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Douglas M. Auclair (geophf): September 2016 1HaskellADay problems and solutions

Planet Haskell - Sun, 10/02/2016 - 8:04pm
Categories: Offsite Blogs

Jasper Van der Jeugt: Patat and Myanmar travels

Planet Haskell - Sat, 10/01/2016 - 6:00pm
Presentations in the terminal

At work, I frequently need to give (internal) presentations and demos using video conferencing. I prefer to do these quick-and-dirty presentations in the terminal for a few reasons:

  • I don’t spend time worrying about layout, terminal stuff always looks cool.
  • I want to write markdown if possible.
  • You can have a good “Questions?” slide just by running cowsay 'Questions?'
  • Seamless switching between editor/shell and presentation using tmux.

The last point is important for video conferencing especially. The software we use allows you to share a single window from your desktop. This is pretty neat if you have a multi-monitor setup. However, it does not play well with switching between a PDF viewer and a terminal.

Introducing patat

To this end, I wrote patatPresentations And The ANSI Terminal – because I was not entirely happy with the available solutions. You can get it from Hackage: cabal install patat.

patat screenshot

You run it simply by doing:

patat presentation.md

The key features are:

  • Built on Pandoc:

    The software I was using before contained some Markdown parsing bugs. By using Pandoc under the hood, this should not happen.

    Additionally, we get all the input formats Pandoc supports (Literate Haskell is of particular importance to me) and some additional elements like tables and definition lists.

  • Smart slide splitting:

    Most Markdown presentation tools seem to split slides at --- (horizontal rulers). This is a bit verbose since you usually start each slide with an h1 as well. patat will check if --- is used and if it’s not, it will split on h1s instead.

  • Live reload:

    If you run patat --watch presentation.md, patat will poll the file for changes and reload automatically. This is really handy when you are writing the presentation, I usually use it with split-pane in tmux.

An example of a presentation is:

--- title: This is my presentation author: Jane Doe ... # This is a slide Slide contents. Yay. # Important title Things I like: - Markdown - Haskell - Pandoc - Traveling How patat came to be

I started writing a simple prototype of patat during downtime at ICFP2016, when I discovered that MDP was not able to parse my presentation correctly.

After ICFP, I flew to Myanmar, and I am currently traveling around the country with my girlfriend. It’s a super interesting place to visit, with a rich history. Now that NLD is the ruling party, I think it is a great time to visit the country responsibly.

Riding around visiting temples in Bagan

However, it is a huge country – the largest in south-east Asia – so there is some downtime traveling on domestic flights, buses and boats. I thought it was a good idea to improve the tool a bit further, since you don’t need internet to hack on this sort of thing.

Pull requests are welcome as always! Note that I will be slow to respond: for the next three days I will be trekking from Kalaw to Inle Lake, so I have no connectivity (or electricity, for that matter).

Sunset at U Bein bridge

Sidenote: “Patat” is the Flemish word for “potato”. Dutch people also use it to refer to French Fries but I don’t really do that – in Belgium we just call fries “Frieten”.

Categories: Offsite Blogs

JP Moresmau: Everything is broken

Planet Haskell - Sat, 10/01/2016 - 7:09am
This week was I suppose fairly typical. Started using a new library, the excellent sqlg that provides the TinkerPop graph API on top of relational databases. Found a bug pretty quickly. Off we go to contribute to another open source project, good for my street cred I suppose. Let’s fork it, and open the source code in IDEA (Community edition). After years of hearing abuse about Eclipse, I’m now trying to use “the best IDE ever” (say all the fan boys) instead. Well, that didn’t go so well, apparently importing a Maven project and resolving the dependencies proves too much for IDEA. I fought with it for a while, then gave up.
Fired up Eclipse, it opened and built the sqlg projects without a hitch. Wrote a test, fixed the bug, raised a PR, got it accepted with a thank you, life is good.
Then I find another bug. Except that upon investigation, it’s not in sqlg, it’s in the actual TinkerPop code. The generics on a map are wrong, there are values that are not instances of the key class (thanks generics type erasure!). So I can fix by changing the method signature, or change the keys. Both will break existing code. Sigh…
Oh, and the TinkerPop project doesn’t build in Eclipse. The Eclipse compiler chokes on some Java 8 code. Off to the Eclipse bug tracker. Maybe I need to have three different Java IDEs to be able to handle all the projects I may find bugs in.

Everything isbroken. Off I go to my own code to add my own bugs.
Categories: Offsite Blogs

Well-Typed.Com: Hackage reliability via mirroring

Planet Haskell - Fri, 09/30/2016 - 9:08am
TL;DR: Hackage now has multiple secure mirrors which can be used fully automatically by clients such as cabal.

In the last several years, as a community, we’ve come to greatly rely on services like Hackage and Stackage being available 24/7. There is always enormous frustration when either of these services goes down.

I think as a community we’ve also been raising our expectations. We’re all used to services like Google which appear to be completely reliable. Of course these are developed and operated by huge teams of professionals, whereas our community services are developed, maintained and operated by comparatively tiny teams on shoestring budgets.

A path to greater reliability

Nevertheless, reliability is important to us all, and so there has been a fair bit of effort put in over the last few years to improve reliability. I’ll talk primarily about Hackage since that is what I am familiar with.

Firstly, a couple years ago Hackage and haskell.org were moved from super-cheap VM hosting (where our machines tended to go down several times a year) to actually rather good quality hosting provided by Rackspace. Thanks to Rackspace for donating that, and the haskell.org infrastructure team for getting that organised and implemented. That in itself has made a huge difference: we’ve had far fewer incidents of downtime since then.

Obviously even with good quality hosting we’re still only one step away from unscheduled downtime, because the architecture is too centralised.

There were two approaches that people proposed. One was classic mirroring: spread things out over multiple mirrors for redundancy. The other proposal was to adjust the Hackage architecture somewhat so that while the main active Hackage server runs on some host, the the core Hackage archive would be placed on an ultra-reliable 3rd party service like AWS S3, so that this would stay available even if the main server was unavailable.

The approach we decided to take was the classic mirroring one. In some ways this is the harder path, but I think ultimately it gives the best results. This approach also tied in with the new security architecture (The Update Framework – TUF) that we were implementing. The TUF design includes mirrors and works in such a way that mirrors do not need to be trusted. If we (or rather end users) do not have to trust the operators of all the mirrors then this makes a mirroring approach much more secure and much easier to deploy.

Where we are today

The new system has been in beta for some time and we’re just short of flipping the switch for end users. The new Hackage security system in place on the server side, while on the client side, the latest release of cabal-install can be configured to use it, and the development version uses it by default.

