I know that there are several methods of inter-process communication (ipc), like:
File
Signal
Socket
Message Queue
Pipe
Named pipe
Semaphore
Shared memory
Message passing
Memory-mapped file
However I was unable to find a list or a paper comparing these mechanism to each other and pointing out the benefits of them in different environemnts.
E.g I know that if I use a file which gets written by process A and process B reads it out it will work on any OS and is pretty robust, on the other hand - why shouldn't I use TCP Socket ? Has anyone a kind of overview in which cases which methods are the most suitable ?
Long story short:
Use lock files, mutexes, semaphores and barriers when processes compete for a scarce resource. They operate in a similar manner: several process try to acquire a synchronisation primitive, some of them acquire it, others are put in sleeping state until the primitive is available again. Use semaphores to limit the amount of processes working with a resource. Use a mutex to limit the amount to 1.
You can partially avoid using synchronisation primitives by using non-blocking thread-safe data structures.
Use signals, queues, pipes, events, messages, unix sockets when processes need to exchange data. Signals and events are usually used for notifying a process of something (for instance, ctrl+c in unix terminal sends a SIGINT signal to a process). Pipes, shared memory and unix sockets are for transmitting data.
Use sockets for networking (or, speaking formally, for exchanging data between processes located on different machines).
Long story long: take a look at Modern Operating Systems book by Tanenbaum & Bos, namely IPC chapter. The topic is vast and can't be completely covered within a list or a paper.
Related
I was going through the following lecture notes on OS :
http://williamstallings.com/Extras/OS-Notes/h2.html
What I could draw is that "A process is a stream of execution ,i.e.basically a sequence of statements and so is a thread .However , the register states of one process are independent of the register states of another process but the register states of another thread can be accessed inside a thread. For every process at least one thread is allotted or dedicated ,when a process is started the OS activities for that process are taken over by the thread ( or a thread)"
What was the rationale behind conceiving the idea of threads ? When the OS is running a particular process why do we need some intermediate like a thread between them ?
"However , the register states of one process are independent of the register states of another process but the register states of another thread can be accessed inside a thread".
Can the above statement be taken as in the code for a process we cannot access the register states of a another process but in a code for a thread we can access the register states of another thread ?
(The above question did have the substitution of process and thread by their definition as codes or sequences of streams )
P.S : The title of the question is a metaphoric one .Please forgive if it misleads in any way . :P Could I take the liberty to broaden up and ask that if
the processor generates a thread for every process what does it write in the code for a thread ?(How does the code for a thread look like ? )
Terminology - for a system with virtual memory, threads share the same virtual memory address space, while each process has it's own address space. Processes can share physical memory by having a portion of that memory shared into their virtual address spaces (but the virtual address for each process may be different even though it is the same physical memory block).
Early (1960's) instances of multi-processing were mainframes that ran multiple processes that usually did not communicate with each other. Most of this activity was for batch oriented jobs, with a stream of jobs to be run, often from a punched card reader, or in more advanced situations, from remote job entry sites, which were other computers with a few peripherals (card readers, tape drives, line printers, ... ) that communicated with the mainframe to run jobs. There were also time sharing applications, similar to servers, except in many cases, relatively dumb terminals were used to communicate with the main frame. By the 1970's, APL/SV (A Programmming Language / Share Variables) was a time sharing application / programming language that could share variables between users.
For multi-process / multi-threaded operating systems, the device drivers operate from a queue of requests (such as a file read or write). Each request to be added to a device driver queue is done similar to a context switch so there won't be conflicts between process or thread requests for I/O. Some peripherals, such as mainframe, SCSI, or ... disk drives also operated from an internal queue, and could process I/O requests out of order to reduce random access overhead.
The basic problem that drove thread was how can an application handle multiple tasks at the same time and do it in a system-independent manner?
In classical eunuchs, a process could only do one thing at a time. If you needed to handle multiple things you kicked off multiple processes.
In the olde RSX and VMS systems (and Windoze under the covers), programmers relied on software interrupts. A process could queue I/O requests to multiple devices and receive a software interrupt when the request completed, thus allowing the application to do multiple things at once.
Another approach to the multiple things at once problem was to use event queues (Windoze, X Windows).
The ADA programming language was the first (and still really the only) mainstream programming language to support threads (tasks) as a system independent way to handle these kinds of problems. DOD compliance mandates drove the creation of threads.
Originally, threads were implemented through libraries ("use threads", "many to one model"). With the rise of multiprocessor systems, there became an increased demand to be able to have threads execute in parallel on different processors. This drove the creates of kernel threads in operating systems. (Many operating systems still do not support kernel threads).
