Here's my scenario:
In my application i have several processes which communicate with each other using Quickfix which internally use tcp sockets.the flow is like:
Process1 sends quickfix messaage-> process 2 sends quickfix message after processing message from
process 1 -> .....->process n
Similarly the acknowledgement messages flow like,
process n->....->process 1
Now, All of these processes except the last process( process n ) are on the same machine.
I googled and found that tcp sockets are the slowest of ipc mechanisms.
So, is there a way to transmit and recieve quick fix messages( obviously using their apis)
through other ipc mechanisms. If yes, i can then reduce the latency by using that ipc mechanism between all the processes which are on the same machine.
However if i do so, do those mechanisms guarentee the tranmission of complete message like tcp sockets do?
I think you are doing premature optimization, and I don't think that TCP will be your performance bottleneck. Your local LAN latency will be faster than that of your exterior FIX connection. From experience, I'd expect perf issues to originate in your app's message handling (perhaps due to accidental blocking in OnMessage() callbacks) rather than the IPC stuff going on afterward.
Advice: Write your communication component with an abstraction-layer interface so that later down the line you can swap out TCP for something else (e.g ActiveMQ, ZeroMQ, whatever else you may consider) if you decide you may need it.
Aside from that, just focus on making your system work correctly. Once you are sure teh behavior are correct (hopefully with tests to confirm them), then you can work on performance. Measure your performance before making any optimizations, and then measure again after you make "improvements". Don't trust your gut; get numbers.
Although it would be good to hear more details about the requirements associated with this question, I'd suggest looking at a shared memory solution. I'm assuming that you are running a server in a colocated facility with the trade matching engine and using high speed, kernel bypass communication for external communications. One of the issues with TCP is the user/kernel space transitions. I'd recommend considering user space shared memory for IPC and use a busy polling technique for synchronization rather than using synchronization mechanisms that might also involve kernel transitions.
Related
This may appear as a silly question, but I am really confused about the terminology of the ZeroMQ regarding synchronous sockets like REQ and REP.
By my understanding a synchronous communication occurs when a client sends a message an then it blocks, until the response arrives. If ZeroMQ implemented a synchronous communication then only a .send() method would be enough for a synchronous socket.
I think that synchronous sockets terminology of ZeroMQ refers only to the inability of sending more messages until the response of the last message arrives, but the "sender" can still continue its processing ( doing more stuff ) asynchronously.
Is this true?
In that case, is there any straightforward way to implement a synchronous communication using ZeroMQ?
EDIT: Synchronous communication makes sense when I want to invoke a method in a remote process (like RPC). If I want to execute a series of commands in a remote process and each command needs the result of the previous one to do its job then asynchronous communication is not the best option.
To use ZMQ for implementing a synchronous framework, you can very nearly do it using just ZMQ; you can set the high water mark to 1. Unfortunately that's not quite it; what you want is an out going queue length of 0. Even more unfortunately, setting the high water mark to 0 is interpretted by ZMQ as infinity...
So the only option is to implement a synchronous transfer protocol on top of ZMQ. That's not very difficult to do. The conversation between the two ends will be something like "can I send?", "yes you can send now", "ok here it is", "ok I have received it" (both ends return to caller) (or at least the programatic version of that). This sets up what is called an execution rendevous - both ends know that they both reached a certain point of execution.
Technically speaking what you're doing is taking ZeroMQ (Actor Model) and turning it into something more like Communicating Sequential Processes.
RPC
Having said all that, from your edit I think you might like to consider Cap'n Proto. This is a C++ serialisation technology that has a neat RPC trick. If the return from one RPC call is the input to another, you can chain those all together somehow in advance (see here).
Let's start with a first stepforget everything you know about sockets.
ZeroMQ is more a concept of thinking about distributed-systems ( multi-agent like ) and how to design a software, with a use of such a smart signalling / messaging framework.
This is the core aim of the ZeroMQ, to allow designers remain thinking in the application domain and let all the low level dirty work to be operated actually without much of the designers' need to care of.
If have just recently started with ZeroMQ, one may enjoy a short read about a ZeroMQ global view first, before discussing details.
Having read and understood the concept of the ZeroMQ hierarchy, it is way simpler to start on details:
given a local Context() instance is a data-pumping engine and having a REQ/REP Scalable Formal Communications Archetype pattern in mind, the story is now actually a story about a network of distributed-Finite-State-Automata.
local process, operating just one side of the distributed REQ/REP communication archetype has zero power to influence the remote process to receive or not the message that was passed from the local process over to the ZeroMQ delivery services towards the indended recipient(s) in a fair belief. The less the local process can influence the remote process' intent to respond at all or not, so welcome to the realms of distributed multi-agent games.
Both the REQ and the REP formal behaviour has to meet its both the { local | distributed-mode }-expected sort of behaviour -- REQ asks first, REP answers then, so as to keep the contracted promise. The point is, that this behaviour is distributed and split among a pair of nodes, plus there are cases, when network incidents may throw the distributed-FSA into an unsalvageable mutual deadlock ( one may find more posts on this here zeromq quite often ).
