Browse Source

initial commit, separated from transient

addedsingle
Alberto G. Corona 6 years ago
commit
78b9888e36
  1. 24
      Dockerfile
  2. 674
      LICENSE
  3. 679
      README.md
  4. 2
      Setup.hs
  5. 4
      buildrun.bat
  6. 5
      buildrun.sh
  7. 46
      examples/DistrbDataSets.hs
  8. 115
      examples/PiDistribCountinuous.hs
  9. 56
      examples/PiDistribOnce.hs
  10. 158
      examples/distributedExamples.hs
  11. 141
      examples/webapp.hs
  12. BIN
      logo.ico
  13. 0
      outfile
  14. 386
      src/Transient/DDS.hs
  15. 1268
      src/Transient/Move.hs
  16. 156
      src/Transient/Move/Services.hs
  17. 125
      src/Transient/Stream/Resource.hs
  18. 5
      stack.yaml
  19. 65
      tests/Test.hs
  20. 72
      tests/Test2.hs
  21. 54
      tests/Test3.hs
  22. 87
      tests/TestSuite.hs
  23. 13
      tests/Testspark.hs
  24. 47
      tests/ghcjs-websockets.hs
  25. 11
      tests/snippet
  26. 55
      tests/test5.hs
  27. 127
      tests/teststream.hs
  28. 74
      tests/teststreamsocket.hs
  29. 67
      transient-universe.cabal
  30. 13
      transient.lkshf

24
Dockerfile

@ -0,0 +1,24 @@
FROM agocorona/herokughcjs
RUN git clone https://github.com/agocorona/transient transgit \
&& cd transgit \
&& git checkout ghcjs \
&& cabal install
RUN cd transgit && cabal install --ghcjs
RUN git clone https://github.com/agocorona/ghcjs-perch \
&& cd ghcjs-perch \
&& cabal install \
&& cabal install --ghcjs #
RUN git clone https://github.com/agocorona/ghcjs-hplay \
&& cd ghcjs-hplay \
&& cabal install --ghcjs \
&& cabal install
ADD . /transient/
CMD cd /transient && chmod 777 buildrun.sh && ./buildrun.sh

674
LICENSE

@ -0,0 +1,674 @@
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To do so, attach the following notices to the program. It is safest
to attach them to the start of each source file to most effectively
state the exclusion of warranty; and each file should have at least
the "copyright" line and a pointer to where the full notice is found.
<one line to give the program's name and a brief idea of what it does.>
Copyright (C) <year> <name of author>
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
Also add information on how to contact you by electronic and paper mail.
If the program does terminal interaction, make it output a short
notice like this when it starts in an interactive mode:
<program> Copyright (C) <year> <name of author>
This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
This is free software, and you are welcome to redistribute it
under certain conditions; type `show c' for details.
The hypothetical commands `show w' and `show c' should show the appropriate
parts of the General Public License. Of course, your program's commands
might be different; for a GUI interface, you would use an "about box".
You should also get your employer (if you work as a programmer) or school,
if any, to sign a "copyright disclaimer" for the program, if necessary.
For more information on this, and how to apply and follow the GNU GPL, see
<http://www.gnu.org/licenses/>.
The GNU General Public License does not permit incorporating your program
into proprietary programs. If your program is a subroutine library, you
may consider it more useful to permit linking proprietary applications with
the library. If this is what you want to do, use the GNU Lesser General
Public License instead of this License. But first, please read
<http://www.gnu.org/philosophy/why-not-lgpl.html>.