I'm writing a test program that needs to emulate several connections between virtual machines, and it seems like the best way to do that is to use Unix domain sockets, for various reasons. It doesn't really matter whether I use SOCK_STREAM or SOCK_DGRAM, but it seems like SOCK_STREAM is easier/simpler for my usage.
My problem seems to be a little backwards from the typical scenario. I want to have a single client communicating with the server over 4 distinct sockets. (I could have 4 clients with one socket each, but that distinction shouldn't matter.) Now, the thing I'm emulating doesn't have multiple threads and gets an interrupt whenever a data packet is received over one of the "sockets". Is there some easy way to emulate this with Unix sockets?
I believe that I have to do the socket(), bind(), and listen() for all 4 sockets first, then do an accept() for all 4, and do fcntl( fd, F_SETFF, FNDELAY ) for each one to make them nonblocking, so that I can check each one for data with read() in a round-robin fashion. Is there any way to make it interrupt-driven or event-driven, so that my main loop only checks for data in the socket if there's data there? Or is it better to poll them all like this?
Yes. Handling multiple connections is almost synonymous with "server", and they are often single threaded -- but please not this way:
check each one for data with read() in a round-robin fashion
That would require, as you mention, non-blocking sockets and some kind of delay to prevent your "round-robin" from becoming a system killing busy loop.
A major problem with that is the granularity of the delay. You can't make it too small, or the loop will still hog too much CPU time when nothing is happening. But what about when something is happening, and that something is data incoming simultaneously on multiple connections? Now your delay can produce a snowballing backlog of tish leading to refused connections, etc.
It just is not feasible, and no one writes a server that way, although I am sure anyone would give it serious thought if they were unaware of the library functions intended to tackle the problem. Note that networking is a platform specific issue, so these are not actually part of the C standard (which does not deal with sockets at all).
The functions are select(), poll(), and epoll(); the last one is linux specific and the other two are POSIX. The basic idea is that the call blocks, waiting until one or more of any number of active connections is ready to read or write. Waiting for a socket to be ready to write only meaningfully applies to NON_BLOCK sockets. You don't have to use NON_BLOCK, however, and the select() call blocks regardless. Using NON_BLOCK on the individual sockets makes the implementation more complex, but increases performance potential in a single threaded server -- this is the idea behind asynchronous servers (such as nginx), a paradigm which contrasts with the more traditional threaded synchronous model.
However, I would recommend that you not use NON_BLOCK initially because of the added complexity. When/if it ends up being called for, you'll know. You still do not need threads.
There are many, many, many examples and tutorials around about how to use select() in particular.
I have to write a program that serves multiple clients that access multiple resources (webcams) at the same time.
Example: clients A and B both asks for the current position of two pan-tilt cameras A and B.
I have to avoid that the clients speak directly to that cameras (as there can be many clients)
So my idea was to have a process for each client (who connects through a socket) and a process for each cam.
If a client requests the position for cam A the program forks new process for that cam, and that process polls the cam position repeatedly for 10 seconds and then exits. Within that 10 second period each position request from any client should be served by this cam-A process.
The problem is: How can the cam processes communicate with the client processes?
My naive approach is the use of global variables (camA-posX, camA-posY, camB-posX, camB-posY,...) that the cam processes write to and the client processes read from. I even don't know if globals between forked processes are possible at all.
My second approach is to use pipes like in perlipc/Safe Pipe Opens but this only covers parent-child communication.
Another problem: There must be someone (the parent process?) who has to decide if I have to fork a new cam process or if it is still running.
Maybe it's even better to write two programs (using the second approach), one for the clients and one for the cameras, that communicates through a single socket with each other.
If the number of cams and clients raise there even might be a need of scaling the whole thing to distribute the load.
You can't use global variables. Once the processes are forked, they no longer share memory space and therefore global variables are distinct between them. You can only do this with threads, and using shared memory for communication needs to be done very carefully (as does anything in thread concurrent programming :)
For lower level IPC, use IPC::Msg
To be honest, if you need to worry about scaling, I would seriously recommend looking outside the IPC box, and using a real database to manage your communication.
It can either be a relational database, or noSQL one, as long as it is one that guarantees transaction atomicity. mySQL should work perfectly fine.
Another similar approach (if DB is a bit of an overkill) is to use messaging queues, as discussed here: " A queueing system for Perl "
Some other solutions discussed:
What's the fastest Perl IPC/message queue for a single machine?
Message queues in Perl, PHP, Python lists several options for message queues.
I am starting to learn Scala and functional programming. I was reading the book !Programming scala: Tackle Multi-Core Complexity on the Java Virtual Machine". Upon the first chapter I've seen the word Event-Driven concurrency and Actor model. Before I continue reading this book I want to have an idea about Event-Driven concurrency or Actor Model.
