How to impurely modify a state associated with an object? - haskell

In Haskell, I have a container like:
data Container a = Container { length :: Int, buffer :: Unboxed.Vector (Int,a) }
This container is a flattened tree. Its accessor (!) performs a binary (log(N)) search through the vector in order to find the right bucket where index is stored.
(!) :: Container a -> Int -> a
container ! index = ... binary search ...
Since consecutive accesses are likely to be in the same bucket, this could be optimized in the following way:
if `index` is on the the last accessed bucket, skip the search
The tricky point is the last accessed bucket part. In JavaScript, I'd just impurely modify a hidden variable on the container object.
function read(index,object){
var lastBucket = object.__lastBucket;
// if the last bucket contains index, no need to search
if (contains(object, lastBucket, index))
var bucket = lastBucket;
// if it doesn't
else {
// then we search the bucket
var bucket = searchBucket(index,object);
// And impurely annotate it on the container, so the
// next time we access it we could skip the search.
container.__lastBucket = bucket;
}
return object.buffer[bucket].value;
}
Since this is just an optimization and the result is the same independent of the branch taken, I believe it doesn't break referential transparency. How is it possible, in Haskell, to impurely modify an state associated with a runtime value?
~
I have thought in 2 possible solutions.
A global, mutable hashmap linking pointers to the lastBucket value, and use unsafePerformIO to write on it. But I'd need a way to get the runtime pointer of an object, or at least an unique id of some sort (how?).
Add an extra field to Container, lastBucket :: Int, and somehow impurely modify it within (!), and consider that field internal (because it obviously break referential transparency).

Using solution (1), I managed to get the following design. First, I added a __lastAccessedBucket :: IORef Int field to my datatype, as suggested by #Xicò:
data Container a = Container {
length :: Int,
buffer :: V.Vector (Int,a),
__lastAccessedBucket :: IORef Int }
Then, I had to update the functions that create a new Container in order to create a new IORef using unsafePerformIO:
fromList :: [a] -> Container a
fromList list = unsafePerformIO $ do
ref <- newIORef 0
return $ Container (L.length list) buffer ref
where buffer = V.fromList (prepare list)
Finally, I created two new functions, findBucketWithHint, a pure function which searches the bucket of an index with guess (i.e., the bucket where you think it might be), and the unsafeFindBucket function, which replaces the pure findBucket when performance is needed, by always using the last accessed bucket as the hint:
unsafeFindBucket :: Int -> Container a -> Int
unsafeFindBucket findIdx container = unsafePerformIO $ do
let lastBucketRef = __lastAccessedBucket contianer
lastBucket <- readIORef lastBucketRef
let newBucket = findBucketWithHint lastBucket findIdx container
writeIORef lastBucketRef newBucket
return $ newBucket
With this, unsafeFindBucket is technically a pure function with the same API of the original findBucket function, but is an order of magnitude faster in some benchmarks. I have no idea how safe this is and where it could cause bugs. Threads are certainly a concern.

(This is more an extended comment than an answer.)
First I'd suggest to check if this isn't a case of premature optimization. After all, O(log n) ins't that bad.
If this part is indeed performance-critical, your intention is definitely valid. The usual warning for unsafePerformIO is "use it only if you know what you're doing", which you obviously do, and it can help to make things pure and fast at the same time.
Be sure that you follow all the precautions in the docs, in particular setting the proper compiler flags (you might want to use the OPTIONS_GHC pragma).
Also make sure that the IO operation is thread safe. The easiest way to ensure that is to use IORef together with atomicModifyIORef.
The disadvantage of an internal mutable state is that the performance of the cache will deteriorate if it's accessed from multiple threads, if they lookup different elements.
One remedy would be to explicitly thread the updated state instead of using the internal mutable state. This is obviously what you want to avoid, but if your program is using monads, you could just add another monadic layer that'd internally keep the state for you and expose the lookup operation as a monadic action.
Finally, you could consider using splay trees instead of the array. You'd still have (amortized) O(log n) complexity, but their big advantage is that by design they move frequently accessed elements near the top. So if you'll be accessing a subset of elements of size k, they'll be soon moved to the top, so the lookup operations will be just O(log k) (constant for a single, repeatedly accessed element). Again, they update the structure on lookups, but you could use the same approach with unsafePerformIO and atomic updates of IORef to keep the outer interface pure.