679
README.md

@ -0,0 +1,679 @@
![](https://raw.githubusercontent.com/agocorona/transient/master/logo.ico)
New: GHCJS integration and improved map-reduce (and wormholes and teleporting of computations)
======================
Browser nodes, running transient programs compiled with ghcjs are
integrated with server nodes using websockets communnications.
Just compile the program with ghcjs and point the browser to http://server:port.
The server nodes have a HTTP server that will send the compiled program to
the browser.
Browser nodes integrate Hplayground (package ghcjs-hplay) they can create
widgets and control the server nodes. A computation can move from browser
to server and back at runtime despite the different architecture.
Widgets with code running in browser and servers cancompose with other widgets.
A Browser node can access to many server nodes using `clustered` primitives.
map-reduce (Transient.DDS module) now has a true shuffle stage. Before the final reduction was in the node that initiated the computation. Now it is also distributed. Look for the concept of shuffle in the spark cloud platform
`teleport` is a new primitive that translates computations back and forth reusing an
already opened connection.
The connection is initiated by `wormhole` which opens a connection with another node anywhere in a computation.
Don't worry: as always, everything is composable. All the previous distributed primitives are rewritten in terms of these two new ones.
How to run the new example with ghcjs code:
You nee ghc and ghcjs installed.
clone and install perch:
> git clone https://github.com/geraldus/ghcjs-perch
> cd ghcjs-perch
> cabal install --ghcjs -f ghcjs
clone and install this branch (ghcjs) of transient:
> git clone https://github.com/agocorona/transient
> cd transient
> git checkout ghcjs
clone and install hplay:
> git clone https://github.com/agocorona/ghcjs-hplay
> cd ghcjs-hplay
> cabal install
> cabal install --ghcjs -f ghcjs
for fast development interactions, use the script
> buildrun examples/webapp.hs
This will compile examples/webapp.hs for ghcjs and run it interpreted with runghc
then point a browser to: http:localhost:2020
See this video to see this example running: https://www.livecoding.tv/agocorona/videos/Ke1Qz-seamless-composable-web-programming
The test program run among other things, two copies of a widget that start, stop and display a counter that run in the server.
transient
=========
One of the biggest dreams of software engineering is unrestricted composability.
This may be put in these terms:
let `ap1` and `ap2` two applications with arbitrary complexity, with all effects including multiple threads, asynchronous IO, indeterminism, events and perhaps, distributed computing.
Then the combinations:
- ap1 <|> ap2 -- Alternative expression
- ap1 >>= \x -> ap2 -- monadic sequence
- ap1 <> ap2 -- monoidal expression
- (,) <$> ap1 <*> ap2 -- Applicative expression
are possible if the types match, and generate new applications that are composable as well.
Transient does exactly that.
The operators `<|>` and `<>` can be used for concurrency, the operator `<|>` can be used for parallelism and `>>=` for sequencing of threads and/or distributed processes. So even in the presence of these effects and others, everything is composable.
For this purpose transient is an extensible effects monad with all major effects and primitives for parallelism, events, asyncronous IO, early termination, non-determinism logging and distributed computing. Since it is possible to extend it with more effects without adding monad transformers, the composability is assured.
The [Wiki](https://github.com/agocorona/transient/wiki) is more user oriented
The articles are more tecnical:
- [Philosophy, async, parallelism, thread control, events, Session state](https://www.fpcomplete.com/user/agocorona/EDSL-for-hard-working-IT-programmers?show=tutorials)
- [Backtracking and undoing IO transactions](https://www.fpcomplete.com/user/agocorona/the-hardworking-programmer-ii-practical-backtracking-to-undo-actions?show=tutorials)
- [Non-deterministic list like processing, multithreading](https://www.fpcomplete.com/user/agocorona/beautiful-parallel-non-determinism-transient-effects-iii?show=tutorials)
- [Distributed computing](https://www.fpcomplete.com/user/agocorona/moving-haskell-processes-between-nodes-transient-effects-iv?show=tutorials)
- [Publish-Subscribe variables](https://www.schoolofhaskell.com/user/agocorona/publish-subscribe-variables-transient-effects-v)
- [Distributed streaming, map-reduce](https://www.schoolofhaskell.com/user/agocorona/estimation-of-using-distributed-computing-streaming-transient-effects-vi-1)
These articles contain executable examples (not now, since the site no longer support the execution of haskell snippets).
This is the text of the first article:
# Introduction #
I have a problem: How can I present the few applicative and monadic combinators that I just developed. I could present it as
- something for multithreaded event handling without inversion of control. Or
- something for parallelization of processes: [async](https://hackage.haskell.org/package/async) without the wait
- for automatic thread control
- for alternative and applicative composition of parallel IO actions
- for indeterminism and asynchronicity effects
- for high level programming at the specification level
- for creating and composing applications by means of a single expression
- for overcoming futures and promises of Scala and JavaScript making them unnecessary.
Too much stuff for a single article. Maybe I should split this article and take time to write something more extensive and less dense. But I'm lazy and moreover they are only a few primitives, four or five, and no new operators. Breaking the article would hide the big picture. It would not display the beautiful unity of the common solution.
- If you are interested in how the idea came about read the next paragraph
- If you are interested in the internals, read the section "Enter the monad"
- If you are interested in how the monad controls multiple threads see "Implicit thread control"
- If you are interested in examples read from "Example" on
- If you are interested in Async, promises and futures, read "Beyond futures and promises"
- If you are interested in running the examples read "Composition of Programs"
- If you are interested in de-inverting the control of callbacks see "deinverting callbacks"
#The problem: parallelization, concurrency and inversion of control#
Suppose that I have a blocking computation that returns data when something happens. It may be also a long running computation that blocks the thread for a time:
``` haskell
receive: IO a
```
I can use it as such, but it blocks. I cannot use it in a context where other events are firing. I must create a thread for each blocking IO call. All of these threads probably modify a central state. Otherwise there will be no communication of data among threads. Alternatively someone may have created a kind of framework for this particular problem, where these blocking calls are managed. It may be a GUI toolkit, or a Web application framework, A browser environment or a library for the management of an ATM machine etc. In any case, what the programmer sees is a set of blocking synchronous calls and a set of callbacks or handlers that he has to program and configure in the framework.