What is Event-Driven concurrency, and how is it related to Actor Model?
An Event Driven programming model involves registering code to be run when a given event fires. An example is, instead of calling a method that returns some data from a database:
val user = db.getUser(1)
println(user.name)
You could instead register a callback to be run when the data is ready:
db.getUser(1, u => println(u.name))
In the first example, no concurrency was happening; The current thread would block until db.getUser(1) returned data from the database. In the second example db.getUser would return immediately and carry on executing the next code in the program. In parallel to this, the callback u => println(u.name) will be executed at some point in the future.
Some people prefer the second approach as it doesn't mean memory hungry Threads are needlessly sat around waiting for slow I/O to return.
The Actor Model is an example of how Event-Driven concepts can be used to help the programmer easily write concurrent programs.
From a super high level, Actors are objects that define a series of Event Driven message handlers that get fired when the Actor receives messages. In Akka, each instance of an Actor is single Threaded, however when many of these Actors are put together they create a system with concurrency.
For example, Actor A could send messages to Actor B and C in parallel. Actor B and C could fire messages back to Actor A. Actor A would have message handlers to receive these messages and behave as desired.
To learn more about the Actor model I would recommend reading the Akka documentation. It is really well written: http://doc.akka.io/docs/akka/2.1.4/
There is also lot's of good documentation around the web about Event Driven Concurrency that us much more detailed than what I've written here. http://berb.github.io/diploma-thesis/original/055_events.html
Theon's answer provides a good modern overview. I'd like to add some historical perspective.
Tony Hoare and Robert Milner both developed mathematical algebra for analysing concurrent systems (Communicating Sequential Processes, CSP, and Communicating Concurrent Systems, CCS). Both of these look like heavy mathematics to most of us but the practical application is relatively straightforward. CSP led directly to the Occam programming language amongst others, with Go being the newest example. CCS led to Pi calculus and the mobility of communicating channel ends, a feature that is part of Go and was added to Occam in the last decade or so.
CSP models concurrency purely by considering automomous entities ('processes', v.lightweight things like green threads) interacting simply by event exchange. The medium for passing events is along channels. Processes may have to deal with several inputs or outputs and they do this by selecting the event that is ready first. The events usually carry data from the sender to the receiver.
A principle feature of the CSP model is that a pair of processes engage in communication only when both are ready - in practical terms this leads to what is usually called 'synchronous' communication. However, the actual implementations (Go, Occam, Akka) allow channels to be buffered (the normal state in Akka) so that the lock-step exchange of events is often actually decoupled instead.
So in summary, an event-driven CSP-based system is really a data-flow network of processes connected by channels.
Besides the CSP interpretation of event-driven, there have been others. An important example is the 'event-wheel' approach, once popular for modelling concurrent systems whilst actually having a single processing thread. Such systems handle events by putting them into a processing queue and dealing with them due course, usually via a callback. Java Swing's event processing engine is a good example. There were others, e.g. for time-based simulation engines. One might think of the Javascript / NodeJS model as fitting into this category as well.
So in summary, an event-wheel was a way to express concurrency but without parallelism.
The irony of this is that the two approaches I've described above are both described as event driven but what they mean by event driven is different in each case. In one case, hardware-like entities are wired together; in the other, almost all actions are executed by callbacks. The CSP approach claims to be scalable because it's fully composable; it's naturally adept at parallel execution also. If there are any reasons to favour one over the other, these are probably it.
To understand the answer to this you have to look at event concurrency from the OS layer up. First you start with threads which are the smallest section of code that can be run by the OS and eventually deal with I/O, timing and other kinds of events.
The OS groups threads into a process in which they share the same memory, protection and security permissions. Above that layer you have user programs which typically make I/O requests that are handled by user libraries.
The I/O libraries handle these requests in one of two ways. Unix-like systems use a "reactor" model in which the library registers I/O handlers for all the different types of I/O and events in the system. These handlers are activated when I/O is ready on a specific device. Windows-like systems use an I/O completion model in which I/O requests are made and a callback is triggered when the request is complete.
Both of these models require a significant amount of overhead to manage overall program state if you were to use them directly. However some programming tasks (web apps / services) lend themselves to a seemingly more direct implementation if you use an event model directly, but you still need to manage all of that program state. In order to track program logic across dispatches of several related events you have to manually track state and pass it around to the callbacks. This tracking structure is usually called a state context or baton. As you might imagine passing batons around all over the place to numerous seemingly unrelated handlers makes for some extremely hard to read and spaghetti-like code. It's also a pain to write and debug -- especially when you're trying to handle the synchronization of various concurrent paths of execution. You start getting into Futures and then the code becomes really difficult to read.
