I've read through the boost:asio documentation (which appears silent on async clients), and looked through here, but can't seem to find the forest for the trees here.
I've got a simulation that has a main loop that looks like this:
for(;;)
{
a = do_stuff1();
do_stuff2(a);
}
Easy enough.
What I'd like to do, is modify it so that I have:
for(;;)
{
a = do_stuff1();
check_for_new_received_udp_data(&b);
modify_a_with_data_from_b(a,b);
do_stuff2(a);
}
Where I have the following requirements:
I cannot lose data just because I wasn't actively listening. IE I don't want to lose packets because I was in do_stuff2() instead of check_for_new_received_udp_data() at the time the server sent the packet.
I can't have check_for_new_received_udp_data() block for more than about 2ms, since the main for loop needs to execute at 60Hz.
The server will be running elsewhere, and has a completely erratic schedule. Sometimes there will be no data, othertimes I may get the same packet repeatedly.
I've played with the async UDP, but that requires calling io_service.run(), which blocks indefinitely, so that doesn't really help me.
I thought about timing out a blocking socket read, but it seems you have to cheat and get out of the boost calls to do that, so that's a non-starter.
Is the answer going to involve threading? Either way, could someone kindly point me to an example that is somewhat similar? Surely this has been done before.
To avoid blocking in the io_service::run() you can use io_service::poll_one().
Regarding loosing UDP packets, I think you are out of luck. UDP does not guarantee delivery, and any part of the network may decide to drop UDP packets if there is much traffic. If you need to ensure delivery you need to have either implement some sort of flow control or just use TCP.
I think your problem is that you're still thinking synchronously. You need to think asynchronously.
Async read on UDP socket - will call handler when data arrives.
Within that handler do your processing on the incoming data. Keep in mind that while you're processing, if you have a single thread, nothing else dispatches. This can be perfectly OK (UDP messages will still be queued in the network stack...).
As a result of this you could be starting other asynchronous operations.
If you need to do work in parallel that is essentially unrelated or offline that will involve threads. Create a thread that calls io_service.run().
If you need to do periodic work in an asynch framework use timers.
In your particular example we can rearrange things like this (psuedo-code):
read_handler( ... )
{
modify_a_with_data_from_b(a,b);
do_stuff2(a);
a = do_stuff1();
udp->async_read( ..., read_handler );
}
periodic_handler(...)
{
// do periodic stuff
timer.async_wait( ..., periodic_handler );
}
main()
{
...
a = do_stuff1();
udp->async_read( ..., read_handler )
timer.async_wait( ..., periodic_handler );
io_service.run();
}
Now I'm sure there are other requirements that aren't evident from your question but you'll need to figure out an asynchronous answer to them, this is just an idea. Also ask yourself if you really need an asynchronous framework or just use the synchronous socket APIs.
Related
I have a reactive pipeline to process incoming requests. For each request I need to call a business-relevant function (doSomeRelevantProcessing).
After that is done, I need to notify some external service about what happened. That part of the pipeline should not increase the overall response time.
Also, notifying this external system is not business critical: giving a quick response after the main part of the pipeline is finished is more important than making sure the notification is successful.
As far as I learned, the only way to run something in the background without slowing down the overall process is to subscribe to in directly in the pipeline, thus achieving a fire-and-forget mentality.
Is there a good alternative to subscribing inside the flatmap?
I am a little worried about what might happen if notifying the external service takes longer than the original processing and a lot of requests are coming in at once. Could this lead to a memory exhaustion or the overall process to block?
fun runPipeline(incoming: Mono<Request>) = incoming
.flatMap { doSomeRelevantProcessing(it) } // this should not be delayed
.flatMap { doBackgroundJob(it) } // this can take a moment, but is not super critical
fun doSomeRelevantProcessing(request: Request) = Mono.just(request) // do some processing
fun doBackgroundJob(request: Request) = Mono.deferContextual { ctx: ContextView ->
val notification = "notification" // build an object from context
// this uses non-blocking HTTP (i.e. webclient), so it can take a second or so
notifyExternalService(notification).subscribeOn(Schedulers.boundedElastic()).subscribe()
Mono.just(Unit)
}
fun notifyExternalService(notification: String) = Mono.just(Unit) // might take a while
I'm answering this assuming that you notify the external service using purely reactive mechanisms - i.e. you're not wrapping a blocking service. If you are then the answer would be different as you're bound by the size of your bounded elastic thread pool, which could quickly become overwhelmed if you have hundreds of requests a second incoming.
(Assuming you're using reactive mechanisms, then there's no need for .subscribeOn(Schedulers.boundedElastic()) as you give in your example, as that's not buying you anything - it's designed for wrapping legacy blocking services.)
Could this lead to a memory exhaustion
It's only a possibility in really extreme cases, the memory used by each individual request will be tiny. It's almost certainly not worth worrying about, if you start seeing memory issues here then you'll almost certainly be hit by other issues elsewhere.