Blocking IO creates the need to resort to manual thread management and concurrency. That means that the code is split into parallel and concurrent chunks which are hard to code and debug. In the second case I have non blocking IO, since the thread management is done implicitly by the framework, but, on the other side, I have to split the program logic into disconnected chunks. As a result, the program logic is very hard to grasp. This is known as the callback hell, a consequence of the inversion of control.
#The OOP non-solution#
The second scenario appears when threading is managed by a framework. Essentially it is the same case. In both cases we end up with disconnected chunks of code and a mutable state. The standard way to manage this messy central state has been to divide it into smaller states and encapsulate them with the methods that modify and serve the state. This is the Object Oriented Programming solution; The first OOP languages were created for managing events (SIMULA), and mouse events in an interactive GUI (Smalltalk).
*Object-oriented programming is an exceptionally bad idea which could only have originated in California. - Dijkstra*
Object Oriented Programming naturally fits with this inversion of control, that pervades IT problems. Whenever there are more than one asynchronous input, there is multitasking or inversion of control with state. The solution is a state machine. What OOP does is to split this state machine into smaller state machines called objects, that interact among them. But that implies the need to "deconstruct" the specifications.
##Deconstruct the specification recipe considered harmful##
Usually the specification of something is naturally expressed as if this "something" is a process, in the third person active perspective. People describe things that are the focus of their analysis as active, and the environment is described as passive even if there are active parts is the environment. In a recipe they say: you "fry the eggs" not "there is fire and there is eggs, you start the fire and the eggs are fried by the fire". You see that the passive perspective implies to give protagonism to low level elements that you are not interested in when writing an specification. Creating an OOP solution implies the deconstruction of the specification recipe into multiple third person passive perspectives, one for each class or object.
In any case, the programs are made of disconnected pieces, the software does not follow the natural flow as it would naturally extracted from the specifications and has many low level details, so the resulting code is low level, it is hard to maintain and the final service created is not composable. There is no way to insert your service within something bigger with a single invocation. This derives in the scarce reusability of the software, and the need of profuse documentation. It is inelegant, buggy, hard to maintain, and permits an huge number of arbitrary alternatives in the design space that aggravates the mentioned problems. It may be though that this is good for the IT business, but not in the medium-long term. And moreover, there are better ways to do it.
Functional languages help little on that if people limit themselves to porting already existing solutions from OOP languages.
#What we need#
*Simplicity is prerequisite for reliability. - Dijkstra*
The application must be programmed following the natural flow defined in the specification. The code must not split the specifications into parallel running tasks, neither invert the control and deconstruct the specification into objects. The design space must be limited so everyone should program the same specification in the same way. So other's code can be grasped immediately without the aid of external documentation. The application must transport user-defined state, that can be inspected and updated, added and deleted, but this state is instrumental it is not the center, because the center is the process described in the specification.
**We need an EDSL for hardworking IT programmers, that use Java, JavaScript, Scala, C#, PHP, Ruby or Python and don't know Haskell. Not a monad stack but a simple Monad that may liberate them from the Oppressive Object Paradigm, or OOP, without forcing them to sacrifice to the gods of Category Theory. With applicative and alternative combinators and a few primitives for implicit parallelization and thread control and for de-inversion of callbacks in the IO monad. Plus user-defined state management and early termination**
*“Simplicity is a great virtue but it requires hard work to achieve it and education to appreciate it. And to make matters worse: complexity sells. - Dijkstra*
#Enter the monad#
A monad with the asynchronicity effect can rescue the IT industry from the inversion of control trap for which OOP was originally designed while allowing implicit parallelization and thread control. A entire application can be coded in a single monadic expression with little or no plumbing. That allows the creation of composable applications and services of the [A -> A -> A](http://www.haskellforall.com/2014/04/scalable-program-architectures.html) kind.
In [A monad for reactive programming](https://www.fpcomplete.com/user/agocorona/monad-reactive-programming-2) I defined a monad that de-inverts the control when there are different events. The `Transient` monad can listen for events at different points in the monadic expression. It is no longer necessary to have a single listen point like in the case of the event loop or in classical reactive frameworks. Moreover, these event listeners do not block, so every event watchig point is active in the monad at the same time.
The events in the above article are injected by a simulated event loop in the state monad. This time I will show how to listen for IO computations without the help of a framework that brings events. These events may be hardware buttons, device driver inputs, requests from users, responses from databases or requests from other systems in the cloud, etc.
What we intend here is to formulate a general solution that permits coding close to the user requirement document and presenting the application as a single process even if involves multiple inputs and parallel executions in a single monadic expression. This expression will automatically spawn, communicate and kill tasks whenever necessary. We will see that we can improve the readability and reduce the complexity, so we can increase the maintainability and make entire services or applications composable.
Since I have to deal with dirty things like blocking, threads and IO, don't expect what follows to be a walk in the Platonic realm. I start with a monad like the `Transient` monad, that can be stopped with `empty` and continued with `runCont cont` where `cont` is the continuation context, set with `getCont`. (see explanation below)
