I beg for your help, speeding up the following program:
main = do
jobsToProcess <- fmap read getLine
forM_ [1..jobsToProcess] $ \_ -> do
[r, k] <- fmap (map read . words) getLine :: IO [Int]
putStrLn $ doSomeReallyLongWorkingJob r k
There could(!) be a lot of identical jobs to do, but it's not up to me modifying the inputs, so I tried to use Data.HashMap for backing up already processed jobs. I already optimized the algorithms in the doSomeReallyLongWorkingJob function, but now it seems, it's quite as fast as C.
But unfortunately it seems, I'm not able to implement a simple cache without producing a lot of errors. I need a simple cache of Type HashMap (Int, Int) Int, but everytime I have too much or too few brackets. And IF I manage to define the cache, I'm stuck in putting data into or retrieving data from the cache cause of lots of errors.
I already Googled for some hours but it seems I'm stuck. BTW: The result of the longrunner is an Int as well.
It's pretty simple to make a stateful action that caches operations. First some boilerplate:
{-# LANGUAGE FlexibleContexts #-}
import Control.Monad.State
import Data.Map (Map)
import qualified Data.Map as M
import Debug.Trace
I'll use Data.Map, but of course you can substitute in a hash map or any similar data structure without much trouble. My long-running computation will just add up its arguments. I'll use trace to show when this computation is executed; we'll hope not to see the output of the trace when we enter a duplicate input.
reallyLongRunningComputation :: [Int] -> Int
reallyLongRunningComputation args = traceShow args $ sum args
Now the caching operation will just look up whether we've seen a given input before. If we have, we'll return the precomputed answer; otherwise we'll compute the answer now and store it.
cache :: (MonadState (Map a b) m, Ord a) => (a -> b) -> a -> m b
cache f x = do
mCached <- gets (M.lookup x)
case mCached of
-- depending on your goals, you may wish to force `result` here
Nothing -> modify (M.insert x result) >> return result
Just cached -> return cached
where
result = f x
The main function now just consists of calling cache reallyLongRunningComputation on appropriate inputs.
main = do
iterations <- readLn
flip evalStateT M.empty . replicateM_ iterations
$ liftIO getLine
>>= liftIO . mapM readIO . words
>>= cache reallyLongRunningComputation
>>= liftIO . print
Let's try it in ghci!
> main
5
1 2 3
[1,2,3]
6
4 5
[4,5]
9
1 2
[1,2]
3
1 2
3
1 2 3
6
As you can see by the bracketed outputs, reallyLongRunningComputation was called the first time we entered 1 2 3 and the first time we entered 1 2, but not the second time we entered these inputs.
I hope i'm not too far off base, but first you need a way to carry around the past jobs with you. Easiest would be to use a foldM instead of a forM.
import Control.Monad
import Data.Maybe
main = do
jobsToProcess <- fmap read getLine
foldM doJobAcc acc0 [1..jobsToProcess]
where
acc0 = --initial value of some type of accumulator, i.e. hash map
doJobAcc acc _ = do
[r, k] <- fmap (map read . words) getLine :: IO [Int]
case getFromHash acc (r,k) of
Nothing -> do
i <- doSomeReallyLongWorkingJob r k
return $ insertNew acc (r,k) i
Just i -> do
return acc
Note, I don't actually use the interface for putting and getting the hash table key. It doesn't actually have to be a hash table, Data.Map from containers could work. Or even a list if its going to be a small one.
Another way to carry around the hash table would be to use a State transformer monad.
I am just adding this answer since I feel like the other answers are diverging a bit from the original question, namely using hashtable constructs in Main function (inside IO monad).
Here is a minimal hashtable example using hashtables module. To install the module with cabal, simply use
cabal install hashtables
In this example, we simply put some values in a hashtable and use lookup to print a value retrieved from the table.
import qualified Data.HashTable.IO as H
main :: IO ()
main = do
t <- H.new :: IO (H.CuckooHashTable Int String)
H.insert t 22 "Hello world"
H.insert t 5 "No problem"
msg <- H.lookup t 5
print msg
Notice that we need to use explicit type annotation to specify which implementation of the hashtable we wish to use.