That being said, I'd probably recommend adding .timeout(Duration.ofSeconds(5)) or similar before your inner subscribe method to make sure the requests are killed off after a while if they haven't worked for any reason - this will prevent them building up.
...or [can this cause] the overall process to block?
This one is easier - a short no, it can't.
I've been using ZMQ in some Python applications for a while, but only very recently I decided to reimplement one of them in Go and I realized that ZMQ sockets are not thread-safe.
The original Python implementation uses an event loop that looks like this:
while running:
socks = dict(poller.poll(TIMEOUT))
if socks.get(router) == zmq.POLLIN:
client_id = router.recv()
_ = router.recv()
data = router.recv()
requests.append((client_id, data))
for req in requests:
rep = handle_request(req)
if rep:
replies.append(rep)
requests.remove(req)
for client_id, data in replies:
router.send(client_id, zmq.SNDMORE)
router.send(b'', zmq.SNDMORE)
router.send(data)
del replies[:]
The problem is that the reply might not be ready on the first pass, so whenever I have pending requests, I have to poll with a very short timeout or the clients will wait for more than they should, and the application ends up using a lot of CPU for polling.
When I decided to reimplement it in Go, I thought it would be as simple as this, avoiding the problem by using infinite timeout on polling:
for {
sockets, _ := poller.Poll(-1)
for _, socket := range sockets {
switch s := socket.Socket; s {
case router:
msg, _ := s.RecvMessage(0)
client_id := msg[0]
data := msg[2]
go handleRequest(router, client_id, data)
}
}
}
But that ideal implementation only works when I have a single client connected, or a light load. Under heavy load I get random assertion errors inside libzmq. I tried the following:
Following the zmq4 docs I tried adding a sync.Mutex and lock/unlock on all socket operations. It fails. I assume it's because ZMQ uses its own threads for flushing.
Creating one goroutine for polling/receiving and one for sending, and use channels in the same way I used the req/rep queues in the Python version. It fails, as I'm still sharing the socket.
Same as 2, but setting GOMAXPROCS=1. It fails, and throughput was very limited because replies were being held back until the Poll() call returned.
Use the req/rep channels as in 2, but use runtime.LockOSThread to keep all socket operations in the same thread as the socket. Has the same problem as above. It doesn't fail, but throughput was very limited.
Same as 4, but using the poll timeout strategy from the Python version. It works, but has the same problem the Python version does.
Share the context instead of the socket and create one socket for sending and one for receiving in separate goroutines, communicating with channels. It works, but I'll have to rewrite the client libs to use two sockets instead of one.
Get rid of zmq and use raw TCP sockets, which are thread-safe. It works perfectly, but I'll also have to rewrite the client libs.
So, it looks like 6 is how ZMQ was really intended to be used, as that's the only way I got it to work seamlessly with goroutines, but I wonder if there's any other way I haven't tried. Any ideas?
Update
With the answers here I realized I can just add an inproc PULL socket to the poller and have a goroutine connect and push a byte to break out of the infinite wait. It's not as versatile as the solutions suggested here, but it works and I can even backport it to the Python version.
I opened an issue a 1.5 years ago to introduce a port of https://github.com/vaughan0/go-zmq/blob/master/channels.go to pebbe/zmq4. Ultimately the author decided against it, but we have used this in production (under VERY heavy workloads) for a long time now.
This is a gist of the file that had to be added to the pebbe/zmq4 package (since it adds methods to the Socket). This could be re-written in such a way that the methods on the Socket receiver instead took a Socket as an argument, but since we vendor our code anyway, this was an easy way forward.
The basic usage is to create your Socket like normal (call it s for example) then you can:
channels := s.Channels()
outBound := channels.Out()
inBound := channels.In()
Now you have two channels of type [][]byte that you can use between goroutines, but a single goroutine - managed within the channels abstraction, is responsible for managing the Poller and communicating with the socket.
The blessed way to do this with pebbe/zmq4 is with a Reactor. Reactors have the ability to listen on Go channels, but you don't want to do that because they do so by polling the channel periodically using a poll timeout, which reintroduces the same exact problem you have in your Python version. Instead you can use zmq inproc sockets, with one end held by the reactor and the other end held by a goroutine that passes data in from a channel. It's complicated, verbose, and unpleasant, but I have used it successfully.
I am trying to understand the difference between the 2 methods, in terms of functionality.
class MyService (blockService: BlockService){
def doSomething1(): Future[Boolean] = {
//do
//some non blocking
//stuff
val result = blockService.block()
Future.successful(result)
}
def doSomething2(): Future[Boolean] = {
Future{
//do
//some non blocking
//stuff
blockService.block()
}
}
}
To my understanding the difference between the 2 is which thread is the actual thread that will be blocked.
So if there is a thread: thread_1 that execute something1, thread_1 will be the one that is blocked, while if a thread_1 executed something2a new thread will run it - thread_2, and thread_2 is the one to be blocked.
Is this true?
If so, than there is no really a preferred way to write this code? if I don't care which thread will eventually be blocked, then the end result will be the same.
dosomething1 seems like a weird way to write this code, I would choose dosomething2.