``` haskell
data Transient m x= Transient {runTrans :: m (Maybe x)}
data EventF = forall a b . EventF
{xcomp :: (TransientIO a)
,fcomp :: [a -> TransientIO b]
, ... other ....}
type StateIO= StateT EventF IO
type TransientIO= Transient StateIO
instance Monad TransientIO where
return x = Transient $ return $ Just x
x >>= f = Transient $ do
cont <- setEventCont x f
mk <- runTrans x
resetEventCont cont
case mk of
Just k -> runTrans $ f k
Nothing -> return Nothing
instance Applicative TransientIO where
pure a = Transient . return $ Just a
Transient f <*> Transient g= Transient $ do
k <- f
x <- g
return $ k <*> x
instance Alternative TransientIO where
empty= Transient $ return Nothing
Transient f <|> Transient g= Transient $ do
k <- f
x <- g
return $ k <|> x
getCont ::(MonadState EventF m) => m EventF
getCont = get
runCont :: EventF -> StateIO ()
runCont (EventF x fs ...)= do runIt x (unsafeCoerce fs); return ()
where
runIt x fs= runTrans $ x >>= compose fs
compose []= const empty
compose (f: fs)= \x -> f x >>= compose fs
```
For a view of how this monad has evolved, look at the first article [A monad for reactive programming part 1](https://www.fpcomplete.com/user/agocorona/a-monad-for-reactive-programming-part-1) where I present a simpler version of this monad that has some shortcomings. In the second part I solved these shortcomings. I think that this is the best way to understand it.
What this monad does is to store the closure `x` and the continuations `f` in the state. `getCont` captures the execution state at the point and `runCont` executes it.
As far as "continuation" is taken here, there may be more than one of them.
For example, in this expression:
x0 >>=((x >>= f1) >>= f2) >>= f3
for the closure generated at the execution point `x`, the continuations are
f1 >>= f2 >>= f3
and the closure is the result of the execution of `x0 >>= x`
What `setEventCont` and `resetEventCont` do is to compose the list of continuations (one for each nested expression) in a 'flattened' representation, as a list in `fcomp`. Since the list does not "know" that the continuations types match, I have to erase the types using `unsafeCoerce`.
# Parallelization #
With these three primitives `getCont`, `runCont` and `empty` I will define a `async` primitive that will run a blocking IO action in a new thread and will execute the continuation in that thread whenever in receives something:
``` haskell
buffer :: Dynamic
buffer= unsafePerformIO $ newEmptyMVar
async :: IO a -> TransientIO a
async receive = do
cont <- getCont
r <- liftIO $ tryTakeMVar buffer
case r of
Nothing ->do
liftIO . forkIO $ do
r <- receive
putMVar buffer $ toDync r
runCont cont
return()
empty
Just r -> return $ formDynamic r
```
Essentially, `async` gets the continuation, then inspects the buffer. If there is `Nothing` then spawn `receive` in a new thread. The current thread is finished (`empty`). When something arrives, it is put in the buffer, then `runCont` will continue at the beginning of `receive` in the new thread. It does so because `getCont` got the `Transient` continuation there. This time, there will be something in the buffer and will return it, so the procedure will continue after the event arrives, but in a new thread.
Note that `receive` only fills the buffer. When `runCont` executes the closure, it will inspect the buffer again. This time there will be something, the closure will succeed and the continuation will fire.
`getCont` and `runCont` are similar to [setjmp and longjmp](http://en.wikipedia.org/wiki/Setjmp.h) in C. Moreover, the mechanism is not very different form the IO scheduler in GHC or in any operating system. But this time it runs at the application level rather than at the GHC level.
#Wait for events#
If we want to trigger the continuation repeatedly whenever something is received by `receive`, it is a matter of adding a loop to the `Nothing` branch. Then the continuation will be called for every received event.
Let's call this variant `waitEvents`:
``` haskell
waitEvents :: IO a -> TransientIO a
waitEvents receive = do
cont <- getCont
r <- tryTakeMVar buffer
case r of
Nothing ->do
liftIO . forkIO $ loop $ do
r <- receive
putMVar buffer r
runCont cont
return()
empty
Just r -> return r
where
loop x= x >> loop x
```
##Example##
This program will say "hello" to every name entered.
``` haskell
runTransient :: TransientIO x -> IO (Maybe x, EventF)
runTransient t= runStateT (runTrans t) eventf0
main= do
runTransient $ do
name <- waitEvents getLine
liftIO $ putStrLn $ "hello "++ name
stay
```
Note that there is no loop. `waitEvent` install `getLine` at the start of a process that execute the continuation, what is after `getLine`, for each entry. The loop is internal to `waitEvents`
Here `runTransient` executes a transient computation down to the IO monad.
`stay` is whatever that keeps the console application from exiting. That is because since the transient branch that waits for events is non-blocking, it would finish immediately. **After `async` or `waitEvents`, the current thread dies and the rest of the monadic computation runs in a different thread**
#Implicit thread control#
Since each event in any part of the monadic computation is active and triggers the continuation of the monad at that point, the monadic expression is multithreaded and non deterministic.
How to control the threads? It is natural to think that since `waitEvents` and `async` execute continuations within the monadic expression, then once something happens in a statement then their continuations must be invalidated.
That means that whenever `async` of `waitEvents` receive something, the threads that are running below must be killed. Then this statement, with the new buffered input will execute his closure and rebuild the continuation again.
This is the natural thread management that I implemented. I do not detail the modifications necessary for `waitEvents` to permit this behaviour. It is a matter of keeping in the state the list of spawned threads so that each `waitEvents` has the information about all the threads that are triggered after it. Additionally, this list contains also a buffer for each of these threads.
In this example:
``` haskell
main= do
runTransient $ do
waitEvents watchReset <|> return ()
name <- waitEvents getLine
liftIO $ putStrLn $ "hello "++ name
stay
```
the `return()` composed with the alternative operator `<|>` would bypass immediately the wait for the reset event, but as soon as the reset is pressed, all the event handlers spawned after it will be killed. Immediately they will be spawned again.
This is a slightly different version:
main= do
runTransient $ do
r <- (waitEvents watchStop >> return True) <|> return False
if r then liftIO $ putStrln "STOP" else do
name <- waitEvents getLine
liftIO $ putStrLn $ "hello "++ name
stay
In this case the program will be stopped and will not be re-spawned when `watchStop` is activated. Since now the branch of the monad executed is different. It prints the stop message and finalizes.
#Non blocking IO#