One well-known event processing library is call libuv. It's a portable event loop that integrates Unix's reactor model with Windows' completion model into a single model usually called a "proactor". Its the event handler that drives NodeJS.
Which brings us to communicating sequential processes.
https://en.wikipedia.org/wiki/Communicating_sequential_processes
Rather than writing asynchronous I/O dispatch and synchronization code using one or more concurrency models (and their often competing conventions), we flip the problem on its head. We use a "coroutine" which looks like normal sequential code.
A simple example is a coroutine that receives a single byte over an event channel from another coroutine that sends a single byte. This effectively synchronizes I/O producer and consumer because the writer/sender has to wait for a reader/receiver and vice-versa. While either process is waiting they explicitly yield execution to other processes. When a coroutine yields, its scoped program state is saved on a stack frame thus saving you from the confusion of managing multi-layered baton state in an event loop.
Using applications built on these event channels we can construct arbitrary, reusable, concurrent logic and the algorithms no longer look like spaghetti code. In pure CSP systems if you write to a channel and there is no reader, you will be blocked. The channel endpoints are known via handles internally to the program.
Actor systems are different in a couple of ways. First, the endpoints are the actor threads and they are named and known external to the mainline program. The second difference is that sends and receives on these channels are buffered. In other words if you send a message to an actor and there isn't one listening or its busy you aren't blocked until one reads from their input channel. Other differences exist like one actor can publish to two different actors concurrently.
As you might guess Actor systems can easily be built from CSP systems. There are other details like waiting for specific event patterns and selecting from them, but that's the basics.
I hope that clarifies things a bit.
Other constructs can be built from these ideas. Various programming systems (Go, Erlang, etc) include CSP implementations within them. Operating systems like Inferno and Node9 use CSPs and Channels as the basis of their distributed computing model.
Go: https://en.wikipedia.org/wiki/Go_(programming_language)
Erlang: https://en.wikipedia.org/wiki/Erlang_(programming_language)
Inferno: https://en.wikipedia.org/wiki/Inferno_(operating_system)
Node9: https://github.com/jvburnes/node9
This is just a technical question to improve my understanding of OS architecture.
I understand when the Application.Run() method is executed, a new form with its message pump is created. From MSDN and other online articles, I understand its thread safe nature and even understand that the Windows OS components like HAL layer, core OS services and applications on the top of the hierarchy all communicate between one another using messaging too.
Is this custom only to Windows or does this happen in the Linux environment too?
Can this be thought of as a semaphore? Or does the definition and context of a semaphore only make sense in a multi-threaded environment?
Please advice.
Thanks,
Subbu
There are many ways how processes can communicate, together called IPC - inter-process communication. From historical reasons, in UNIX-like systems use other mechanisms for communicating between processes than the message loop. UNIX processes are usually communicating through pipes (one can think about them as temporary files which can be only written in one process and read in another one), signals (code preempting the actual execution of some process) or process return values (similar to function returning). There are many other ways how to communicate (sockets, shared memory, files) but these are the most usual.
As for the semaphores: I am not sure how should these be related to message passing, semaphores objects designed for allowing programmers to create critical sections of code. Because in UNIX can be semaphore shared even between different processes (not only different threads in one process), they make sense in any multi-process OS (which is almost every today's OS), even with no threading support.
Well, semaphores can be used even with fibrils - userspace threads which are not preempted by exhausting their time quantum, as threads do, but which yield control to another fibril manually (for example when the fibril is about to begin a long blocking operation such as reading data from harddisk, it may request the data and instead of blocking switch to another fibril which wants CPU).
Unix systems have the message queues:
#include <sys/types.h>
#include <sys/ipc.h>
#include <sys/msg.h>
int msgsnd(int msqid, const void *msgp, size_t msgsz, int msgflg);
ssize_t msgrcv(int msqid, void *msgp, size_t msgsz, long msgtyp, int msgflg);
which are much less used than Windows messages but operate in a very similar fashion. Also a very similar concept, the Go language nicely implements the CSV (communicating sequential processes), which is an excellent multitasking paradigm, because does not suffer from exponential complexity growth. I would recommend Unix system programmers to use message queues more.
Windows messages are also somewhat similar to Unix signals, but Unix signals (usually) don't have arguments, are very limited in number (often only 32, compared to thousands of Windows messages) and the signal handlers have to execute in a weird suspended environment, which makes them much less practical. Nonetheless, signals are much more popular in Unix programming than message queues.
Regarding semaphores
Rather than using semaphores (which have an attached counter), you should first try to use mutexes, which are more lightweight and usable for synchronizing threads inside the same process.