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I'm bit confused to understand the difference between Asynchronous calls and Callbacks.
I read this posts which teach about CallBacks but none of the answers addresses how it differs from Asynchronous calls.
Is this Callbacks = Lambda Expressions?
Callbacks are running in a different thread?
Can anyone explains this with plain simple English?
Very simply, a callback needn't be asynchronous.
http://docs.apigee.com/api-baas/asynchronous-vs-synchronous-calls
Synchronous:
If an API call is synchronous, it means that code execution will
block (or wait) for the API call to return before continuing. This
means that until a response is returned by the API, your application
will not execute any further, which could be perceived by the user as
latency or performance lag in your app. Making an API call
synchronously can be beneficial, however, if there if code in your app
that will only execute properly once the API response is received.
Asynchronous:
Asynchronous calls do not block (or wait) for the API call to return
from the server. Execution continues on in your program, and when the
call returns from the server, a "callback" function is executed.
In Java, C and C#, "callbacks" are usually synchronous (with respect to a "main event loop").
In Javascript, on the other hand, callbacks are usually asynchronous - you pass a function that will be invoked ... but other events will continue to be processed until the callback is invoked.
If you don't care what Javascript events occur in which order - great. Otherwise, one very powerful mechanism for managing asynchronous behavior in Javascript is to use "promises":
http://www.html5rocks.com/en/tutorials/es6/promises/
PS:
To answer your additional questions:
Yes, a callback may be a lambda - but it's not a requirement.
In Javascript, just about every callback will be an "anonymous function" (basically a "lambda expression").
Yes, callbacks may be invoked from a different thread - but it's certainly not a requirement.
Callbacks may also (and often do) spawn a thread (thus making themselves "asynchronous").
'Hope that helps
====================================================================
Hi, Again:
Q: #paulsm4 can you please elaborate with an example how the callback
and asynchronous call works in the execution flow? That will be
greatly helpful
First we need to agree on a definition for "callback". Here's a good one:
https://en.wikipedia.org/wiki/Callback_%28computer_programming%29
In computer programming, a callback is a piece of executable code that
is passed as an argument to other code, which is expected to call back
(execute) the argument at some convenient time. The invocation may be
immediate as in a synchronous callback, or it might happen at a later
time as in an asynchronous callback.
We must also define "synchronous" and "asynchronous". Basically - if a callback does all it's work before returning to the caller, it's "synchronous". If it can return to the caller immediately after it's invoked - and the caller and the callback can work in parallel - then it's "asynchronous".
The problem with synchronous callbacks is they can appear to "hang". The problem with asynchronous callbacks is you can lose control of "ordering" - you can't necessarily guarantee that "A" will occur before "B".
Common examples of callbacks include:
a) a button press handler (each different "button" will have a different "response"). These are usually invoked "asynchronousy" (by the GUI's main event loop).
b) a sort "compare" function (so a common "sort()" function can handle different data types). These are usually invoked "synchronously" (called directly by your program).
A CONCRETE EXAMPLE:
a) I have a "C" language program with a "print()" function.
b) "print()" is designed to use one of three callbacks: "PrintHP()", "PrintCanon()" and "PrintPDF()".
c) "PrintPDF()" calls a library to render my data in PDF. It's synchronous - the program doesn't return back from "print()" until the .pdf rendering is complete. It usually goes pretty quickly, so there's no problem.
d) I've coded "PrintHP()" and "PrintCanon()" to spawn threads to do the I/O to the physical printer. "Print()" exits as soon as the thread is created; the actual "printing" goes on in parallel with program execution. These two callbacks are "asynchronous".
Q: Make sense? Does that help?
They are quite similar but this is just mho.
When you use callbacks you specify which method should you should be called back on and you rely on the methods you call to call you back. You could specify your call back to end up anywhere and you are not guaranteed to be called back.
In Asynchronous programming, the call stack should unwind to the starting position, just as in normal synchronous programming.
Caveat: I am specifically thinking of the C# await functionality as there are other async techniques.