Let's create a nonblocking keyboard input thing called `option`. At the same time this is a good example of inter-thread communication within the `Transient` monad:
``` haskell
option :: (Typeable a, Show a, Read a, Eq a) =>
a -> [Char] -> TransientIO a
option ret message= do
liftIO $ putStrLn $ message++"("++show ret++")"
waitEvents "" getLine'
where
getLine'= do
atomically $ do
mr <- readTVar getLineRef
case mr of
Nothing -> retry
Just r ->
case readsPrec 0 r of
[] -> retry
(s,_):_ -> if ret== s
then do
writeTVar getLineRef Nothing
return ret
else retry
_ -> retry
getLineRef= unsafePerformIO $ newTVarIO Nothing
inputLoop :: IO ()
inputLoop= do
r<- getLine !> "started inputLoop"
if r=="end" return True else do
atomically . writeTVar getLineRef $ Just r
inputLoop
```
# Applicative and Alternative combinators#
`option` reads the standard input in nonblocking mode, so that many options can be combined using applicative or alternative operators. `option` shows a message and waits for `inputLoop` to enter an input line. If some `option` match, it returns the value. If it does not match, it fails with `empty`, but the loop in `waitEvents` re-executes `getLine'` again for this option. In this way, the options are continuously watching the input. Note that more than one option can be triggered simultaneously, in a different thread.
`inputLoop` is initialized by `async`. It waits for input, and exposes it to all the running `getLine'` processes (one per `option`) in a `TVar`. If the user presses "end" `inputLoop` returns and `async` kills all the watching threads below.
``` haskell
main= do
runTransient choose
stay
choose :: TransientIO()
choose= do
r <- async inputLoop <|> return False
case r of
True -> return ()
False-> do
r <- option (1 :: Int) "red" <|> option 2 "green" <|> option 3 "blue"
liftIO $ print r
```
The above program will print repeatedly the chosen option. We see that `option` is composable using the alternative operator.
Now let's create another event generator, a number is sent every second, while two options are waiting for keyboard input:
``` haskell
data Option= Option String String | Number Int deriving Show
choose= do
r <- ( Option <$> ( option "1" "red" <*> option "2" "green"))
<|> ( Number <$> waitEvents waitnumber )
liftIO $ putStrLn $ "result=" ++ show r
where
waitnumber= do
threadDelay 1000000
return 42
```
Applicative and alternative combinators can be used fully. The Applicative waits for both events to be triggered to have data in their respective buffers. `waitnumber` produces an event each second.
Each Option runs a different `waitEvent` in a different thread, but the closure is the same applicative expression in the three threads. The three have a TVar waiting for new input. They fill their respective buffers when they validate. The thread that fills the last buffer empties them and executes the continuation. The other two fail, but stay ready for the next input, since `waitEvents` has a loop.
#Beyond futures and promises#
Scala Futures and the haskell library async manage placeholders that receive the result and place them where the result of the computation is needed.
Scala Futures also uses futures in nice chains of multi-threaded lists that can be transformed in the style of map-reduce.
In this sense they are similar to the javaScript promises, which chain code with `then`, but the latter does not perform multiple tasks like in the case of Scala futures.
For some needs Scala and JavaScript must use callbacks since the constraints of their frameworks do not allow enough flexibility. futures and promises force the programmer to enter in a different kind of computation model, different from the one of the native languages. In the case of Scala it is monoidal. In the case of Javascript it is a restricted form of bind operation.
This library puts the continuation code at the end of the receiving pipeline and parallelizes the execution, but the continuation is the plain monadic code that is after the receiving call in the monadic expression, so there is no restriction about what can be done.
`asyn` can be used for any process that we want to parallelize. Now we can do it better than the [async](https://hackage.haskell.org/package/async-2.0.2/docs/Control-Concurrent-Async.html) library. This program sums the words in google and haskell homepages **in parallel**. Using [Network.HTTP](http://hackage.haskell.org/package/HTTP-4000.2.19/docs/Network-HTTP.html)
``` haskell
sum= do
(r,r') <- (,) <$> async (worker "http://www.haskell.org/")
<*> async (worker "http://www.google.com/")
liftIO $ putStrLn $ "result=" ++ show r + r'
where
getURL= simpleHTTP . getRequest
worker :: String -> IO Int
worker url=do
r <- getURL url
body <- getResponseBody r
return . length . words $ body
```
That is a complete working example. Note that unlike in the async library, there is no `wait` primitive. All the processing is done in the `worker` in his own thread. When both workers finish, they return the result to the monad and continue the thread.
We also can do parallel IO processing in the style of futures of the Scala language using the `Monoid` instance of `TransientIO`. But this time since we use continuations, futures are no longer necessary: the last download triggers the continuation.
``` haskell
instance Monoid a => Monoid (TransientIO a) where
mappend x y = mappend <$> x <*> y
mempty= return mempty
sum= do
rs <- foldl (<>) (return 0) $ map (async . worker)
[ "http://www.haskell.org/", "http://www.google.com/"]
liftIO $ putStrLn $ "result=" ++ show rs
```
Since the `worker`returns an `Int`, to sum the results we need a `Monoid` instance for `Int`
``` haskell
instance Monoid Int where
mappend= (+)
mempty= 0
```
Note that there is a full de-inversion of control, since the result returns to the monad. The Futures and promises of Scala (or in javaScript) can not return the execution flow to the calling procedure. Since the Transient monad continues in the thread that completed the worker, the processing can continue in the monad. That permits more complex and yet clearer computations. It is not reduced to list-like processing.
# A Web Server #
Here is a Web Server:
``` haskell
server= do
sock <- liftIO $ listenOn $ PortNumber 80
(h,_,_) <- spawn $ accept sock
liftIO $ do
hPutStr h msg
hFlush h
hClose h
msg = "HTTP/1.0 200 OK\r\nContent-Length: 5\r\n\r\nPong!\r\n"
```
In the current code, the primitives `async`, `waitEvents` and `spawn` are defined in terms of `parallel`, which is a generalization of `async` and `waitEvents` explained above:
``` haskell
data Loop= Once | Loop | Multithread
waitEvents :: IO b -> TransientIO b
waitEvents= parallel Loop
async :: IO b -> TransientIO b
async = parallel Once
spawn= parallel Multithread
parallel :: Loop -> IO b -> TransientIO b
```
When `parallel` is called with `Multithread`, it spawns the continuation in a thread for each event received immediately without waiting for the termination of the previous one. `waitEvent` executes the continuation within the thread so the receive method is not called again until the previous event is processed.