Related

Haskell: find out how many bytes a Get expression would consume

I am writing a tool which includes a deserialization mechanism for my bachelor thesis, for which I use the Get Monad (Data.Binary.Get). I ran into the following problem:
During deserialization, there is a part where I have a getter of type Get a and I need to read a ByteString of length n, where n is the amount of bytes that would be consumed if I ran my getter at this position. In other words, I need to know how much bytes my getter would consume without consuming them.
There is a way to do this:
readBytes :: Get a -> Get ByteString
readBytes getter = do safe <- lookAhead getRemainingLazyByteString
let info = runGetOrFail getter safe
-- n_cB = number of consumed bytes
case info of Right (_, n_cB, _) -> getLazyByteString n_cB
But this is hideous beyond description. Every time this method is called, it copies the entire remainder of the file.
Even though this doesn't seem like a hard problem in theory, and so far the Get Monad has been capable of doing everything I needed, I cannot find a better solution.
I need to know how much bytes my getter would consume without
consuming them.
Perhaps you could perform two calls to the bytesRead :: Get Int64 function, the second call inside a lookAhead, after having parsed the a value. Something like
bytesRead1 <- bytesRead
bytesRead2 <- lookAhead (getter *> bytesRead)
return (bytesRead2 - bytesRead1)
I'm not sure about how bytesRead behaves inside lookAhead, however.