There is lots to say about the security system, but that has (1, 2, 3) and will be covered elsewhere. This post is about mirroring.

For mirrors, we currently have two official public mirrors, and a third in the works. One mirror is operated by FP Complete and the other by Herbert Valerio Riedel. For now, Herbert and I manage the list of mirrors and we will be accepting contributions of further public mirrors. It is also possible to run private mirrors.

Once you are using a release of cabal-install that uses the new system then no further configuration is required to make use of the mirrors (or indeed the security). The list of public mirrors is published by the Hackage server (along with the security metadata) and cabal-install (and other clients using hackage-security) will automatically make use of them.

Reliability in the new system

Both of the initial mirrors are individually using rather reliable hosting. One is on AWS S3 and one on DreamHost S3. Indeed the weak point in the system is no longer the hosting. It is other factors like reliability of the hosts running the agents that do the mirroring, and the ever present possibility of human error.

The fact that the mirrors are hosted and operated independently is the key to improved reliability. We want to reduce the correlation of failures.

Failures in hosting can be mitigated by using multiple providers. Even AWS S3 goes down occasionally. Failures in the machines driving the mirroring are mitigated by using a normal decentralised pull design (rather than pushing out from the centre) and hosting the mirroring agents separately. Failures due to misconfiguration and other human errors are mitigated by having different mirrors operated independently by different people.

So all these failures can and will happen, but if they are not correlated and we have enough mirrors then the system overall can be quite reliable.

There is of course still the possibility that the upstream server goes down. It is annoying not to be able to upload new packages, but it is far more important that people be able to download packages. The mirrors mean there should be no interruption in the download service, and it gives the upstream server operators the breathing space to fix things.

Categories: Offsite Blogs

Neil Mitchell: Full-time Haskell jobs in London, at Barclays

Planet Haskell - Thu, 09/29/2016 - 6:26am
Summary: I'm hiring 9 Haskell programmers. Email neil.d.mitchell AT barclays.com to apply.

I work for Barclays, in London, working on a brand new Haskell project. We're looking for nine additional Haskell programmers to come and join the team.

What we offer

A permanent job, writing Haskell, using all the tools you know and love – GHC/Cabal/Stack etc. In the first two weeks in my role I've already written parsers with attoparsec, both Haskell and HTML generators and am currently generating a binding to C with lots of Storable/Ptr stuff. Since it's a new project you will have the chance to help shape the project.

The project itself is to write a risk engine – something that lets the bank calculate the value of the trades it has made, and how things like changes in the market change their value. Risk engines are important to banks and include lots of varied tasks – talking to other systems, overall structuring, actual computation, streaming values, map/reduce.

We'll be following modern but lightweight development principles – including nightly builds, no check-ins to master which break the tests (enforced automatically) and good test coverage.

These positions have attractive salary levels.

What we require

We're looking for the best functional programmers out there, with a strong bias towards Haskell. We have a range of seniorities available to suit even the most experienced candidates. We don't have anything at the very junior end; instead we're looking for candidates that are already fluent and productive. That said, a number of very good Haskell programmers think of themselves as beginners even after many years, so if you're not sure, please get in touch.

We do not require any finance knowledge.

The role is in London, Canary Wharf, and physical presence in the office on a regular basis is required – permanent remote working is not an option.

How to apply

To apply, email neil.d.mitchell AT barclays.com with a copy of your CV. If you have any questions, email me.

The best way to assess technical ability is to look at code people have written. If you have any packages on Hackage or things on GitHub, please point me at the best projects. If your best code is not publicly available, please describe the Haskell projects you've been involved in.

Categories: Offsite Blogs

Don Stewart (dons): Haskell dev roles with Strats @ Standard Chartered

Planet Haskell - Thu, 09/29/2016 - 2:17am

The Strats team at Standard Chartered is growing. We have 10 more open roles currently, in a range of areas:

  • Haskell dev for hedging effectiveness analytics, and build hedging services.
  • Haskell devs for derivatives pricing services. Generic roles using Haskell.
  • Web-experienced Haskell devs for frontends to analytics services written in Haskell. PureScript and or data viz, user interfaces skills desirable
  • Haskell dev for trading algorithms and strategy development.
  • Dev/ops role to extend our continuous integration infrastructure (Haskell+git)
  • Contract analysis and manipulation in Haskell for trade formats (FpML + Haskell).
  • Haskell dev for low latency (< 100 microsecond) components in soft real-time non-linear pricing charges service.

You would join an existing team of 25 Haskell developers in Singapore or London. Generally our roles involve directly working with traders to automate their work and improve their efficiency. We use Haskell for all tasks. Either GHC Haskell or our own (“Mu”) implementation, and this is a rare chance to join a large, experienced Haskell dev team.

We offer permanent or contractor positions, at Director and Associate Director level, with very competitive compensation. Demonstrated experience in typed FP (Haskell, OCaml, F# etc) is required or other typed FP.

All roles require some physical presence in either Singapore or London, and we offer flexiblity with these constraints (with work from home available). No financial background is required or assumed.

More info about our development process is in the 2012 PADL keynote, and a 2013 HaskellCast interview.

If this sounds exciting to you, please send your PDF resume to me – donald.stewart <at> sc.com


Tagged: jobs
Categories: Offsite Blogs

Well-Typed.Com: Sharing, Space Leaks, and Conduit and friends

Planet Haskell - Thu, 09/29/2016 - 12:20am
TL;DR: Sharing conduit values leads to space leaks. Make sure that conduits are completely reconstructed on every call to runConduit; this implies we have to be careful not to create any (potentially large) conduit CAFs (skip to the final section “Avoiding space leaks” for some details on how to do this). Similar considerations apply to other streaming libraries and indeed any Haskell code that uses lazy data structures to drive computation. Motivation

We use large lazy data structures in Haskell all the time to drive our programs. For example, consider

main1 :: IO () main1 = forM_ [1..5] $ \_ -> mapM_ print [1 .. 1000000]

It’s quite remarkable that this works and that this program runs in constant memory. But this stands on a delicate cusp. Consider the following minor variation on the above code:

ni_mapM_ :: (a -> IO b) -> [a] -> IO () {-# NOINLINE ni_mapM_ #-} ni_mapM_ = mapM_ main2 :: IO () main2 = forM_ [1..5] $ \_ -> ni_mapM_ print [1 .. 1000000]

This program runs, but unlike main1, it has a maximum residency of 27 MB; in other words, this program suffers from a space leak. As it turns out, main1 was running in constant memory because the optimizer was able to eliminate the list altogether (due to the fold/build rewrite rule), but it is unable to do so in main2.