Related
I'm trying to write code in source -> transform -> sink style, for example:
let (|>) = flip ($)
repeat 1 |> take 5 |> sum |> print
But would like to do that using IO. I have this impression that my source can be an infinite list of IO actions, and each one gets evaluated once it is needed downstream. Something like this:
-- prints the number of lines entered before "quit" is entered
[getLine..] >>= takeWhile (/= "quit") >>= length >>= print
I think this is possible with the streaming libraries, but can it be done along the lines of what I'm proposing?
Using the repeatM, takeWhile and length_ functions from the streaming library:
import Streaming
import qualified Streaming.Prelude as S
count :: IO ()
count = do r <- S.length_ . S.takeWhile (/= "quit") . S.repeatM $ getLine
print r
This seems to be in that spirit:
let (|>) = flip ($)
let (.>) = flip (.)
getContents >>= lines .> takeWhile (/= "quit") .> length .> print
The issue here is that Monad is not the right abstraction for this, and attempting to do something like this results in a situation where referential transparency is broken.
Firstly, we can do a lazy IO read like so:
module Main where
import System.IO.Unsafe (unsafePerformIO)
import Control.Monad(forM_)
lazyIOSequence :: [IO a] -> IO [a]
lazyIOSequence = pure . go where
go :: [IO a] -> [a]
go (l:ls) = (unsafePerformIO l):(go ls)
main :: IO ()
main = do
l <- lazyIOSequence (repeat getLine)
forM_ l putStrLn
This when run will perform cat. It will read lines and output them. Everything works fine.
But consider changing the main function to this:
main :: IO ()
main = do
l <- lazyIOSequence (map (putStrLn . show) [1..])
putStrLn "Hello World"
This outputs Hello World only, as we didn't need to evaluate any of l. But now consider replacing the last line like the following:
main :: IO ()
main = do
x <- lazyIOSequence (map (putStrLn . show) [1..])
seq (head x) putStrLn "Hello World"
Same program, but the output is now:
1
Hello World
This is bad, we've changed the results of a program just by evaluating a value. This is not supposed to happen in Haskell, when you evaluate something it should just evaluate it, not change the outside world.
So if you restrict your IO actions to something like reading from a file nothing else is reading from, then you might be able to sensibly lazily evaluate things, because when you read from it in relation to all the other IO actions your program is taking doesn't matter. But you don't want to allow this for IO in general, because skipping actions or performing them in a different order can matter (and above, certainly does). Even in the reading a file lazily case, if something else in your program writes to the file, then whether you evaluate that list before or after the write action will affect the output of your program, which again, breaks referential transparency (because evaluation order shouldn't matter).
So for a restricted subset of IO actions, you can sensibly define Functor, Applicative and Monad on a stream type to work in a lazy way, but doing so in the IO Monad in general is a minefield and often just plain incorrect. Instead you want a specialised streaming type, and indeed Conduit defines Functor, Applicative and Monad on a lot of it's types so you can still use all your favourite functions.
Consider the two following variations:
myReadListTailRecursive :: IO [String]
myReadListTailRecursive = go []
where
go :: [String] -> IO [String]
go l = do {
inp <- getLine;
if (inp == "") then
return l;
else go (inp:l);
}
myReadListOrdinary :: IO [String]
myReadListOrdinary = do
inp <- getLine
if inp == "" then
return []
else
do
moreInps <- myReadListOrdinary
return (inp:moreInps)
In ordinary programming languages, one would know that the tail recursive variant is a better choice.
However, going through this answer, it is apparent that haskell's implementation of recursion is not similar to that of using the recursion stack repeatedly.
But because in this case the program in question involves actions, and a strict monad, I am not sure if the same reasoning applies. In fact, I think in the IO case, the tail recursive form is indeed better. I am not sure how to correctly reason about this.
EDIT: David Young pointed out that the outermost call here is to (>>=). Even in that case, does one of these styles have an advantage over the other?