STM-friendly list as a change log

I need an advice on the data structure to use as an atomic change log.
I'm trying to implement the following algorithm. There is a flow of incoming
changes updating an in-memory map. In Haskell-like pseudocode it is
update :: DataSet -> SomeListOf Change -> Change -> STM (DataSet, SomeListOf Change)
update dataSet existingChanges newChange = do
...
return (dataSet, existingChanges ++ [newChange])
where DataSet is a map (currently it is the Map from the stm-containers package, https://hackage.haskell.org/package/stm-containers-0.2.10/docs/STMContainers-Map.html). The whole "update" is called from arbitrary number of threads. Some of the Change's can be rejected due to domain semantics, I use throwSTM for that to throw away the effect of the transaction. In case of successful commit the "newChange" is added to the list.
There exists separate thread which calls the following function:
flush :: STM (DataSet, SomeListOf Change) -> IO ()
this function is supposed to take the current snapshot of DataSet together with the list of changes (it has to a consistent pair) and flush it to the filesystem, i.e.
flush data = do
(dataSet, changes) <- atomically $ readTVar data_
-- write them both to FS
-- ...
atomically $ writeTVar data_ (dataSet, [])
I need an advice about the data structure to use for "SomeListOf Change". I don't want to use [Change] because it is "too ordered" and I'm afraid there will be too many conflicts, which will force the whole transaction to retry. Please correct me, if I'm wrong here.
I cannot use the Set (https://hackage.haskell.org/package/stm-containers-0.2.10/docs/STMContainers-Set.html) because I still need to preserve some order, e.g. the order of transaction commits. I could use TChan for it and it looks like a good match (exactly the order of transaction commits), but I don't know how to implement the "flush" function so that it would give the consistent view of the whole change log together with the DataSet.
The current implementation of that is here https://github.com/lolepezy/rpki-pub-server/blob/add-storage/src/RRDP/Repo.hs, in the functions applyActionsToState and rrdpSyncThread, respectively. It uses TChan and seems to do it in a wrong way.
Thank you in advance.
Update: A reasonable answer seems to be like that
type SomeListOf c = TChan [c]
update :: DataSet -> TChan [Change] -> Change -> STM DataSet
update dataSet existingChanges newChange = do
...
writeTChan changeChan $ reverse (newChange : existingChanges)
return dataSet
flush data_ = do
(dataSet, changes) <- atomically $ (,) <$> readTVar data_ <*> readTChan changeChan
-- write them both to FS
-- ...
But I'm still not sure whether it's a neat solution to pass the whole list as an element of the channel.
I'd probably just go with the list and see how far it takes performance-wise. Given that, you should consider that both, appending to the end of a list and reversing it are O(n) operations, so you should try to avoid this. Maybe you can just prepend the incoming changes like this:
update dataSet existingChanges newChange = do
-- ...
return (dataSet, newChange : existingChanges)
Also, your example for flush has the problem that reading and updating the state is not atomic at all. You must accomplish this using a single atomically call like so:
flush data = do
(dataSet, changes) <- atomically $ do
result <- readTVar data_
writeTVar data_ (dataSet, [])
return result
-- write them both to FS
-- ...
You could then just write them out in reverse order (because now changes contains the elements from newest to oldest) or reverse here once if it's important to write them out oldest to newest. If that's important I'd probably go with some data structure which allows O(1) element access like a good old vector.
When using a fixed-size vector you would obviously have to deal with the problem that it can become "full" which would mean your writers would have to wait for flush to do it's job before adding fresh changes. That's why I'd personally go for the simple list first and see if it's sufficient or where it needs to be improved.
PS: A dequeue might be a good fit for your problem as well, but going fixed size forces you to deal with the problem that your writers can potentially produce more changes than your reader can flush out. The dequeue can grow infinitely, but you your RAM probably isn't. And the vector has pretty low overhead.
I made some (very simplistic) investigation
https://github.com/lolepezy/rpki-pub-server/tree/add-storage/test/changeLog
imitating exactly the type of load I supposedly going to have. I used the same STMContainers.Map for the data set and usual list for the change log. To track the number of transaction retries, I used Debug.Trace.trace, meaning, the number of lines printed by trace. And the number of unique lines printed by trace gives me the number of committed transactions.
The result is here (https://github.com/lolepezy/rpki-pub-server/blob/add-storage/test/changeLog/numbers.txt). The first column is the number of threads, the second is the number of change sets generated in total. The third column is the number of trace calls for the case without change log and the last one is the number of trace calls with the change log.
Apparently most of the time change log adds some extra retries, but it's pretty much insignificant. So, I guess, it's fair to say that any data structure would be good enough, because most of the work is related to updating the map and most of the retries are happening because of it.