But why is main2 leaking? In fact, we can recover constant space behaviour by recompiling the code with -fno-full-laziness. The full laziness transformation is effectively turning main2 into

longList :: [Integer] longList = [1 .. 1000000] main3 :: IO () main3 = forM_ [1..5] $ \_ -> ni_mapM_ print longList

The first iteration of the forM_ loop constructs the list, which is then retained to be used by the next iterations. Hence, the large list is retained for the duration of the program, which is the beforementioned space leak.

The full laziness optimization is taking away our ability to control when data structures are not shared. That ability is crucial when we have actions driven by large lazy data structures. One particularly important example of such lazy structures that drive computation are conduits or pipes. For example, consider the following conduit code:

import qualified Data.Conduit as C countConduit :: Int -> C.Sink Char IO () countConduit cnt = do mi <- C.await case mi of Nothing -> liftIO (print cnt) Just _ -> countConduit $! cnt + 1 getConduit :: Int -> C.Source IO Char getConduit 0 = return () getConduit n = do ch <- liftIO getChar C.yield ch getConduit (n - 1)

Here countConduit is a sink that counts the characters it receives from upstream, and getConduit n is a conduit that reads n characters from the console and passes them downstream.

To illustrate what might go wrong, we will use the following exception handler throughout this blog post5:

retry :: IO a -> IO a retry io = do ma <- try io case ma of Right a -> return a Left (_ :: SomeException) -> retry io

The important point to notice about this exception handler is that it retains a reference to the action io as it executes that action, since it might potentially have to execute it again if an exception is thrown. However, all the space leaks we discuss in this blog post arise even when an exception is never thrown and hence the action is run only once; simply maintaining a reference to the action until the end of the program is enough to cause the space leak.

If we use this exception handler as follows:

main :: IO () main = retry $ C.runConduit $ getConduit 1000000 C.=$= countConduit 0

we again end up with a large space leak, this time of type Pipe and ->Pipe (conduit’s internal type):

Although the values that stream through the conduit come from IO, the conduit itself is fully constructed and retained in memory. In this blog post we examine what exactly is being retained here, and why. We will finish with some suggestions on how to avoid such space-leaks, although sadly there is no easy answer. Note that these problems are not specific to the conduit library, but apply equally to all other similar libraries.

We will not assume any knowledge of conduit but start from first principles; however, if you have never used any of these libraries before this blog post is probably not the best starting point; you might for example first want to watch my presentation Lazy I/O and Alternatives in Haskell.

Lists

Before we look at the more complicated case, let’s first consider another program using just lists:

main :: IO () main = retry $ ni_mapM_ print [1..1000000]

This program suffers from a space leak for similar reasons to the example with lists we saw in the introduction, but it’s worth spelling out the details here: where exactly is the list being maintained?

Recall that the IO monad is effectively a state monad over a token RealWorld state (if that doesn’t make any sense to you, you might want to read ezyang’s article Unraveling the mystery of the IO monad first). Hence, ni_mapM_ (just a wrapper around mapM_) is really a function of three arguments: the action to execute for every element of the list, the list itself, and the world token. That means that

ni_mapM_ print [1..1000000]

is a partial application, and hence we are constructing a PAP object. Such a PAP object is an runtime representation of a partial application of a function; it records the function we want to execute (ni_mapM_), as well as the arguments we have already provided. It is this PAP object that we give to retry, and which retry retains until the action completes because it might need it in the exception handler. The long list in turn is being retained because there is a reference from the PAP object to the list (as one of the arguments that we provided).

Full laziness does not make a difference in this example; whether or not that [1 .. 10000000] expression gets floated out makes no difference.

Reminder: Conduits/Pipes

Just to make sure we don’t get lost in the details, let’s define a simple conduit-like or pipe-like data structure:

data Pipe i o m r = Yield o (Pipe i o m r) | Await (Either r i -> Pipe i o m r) | Effect (m (Pipe i o m r)) | Done r

A pipe or a conduit is a free monad which provides three actions:

  1. Yield a value downstream
  2. Await a value from upstream
  3. Execute an effect in the underlying monad.

The argument to Await is passed an Either; we give it a Left value if upstream terminated, or a Right value if upstream yielded a value.1

This definition is not quite the same as the one used in real streaming libraries and ignores various difficulties (in particular exception safely, as well as other features such as leftovers); however, it will suffice for the sake of this blog post. We will use the terms “conduit” and “pipe” interchangeably in the remainder of this article.

Sources

The various Pipe constructors differ in their memory behaviour and the kinds of space leaks that they can create. We therefore consider them one by one. We will start with sources, because their memory behaviour is relatively straightforward.

A source is a pipe that only ever yields values downstream.2 For example, here is a source that yields the values [n, n-1 .. 1]:

yieldFrom :: Int -> Pipe i Int m () yieldFrom 0 = Done () yieldFrom n = Yield n $ yieldFrom (n - 1)

We could “run” such a pipe as follows:

printYields :: Show o => Pipe i o m () -> IO () printYields (Yield o k) = print o >> printYields k printYields (Done ()) = return ()

If we then run the following program:

main :: IO () main = retry $ printYields (yieldFrom 1000000)

we get a space leak. This space leak is very similar to the space leak we discussed in section Lists above, with Done () playing the role of the empty list and Yield playing the role of (:). As in the list example, this program has a space leak independent of full laziness.

Sinks

A sink is a conduit that only ever awaits values from upstream; it never yields anything downstream.2 The memory behaviour of sinks is considerably more subtle than the memory behaviour of sources and we will examine it in detail. As a reminder, the constructor for Await is

data Pipe i o m r = Await (Either r i -> Pipe i o m r) | ...

As an example of a sink, consider this pipe that counts the number of characters it receives:

countChars :: Int -> Pipe Char o m Int countChars cnt = Await $ \mi -> case mi of Left _ -> Done cnt Right _ -> countChars $! cnt + 1

We could “run” such a sink by feeding it a bunch of characters; say, 1000000 of them:

feed :: Char -> Pipe Char o m Int -> IO () feed ch = feedFrom 10000000 where feedFrom :: Int -> Pipe Char o m Int -> IO () feedFrom _ (Done r) = print r feedFrom 0 (Await k) = feedFrom 0 $ k (Left 0) feedFrom n (Await k) = feedFrom (n-1) $ k (Right ch)

If we run this as follows and compile with optimizations enabled, we once again end up with a space leak:

main :: IO () main = retry $ feed 'A' (countChars 0)

We can recover constant space behaviour by disabling full laziness; however, the effect of full laziness on this example is a lot more subtle than the example we described in the introduction.