I want to comment paulsm4 above, but I have no enough reputation, so I have to give another new answer.
according to wikipedia, "a callback is a piece of executable code that is passed as an argument to other code, which is expected to call back (execute) the argument at some convenient time. The invocation may be immediate as in a synchronous callback, or it might happen at a later time as in an asynchronous callback.", so the decorative word "synchronous" and "asynchronous" are on "callback", which is the key-point. we often confuse them with an "asynchronous function", which is the caller function indeed. For example,
const xhr = new XMLHttpRequest();
xhr.addEventListener('loadend', () => {
log.textContent = `${log.textContent}Finished with status: ${xhr.status}`;
});
xhr.open('GET', 'https://raw.githubusercontent.com/mdn/content/main/files/en-us/_wikihistory.json');
xhr.send();
here, xhr.send() is an asynchronous caller function, while the anonymous function defined in xhr.addEventListener is an asynchronous callback function.
for clarification, the following is synchronous callback example:
function doOperation(callback) {
const name = "world";
callback(name);
}
function doStep(name) {
log.console(`hello, ${name}`);
}
doOperation(doStep)
then, let's answer the specified question:
Is this Callbacks = Lambda Expressions?
A: nop, callback is just a normal function, it can be named or anonymous(lambda expressions).
Callbacks are running in a different thread?
A: if callbacks are synchronous, they are running within the same thread of the caller function. if callbacks are asynchronous, they are running in another thread to the caller function to avoid blocking the execution of the caller.
A call is Synchronous: It returns control to the caller when it's done.
A call is Async.: Otherwise.
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.
I have a workflow (simple sequence) that calls InvokeMethod on an object. I would like to abort the entire workflow based on code within the object.
This is kind of like http://msdn.microsoft.com/en-us/site/dd560894 but that is aborting from top-down, and I want to just halt the whole workflow from bottom-up. How to do that?
Thanks.
You can do this from a NativeActivity by calling NativeActivityContext.Abort. How are you calling the InvokeMethod?
Do you really need to abort the workflow or is it OK if the workflow just ends? If you are happy with it ending, then I think you have two options depending on the complexity of the workflow:
Use an If activity and have one branch doing nothing when the condition (based on the result of your InvokeMethod call) is not met.
Use a Flowchart instead of a Sequence, as that will allow you to implement more complex exit logic.
My answer for a recent question about GOTOs and tail recursion was phrased in terms of a call stack. I'm worried that it wasn't sufficiently general, so I ask you: how is the notion of a tail call (or equivalent) useful in architectures without a call stack?
In continuation passing, all called functions replace the calling function, and are thus tail calls, so "tail call" doesn't seem to be a useful distinction. In messaging and event based architectures, there doesn't seem to be an equivalent, though please correct me if I'm wrong. The latter two architectures are interesting cases as they are associated with OOP, rather than FP. What about other architectures? Were the old Lisp machines based on call-stacks?
Edit: According to "What the heck is: Continuation Passing Style (CPS)" (and Alex below), the equivalent of a tail call under continuation passing is not "called function replaces calling function" but "calling function passes the continuation it was given, rather than creating a new continuation". This type of tail call is useful, unlike what I asserted.
Also, I'm not interested in systems that use call stacks at a lower level, for the higher level doesn't require a call stack. This restriction doesn't apply to Alex's answer because he's writing about the fact that other invocation architectures (is this the right term?) often have a call stack equivalent, not that they have a call stack somewhere under the hood. In the case of continuation passing, the structure is like an arborescence, but with edges in the opposite direction. Call stack equivalents are highly relevant to my interests.
"Architectures without a call stack" typically "simulate" one at some level -- for example, back in the time of IBM 360, we used the S-Type Linkage Convention using register-save areas and arguments-lists indicated, by convention, by certain general-purpose registers.
So "tail call" can still matter: does the calling function need to preserve the information necessary to resume execution after the calling point (once the called function is finished), or does it know there IS going to be no execution after the calling point, and so simply reuse its caller's "info to resume execution" instead?
So for example a tail call optimization might mean not appending the continuation needed to resume execution on whatever linked list is being used for the purpose... which I like to see as a "call stack simulation" (at some level, though it IS obviously a more flexible arrangement -- don't want to have continuation-passing fans jumping all over my answer;-).
On the off chance that this question interests someone other than me, I have an expanded answer for the other question that also answers this one. Here's the nutshell, non-rigorous version.
When a computational system performs sub-computations (i.e. a computation starts and must pause while another computation is performed because the first depends on the result of the second), a dependency relation between execution points naturally arises. In call-stack based architectures, the relation is topologically a path graph. In CPS, it's a tree, where every path between the root and a node is a continuation. In message passing and threading, it's a collection of path graphs. Synchronous event handling is basically message passing. Starting a sub-calculation involves extending the dependency relation, except in a tail call which replaces a leaf rather than appending to it.