Concurrency considerations between pipes and non-pipes code

I'm in the process of wrapping a C library for some encoding in a pipes interface, but I've hit upon some design decisions that need to be made.
After the C library is set up, we hold on to an encoder context. With this, we can either encode, or change some parameters (let's call the Haskell interface to this last function tune :: Context -> Int -> IO ()). There are two parts to my question:
The encoding part is easily wrapped up in a Pipe Foo Bar IO (), but I would also like to expose tune. Since simultaneous use of the encoding context must be lock protected, I would need to take a lock at every iteration in the pipe, and protect tune with taking the same lock. But now I feel I'm forcing hidden locks on the user. Am I barking up the wrong tree here? How is this kind of situation normally resolved in the pipes ecosystem? In my case I expect the pipe that my specific code is part of to always run in its own thread, with tuning happening concurrently, but I don't want to force this point of view upon any users. Other packages in the pipes ecosystem do not seem to force their users like either.
An encoding context that is no longer used needs to be properly de-initialized. How does one, in the pipes ecosystem, ensure that such things (in this case performing som IO actions) are taken care of when the pipe is destroyed?
A concrete example would be wrapping a compression library, in which case the above can be:
The compression strength is tunable. We set up the pipe and it runs along merrily. How should one best go about allowing the compression strength setting to be changed while the pipe keeps running, assuming that concurrent access to the compression codec context must be serialized?
The compression library allocated a bunch of memory off the Haskell heap when set up, and we'll need to call some library function to clean this up when the pipe is torn down.
Thanks… this might all be obvious, but I'm quite new to the pipes ecosystem.
Edit: Reading this after posting, I'm quite sure it's the vaguest question I've ever asked here. Ugh! Sorry ;-)
Regarding (1), the general solution is to change your Pipe's type to:
Pipe (Either (Context, Int) Foo) Bar IO ()
In other words, it accepts both Foo inputs and tune requests, which it processes internally.
So let's then assume that you have two concurrent Producers corresponding to inputs and tune requests:
producer1 :: Producer Foo IO ()
producer2 :: Producer (Context, Int) IO ()
You can use pipes-concurrency to create a buffer that they both feed into, like this:
example = do
(output, input) <- spawn Unbounded
-- input :: Input (Either (Context, Int) Foo)
-- output :: Output (Either (Context, Int) Foo)
let io1 = runEffect $ producer1 >-> Pipes.Prelude.map Right >-> toOutput output
io2 = runEffect $ producer2 >-> Pipes.Prelude.map Left >-> toOutput output
as <- mapM async [io1, io2]
runEffect (fromInput >-> yourPipe >-> someConsumer)
mapM_ wait as
You can learn more about the pipes-concurrency library by reading this tutorial.
By forcing all tune requests to go through the same single-threaded Pipe you can ensure that you don't accidentally have two concurrent invocations of the tune function.
Regarding (2) there are two ways you can acquire a resource using pipes. The more sophisticated approach is to use the pipes-safe library, which provides a bracket function that you can use within a Pipe, but that is probably overkill for your purpose and only exists for acquiring and releasing multiple resources over the lifetime of a pipe. A simpler solution is just to use the following with idiom to acquire the pipe:
withEncoder :: (Pipe Foo Bar IO () -> IO r) -> IO r
withEncoder k = bracket acquire release $ \resource -> do
k (createPipeFromResource resource)
Then a user would just write:
withEncoder $ \yourPipe -> do
runEffect (someProducer >-> yourPipe >-> someConsumer)
You can optionally use the managed package, which simplifies the types a bit and makes it easier to acquire multiple resources. You can learn more about it from reading this blog post of mine.

Is it safe to reuse a conduit?

Is it safe to perform multiple actions using the same conduit value? Something like
do
let sink = sinkSocket sock
something $$ sink
somethingElse $$ sink
I recall that in the early versions of conduit there were some dirty hacks that made this unsafe. What's the current status?
(Note that sinkSocket doesn't close the socket.)
That usage is completely safe. The issue in older versions had to do with blurring the line between resumable and non-resumable components. With modern versions (I think since 0.4), the line is very clear between the two.
It might be safe to reuse sinks in the sense that the semantics for the "used" sink doesn't change. But you should be aware of another threat: space leaks.
The situation is analogous to lazy lists: you can consume a huge list lazily in a constant space, but if you process the list twice it will be kept in memory. The same thing might happen with a recursive monadic expression: if you use it once it's constant size, but if you reuse it the structure of the computation is kept in memory, resulting in space leak.
Here's an example:
import Data.Conduit
import Data.Conduit.List
import Control.Monad.Trans.Class (lift)
consumeN 0 _ = return ()
consumeN n m = do
await >>= (lift . m)
consumeN (n-1) m
main = do
let sink = consumeN 1000000 (\i -> putStrLn ("Got one: " ++ show i))
sourceList [1..9000000::Int] $$ sink
sourceList [1..22000000::Int] $$ sink
This program uses about 150M of ram on my machine, but if you remove the last line or repeat the definition of sink in both places, you get a nice constant space usage.
I agree that this is a contrived example (this was the first that came to my mind), and this is not very likely to happen with most Sinks. For example this will not happen with your sinkSocket. (Why is this contrived: because the control structure of the sink doesn't depend on the values it gets. And that is also why it can leak.) But, for example, for sources this would be much more common. (Many of the common Sources exhibit this behavior. The sourceList would be an obvious example, because it would actually keep the source list in memory. But, enumFromTo is no different, although there is no data to keep in memory, just the structure of the monadic computation.)
So, all in all, I think it's important to be aware of this.