Full laziness

Let’s take a brief moment to describe what full laziness is, exactly. Full laziness is one of the optimizations that ghc applies by default when optimizations are enabled; it is described in the paper “Let-floating: moving bindings to give faster programs”. The idea is simple; if we have something like

f = \x y -> let e = .. -- expensive computation involving x but not y in ..

full laziness floats the let binding out over the lambda to get

f = \x = let e = .. in \y -> ..

This potentially avoids unnecessarily recomputing e for different values of y. Full laziness is a useful transformation; for example, it turns something like

f x y = .. where go = .. -- some local function

into

f x y = .. f_go .. = ..

which avoids allocating a function closure every time f is called. It is also quite a notorious optimization, because it can create unexpected CAFs (constant applicative forms; top-level definitions of values); for example, if you write

nthPrime :: Int -> Int nthPrime n = allPrimes !! n where allPrimes :: [Int] allPrimes = ..

you might expect nthPrime to recompute allPrimes every time it is invoked; but full laziness might move that allPrimes definition to the top-level, resulting in a large space leak (the full list of primes would be retained for the lifetime of the program). This goes back to the point we made in the introduction: full laziness is taking away our ability to control when values are not shared.

Full laziness versus sinks

Back to the sink example. What exactly is full laziness doing here? Is it constructing a CAF we weren’t expecting? Actually, no; it’s more subtle than that. Our definition of countChars was

countChars :: Int -> Pipe Char o m Int countChars cnt = Await $ \mi -> case mi of Left _ -> Done cnt Right _ -> countChars $! cnt + 1

Full laziness is turning this into something more akin to

countChars' :: Int -> Pipe Char o m Int countChars' cnt = let k = countChars' $! cnt + 1 in Await $ \mi -> case mi of Left _ -> Done cnt Right _ -> k

Note how the computation of countChars' $! cnt + 1 has been floated over the lambda; ghc can do that, since this expression does not depend on mi. So in memory the countChars 0 expression from our main function (retained, if you recall, because of the surrounding retry wrapper), develops something like this. It starts of as a simple thunk:

Then when feed matches on it, it gets reduced to weak head normal form, exposing the top-most Await constructor:

The body of the await is a function closure pointing to the function inside countChars (\mi -> case mi ..), which has countChars $! (cnt + 1) as an unevaluated thunk in its environment. Evaluating it one step further yields

So where for a source the data structure in memory was a straightforward “list” consisting of Yield nodes, for a sink the situation is more subtle: we build up a chain of Await constructors, each of which points to a function closure which in its environment has a reference to the next Await constructor. This wouldn’t matter of course if the garbage collector could clean up after us; but if the conduit itself is shared, then this results in a space leak.

Without full laziness, incidentally, evaluating countChars 0 yields

and the chain stops there; the only thing in the function closure now is cnt. Since we don’t allocate the next Yield constructor before running the function, we never construct a chain of Yield constructors and hence we have no space leak.

Depending on values

It is tempting to think that if the conduit varies its behaviour depending on the values it receives from upstream the same chain of Await constructors cannot be constructed and we avoid a space leak. For example, consider this variation on countChars which only counts spaces:

countSpaces :: Int -> Pipe Char o m Int countSpaces cnt = Await $ \mi -> case mi of Left _ -> Done cnt Right ' ' -> countSpaces $! cnt + 1 Right _ -> countSpaces $! cnt

If we substitute this conduit for countChars in the previous program, do we fare any better? Alas, the memory behaviour of this conduit, when shared, is in fact far, far worse.

The reason is that both the countSpaces $! cnt + 1 and the expression countSpaces $! cnt can both be floated out by the full laziness optimization. Hence, now every Await constructor will have a function closure in its payload with two thunks, one for each alternative way to execute the conduit. What’s more, both of these thunks will are retained as long as we retain a reference to the top-level conduit.

We can neatly illustrate this using the following program:

main :: IO () main = do let count = countSpaces 0 feed ' ' count feed ' ' count feed ' ' count feed 'A' count feed 'A' count feed 'A' count

The first feed ' ' explores a path through the conduit where every character is a space; so this constructs (and retains) one long chain of Await constructors. The next two calls to feed ' ' however walk over the exact same path, and hence memory usage does not increase for a while. But then we explore a different path, in which every character is a non-space, and hence memory behaviour will go up again. Then during the second call to feed 'A' memory usage is stable again, until we start executing the last feed 'A', at which point the garbage collector can finally start cleaning things up:

What’s worse, there is an infinite number of paths through this conduit. Every different combination of space and non-space characters will explore a different path, leading to combinatorial explosion and terrifying memory usage.

Effects

The precise situation for effects depends on the underlying monad, but let’s explore one common case: IO. As we will see, for the case of IO the memory behaviour of Effect is actually similar to the memory behaviour of Await. Recall that the Effect constructor is defined as

data Pipe i o m r = Effect (m (Pipe i o m r)) | ...

Consider this simple pipe that prints the numbers [n, n-1 .. 1]:

printFrom :: Int -> Pipe i o IO () printFrom 0 = Done () printFrom n = Effect $ print n >> return (printFrom (n - 1))

We might run such a pipe using3:

runPipe :: Show r => Pipe i o IO r -> IO () runPipe (Done r) = print r runPipe (Effect k) = runPipe =<< k

In order to understand the memory behaviour of Effect, we need to understand how the underlying monad behaves. For the case of IO, IO actions are state transformers over a token RealWorld state. This means that the Effect constructor actually looks rather similar to the Await constructor. Both have a function as payload; Await a function that receives an upstream value, and Effect a function that receives a RealWorld token. To illustrate what printFrom might look like with full laziness, we can rewrite it as

printFrom :: Int -> Pipe i o IO () printFrom n = let k = printFrom (n - 1) in case n of 0 -> Done () _ -> Effect $ IO $ \st -> unIO (print n >> return k) st

If we visualize the heap (using ghc-vis), we can see that it does indeed look very similar to the picture for Await:

Increasing sharing

If we cannot guarantee that our conduits are not shared, then perhaps we should try to increase sharing instead. If we can avoid allocating these chains of pipes, but instead have pipes refer back to themselves, perhaps we can avoid these space leaks.