Translating tail calling to asynchronous event handling is more complex, so instead consider a more general version. If A is subscribed to an event on channel 1, B is subscribed to the same event on channel 2 and B's handler merely fires the event on channel 1 (it translates the event across channels), then A can be subscribed to the event on channel 2 instead of subscribing B. This is more general because the equivalent of a tail call requires that
A's subscription on channel 1 be canceled when A is subscribed on channel 2
the handlers are self-unsubscribing (when invoked, they cancel the subscription)
Now for two systems that don't perform sub-computations: lambda calculus (or term rewriting systems in general) and RPN. For lambda calculus, tail calls roughly correspond to a sequence of reductions where the term length is O(1) (see iterative processes in SICP section 1.2). Take RPN to use a data stack and an operations stack (as opposed to a stream of operations; the operations are those yet to be processed), and an environment that maps symbols to a sequence of operations. Tail calls could correspond to processes with O(1) stack growth.
Just to get it straight in my head. Consider this example bit of Erlang code:
test() ->
receive
{From, whatever} ->
%% do something
test();
{From, somethingelse} ->
%% do something else
test();
end.
Isn't the test() call, just a goto?
I ask this because in C we learned, if you do a function call, the return location is always put on the stack. I can't imagine this must be the case in Erlang here since this would result in a stackoverflow.
We had 2 different ways of calling functions:
goto and gosub.
goto just steered the program flow somewhere else, and gosub remembered where you came from so you could return.
Given this way of thinking, I can look at Erlang's recursion easier, since if I just read: test() as a goto, then there is no problem at all.
hence my question: isn't :Erlang just using a goto instead of remembering the return address on a stack?
EDIT:
Just to clarify my point:
I know goto's can be used in some languages to jump all over the place. But just supose instead of doing someFunction() you can also do: goto someFunction()
in the first example the flow returns, in the second example the flow just continues in someFunction and never returns.
So we limit the normal GOTO behaviour by just being able to jump to method starting points.
If you see it like this, than the Erlang recursive function call looks like a goto.
(a goto in my opinion is a function call without the ability to return where you came from). Which is exactly what is happening in the Erlang example.
A tail recursive call is more of a "return and immediately call this other function" than a goto because of the housekeeping that's performed.
Addressing your newest points: recording the return point is just one bit of housekeeping that's performed when a function is called. The return point is stored in the stack frame, the rest of which must be allocated and initialized (in a normal call), including the local variables and parameters. With tail recursion, a new frame doesn't need to be allocated and the return point doesn't need to be stored (the previous value works fine), but the rest of the housekeeping needs to be performed.
There's also housekeeping that needs to be performed when a function returns, which includes discarding locals and parameters (as part of the stack frame) and returning to the call point. During tail recursive call, the locals for the current function are discarded before invoking the new function, but no return happens.
Rather like how threads allow for lighter-weight context switching than processes, tail calls allow for lighter-weight function invocation, since some of the housekeeping can be skipped.
The "goto &NAME" statement in Perl is closer to what you're thinking of, but not quite, as it discards locals. Parameters are kept around for the newly invoked function.
One more, simple difference: a tail call can only jump to a function entry point, while a goto can jump most anywhere (some languages restrict the target of a goto, such as C, where goto can't jump outside a function).
You are correct, the Erlang compiler will detect that it is a tail recursive call, and instead of moving on on the stack, it reuses the current function's stack space.
Furthermore it is also able to detect circular tail-recursion, e.g.
test() -> ..., test2().
test2() -> ..., test3().
test3() -> ..., test().
will also be optimized.
The "unfortunate" side-effect of this is that when you are tracing function calls, you will not be able to see each invocation of a tail recursive function, but the entry and exit point.
You've got two questions here.
First, no, you're not in any danger of overrunning the stack in this case because these calls to test() are both tail-recursive.
Second, no, function calls are not goto, they're function calls. :) The thing that makes goto problematic is that it bypasses any structure in your code. You can jump out of statements, jump into statements, bypass assignments...all kinds of screwiness. Function calls don't have this problem because they have an obvious flow of control.
I think the difference here is between a "real" goto and what can in some cases seem like a goto. In some special cases the compiler can detect that it is free to cleanup the stack of the current function before calling another function. This is when the call is the last call in a function. The difference is, of course, that as in any other call you can pass arguments to the new function.
As others have pointed out this optimisation is not restricted to recursive calls but to all last calls. This is used in the "classic" way of programming FSMs.
It's a goto in the same why that if is goto and while is goto. It is implemented using (the moral equivalent of) goto, but it does not expose the full shoot-self-in-foot potential of goto directly to the programmer.
In fact, these recursive functions are the ultimate GOTO according to Guy Steele.
Here's a more general answer, which supercedes my earlier answer based on call-stacks. Since the earlier answer has been accepted, I won't replace the text.