Make sense?
Yes, doSomething1 and doSomething2 blocks different threads, but depending on your scenario, this is an important decision.
As #AndreasNeumann said, you can have different execution contexts in doSomething2. Imagine that the main execution context is the one receiving HTTP requests from your users. Block threads in this context is bad because you can easily exhaust the execution context and impact requests that have nothing to do with doSomething.
Play docs have a better explanation about the possible problems with having blocking code:
If you plan to write blocking IO code, or code that could potentially do a lot of CPU intensive work, you need to know exactly which thread pool is bearing that workload, and you need to tune it accordingly. Doing blocking IO without taking this into account is likely to result in very poor performance from Play framework, for example, you may see only a few requests per second being handled, while CPU usage sits at 5%. In comparison, benchmarks on typical development hardware (eg, a MacBook Pro) have shown Play to be able to handle workloads in the hundreds or even thousands of requests per second without a sweat when tuned correctly.
In your case, both methods are being executed using Play default thread pool. I suggest you to take a look at the recommended best practices and see if you need a different execution context or not. I also suggest you to read Akka docs about Dispatchers and Futures to gain a better understanding about what executing Futures and have blocking/non-blocking code.
This approach makes sense if you make use of different execution contexts in the second method.
So having for example one for answering requests and another for blocking requests.
So you would use the normal playExecutionContext to keep you application running and answering and separate blocking operation in a different one.
def doSomething2(): Future[Boolean] = Future{
blocking { blockService.block() }
}( mySpecialExecutionContextForBlockingOperations )
For a little more information: http://docs.scala-lang.org/overviews/core/futures.html#blocking
You are correct. I don't see a point in doSomething1. It simply complicates the interface for the caller while not providing the benefits of an asynchronous API.
Does BlockService handle blocking operation?
Normally, use blocking ,as #Andreas remind,to make blocking operation into another thread is meanful.
a typical socket program example would be like this:
while(1){
data = socket.recv()
//do some work
}
since you don't know when package arrive,it must block to wait until get some data from the listening port,suppose if the program start a heavy work after received the command from another side,during the work , another package arrived,but because at that moment you are doing the work,you are not listening the port, you might missed the package ,no matter how fast you handle the work.
so how does the socket work to handle all the data without any lost?
The operating system has a receive buffer which holds packets that have been received from the network but not yet recv()ed by the application. If that buffer fills up packets will be lost. You don't have to be in a recv() call when packets arrive, though you should make sure you call it often enough to keep the buffer from overflowing.
I'm designing a multi-threaded server with a thread pool. This system is designed to use persistent TCP connections, as clients will maintain connects close to 24/7. The problem I run into is how to manage shutdowns. Currently, a connection comes in through "accept(listen_fd....)" and gets assigned to a work order struct. This struct is dumped onto the work queue, and is picked up by a thread. From this point on, this thread is devoted to the current connection. My code inside the thread is:
/* Function which runs in a thread to handle a request */
void *
handle_req( void *in)
{
ssize_t n;
char read;
/* Convert the input to a workorder_ptr */
workorder_t *workorder_ptr = (workorder_t *)in;
while( !serv_shutdown
&& (n=recv(workorder_ptr->sock_fd,&read,1,0) != 0))
{
printf("Read a character: %c\n",read);
}
printf("Peer has shutdown.\n");
/* Free the workorder memory */
close(workorder_ptr->sock_fd);
free(workorder_ptr);
return NULL;
}
Which simply listens to the socket and echos the characters indefinitely, and operates correctly when the client terminates the connection. You see the "!serv_shutdown" part in the while loop - this is my attempt to get the thread to break out of its loop on a shutdown signal. When a SIGINT is caught, the global variable is set to 1. Unfortunately, the program is currently blocking on the recv statement, and won't check this flag until another character is read. I want to avoid that, since it could be an arbitrary amount of time before another character is sent on this connection.
Also, I read on another post here that it's better to use "select" than "accept" to wait on a socket connection, but I didn't quite understand. Would you do a select to wait, and then do an accept right after that? I'm not sure how select creates a socket connection. I ask this, because if my understanding of select is cleared up, maybe it applies to the question I am asking?
Also also, how do I detect the case where a connection simply times out?
Thanks!
EDIT
I think I may have finally found a solution, after further digging:
Wake up thread blocked on accept() call
Basically, I could create a global pipe and have each thread do a select on its own socket_fd as well as this global pipe. Then, when a signal is caught, I'll just write something to the pipe. All threads should be woken, no?
Well, on FreeBSD, MacOSX and maybe somewhere else there is kevent() call, that allows listening on a broad range of system events including connect requests and signaling when data arrives to the socket.
It will solve all of your problems in a neat way, but it's not portable. There are libs such libevent and libev, that wraps OS-specific functionality like kevent() on BSD's, epoll() on Linux and so on. May be it would help you.
You can use the recv() primitive. If it returns 0, that means that the socket has been closed.
More information: http://beej.us/guide/bgnet/output/html/singlepage/bgnet.html#recvman