In theory, this is possible. For example, when using the conduit library, we could try to take advantage of monad transformers and rewrite our feed source and our count sink as:

feed :: Source IO Char feed = evalStateC 1000000 go where go :: Source (StateT Int IO) Char go = do st <- get if st == 0 then return () else do put $! (st - 1) ; yield 'A' ; go count :: Sink Char IO Int count = evalStateC 0 go where go :: Sink Char (StateT Int IO) Int go = do mi <- await case mi of Nothing -> get Just _ -> modify' (+1) >> go

In both definitions go refers back to itself directly, with no arguments; hence, it ought to be self-referential, without any long chain of sources or sinks ever being constructed. This works; the following program runs in constant space:

main :: IO () main = retry $ print =<< (feed $$ count)

However, this kind of code is extremely brittle. For example, consider the following minor variation on count:

count :: Sink Char IO Int count = evalStateC 0 go where go :: Sink Char (StateT Int IO) Int go = withValue $ \_ -> modify' (+1) >> go withValue :: (i -> Sink i (StateT Int IO) Int) -> Sink i (StateT Int IO) Int withValue k = do mch <- await case mch of Nothing -> get Just ch -> k ch

This seems like a straight-forward variation, but this code in fact suffers from a space leak again4. The optimized core version of this variation of count looks something like this:

count :: ConduitM Char Void (StateT Int IO) Int count = ConduitM $ \k -> let countRec = modify' (+ 1) >> count in unConduitM await $ \mch -> case mch of Nothing -> unConduitM get k Just _ -> unConduitM countRec k

In the conduit library, ConduitM is a codensity transformation of an internal Pipe datatype; the latter corresponds more or less to the Pipe datastructure we’ve been describing here. But we can ignore these details: the important point here is that this has the same typical shape that we’ve been studying above, with an allocation inside a lambda but before an await.

We can fix it by writing our code as

count :: Sink Char IO Int count = evalStateC 0 go where go :: Sink Char (StateT Int IO) Int go = withValue goWithValue goWithValue :: Char -> Sink Char (StateT Int IO) Int goWithValue _ = modify' (+1) >> go withValue :: (i -> Sink i (StateT Int IO) Int) -> Sink i (StateT Int IO) Int withValue k = do mch <- await case mch of Nothing -> get Just ch -> k ch

Ironically, it would seem that full laziness here could have helped us by floating out that modify' (+1) >> go expression for us. The reason that it didn’t is probably related to the exact way the k continuation is threaded through in the compiled code (I simplified a bit above). Whatever the reason, tracking down problems like these is difficult and incredibly time consuming; I’ve spent many many hours studying the output of -ddump-simpl and comparing before and after pictures. Not a particularly productive way to spend my time, and this kind of low-level thinking is not what I want to do when writing application level Haskell code!

Composed pipes

Normally we construct pipes by composing components together. Composition of pipes can be defined as

(=$=) :: Monad m => Pipe a b m r -> Pipe b c m r -> Pipe a c m r {-# NOINLINE (=$=) #-} _ =$= Done r = Done r u =$= Effect d = Effect $ (u =$=) <$> d u =$= Yield o d = Yield o (u =$= d) Yield o u =$= Await d = u =$= d (Right o) Await u =$= Await d = Await $ \ma -> u ma =$= Await d Effect u =$= Await d = Effect $ (=$= Await d) <$> u Done r =$= Await d = Done r =$= d (Left r)

The downstream pipe “is in charge”; the upstream pipe only plays a role when downstream awaits. This mirrors Haskell’s lazy “demand-driven” evaluation model.

Typically we only run self-contained pipes that don’t have any Awaits or Yields left (after composition), so we are only left with Effects. The good news is that if the pipe components don’t consist of long chains, then their composition won’t either; at every Effect point we wait for either upstream or downstream to complete its effect; only once that is done do we receive the next part of the pipeline and hence no chains can be constructed.

On the other hand, of course composition doesn’t get rid of these space leaks either. As an example, we can define a pipe equivalent to the getConduit from the introduction

getN :: Int -> Pipe i Char IO Int getN 0 = Done 0 getN n = Effect $ do ch <- getChar return $ Yield ch (getN (n - 1))

and then compose getN and countChars to get a runnable program:

main :: IO () main = retry $ runPipe $ getN 1000000 =$= countChars 0

This program suffers from the same space leaks as before because the individual pipelines component are kept in memory. As in the sink example, memory behaviour would be much worse still if there was different paths through the conduit network.

Summary

At Well-Typed we’ve been developing an application for a client to do streaming data processing. We’ve been using the conduit library to do this, with great success. However, occassionally space leaks arise that difficult to fix, and even harder to track down; of course, we’re not the first to suffer from these problems; for example, see ghc ticket #9520 or issue #6 for the streaming library (a library similar to conduit).

In this blog post we described how such space leaks arise. Similar space leaks can arise with any kind of code that uses large lazy data structures to drive computation, including other streaming libraries such as pipes or streaming, but the problem is not restricted to streaming libraries.

The conduit library tries to avoid these intermediate data structures by means of fusion rules; naturally, when this is successful the problem is avoided. We can increase the likelihood of this happening by using combinators such as folds etc., but in general the intermediate pipe data structures are difficult to avoid.

The core of the problem is that in the presence of the full laziness optimization we have no control over when values are not shared. While it is possible in theory to write code in such a way that the lazy data structures are self-referential and hence keeping them in memory does not cause a space leak, in practice the resulting code is too brittle and writing code like this is just too difficult. Just to provide one more example, in our application we had some code that looked like this:

go x@(C y _) = case y of Constr1 -> doSomethingWith x >> go Constr2 -> doSomethingWith x >> go Constr3 -> doSomethingWith x >> go Constr4 -> doSomethingWith x >> go Constr5 -> doSomethingWith x >> go

This worked and ran in constant space. But after adding a single additional clause to this pattern match, suddenly we reintroduced a space leak again:

go x@(C y _) = case y of Constr1 -> doSomethingWith x >> go Constr2 -> doSomethingWith x >> go Constr3 -> doSomethingWith x >> go Constr4 -> doSomethingWith x >> go Constr5 -> doSomethingWith x >> go Constr6 -> doSomethingWith x >> go

This was true even when that additional clause was never used; it had nothing to do with the change in the runtime behaviour of the code. Instead, when we added the additional clause some limit got exceeded in ghc’s bowels and suddenly something got allocated that wasn’t getting allocated before.

Full laziness can be disabled using -fno-full-laziness, but sadly this throws out the baby with the bathwater. In many cases, full laziness is a useful optimization. In particular, there is probably never any point allocation a thunk for something that is entirely static. We saw one such example above; it’s unexpected that when we write

go = withValue $ \_ -> modify' (+1) >> go

we get memory allocations corresponding to the modify' (+1) >> go expression.

Avoiding space leaks

So how do we avoid these space leaks? The key idea is pretty simple: we have to make sure the conduit is fully reconstructed on every call to runConduit. Conduit code typically looks like

runMyConduit :: Some -> Args -> IO r runMyConduit some args = runConduit $ stage1 some =$= stage2 args ... =$= stageN

You should put all top-level calls to runConduit into a module of their own, and disable full laziness in that module by declaring

{-# OPTIONS_GHC -fno-full-laziness #-}

at the top of the file. This means the computation of the conduit (stage1 =$= stage2 .. =$= stageN) won’t get floated to the top and the conduit will be recomputed on every invocation of runMyConduit (note that this relies on runMyConduit to have some arguments; if it doesn’t, you should add a dummy one).