FWIW, I'd go for existing monadic combinators and focus on readability/consiseness. Using unfoldM :: Monad m => m (Maybe a) -> m [a]:
import Control.Monad (liftM, mfilter)
import Control.Monad.Loops (unfoldM)
myReadListTailRecursive :: IO [String]
myReadListTailRecursive = unfoldM go
where
go :: IO (Maybe String)
go = do
line <- getLine
return $ case line of
"" -> Nothing
s -> Just s
Or using MonadPlus instance of Maybe, with mfilter :: MonadPlus m => (a -> Bool) -> m a -> m a:
myReadListTailRecursive :: IO [String]
myReadListTailRecursive = unfoldM (liftM (mfilter (/= "") . Just) getLine)
Another, more versatile option, might be to use LoopT.
That’s really not how I would write it, but it’s clear enough what you’re doing. (By the way, if you want to be able to efficiently insert arbitrary output from any function in the chain, without using monads, you might try a Data.ByteString.Builder.)
Your first implementation is very similar to a left fold, and your second very similar to a right fold or map. (You might try actually writing them as such!) The second one has several advantages for I/O. One of the most important, for handling input and output, is that it can be interactive.
You’ll notice that the first builds the entire list from the outside in: in order to determine what the first element of the list is, the program needs to compute the entire structure to get to the innermost thunk, which is return l. The program generates the entire data structure first, then starts to process it. That’s useful when you’re reducing a list, because tail-recursive functions and strict left folds are efficient.
With the second, the outermost thunk contains the head and tail of the list, so you can grab the tail, then call the thunk to generate the second list. This can work with infinite lists, and it can produce and return partial results.
Here’s a contrived example: a program that reads in one integer per line and prints the sums so far.
main :: IO ()
main = interact( display . compute 0 . parse . lines )
where parse :: [String] -> [Int]
parse [] = []
parse (x:xs) = (read x):(parse xs)
compute :: Int -> [Int] -> [Int]
compute _ [] = []
compute accum (x:xs) = let accum' = accum + x
in accum':(compute accum' xs)
display = unlines . map show
If you run this interactively, you’ll get something like:
$ 1
1
$ 2
3
$ 3
6
$ 4
10
But you could also write compute tail-recursively, with an accumulating parameter:
main :: IO ()
main = interact( display . compute [] . parse . lines )
where parse :: [String] -> [Int]
parse = map read
compute :: [Int] -> [Int] -> [Int]
compute xs [] = reverse xs
compute [] (y:ys) = compute [y] ys
compute (x:xs) (y:ys) = compute (x+y:x:xs) ys
display = unlines . map show
This is an artificial example, but strict left folds are a common pattern. If, however, you write either compute or parse with an accumulating parameter, this is what you get when you try to run interactively, and hit EOF (control-D on Unix, control-Z on Windows) after the number 4:
$ 1
$ 2
$ 3
$ 4
1
3
6
10
This left-folded version needs to compute the entire data structure before it can read any of it. That can’t ever work on an infinite list (When would you reach the base case? How would you even reverse an infinite list if you did?) and an application that can’t respond to user input until it quits is a deal-breaker.
On the other hand, the tail-recursive version can be strict in its accumulating parameter, and will run more efficiently, especially when it’s not being consumed immediately. It doesn’t need to keep any thunks or context around other than its parameters, and it can even re-use the same stack frame. A strict accumulating function, such as Data.List.foldl', is a great choice whenver you’re reducing a list to a value, not building an eagerly-evaluated list of output. Functions such as sum, product or any can’t return any useful intermediate value. They inherently have to finish the computation first, then return the final result.