Prologue
Some architectures don't have things they call "functions" that are "called", but do have something analogous (messaging may call them "methods" or "message handlers"; event based architectures have "event handlers" or simply "handlers"). I'll be using the terms "code block" and "invocation" for the general case, though (strictly speaking) "code block" can include things that aren't quite functions. You can substitute the appropriately inflected form of "call" for "invocation" or "invoke", as I might in a few places. The features of an architecture that describe invocation are sometimes called "styles", as in "continuation passing style" (CPS), though this isn't previously an official term. To keep things from being too abstract, we'll examine call stack, continuation passing, messaging (à la OOP) and event handling invocation styles. I should specify the models I'm using for these styles, but I'm leaving them out in the interest of space.
Invocation Features
or, C Is For Continuation, Coordination and Context, That's Good Enough For Me
Hohpe identifies three nicely alliterative invocation features of the call-stack style: Continuation, Coordination, Context (all capitalized to distinguish them from other uses of the words).
Continuation decides where execution will continue when a code block finishes. The "Continuation" feature is related to "first-class continuations" (often simply called "continuations", including by me), in that continuations make the Continuation feature visible and manipulable at a programmatic level.
Coordination means code doesn't execute until the data it needs is ready. Within a single call stack, you get Coordination for free because the program counter won't return to a function until a called function finishes. Coordination becomes an issue in (e.g.) concurrent and event-driven programming, the former because a data producer may fall behind a data consumer and the latter because when a handler fires an event, the handler continues immediately without waiting for a response.
Context refers to the environment that is used to resolve names in a code block. It includes allocation and initialization of the local variables, parameters and return value(s). Parameter passing is also covered by the calling convention (keeping up the alliteration); for the general case, you could split Context into a feature that covers locals, one that covers parameters and another for return values. For CPS, return values are covered by parameter passing.
The three features aren't necessarily independent; invocation style determines their interrelationships. For instance, Coordination is tied to Continuation under the call-stack style. Continuation and Context are connected in general, since return values are involved in Continuation.
Hohpe's list isn't necessarily exhaustive, but it will suffice to distinguish tail-calls from gotos. Warning: I might go off on tangents, such as exploring invocation space based on Hohpe's features, but I'll try to contain myself.
Invocation Feature Tasks
Each invocation feature involves tasks to be completed when invoking a code block. For Continuation, invoked code blocks are naturally related by a chain of invoking code. When a code block is invoked, the current invocation chain (or "call chain") is extended by placing a reference (an "invocation reference") to the invoking code at the end of the chain (this process is described more concretely below). Taking into account invocation also involves binding names to code blocks and parameters, we see even non-bondage-and-discipline languages can have the same fun.
Tail Calls
or, The Answer
or, The Rest Is Basically Unnecessary
Tail calling is all about optimizing Continuation, and it's a matter of recognizing when the main Continuation task (recording an invocation reference) can be skipped. The other feature tasks stand on their own. A "goto" represents optimizing away tasks for Continuation and Context. That's pretty much why a tail call isn't a simple "goto". What follows will flesh out what tail calls look like in various invocation styles.
Tail Calls In Specific Invocation Styles
Different styles arrange invocation chains in different structures, which I'll call a "tangle", for lack of a better word. Isn't it nice that we've gotten away from spaghetti code?
With a call-stack, there's only one invocation chain in the tangle; extending the chain means pushing the program counter. A tail call means no program counter push.
Under CPS, the tangle consists of the extant continuations, which form a reverse arborescence (a directed tree where every edge points towards a central node), where each path back to the center is a invocation chain (note: if the program entry point is passed a "null" continuation, the tangle can be a whole forest of reverse arborescences). One particular chain is the default, which is where an invocation reference is added during invocation. Tail calls won't add an invocation reference to the default invocation chain. Note that "invocation chain" here is basically synonymous with "continuation", in the sense of "first class continuation".
Under message passing, the invocation chain is a chain of blocked methods, each waiting for a response from the method before it in the chain. A method that invokes another is a "client"; the invoked method is a "supplier" (I'm purposefully not using "service", though "supplier" isn't much better). A messaging tangle is a set of unconnected invocation chains. This tangle structure is rather like having multiple thread or process stacks. When the method merely echos another method's response as its own, the method can have its client wait on its supplier rather than itself. Note that this gives a slightly more general optimization, one that involves optimizing Coordination as well as Continuation. If the final portion of a method doesn't depend on a response (and the response doesn't depend on the data processed in the final portion), the method can continue once it's passed on its client's wait dependency to its supplier. This is analogous to launching a new thread, where the final portion of the method becomes the thread's main function, followed by a call-stack style tail call.