This might not be enough, however. In the example above, stageN is still a CAF, and the evalation of the conduit stage1 =$= ... =$= stageN will cause that CAF to be evaluated and potentially retained in memory. CAFs are fine for conduits that are guaranteed to be small, or that loop back onto themselves; however, as discussed in section “Increasing sharing”, writing such conduit values is not an easy task, although it is manageable for simple conduits.

To avoid CAFs, conduis like stageN must be given a dummy argument and full laziness must be disabled for the module where stageN is defined. But it’s more subtle than that; even if a conduit does have real (non-dummy) arguments, part of that conduit might still be independent of those arguments and hence be floated to the top by the full laziness optimization, creating yet more unwanted CAF values. Full laziness must again be disabled to stop this from happening.

If you are sure that full laziness cannot float anything harmful to the top, you can leave it enabled; however, verifying that this is the case is highly non-trivial. You can of course test the code, but if you are unlucky the memory leak will only arise under certain specific usage conditions. Moreover, a small modification to the codebase, the libraries it uses, or even the compiler, perhaps years down the line, might change the program and reintroduce a memory leak.

Proceed with caution.

Further reading Addendum 1: ghc’s “state hack”

Let’s go back to the section about sinks; if you recall, we considered this example:

countChars :: Int -> Pipe Char o m Int countChars cnt = let k = countChars $! cnt + 1 in Await $ \mi -> case mi of Left _ -> Done cnt Right _ -> k feedFrom :: Int -> Pipe Char o m Int -> IO () feedFrom n (Done r) = print r feedFrom 0 (Await k) = feedFrom 0 $ k (Left 0) feedFrom n (Await k) = feedFrom (n - 1) $ k (Right 'A') main :: IO () main = retry $ feedFrom 10000000 (countChars 0)

We explained how countChars 0 results in a chain of Await constructors and function closures. However, you might be wondering, why would this be retained at all? After all, feedFrom is just an ordinary function, albeit one that computes an IO action. Why shouldn’t the whole expression

feedFrom 10000000 (countChars 0)

just be reduced to a single print 10000000 action, leaving no trace of the pipe at all? Indeed, this is precisely what happens when we disable ghc’s “state hack”; if we compile this program with -fno-state-hack it runs in constant space.

So what is the state hack? You can think of it as the opposite of the full laziness transformation; where full laziness transforms

\x -> \y -> let e = <expensive> in .. ~~> \x -> let e = <expensive> in \y -> ..

the state hack does the opposite

\x -> let e = <expensive> in \y -> .. ~~> \x -> \y -> let e = <expensive> in ..

though only for arguments y of type State# <token>. In general this is not sound, of course, as it might duplicate work; hence, the name “state hack”. Joachim Breitner’s StackOverflow answer explains why this optimization is necessary; my own blog post Understanding the RealWorld provides more background.

Let’s leave aside the question of why this optimization exists, and consider the effect on the code above. If you ask ghc to dump the optimized core (-ddump-stg), and translate the result back to readable Haskell, you will realize that it boils down to a single line change. With the state hack disabled the last line of feedFrom is effectively:

feedFrom n (Await k) = IO $ unIO (feedFrom (n - 1) (k (Right 'A')))

where IO and unIO just wrap and unwrap the IO monad. But when the state hack is enabled (the default), this turns into

feedFrom n (Await k) = IO $ \w -> unIO (feedFrom (n - 1) (k (Right 'A'))) w

Note how this floats the recursive call to feedFrom into the lambda. This means that

feedFrom 10000000 (countChars 0)

no longer reduces to a single print statement (after an expensive computation); instead, it reduces immediately to a function closure, waiting for its world argument. It’s this function closure that retains the Await/function chain and hence causes the space leak.

Addendum 2: Interaction with cost-centres (SCC)

A final cautionary tale. Suppose we are studying a space leak, and so we are compiling our code with profiling enabled. At some point we add some cost centres, or use -fprof-auto perhaps, and suddenly find that the space leak disappeared! What gives?

Consider one last time the sink example. We can make the space leak disappear by adding a single cost centre:

feed :: Char -> Pipe Char o m Int -> IO () feed ch = feedFrom 10000000 where feedFrom :: Int -> Pipe Char o m Int -> IO () feedFrom n p = {-# SCC "feedFrom" #-} case (n, p) of (_, Done r) -> print r (0, Await k) -> feedFrom 0 $ k (Left 0) (_, Await k) -> feedFrom (n-1) $ k (Right ch)

Adding this cost centre effectively has the same result as specifying -fno-state-hack; with the cost centre present, the state hack can no longer float the computations into the lambda.

Footnotes
  1. The ability to detect upstream termination is one of the characteristics that sets conduit apart from the pipes package, in which this is impossible (or at least hard to do). Personally, I consider this an essential feature. Note that the definition of Pipe in conduit takes an additional type argument to avoid insisting that the type of the upstream return value matches the type of the downstream return value. For simplicity I’ve omitted this additional type argument here.

  2. Sinks and sources can also execute effects, of course; since we are interested in the memory behaviour of the indvidual constructors, we treat effects separately.

  3. runPipe is (close to) the actual runPipe we would normally use; we connect pipes that await or yield into a single self contained pipe that does neither.

  4. For these simple examples actually the optimizer can work its magic and the space leak doesn’t appear, unless evalStateC is declared NOINLINE. Again, for larger examples problems arise whether it’s inlined or not.

  5. The original definition of retry used in this blogpost was

    retry io = catch io (\(_ :: SomeException) -> retry io)

    but as Eric Mertens rightly points out, this is not correct as catch runs the exception handler with exceptions masked. For the purposes of this blog post however the difference is not important; in fact, none of the examples in this blog post run the exception handler at all.

Categories: Offsite Blogs

Michael Snoyman: Respect

Planet Haskell - Wed, 09/28/2016 - 6:00pm

As I'm sure many people in the Haskell community have seen, Simon PJ put out an email entitled "Respect". If you haven't read it yet, I think you should. As is usually the case, Simon shows by example what we should strive for.

I put out a Tweet referring to a Gist I wrote two weeks back. At the time, I did not put the content on this blog, as I didn't want to make a bad situation worse. However, especially given Simon's comments, now seems like a good time to put out this message in the same medium (this blog) that the original inflammatory messaging came out in:

A few weeks back I wrote a blog post (and a second clarifying post) on what I called the Evil Cabal. There is no sense in repeating the content here, or even referencing it. The title is the main point.