I am building some moderately large DIMACS files, however with the method used below the memory usage is rather large compared to the size of the files generated, and on some of the larger files I need to generate I run in to out of memory problems.
import Control.Monad.State.Strict
import Control.Monad.Writer.Strict
import qualified Data.ByteString.Lazy.Char8 as B
import Control.Monad
import qualified Text.Show.ByteString as BS
import Data.List
main = printDIMACS "test.cnf" test
test = do
xs <- freshs 100000
forM_ (zip xs (tail xs))
(\(x,y) -> addAll [[negate x, negate y],[x,y]])
type Var = Int
type Clause = [Var]
data DIMACSS = DS{
nextFresh :: Int,
numClauses :: Int
} deriving (Show)
type DIMACSM a = StateT DIMACSS (Writer B.ByteString) a
freshs :: Int -> DIMACSM [Var]
freshs i = do
next <- gets nextFresh
let toRet = [next..next+i-1]
modify (\s -> s{nextFresh = next+i})
return toRet
fresh :: DIMACSM Int
fresh = do
i <- gets nextFresh
modify (\s -> s{nextFresh = i+1})
return i
addAll :: [Clause] -> DIMACSM ()
addAll c = do
tell
(B.concat .
intersperse (B.pack " 0\n") .
map (B.unwords . map BS.show) $ c)
tell (B.pack " 0\n")
modify (\s -> s{numClauses = numClauses s + length c})
add h = addAll [h]
printDIMACS :: FilePath -> DIMACSM a -> IO ()
printDIMACS file f = do
writeFile file ""
appendFile file (concat ["p cnf ", show i, " ", show j, "\n"])
B.appendFile file b
where
(s,b) = runWriter (execStateT f (DS 1 0))
i = nextFresh s - 1
j = numClauses s
I would like to keep the monadic building of clauses since it is very handy, but I need to overcome the memory problem. How do I optimize the above program so that it doesn't use too much memory?
If you want good memory behavior, you need to make sure that you write out the clauses as you generate them, instead of collecting them in memory and dumping them as such, either using lazyness or a more explicit approach such as conduits, enumerators, pipes or the like.
The main obstacle to that approach is that the DIMACS format expects the number of clauses and variables in the header. This prevents the naive implementation from being sufficiently lazy. There are two possibilities:
The pragmatic one is to write the clauses first to a temporary location. After that the numbers are known, so you write them to the real file and append the contents of the temporary file.
The prettier approach is possible if the generation of clauses has no side effects (besides the effects offered by your DIMACSM monad) and is sufficiently fast: Run it twice, first throwing away the clauses and just calculating the numbers, print the header line, run the generator again; now printing the clauses.
(This is from my experience with implementing SAT-Britney, where I took the second approach, because it fitted better with other requirements in that context.)
Also, in your code, addAll is not lazy enough: The list c needs to be retained even after writing (in the MonadWriter sense) the clauses. This is another space leak. I suggest you implement add as the primitive operation and then addAll = mapM_ add.
As explained in Joachim Breitner's answer the problem was that DIMACSM was not lazy enough, both because the strict versions of the monads was used and because the number of variables and clauses are needed before the ByteString can be written to the file. The solution is to use the lazy versions of the Monads and execute them twice. It turns out that it is also necessary to have WriterT be the outer monad:
import Control.Monad.State
import Control.Monad.Writer
...
type DIMACSM a = WriterT B.ByteString (State DIMACSS) a
...
printDIMACS :: FilePath -> DIMACSM a -> IO ()
printDIMACS file f = do
writeFile file ""
appendFile file (concat ["p cnf ", show i, " ", show j, "\n"])
B.appendFile file b
where
s = execState (execWriterT f) (DS 1 0)
b = evalState (execWriterT f) (DS 1 0)
i = nextFresh s - 1
j = numClauses s
Greetings,
I'm trying to understand why I'm seeing the entire file loaded into memory with the following program, yet if you comment out the line below "(***)" then the program runs in constant (about 1.5M) space.
EDIT: The file is about 660MB, the field in column 26 is a date string like '2009-10-01', and there are one million lines. The process uses about 810MB by the time it hits the 'getLine'
Am I right in thinking it's related to the splitting of the string using 'split', and that somehow the underlying ByteString that has been read from the file can't be garbage-collected because it's still referenced? But if so, then I thought BS.copy would work around that. Any ideas how to force the computation - I can't seem to get 'seq' into the right place to have an effect.