How can one implement a forking try-catch in Haskell?

I want to write a function
forkos_try :: IO (Maybe α) -> IO (Maybe α)
which Takes a command x. x is an imperative operation which first mutates state, and then checks whether that state is messed up or not. (It does not do anything external, which would require some kind of OS-level sandboxing to revert the state.)
if x evaluates to Just y, forkos_try returns Just y.
otherwise, forkos_try rolls back state, and returns Nothing.
Internally, it should fork() into threads parent and child, with x running on child.
if x succeeds, child should keep running (returning x's result) and parent should die
otherwise, parent should keep running (returning Nothing) and child should die
Question: What's the way to write something with equivalent, or more powerful semantics than forkos_try? N.B. -- the state mutated (by x) is in an external library, and cannot be passed between threads. Hence, the semantic of which thread to keep alive is important.
Formally, "keep running" means "execute some continuation rest :: Maybe α -> IO () ". But, that continuation isn't kept anywhere explicit in code.
For my case, I think it will (for the time) work to write it in different style, using forkOS (which takes the entire computation child will run), since I can write an explicit expression for rest. But, it troubles me that I can't figure out how do this with the primitive function forkOS -- one would think it would be general enough to support any specific case (which could appear as a high-level API, like forkos_try).
EDIT -- please see the example code with explicit rest if the problem's still not clear [ http://pastebin.com/nJ1NNdda ].
p.s. I haven't written concurrency code in a while; hopefully my knowledge of POSIX fork() is correct! Thanks in advance.
Things are a lot simpler to reason about if you model state explicitly.
someStateFunc :: (s -> Maybe (a, s))
-- inside some other function
case someStateFunc initialState of
Nothing -> ... -- it failed. stick with initial state
Just (a, newState) -> ... -- it suceeded. do something with
-- the result and new state
With immutable state, "rolling back" is simple: just keep using initialState. And "not rolling back" is also simple: just use newState.
So...I'm assuming from your explanation that this "external library" performs some nontrivial IO effects that are nevertheless restricted to a few knowable and reversible operations (modify a file, an IORef, etc). There is no way to reverse some things (launch the missiles, write to stdout, etc), so I see one of two choices for you here:
clone the world, and run the action in a sandbox. If it succeeds, then go ahead and run the action in the Real World.
clone the world, and run the action in the real world. If it fails, then replace the Real World with the snapshot you took earlier.
Of course, both of these are actually the same approach: fork the world. One world runs the action, one world doesn't. If the action succeeds, then that world continues; otherwise, the other world continues. You are proposing to accomplish this by building upon forkOS, which would clone the entire state of the program, but this would not be sufficient to deal with, for example, file modifications. Allow me to suggest instead an approach that is nearer to the simplicity of immutable state:
tryIO :: IO s -> (s -> IO ()) -> IO (Maybe a) -> IO (Maybe a)
tryIO save restore action = do
initialState <- save
result <- action
case result of
Nothing -> restore initialState >> return Nothing
Just x -> return (Just x)
Here you must provide some data structure s, and a way to save to and restore from said data structure. This allows you the flexibility to perform any cloning you know to be necessary. (e.g. save could copy a certain file to a temporary location, and then restore could copy it back and delete the temporary file. Or save could copy the value of certain IORefs, and then restore could put the value back.) This approach may not be the most efficient, but it's very straightforward.

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