It was a mistake, and an offensive one, to use insulting terms like evil in that blog post. What I said is true: I have taken to using that term when discussing privately some of the situation that has occured. I now see that that was the original problem: while the term started as a joke and a pun, it set up a bad precedent for conversation. I should not have used it privately, and definitely should not have publicized it.

To those active members in projects I maligned, I apologize. I should not have brought the discourse to that level.

Categories: Offsite Blogs

FP Complete: Updated Hackage mirroring

Planet Haskell - Tue, 09/27/2016 - 6:00am

As we've discussed on this blog before, FP Complete has been running a Hackage mirror for quite a few years now. In addition to a straight S3-based mirror of raw Hackage content, we've also been running some Git repos providing the same content in an arguably more accessible format (all-cabal-files, all-cabal-hashes, and all-cabal-metadata).

In the past, we did all of this mirroring using Travis, but had to stop doing so a few months back. Also, a recent revelation showed that the downloads we were making were not as secure as I'd previously believed (due to lack of SSL between the Hackage server and its CDN). Finally, there's been off-and-on discussion for a while about unifying on one Hackage mirroring tool. After some discussion among Duncan, Herbert, and myself, all of these goals ended up culminating in this mailing list post

This blog post details the end result of these efforts: where code is running, where it's running, how secret credentials are handled, and how we monitor the whole thing.

Code

One of the goals here was to use the new hackage-security mechanism in Hackage to validate the package tarballs and cabal file index downloaded from Hackage. This made it natural to rely on Herbert's hackage-mirror-tool code, which supports downloads, verification, and uploading to S3. There were a few minor hiccups getting things set up, but overall it was surprisingly easy to integrate, especially given that Herbert's code had previously never been used against Amazon S3 (it had been used against the Dreamhost mirror).

I made a few downstream modifications to the codebase to make it compatible with officially released versions of Cabal, Stackify it, and in the process generate Docker images. I also included a simple shell script for running the tool in a loop (based on Herbert's README instructions). The result is the snoyberg/hackage-mirror-tool Docker image.

After running this image (we'll get to how it's run later), we have a fully populated S3 mirror of Hackage guaranteeing a consistent view of Hackage (i.e., all package tarballs are available, without CDN caching issues in place). The next step is to use this mirror to populated the Git repositories. We already have all-cabal-hashes-tool and all-cabal-metadata-tool for updating the appropriate repos, and all-cabal-files is just a matter of running a tar xf on the tarball containing .cabal files. Putting all of this together, I set up the all-cabal-tool repo, containing:

  • run-inner.sh will:
    • Grab the 01-index.tar.gz file from the S3 mirror
    • Update the all-cabal-files repo
    • Use git archive in that repo to generate and update the 00-index.tar.gz file*
    • Update the all-cabal-hashes and all-cabal-metadata repos using the appropriate tools
  • run.sh uses the hackage-watcher to run run-inner.sh each time a new version of 01-index.tar.gz is available. It's able to do a simple ETag check, saving on bandwidth, disk IO, and CPU usage.
  • Dockerfile pulls in all of the relevant tools and provides a commercialhaskell/all-cabal-tool Docker image
  • You may notice some other code in that repo. I did have intention of rewriting the Bash scripts and other Haskell code into a single Haskell executable for simplicity, but didn't get around to it yet. If anyone's interested in taking up the mantle on that, let me know.

* About this 00/01 business: 00-index.tar.gz is the original package format, without hackage-security, and is used by previous cabal-install releases, as well as Stack and possibly some other tools too. hackage-mirror-tool does not mirror this file since it has no security information, so generating it from the known-secure 01-index.tar.gz file (via the all-cabal-files repo) seemed the best option.

In setting up these images, I decided to split them into two pieces instead of combining them so that the straight Hackage mirroring bits would remain unaffected by the rest of the code, since the Hackage mirror (as we'll see later) will be available for users outside of the all-cabal* set of repos.

At the end of this, you can see that we're no longer using the original hackage-mirror code that powered the FP Complete S3 mirror for years. Unification achieved!

Kubernetes

As I mentioned, we previously ran all of this mirroring code on Travis, but had to move off of it. Anyone who's worked with me knows that I hate being a system administrator, so it was a painful few months where I had to run this code myself on an EC2 machine I set up personally. Fortunately, FP Complete runs a Kubernetes cluster these days, and that means I don't need to be a system administrator :). As mentioned, I packaged up all of the code above in two Docker images, so running them on Kubernetes is very straightforward.

For the curious, I've put the Kubernetes deployment configurations in a Gist.

Credentials

We have a few different credentials that need to be shared with these Docker containers:

  • AWS credentials for uploading
  • GPG key for signing tags
  • SSH key for pushing to Github

One of the other nice things about Kubernetes (besides allowing me to not be a sysadmin) is that it has built-in secrets support. I obviously won't be sharing those files with you, but if you look at the deployment configs I shared before, you can see how they are being referenced.

Monitoring

One annoyance I've had in the past is, if there's a bug in the scripts or some system problem, mirroring will stop for many hours before I become aware of it. I was determined to not let that be a problem again. So I put together the Hackage Mirror status page. It compares the last upload date from Hackage itself against the last modified time on various S3 artifacts, as well as the last commit for the Git repos. If any of the mirrors fall more than an hour behind Hackage itself, it returns a 500 status code. That's not technically the right code to use, but it does mean that normal HTTP monitoring/alerting tools can be used to watch that page and tell me if anything has gone wrong.

If you're curious to see the code powering this, it's available on Github.

Official Hackage mirror

With the addition of the new hackage-security metadata files to our S3 mirror, one nice benefit is that the FP Complete mirror is now an official Hackage mirror, and can be used natively by cabal-install without having to modify any configuration files. Hopefully this will be useful to end users.

And strangely enough, just as I finished this blog post, I got my first "mirrors out of sync" 500 error message ever, proving that the monitoring itself works (even if the mirroring had a bug).

What's next?

Hopefully nothing! I've spent quite a bit more time on this in the past few weeks than I'd hoped, but I'm happy with the end result. I feel confident that the mirroring processes will run reliably, I understand and trust the security model from end to end, and there's less code and machines to maintain overall.

Thank you!

Many thanks to Duncan and Herbert for granting me access to the private Hackage server to work around CDN caching issues, and to Herbert for the help and quick fixes with hackage-mirror-tool.

