bit wasteful (presumably there's still a send buffer under the hood, and my memory channel is like a buffer on top of that buffer).
I posted this on the Trio chat and Nathaniel J. Smith, the creator of Trio, replied with this:
Trio doesn't maintain a buffer "under the hood", no. There's just the kernel's send buffer, but the kernel will apply backpressure whether you want it to or not, so that doesn't help you.
Using a background writer task + an unbounded memory channel is basically what asyncio does for you implicitly.
The other option, if you're putting together a message in multiple pieces and then want to send it when you're done would be to append them into a bytearray and then call send_all once at the end, at the same place where you'd call drain in asyncio
(but obviously that only works if you're calling drain after every logical message; if you're just calling write and letting asyncio drain it in the background then that doesn't help)
So the question was based on a misconception: I wanted to write into Trio's hidden send buffer, but no such thing exists! Using a separate coroutine that waits on a stream and calls send_all() makes more sense than I had thought.
I ended up using a hybrid of the two ideas (using separate coroutine with a memory channel vs using bytearray): save the data to a bytearray, then use a condition variable ParkingLot to signal to the other coroutine that it's ready to be written. That lets me coalesce writes, and also manually check if the buffer's getting too large.
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'd like to have a TCP connection for a gaming application. It's important to be time efficient. I want to receive many objects efficiently. It's also important to be CPU efficient because of the load.
So far, I can make sure handleConnection is called every time a connection is dialed using go's net library. However once the connection is created, I have to poll (check over and over again to see if new data is ready on the connection). This seems inefficient. I don't want to run that check to see if new data is ready if it's needlessly sucking up CPU.
I was looking for something such as the following two options but didn't find what I was looking for.
(1) Do a read operation that somehow blocks (without sucking CPU) and then unblocks when new stuff is ready on the connection stream. I could not find that.
(2) Do an async approach where a function is called when new data arrives on the connection stream (not just when a new connection is dialed). I could not find that.
I don't want to put any sleep calls in here because that will increase the latency of responding to singles messages.
I also considered dialing out for every single message, but I'm not sure if that's efficient or not.
So I came up with code below, but it's still doing a whole lot of checking for new data with the Decode(p) call, which does not seem optimal.
How can I do this more efficiently?
func handleConnection(conn net.Conn) {
dec := gob.NewDecoder(conn)
p := &P{}
for {
result := dec.Decode(p)
if result != nil {
// do nothing
} else {
fmt.Printf("Received : %+v", p)
fmt.Println("result", result, "\n")
}
}
conn.Close()
}
You say:
So I came up with code below, but it's still doing a whole lot of checking for new data with the Decode(p) call.
Why do you think that? The gob decoder will issue a Read to the conn and wait for it to return data before figuring out what it is and decoding it. This is a blocking operation, and will be handled asynchronously by the runtime behind the scenes. The goroutine will sleep until the appropriate io signal comes in. You should not have to do anything fancy to make that more performant.
You can trace this yourself in the code for decoder.Decode.
I think your code will work just fine. CPU will be idle until it receives more data.
Go is not node. Every api is "blocking" for the most part, but that is not as much as a problem as in other platforms. The runtime manages goroutines very efficiently and delegates appropriate signals to sleeping goroutines as needed.
As we know, there is a thing called an MPI send buffer used during a send action.
And for following code:
MPI_Isend(data, ..., req);
...
MPI_Wait(req, &status)
Is it safe to use data between MPI_Isend and MPI_Wait ?
That means, will MPI_Isend use data as the internal send buffer?
And more, if I don't use data anymore, could I indicate MPI to use data as the send buffer rather than waste time to copy data?
BTW, I've heard of MPI_Bsend, but I don't think it could save memory and time in this case.
MPI provides two kinds of operations: blocking and non-blocking. The difference between the two is when it is safe to reuse the data buffer passed to the MPI function.
When a blocking call like MPI_Send returns, the buffer is no longer needed by the MPI library and can be safely reused. On the other hand, non-blocking calls only initiate the corresponding operation and let it continue asynchronously. Only after a successful call to a routine like MPI_Wait or after a positive test result from MPI_Test one can safely reuse the buffer.
As for how the library utilises the user buffer, that is very implementation-specific. Shorter messages are usually copied to internal (for the MPI library) buffers for performance reasons. Longer messages are usually directly read from the user buffer and sent to the network, therefore the buffer will be in use by MPI until the whole message has been sent.
It is absolutely not save to use data between MPI_Isend and MPI_Wait.
Between MPI_Isend and MPI_Wait you actually don't know when data can be reused. Only after MPI_Wait you can be sure that data is sent and you can reuse it.
If you don't use data anymore you should call MPI_Wait at the end of your program.
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.
Need clarification to my understanding of isend and issend as given in Send Types
My understanding is that isend will return once the send buffer is free, i.e. when all the data has been released. Issend on the other hand returns only when it receives an ack from the receive of getting/not getting the entire data. Is this all there is to it?
Both MPI_Isend() and MPI_Issend() return immediately, but in both cases you can't use the send buffer immediately.
Think of the difference that there is between MPI_Send() and MPI_Ssend():
MPI_Send() can be buffered or it can be synchronous if the buffer is too
large to be buffered locally, and in this case it waits to complete sending the
data to the corresponding receive operation.
MPI_Ssend() is always synchronous: it always waits to complete sending the data
to the corresponding receive operation.
The inner working of the corresponding "I"-operations is very similar, except for the fact that they both don't block (return immediately): the difference is only when the MPI library signals to the user program that you can use the send-buffer (that is: MPI_Wait() returns or MPI_Test() returns true - the so called send-complete operation of the non-blocking send):
with MPI_Isend() this can happen either when the data has been copied locally in a buffer owned by the MPI library, if below the "synchronous threshold", or when the data has been actually moved to the sibling task: the send-complete operation can be local, in case the underlying send operation is buffered.
With MPI_Issend() MPI doesn't ever buffer data locally and the "buffer-free condition" is returned only after the data has been actually transferred (and probably ack'ed, at low level): the send-complete operation is non-local.
The MPI standard document is quite pedantic on these aspects. See section 3.7 Nonblocking Communication.
Correct. Obviously both of those will only be true when the request that you get back from the call to MPI_ISEND or MPI_ISSEND is completed via a MPI_WAIT* or MPI_TEST* function.