(NB the source file is tab-separated lines)
Thanks in advance,
Kevin
module Main where
import System.IO
import qualified Data.ByteString.Lazy.Char8 as BS
import Control.Monad
type Record = BS.ByteString
importRecords :: String -> IO [Record]
importRecords filename = do
liftM (map importRecord.BS.lines) (BS.readFile filename)
importRecord :: BS.ByteString -> Record
importRecord txt = r
where
r = getField 26
getField f = BS.copy $ ((BS.split '\t' txt) !! f)
loopInput :: [Record] -> IO ()
loopInput jrs = do
putStrLn $ "Done" ++ (show $ last jrs)
hFlush stdout
x <- getLine
return ()
-- (***)
loopInput jrs
main = do
jrs <- importRecords "c:\\downloads\\lcg1m.txt"
loopInput jrs
Your call to last forces the list, jrs. To figure that out it must run through the entire file building up thunks for each entry in jrs. Because you aren't evaluating each element in jrs (except the last one) these thunks hang out with references to the bytestring, so that must stay in memory.
The solution is to force the evaluation of those thunks. Because we're talking about space the first thing I did was actually to store your info in a smaller format:
type Year = Word16
type Month = Word8
type Day = Word8
data Record = Rec {-# UNPACK #-} !Year {-# UNPACK #-} !Month {-# UNPACK #-} !Day
deriving (Eq, Ord, Show, Read)
This reduces that ugly 10 byte Bytestring (+ overhead of ~16 bytes of structure information) to around 8 bytes.
importRecord now has to call toRecord r to get the right type:
toRecord :: BS.ByteString -> Record
toRecord bs =
case BS.splitWith (== '-') bs of
(y:m:d:[]) -> Rec (rup y) (rup m) (rup d)
_ -> Rec 0 0 0
rup :: (Read a) => BS.ByteString -> a
rup = read . BS.unpack
We'll need to evalute data when we convert from ByteString to Record, so lets use the parallel package and define an NFData instance from DeepSeq.
instance NFData Record where
rnf (Rec y m d) = y `seq` m `seq` d `seq` ()
Now we're ready to go, I modified main to use evalList, thus forcing the whole list before your function that wants the last one:
main = do
jrs <- importRecords "./tabLines"
let jrs' = using jrs (evalList rdeepseq)
loopInput jrs'
And we can see the heap profile looks beautiful (and top agrees, the program uses very little memory).
Sorry about that other misleading wrong answer - I was hooked on the fact that incremental processing fixes it and didn't really realize the thunks really were hanging around, not sure why my brain glided over that. Though I do stand by the gist, you should incrementally process this information making all of this answer moot.
FYI the huge bytestring didn't show up in those previous heap profiles I posted because foreign allocations (which includes ByteString) aren't tracked by the heap profiler.
There seem to be two questions here:
why does the memory usage depend on the presence or absence of the line (***);
why is the memory usage with (***) present about 800MB, rather than, say, 40MB.
I don't really know what to say about the first one that TomMD didn't already say; inside the loopInput loop, jrs can never be freed, because it's needed as an argument to the recursive call of loopInput. (You know that return () doesn't do anything when (***) is present, right?)
As for the second question, I think you are right that the input ByteString isn't being garbage collected. The reason is that you never evaluate the elements of your list jrs besides the last one, so they still contain references to the original ByteString (even though they are of the form BS.copy ...). I would think that replacing show $ last jrs with show jrs would reduce your memory usage; does it? Alternatively, you could try a stricter map, like
map' f [] = []
map' f (x:xs) = ((:) $! (f $! x)) (map' f xs)
Replace the map in importRecords with map' and see whether that reduces your memory usage.
The following program terminates correctly:
import System.Random
randomList = mapM (\_->getStdRandom (randomR (0, 50000::Int))) [0..5000]
main = do
randomInts <- randomList
print $ take 5 randomInts
Running:
$ runhaskell test.hs
[26156,7258,29057,40002,26339]
However, feeding it with an infinite list, the program never terminates, and when compiled, eventually gives a stack overflow error!
import System.Random
randomList = mapM (\_->getStdRandom (randomR (0, 50000::Int))) [0..]
main = do
randomInts <- randomList
print $ take 5 randomInts
Running,
$ ./test
Stack space overflow: current size 8388608 bytes.