# Composition of programs (Runnable example) #
We can compose any of these programs together since none of them blocks and the automatic thread control applies gracefully to all of the elements. This program combines the above programs and some others.
The combination in this case is using the alternative operator:
colors <|> app <|> sum1 <|> sum2 <|> server <|> menu
Since the fpcomplete environment uses ghci and it shares threads among snippets of code, I can run only one example in this article, and the composability of Transient is nice to show them all togeter.
To verify the multitasking press: `app` and then `colors`. `app` would start an iterative counter with an applicative expression, while `colors` will ask for an option among tree of them. Both will run in parallel until you press "main" which will stop both, since main is above in the monad.
Code running at the original article:
https://www.fpcomplete.com/tutorial-edit/An%20EDSL%20for%20hard%20working%20IT%20programmers
#Session data#
I added a type indexed map to the state so the user can store his own session data with these primitives:
``` haskell
setSData :: ( MonadState EventF m,Typeable a) => a -> m ()
getSData :: MonadState EventF m => Typeable a =>Transient m a
```
Session data can be used instead of a state monad transformer for each new kind of user data.
My purpose is to create a monad for general IT purposes, for profane programmers with no knowledge of monad transformers.
Since `empty` stops the computation but does not transport any error condition, session data can be used for this purpose:
``` haskell
data Status= Error String | NoError
fail :: String -> TransientIO a
fail msg= setSData (Error msg) >> empty
```
After the execution, I can inspect the status:
``` haskell
status <- getSData <|> NoError
```
The alternative expression is necessary since if `Status` has not been set, the computation would stop. `NoError` guarantees that it does not stop.
#de-inverting callbacks#
So far so good. But what happens when besides dealing with raw blocking IO there is a framework that deals with some particular events, so it initiates the threads himself and expects you just to set the callbacks?
Suppose that we have this event handling setter:
``` haskell
setHandlerForWhatever :: (a -> IO ()) -> IO ()
```
It is necessary a de-inversion call `whateverHappened` at some point of the computation (may be at the beginning) so that the callback continues the monadic execution:
``` haskell
do
somethingToDo
r <- whatheverHappened
doSomethingWith r
....
```
To define the de-inverted call `whateverHappened` we use the same trick than in `async`, but this time there is no `forkIO` neither thread control, since the framework does it for you:
``` haskell
whateverHappened= do
cont <- getCont
mEvData <- Just <*> getSData <|> return Nothing
case mEvData of
Nothing -> setHandlerForWhatever $\dat -> do
runStateT ( setSData dat >> runTansient cont) cont
empty
Just dat -> return dat
```
Whether the framework is single threaded or multi-threaded is not important, we give it the event handlers that it needs by means of continuations.
To have something more general, I defined:
``` haskell
type EventSetter eventdata response= (eventdata -> IO response) -> IO ()
type ToReturn response= IO response
react
:: Typeable eventdata
=> EventSetter eventdata response
-> ToReturn response
-> TransientIO eventdata
```
The second parameter is the value returned by the callback. So if you have a callback called `OnResponse`
``` haskell
data Control= SendMore | MoMore
OnResponse :: (Response -> IO Control) -> IO()
```
I can display all data received while controlling the reception this way:
``` haskell
rcontrol <- newMVar Control
resp <- react $ OnResponse (const $ readMVar rcontrol)
display resp
r <- (option "more" "more" >> return SendMore) <|> (option "stop" "stop" >> return NoMore)
putMVar rcontrol r
```
Since `react` set as callback all the rest of the computation and since the ToReturn expression is evaluated the latest, the continuation is executed and sets `rcontrol` before the `ToReturn` expression is evaluated.
Note that you can reassign the callback at any moment since react would set whatever continuation that is after it.
#Conclusions and future work#
The code is at
https://github.com/agocorona/transient
My aim is to create a family of combinators for programming in industry. As I said before, that implies no monad transformers, the simplest monad that could produce the simplest error messages.
The haskell applicative, alternative, monoidal and monadic combinators when applied to a monad that manage asynchronous IO permits multi-threaded programming with little plumbing that is close to the specification level with great composability. No inversion of control means no need to deconstruct the specifications and no state machines.
This, together with the uniform and composable thread management, narrows the design space and makes the application more understandable from the requirements, and thus the technical documentation and maintenance costs are reduced to a minimum.
Note that the bulk of the programming is done in the IO monad. That is on purpose. The idea is a simple IT EDSL with the rigth effects that permit rapid and intuitive development. Additional monads can be used by running them within the IO procedures defined by the programmer if they wish. I will add some additional effects like backtracking to undo transactions and to produce execution traces. That would be the base of a new version of MFlow, my server-side framework and integration platform. The ability to perform rollbacks and respond to asynchronous events at the same time is important for cloud applications. Reader- and Writer-effects for any programmer need are almost trivial to implement using `getSData` and `setSData`.
Resource allocation and deallocation for file handlers etc can be done using the same strategy used for thread control, but it is more orthogonal to delegate it to the IO threads themselves. The programmer can use exceptions or monads that guarantee proper release of resources before the thread is killed.
In `Multithread` mode the single entry buffer can be overrun. It is necessary to handle this case or, else, assume that the `receive` procedure has his own buffer and his own event contention mechanism. That is the most orthogonal option.
This is huge. I plan to create interfaces for some GUI toolkit. The GUI objects will be fully composable for the first time.
Spawning threads in other machines is the next big step. [MFlow](http://mflowdemo.herokuapp.com/) and [hplayground](http://tryplayg.herokuapp.com/) will converge with this platform. If you want to collaborate, don't hesitate to send me a message!
With the `react` primitive it is possible to de-invert any framework, including the callbacks of a GUI toolkit, so the widgets can be managed with Applicative and monadic combinators. hplayground does that for the JavaScript callbacks and HTML forms. Since hplayground, that runs in the client and MFlow that runs on the server side share the same widget EDSL, that can be ported to a GUI, an application can run in any environment, including console applications.