So, your local-side REQ code imperatively .send()-s and has no obligation to stop without doing anything reasonable until REP-side .recv( zmq.NOBLOCK )-s or not ( no one has any kind of warranty a remote node exists at all, similarly, one has to set oneselves ready to anticipate and handle all cases, where a remote side will never respond, so many "new" challenges appear from the nature of a distributed multi-agent ecosystem ).
There are smart ways to handle this new breed of distributed chaos and uncertainties, using, best using .poll() and non-blocking forms of either the .send() and .recv()-methods, as these let user-code to remain capable of handling all expected and un-expected events in due time and fashion.
One may also operate rather many co-existent ZeroMQ connections, so as to prioritise and specialise each and any form of the multi-agents' interactions in a distributed system design, even for designing in fault-resilience and similar high-level robustness concept, where asynchronous nature of each of the interactions avoids a need of any sort of coordination or synchronisation with a remote ( possibly even not yet present ) agent, which is principally an autonomous entity, having it's own domain of control, so again, being principally asynchronous to what local-side agent might "expect", the less "influence" in any other form but by an attempt to send "there" a message "telegram".
So yes,ZeroMQ is asynchronous brokerless signalling / messaging framework.
For (almost) synchronous communications, one may take steps and measures to trim down the ( principally distributed ) asynchronous control loops -- best update your post with an MCVE example and details about what are your particular goals for being achieved.
I have a setup in which two nodes are going to be communicating a lot. On Node A, there are going to be thousands of processes, which are meant to access services on Node B. There is going to be a massive load of requests and responses across the two nodes. The two Nodes, will be running on two different servers, each on its own hardware server.
I have 3 Options: HTTP/1.1 , rpc:call/4 and Directly sending a message to a registered gen_server on Node B. Let me explain each option.
HTTP/1.1 Suppose that on Node A, i have an HTTP Client like Ibrowse, and on Node B, i have a web server like Yaws-1.95, the web server being able to handle unlimited connections, the operating system settings tweaked to allow yaws to handle all connections. And then make my processes on Node A to communicate using HTTP. In this case each method call, would mean a single HTTP request and a reply. I believe there is an overhead here, but we are evaluating options here. The erlang Built in mechanism called webtool, may be built for this kind of purpose.
rpc:call/4 I could simply make direct rpc calls from Node A to Node B. I am not very susre how the underlying rpc mechanism works , but i think that when two erlang nodes connect via net_adm:ping/1, the created connection is not closed but all rpc calls use this pipe to transmit requests and pass responses. Please correct me on this one.Sending a Message from Node A to Node B I could make my processes on Node A to just send message to a registered process, or a group of processes on Node B. This too seems a clean option.
Q1. Which of the above options would you recommend and why, for an application in which two erlang nodes are going to have enormous communications between them all the time. Imagine a messaging system, in which two erlang nodes are the routers :) ? Q2. Which of the above methods is cleaner, less problematic and is more fault tolerant (i mean here that, the method should NOT have single point of failure, that could lead to all processes on Node A blind) ? Q3. The mechanism of your choice: how would you make it even more fault tolerant, or redundant? Assumptions: The Nodes are always alive and will never go down, the network connection between the nodes will always be available and non-congested (dedicated to the two nodes only) , the operating system have allocated maximum resources to these two nodes. Thank you for your evaluations
HTTP is definitely out. Just the round-trip overhead of creating a new connection is a problem.
As for Erlang connections and using Pids, you have the advantage that you can subscribe to node-down messages and handle the case where a node goes down. A single TCP connection should be able to give you very fast speeds, however, be aware that it works like a long pipe: messages are muxed and demuxed on a pipe which can affect latency on the line. It also means that large messages will block small messages from getting through.
How much bandwidth are you aiming for, and at what latency? What is the 95th and 99th percentile of answering messages? It is better to put up some rough numbers and then try to target these than just having "as fast as possible". Set your success criteria first.
Q1: HTTP will add additional overhead and will give you nothing in my opinion. HTTP would be useful if you were designing a REST API. Directly sending messages and rpc:call look about the same as far as overhead is regarded.
Q2: Sending messages is much much clearer. It's the way erlang is designed. With RPC calls you must always track which call is executed where and under which circumstances which can be a huge issue if the two servers have state. Also RPC calls are synchronous.
Q3: I would use UBF if I can afford minor overhead, otherwise I would directly send messages between the erlang nodes. If the bandwidth is an issue other trickery would be needed as well. Like encoding the messages in same way and then using some compression algorithm to reduce the size of the message, alternatively I may ditch the erlang message passing altogether and use UDP sockets.
It is not obvious that ! is the best way to go. Definitely, it is the easiest and the code will be the most elegant.
In terms of scalability, take under consideration that to use rpc/! you have to maintain an erlang cluster. I found it painful having just 10-20 nodes even in private cloud. I would never recommend bigger deployments on e.g. EC2, where io/latency/network is not deterministic.
I recommend to structure the project in a way that will let you exchange communication engine in the future. Also HTTP is pretty heavy, but there are options:
socket-socket (tcp/udp/sctp)
amqp (many benefits connected to load balancing)
zeromq (even nicer than amqp)
Betting on !/rpc and OTP cluster is risky. You will fight with full mesh overhead, master election algos and quorum/partition detection.