Use `+RTS -Ksize -RTS' to increase it.
I expected the program to lazily evaluate getStdRandom each time I pick an item off the list, finishing after doing so 5 times. Why is it trying to evaluate the whole list?
Thanks.
Is there a better way to get an infinite list of random numbers? I want to pass this list into a pure function.
EDIT: Some more reading revealed that the function
randomList r = do g <- getStdGen
return $ randomRs r g
is what I was looking for.
EDIT2: after reading camccann's answer, I realized that getStdGen is getting a new seed on every call. Instead, better to use this function as a simple one-shot random list generator:
import System.Random
randomList :: Random a => a -> a -> IO [a]
randomList r g = do s <- newStdGen
return $ randomRs (r,g) s
main = do r <- randomList 0 (50::Int)
print $ take 5 r
But I still don't understand why my mapM call did not terminate. Evidently not related to random numbers, but something to do with mapM maybe.
For example, I found that the following also does not terminate:
randomList = mapM (\_->return 0) [0..]
main = do
randomInts <- randomList
print $ take 50000 randomInts
What gives? By the way, IMHO, the above randomInts function should be in System.Random. It's extremely convenient to be able to very simply generate a random list in the IO monad and pass it into a pure function when needed, I don't see why this should not be in the standard library.
Random numbers in general are not strict, but monadic binding is--the problem here is that mapM has to sequence the entire list. Consider its type signature, (a -> m b) -> [a] -> m [b]; as this implies, what it does is first map the list of type [a] into a list of type [m b], then sequence that list to get a result of type m [b]. So, when you bind the result of applying mapM, e.g. by putting it on the right-hand side of <-, what this means is "map this function over the list, then execute each monadic action, and combine the results back into a single list". If the list is infinite, this of course won't terminate.
If you simply want a stream of random numbers, you need to generate the list without using a monad for each number. I'm not entirely sure why you've used the design you have, but the basic idea is this: Given a seed value, use a pseudo-random number generator to produce a pair of 1) a random number 2) a new seed, then repeat with the new seed. Any given seed will of course provide the same sequence each time. So, you can use the function getStdGen, which will provide a fresh seed in the IO monad; you can then use that seed to create an infinite sequence in completely pure code.
In fact, System.Random provides functions for precisely that purpose, randoms or randomRs instead of random and randomR.
If for some reason you want to do it yourself, what you want is essentially an unfold. The function unfoldr from Data.List has the type signature (b -> Maybe (a, b)) -> b -> [a], which is fairly self-explanatory: Given a value of type b, it applies the function to get either something of type a and a new generator value of type b, or Nothing to indicate the end of the sequence.
You want an infinite list, so will never need to return Nothing. Thus, partially applying randomR to the desired range and composing it with Just gives this:
Just . randomR (0, 50000::Int) :: (RandomGen a) => a -> Maybe (Int, a)
Feeding that into unfoldr gives this:
unfoldr (Just . randomR (0, 50000::Int)) :: (RandomGen a) => a -> [Int]
...which does exactly as it claims: Given an instance of RandomGen, it will produce an infinite (and lazy) list of random numbers generated from that seed.
I would do something more like this, letting randomRs do the work with an initial RandomGen:
#! /usr/bin/env runhaskell
import Control.Monad
import System.Random
randomList :: RandomGen g => g -> [Int]
randomList = randomRs (0, 50000)
main :: IO ()
main = do
randomInts <- liftM randomList newStdGen
print $ take 5 randomInts
As for the laziness, what's happening here is that mapM is (sequence . map)
Its type is: mapM :: (Monad m) => (a -> m b) -> [a] -> m [b]
It's mapping the function, giving a [m b] and then needs to execute all those actions to make an m [b]. It's the sequence that'll never get through the infinite list.
This is explained better in the answers to a prior question: Is Haskell's mapM not lazy?