2
Setup.hs

@ -0,0 +1,2 @@
import Distribution.Simple
main = defaultMain

4
buildrun.bat

@ -0,0 +1,4 @@
ghcjs -isrc -i../ghcjs-hplay/src %1 -o static/out
if %errorlevel% neq 0 exit
runghc -isrc -i../ghcjs-hplay/src %1

5
buildrun.sh

@ -0,0 +1,5 @@
#!/bin/bash
set -e
ghcjs -isrc -i../ghcjs-hplay/src $1 -o static/out
runghc -isrc -i../ghcjs-hplay/src $1

46
examples/DistrbDataSets.hs

File diff suppressed because one or more lines are too long

115
examples/PiDistribCountinuous.hs

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-- Distributed streaming using Transient
-- See the article: https://www.fpcomplete.com/user/agocorona/streaming-transient-effects-vi
{-# LANGUAGE ScopedTypeVariables, DeriveDataTypeable, MonadComprehensions #-}
module MainCountinuous where
import Transient.Base
import Transient.Move
import Transient.Indeterminism
import Transient.Logged
import Transient.Stream.Resource
import Control.Applicative
import Data.Char
import Control.Monad
import Control.Monad.IO.Class
import System.Random
import Data.IORef
import System.IO
import GHC.Conc
import System.Environment
-- continuos streaming version
-- Perform the same calculation but it does not stop, and the results are accumulated in in a mutable reference within the calling node,
-- so the precision in the value of pi is printed with more and more precision. every 1000 calculations.
-- Here instead of `collect` that finish the calculation when the number of samples has been reached, i use `group` which simply
-- group the number of results in a list and then sum of the list is returned to the calling node.
-- Since `group` do not finish the calculation, new sums are streamed from the nodes again and again.
main= do
let numNodes= 5
numCalcsNode= 100
ports= [2000.. 2000+ numNodes -1]
createLocalNode p= createNode "localhost" p
nodes= map createLocalNode ports
rresults <- newIORef (0,0)
keep $ freeThreads $ threads 10 $ runCloud $ do
--setBufSize 1024
local $ addNodes nodes
foldl (<|>) empty (map listen nodes) <|> return()
r <- clustered $ do
--Connection (Just (_,h,_,_)) _ <- getSData <|> error "no connection"
--liftIO $ hSetBuffering h $ BlockBuffering Nothing
r <- local $ group numCalcsNode $ do
n <- liftIO getNumCapabilities
threads n .
spawn $ do
x <- randomIO :: IO Double
y <- randomIO
return $ if x * x + y * y < 1 then 1 else (0 :: Int)
return $ sum r
(n,c) <- local $ liftIO $ atomicModifyIORef' rresults $ \(num, count) ->
let num' = num + r
count'= count + numCalcsNode
in ((num', count'),(num',count'))
when ( c `rem` 1000 ==0) $ local $ liftIO $ do
th <- myThreadId
putStrLn $ "Samples: "++ show c ++ " -> " ++
show( 4.0 * fromIntegral n / fromIntegral c)++ "\t" ++ show th
-- really distributed version
-- generate an executable with this main and invoke it as such:
--
-- program myport remotehost remoteport
--
-- where remotehost remoteport are from a previously initialized node
-- The first node initialize it with:
--
-- program myport localhost myport
mainDistributed= do
args <- getArgs
let localPort = read (args !! 0)
seedHost = read (args !! 1)
seedPort = read (args !! 2)
mynode = createNode "localhost" localPort
seedNode = createNode seedHost seedPort
numCalcsNode= 100
rresults <- liftIO $ newIORef (0,0)
runCloud' $ do
connect mynode seedNode
local $ option "start" "start the calculation once all the nodes have been started" :: Cloud String
r <- clustered $ do
--Connection (Just (_,h,_,_)) _ <- getSData <|> error "no connection"
--liftIO $ hSetBuffering h $ BlockBuffering Nothing
r <- local $ group numCalcsNode $ do
n <- liftIO getNumCapabilities
threads n .
spawn $ do
x <- randomIO :: IO Double
y <- randomIO
return $ if x * x + y * y < 1 then 1 else (0 :: Int)
return $ sum r
(n,c) <- local $ liftIO $ atomicModifyIORef' rresults $ \(num, count) ->
let num' = num + r
count'= count + numCalcsNode
in ((num', count'),(num',count'))
when ( c `rem` 1000 ==0) $ local $ liftIO $ do
th <- myThreadId
putStrLn $ "Samples: "++ show c ++ " -> " ++
show( 4.0 * fromIntegral n / fromIntegral c)++ "\t" ++ show th

56
examples/PiDistribOnce.hs

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-- Distributed streaming using Transient
-- See the article: https://www.fpcomplete.com/user/agocorona/streaming-transient-effects-vi
{-# LANGUAGE ScopedTypeVariables, DeriveDataTypeable, MonadComprehensions #-}
module MainOnce where
import Transient.Base
import Transient.Move
import Transient.Indeterminism
import Transient.Logged
import Transient.Stream.Resource
import Control.Applicative
import Data.Char
import Control.Monad
import Control.Monad.IO.Class
import System.Random
import Data.IORef
import System.IO
import GHC.Conc
import System.Environment
-- distributed calculation of PI
-- This example program is the closest one to the defined in the spark examples: http://tldrify.com/bpr
-- But while the spark example does not contain the setup of the cluster and the confuguration/initalization
-- this examples includes everything
-- The nodes are simulated within the local process, but they communicate trough sockets and serialize data
-- just like real nodes. Each node spawn threads and return the result to the calling node.
-- when the number of result are reached `colect` kill the threads, the sockets are closed and the stream is stopped
-- for more details look at the article: https://www.fpcomplete.com/tutorial-edit/streaming-transient-effects-vi
--
main= do
let numNodes= 5
numSamples= 1000
ports= [2000.. 2000 + numNodes -1]
createLocalNode p= createNode "localhost" p
nodes= map createLocalNode ports
keep $ do
addNodes nodes
-- option "start" "start"
xs <- collect numSamples $ runCloud $ do
foldl (<|>) empty (map listen nodes) <|> return()
local $ threads 2 $ runCloud $
clustered[if x * x + y * y < (1 :: Double) then 1 else (0 :: Int)| x <- random, y <-random]
liftIO $ print (4.0 * (fromIntegral $ sum xs) / (fromIntegral numSamples) :: Double)
exit
where
random= local $ waitEvents' randomIO :: Cloud Double