What About Event Handling Style?
With event handling, invocations don't have responses and handlers don't wait, so "invocation chains" (as used above) isn't a useful concept. Instead of a tangle, you have priority queues of events, which are owned by channels, and subscriptions, which are lists of listener-handler pairs. In some event driven architectures, channels are properties of listeners; every listener owns exactly one channel, so channels become synonymous with listeners. Invoking means firing an event on a channel, which invokes all subscribed listener-handlers; parameters are passed as properties of the event. Code that would depend on a response in another style becomes a separate handler under event handling, with an associated event. A tail call would be a handler that fires the event on another channel and does nothing else afterwards. Tail call optimization would involve re-subscribing listeners for the event from the second channel to the first, or possibly having the handler that fired the event on the first channel instead fire on the second channel (an optimization made by the programmer, not the compiler/interpreter). Here's what the former optimization looks like, starting with the un-optimized version.
Listener Alice subscribes to event "inauguration" on BBC News, using handler "party"
Alice fires event "election" on channel BBC News
Bob is listening for "election" on BBC News, so Bob's "openPolls" handler is invoked
Bob subscribes to event "inauguration" on channel CNN.
Bob fires event "voting" on channel CNN
Other events are fired & handled. Eventually, one of them ("win", for example) fires event "inauguration" on CNN.
Bob's barred handler fires "inauguration" on BBC News
Alice's inauguration handler is invoked.
And the optimized version:
Listener Alice subscribes to event "inauguration" on BBC News
Alice fires event "election" on channel BBC News
Bob is listening for "election" on BBC News, so Bob's "openPolls" handler is invoked
Bob subscribes anyone listening for "inauguration" on BBC News to the inauguration event on CNN*.
Bob fires event "voting" on channel CNN
Other events are fired & handled. Eventually, one of them fires event "inauguration" on CNN.
Alice's inauguration handler is invoked for the inauguration event on CNN.
Note tail calls are trickier (untenable?) under event handling because they have to take into account subscriptions. If Alice were later to unsubscribe from "inauguration" on BBC News, the subscription to inauguration on CNN would also need to be canceled. Additionally, the system must ensure it doesn't inappropriately invoke a handler multiple times for a listener. In the above optimized example, what if there's another handler for "inauguration" on CNN that fires "inauguration" on BBC News? Alice's "party" event will be fired twice, which may get her in trouble at work. One solution is to have *Bob unsubscribe all listeners from "inauguration" on BBC News in step 4, but then you introduce another bug wherein Alice will miss inauguration events that don't come via CNN. Maybe she wants to celebrate both the U.S. and British inaugurations. These problems arise because there are distinctions I'm not making in the model, possibly based on types of subscriptions. For instance, maybe there's a special kind of one-shot subscription (like System-V signal handlers) or some handlers unsubscribe themselves, and tail call optimization is only applied in these cases.
What's next?
You could go on to more fully specify invocation feature tasks. From there, you could figure out what optimizations are possible, and when they can be used. Perhaps other invocation features could be identified. You could also think of more examples of invocation styles. You could also explore the dependencies between invocation features. For instance, synchronous and asynchronous invocation involve explicitly coupling or uncoupling Continuation and Coordination. It never ends.
Get all that? I'm still trying to digest it myself.
References:
Hohpe, Gregor; "Event-Driven Architecture"
Sugalski, Dan; "CPS and tail calls--two great tastes that taste great together"
In this case it is possible to do tail-call optimization, since we don't need to do more work or make use of local variables. So the compiler will convert this into a loop.
(a goto in my opinion is a function call without the ability to return where you came from). Which is exactly what is happening in the erlang example.
That is not what's happening in Erlang, you CAN return to where you came from.
The calls are tail-recursive, which means that it is "sort of" a goto. Make sure you understand what tail-recursion is before you attempt to understand or write any code. Reading Joe Armstrong's book probably isn't a bad idea if you are new to Erlang.
Conceptually, in the case where you call yourself using test() then a call is made to the start of the function using whatever parameters you pass (none in this example) but nothing more is added to the stack. So all your variables are thrown away and the function starts fresh, but you didn't push a new return pointer onto the stack. So it's like a hybrid between a goto and a traditional imperative language style function call like you'd have in C or Java. But there IS still one entry on the stack from the very first call from the calling function. So when you eventually exit by returning a value rather the doing another test() then that return location is popped from the stack and execution resumes in your calling function.