I was running a benchmark on CouchDB when I noticed that even with large bulk inserts, running a few of them in parallel is almost twice as fast. I also know that web browsers use a number of parallel connections to speed up page loading.
What is the reason multiple connections are faster than one? They go over the same wire, or even to localhost.
How do I determine the ideal number of parallel requests? Is there a rule of thumb, like "threadpool size = # cores + 1"?
The gating factor is not the wire itself which, after all, runs pretty quick (ignoring router delays) but the software overhead at each end. Each physical transfer has to be set up, the data sent and stored, and then completely handled before anything can go the other way. So each connection is effectively synchronous, no matter what it claims to be at the socket level: one socket operating asynchronously is still moving data back and forth in a synchronous way because the software demands synchronicity.
A second connection can take advantage of the latency -- the dead time on the wire -- that arises from the software doing its thing for first connection. So, even though each connection is synchronous, multiple connections let things happen much faster. Things seem (but of course only seem) to happen in parallel.
You might want to take a look at RFC 2616, the HTTP spec. It will tell you about the interchanges that happen to get an HTTP connection going.
I can't say anything about optimal number of parallel requests, which is a matter between the browser and the server.
Each connection consume one own thread. Each thread, have a quantum for consume CPU, network and other resources. Mainly, CPU.
When you start a parallel call, thread will dispute CPU time and run things "at the same time".
It's a high level overview of the things. I suggest you to read about asynchronous calls and thread programming to understand it better.
[]'s,
And Past
I am writing an application where the client side will be uploading data to the server through a wireless link.
The connection should be very reliable.The link is expected to break many times and there will be many clients connected to the server.
I am confused whether to use TCP or reliable UDP.
Please share your thoughts.
Thanks.
RUDP is not, of course, a formal standard, and there's no telling if you will find existing implementations you can use. Given a choice between rolling this from scratch and just re-making TCP connections, I'd chose TCP.
To be safe, I would go with TCP just because it's a reliable, standard protocol. RUDP has the disadvantage of not being an established standard (although it's been mentioned in several IETF discussions).
Good luck with your project!
It's likely that both your TCP and RUDP links would be broken by your environment, so the fact that you're using RUDP is unlikely to help there; there will likely be times when no datagrams can get through...
What you actually need to make sure of is that a) you can handle the number of connected clients, b) your application protocol can detect reasonably quickly when you've lost connectivity with a client (or server) and c) you can handle the required reconnection and maintenance of cross connection session state for clients.
As long as you deal with b) and c) it doesn't really matter if the connection keeps being broken. Make sure you design your application protocol so that you can get things done in short batches; so if you're uploading files, make sure that you're sending small blocks and that the application protocol can resume a transfer that was broken half way through; you don't want to get 99% of the way through a 2gb transfer and lose the connection and have to start again.
For this to work your server needs some kind of client session state cache where you can keep the logical state of a client's connection beyond the life of the connection itself. Design from the start to expect a given session to include multiple separate connections. The session state should possibly have some kind of timeout so if the client goes away for along time it doesn't continue to consume resources on the server but, to be honest, it may simply be a case of saving the state off to disk after a while.
In summary, I don't think the choice of transport matters and I'd go with TCP at least to start with. What will really matter is being able to manage your client's session state on the server and deal with the fact that clients will connect and disconnect regularly.
If you aren't sure, odds are that you should use TCP. For one thing, it's certain to be part of the network stack for anything supporting IP. "Reliable UDP" is rarely supported out of the box, so you'll have some extra support work for your clients.
I have a quick and dirty proof of concept app that I wrote in C# that reads high data rate multicast UDP packets from the network. For various reasons the full implementation will be written in C++ and I am considering using boost asio. The C# version used a thread to receive the data using blocking reads. I had some problems with dropped packets if the computer was heavily loaded (generally with processing those packets in another thread).
What I would like to know is if the async read operations in boost (which use overlapped io in windows) will help ensure that I receive the packets and/or reduce the cpu time needed to receive the packets. The single thread doing blocking reads is pretty straightforward, using the async reads seems like a step up in complexity, but I think it would be worth it if it provided higher performance or dropped fewer packets on a heavily loaded system. Currently the data rate should be no higher than 60Mb/s.
I've written some multicast handling code using boost::asio also. I would say that overall, in my experience there is a lot of added complexity to doing things in asio that may not make it easy for other people you work with to understand the code you end up writing.
That said, presumably the argument in favour of moving to asio instead of using lots of different threads to do the work is that you would have to do less context switching. This would clearly be true on a single-core box, but what about when you go multi-core? Are you planning to offload the work you receive to threads or just have a single thread doing the processing work? If you go for a single threaded approach you are going to end up in a situation where you could drop packets waiting for that thread to process the work.
In the end it's swings and roundabouts. I'd say you want to get some fairly solid figures backing up your arguments for going down this route if you are going to do so, just because of all the added complexity it entails (a whole new paradigm for some people I'm sure).