158
examples/distributedExamples.hs

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{-# LANGUAGE ScopedTypeVariables, DeriveDataTypeable, MonadComprehensions #-}
module Main where
import Transient.Move
import Transient.Logged
import Transient.Base
import Transient.Indeterminism
import Transient.EVars
import Network
import Control.Applicative
import Control.Monad.IO.Class
import System.Environment
import System.IO.Unsafe
import Data.Monoid
import System.IO
import Control.Monad
import Data.Maybe
import Control.Exception
import Control.Concurrent (threadDelay)
import Data.Typeable
import Data.IORef
import Data.List((\\))
-- to be executed with two or more nodes
main = do
args <- getArgs
if length args < 2
then do
putStrLn "The program need at least two parameters: localHost localPort remoteHost RemotePort"
putStrLn "Start one node alone. The rest, connected to this node."
return ()
else keep $ do
let localHost= head args
localPort= read $ args !! 1
(remoteHost,remotePort) =
if length args >=4
then(args !! 2, read $ args !! 3)
else (localHost,localPort)
let localNode= createNode localHost localPort
remoteNode= createNode remoteHost remotePort
connect localNode remoteNode
examples
examples = do
logged $ option "main" "to see the menu" <|> return ""
r <- logged $ option "move" "move to another node"
<|> option "call" "call a function in another node"
<|> option "chat" "chat"
<|> option "netev" "events propagating trough the network"
case r of
"call" -> callExample
"move" -> moveExample
"chat" -> chat
"netev" -> networkEvents
data Environ= Environ (IORef String) deriving Typeable
callExample = do
node <- logged $ do
nodes <- getNodes
myNode <- getMyNode
return . head $ nodes \\ [myNode]
logged $ putStrLnhp node "asking for the remote data"
s <- callTo node $ do
putStrLnhp node "remote callTo request"
liftIO $ readIORef environ
liftIO $ putStrLn $ "resp=" ++ show s
{-# NOINLINE environ #-}
environ= unsafePerformIO $ newIORef "Not Changed"
moveExample = do
node <- logged $ do
nodes <- getNodes
myNode <- getMyNode
return . head $ nodes \\ [myNode]
putStrLnhp node "enter a string. It will be inserted in the other node by a migrating program"
name <- logged $ input (const True)
beamTo node
putStrLnhp node "moved!"
putStrLnhp node $ "inserting "++ name ++" as new data in this node"
liftIO $ writeIORef environ name
return()
chat :: TransIO ()
chat = do
name <- logged $ do liftIO $ putStrLn "Name?" ; input (const True)
text <- logged $ waitEvents $ putStr ">" >> hFlush stdout >> getLine' (const True)
let line= name ++": "++ text
clustered $ liftIO $ putStrLn line
networkEvents = do
node <- logged $ do
nodes <- getNodes
myNode <- getMyNode
return . head $ nodes \\ [myNode]
logged $ putStrLnhp node "<- write \"fire\" in this other node"
r <- callTo node $ do
option "fire" "fire event"
return "event fired"
putStrLnhp node $ r ++ " in remote node"
putStrLnhp p msg= liftIO $ putStr (show p) >> putStr " ->" >> putStrLn msg
--call host port proc params= do
-- port <- getPort
-- listen port <|> return
-- parms <- logged $ return params
-- callTo host port proc parms
-- close
--
--distribute proc= do
-- case dataFor proc
-- Nothing -> proc
-- Just dataproc -> do
-- (h,p) <- bestNode dataproc
-- callTo h p proc
--
--bestNode dataproc=
-- nodes <- getNodes
-- (h,p) <- bestMatch dataproc nodes <- user defined
--
--bestMatch (DataProc nodesAccesed cpuLoad resourcesNeeded) nodes= do
-- nodesAccesed: node, response
--
--bestMove= do
-- thisproc <- gets myProc
-- case dataFor thisproc
-- Nothing -> return ()
-- Just dataproc -> do
-- (h,p) <- bestNode dataproc
-- moveTo h p
--
--
--inNetwork= do
-- p <- getPort
-- listen p <|> return ()

141
examples/webapp.hs

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{-# LANGUAGE CPP #-}
module Main where
import Prelude hiding (div,id,span)
import Transient.Base
#ifdef ghcjs_HOST_OS
hiding ( option,runCloud')
#endif
import GHCJS.HPlay.View
#ifdef ghcjs_HOST_OS
hiding (map)
#else
hiding (map, option,runCloud')
#endif
import Transient.Move
import Transient.Indeterminism
import Control.Applicative
import Control.Monad
import Data.Typeable
import Data.IORef
import Control.Concurrent (threadDelay)
import Control.Monad.IO.Class
-- Show the composability of transient web aplications
-- with three examples composed together, each one is a widget that execute
-- code in the browser AND the server.
main = simpleWebApp 2020 $ demo <|> demo2 <|> counters
notebook= do
r <- local . render $ textArea <*** wbutton () "send"
atServer $ do
-- execute with a pipe
exec
demo= do
name <- local . render $ do
rawHtml $ do
hr
p "this snippet captures the essence of this demonstration"
p $ span "it's a blend of server and browser code in a "
>> (span $ b "composable") >> span " piece"
div ! id (fs "fibs") $ i "Fibonacci numbers should appear here"
local . render $ wlink () (p " stream fibonacci numbers")
-- stream fibonancci
r <- atServer $ do
let fibs= 0 : 1 : zipWith (+) fibs (tail fibs) :: [Int] --fibonacci numb. definition
r <- local . threads 1 . choose $ take 10 fibs
lliftIO $ print r
lliftIO $ threadDelay 1000000
return r
local . render . at (fs "#fibs") Append $ rawHtml $ (h2 r)
demo2= do
name <- local . render $ do
rawHtml $ do
hr
br;br
p "In this example you enter your name and the server will salute you"
br