Categories: Offsite Blogs

Ken T Takusagawa: [rotqywrk] foldl foldr

Planet Haskell - Mon, 09/26/2016 - 5:03pm

foldl: (x * y) * z

foldr: x * (y * z)

Also a nice reference: https://wiki.haskell.org/Foldr_Foldl_Foldl'

Categories: Offsite Blogs

Functional Jobs: Senior Backend Engineer at Euclid Analytics (Full-time)

Planet Haskell - Mon, 09/26/2016 - 3:53pm

We are looking to add a senior individual contributor to the backend engineering team! Our team is responsible for creating and maintaining the infrastructure that powers the Euclid Analytics Engine. We leverage a forward thinking and progressive stack built in Scala and Python, with an infrastructure that uses Mesos, Spark and Kafka. As a senior engineer you will build out our next generation ETL pipeline. You will need to use and build tools to interact with our massive data set in as close to real time as possible. If you have previous experience with functional programming and distributed data processing tools such as Spark and Hadoop, then you would make a great fit for this role!

RESPONSIBILITIES:

  • Partnering with the data science team to architect and build Euclid’s big data pipeline
  • Building tools and services to maintain a robust, scalable data service layer
  • Leverage technologies such as Spark and Kafka to grow our predictive analytics and machine learning capabilities in real time
  • Finding innovative solutions to performance issues and bottlenecks

REQUIREMENTS:

  • At least 3 years industry experience in a full time role utilizing Scala or other modern functional programming languages (Haskell, Clojure, Lisp, etc.)
  • Database management experience (MySQL, Redis, Cassandra, Redshift, MemSQL)
  • Experience with big data infrastructure including Spark, Mesos, Scalding and Hadoop
  • Excited about data flow and orchestration with tools like Kafka and Spark Streaming
  • Have experience building production deployments using Amazon Web Services or Heroku’s Cloud Application Platform
  • B.S. or equivalent in Computer Science or another technical field

Get information on how to apply for this position.

Categories: Offsite Blogs

Managed Languages & Runtimes Week '16 - Call forParticipation

General haskell list - Wed, 07/27/2016 - 9:57am
Managed Languages & Runtimes Week '16 PPPJ '16 / JTRES '16 / VMM '16 August 29 - September 2, 2016 Lugano, Switzerland http://manlang16.inf.usi.ch ------------------------------------------------------------------------------- Managed Languages & Runtimes Week '16 is a premier forum for presenting and discussing innovations and breakthroughs in the area of programming languages and runtime systems, which form the basis of many modern computing systems, from small scale (embedded and real-time systems) to large-scale (cloud-computing and big-data platforms). Managed Languages & Runtimes Week '16 features three international academic and industry venues for the first time: - PPPJ '16 - 13th International Conference on Principles and Practices of Programming on the Java Platform: virtual machines, languages, and tools - A forum for researchers, practitioners, and educators to present and discuss novel results on all aspects of managed languages and their runtime systems, including virtual ma
Categories: Incoming News

Forcing the kind in data

haskell-cafe - Tue, 07/26/2016 - 11:36am
Hi, if I have: data Foobar a b = Foobar it has kind: * -> * -> * How can I force the kind to: (* -> *) -> * -> * ? Thank you! _______________________________________________ Haskell-Cafe mailing list To (un)subscribe, modify options or view archives go to: http://mail.haskell.org/cgi-bin/mailman/listinfo/haskell-cafe Only members subscribed via the mailman list are allowed to post.
Categories: Offsite Discussion

ETAPS 2017 1st call for papers

General haskell list - Tue, 07/26/2016 - 9:04am
****************************************************************** JOINT CALL FOR PAPERS 20th European Joint Conferences on Theory And Practice of Software ETAPS 2017 Uppsala, Sweden, 22-29 April 2017 http://www.etaps.org/2017 ******************************************************************
Categories: Incoming News

Use GHC API in standalone executable?

haskell-cafe - Mon, 07/25/2016 - 10:03pm
I guess this depends on what you want to do. I've seen various level of GHC API usage in standalone applications, but it just depends on how much of the compiler pipeline you want to use. Their are ways to override the paths or provide stubs for programs ghc expects to be there. I've packaged API calls into shared libraries myself before, in that case I was mostly accessing the compiler frontend. _______________________________________________ Haskell-Cafe mailing list To (un)subscribe, modify options or view archives go to: http://mail.haskell.org/cgi-bin/mailman/listinfo/haskell-cafe Only members subscribed via the mailman list are allowed to post.
Categories: Offsite Discussion

Is there any way in GHC plugin to refer exposed butdefined in a hidden module?

haskell-cafe - Mon, 07/25/2016 - 4:24pm
Hi Cafe, I'm currently writing a simple GHC Typechecker plugin to augment typelevel naturals with a presburger arithmetic solver [^1]. I want to use type-class constraints to give premisses to the solver, for example, `Empty a` constraint means "a is false (empty type)". So I have to get TyCon information in TcPluginM monad and I wrote as follows: ```haskell do md <- lookupModule (mkModuleName "Proof.Propositional.Empty") (fsLit "equational-reasoning") classTyCon <$> (tcLookupClass =<< lookupOrig md (mkTcOcc "Empty")) ``` But this code doesn't work as expected. For example, the code ```haskell {-# LANGUAGE DataKinds, TypeOperators, GADTs, TypeFamilies, ExplicitForAll, FlexibleContexts #-} import Data.Type.Equality import GHC.TypeLits (type (+), type (<=), type (<=?)) import Proof.Propositional (Empty(..)) predSucc :: Empty (n :~: 0) => proxy n -> (n + 1 <=? n + n) :~: 'True predSucc _ = Witness ``` resulted in the following compile-time error: ``` Can't find interface-file declaration for ty
Categories: Offsite Discussion

linking dlls with relative paths on Windows

haskell-cafe - Mon, 07/25/2016 - 1:52pm
Hi Peter, I forgot to respond to this, if you haven't received an answer to this yet then feel free to open a ticket on the ghc trac and I will take a look as soon as I have working Internet again. I don't know what the status is of this for now, the linked to stackoverfow article should work though. Also 7.10.3 has some issues when loading dependent dlls in the same folder as the main one being loaded when not on the search path. 8.0.1 should be fine. Kind regards, Tamar _______________________________________________ Haskell-Cafe mailing list To (un)subscribe, modify options or view archives go to: http://mail.haskell.org/cgi-bin/mailman/listinfo/haskell-cafe Only members subscribed via the mailman list are allowed to post.
Categories: Offsite Discussion

Munich Haskell Meeting,2016-07-27 < at > 19:30 Augustiner Bierhalle

haskell-cafe - Mon, 07/25/2016 - 1:22pm
Dear all, This week, our monthly Munich Haskell Meeting will take place again on Wednesday, July 27 at Augustiner Bierhalle (Neuhauser Str.) at 19h30. For details see here: http://muenchen.haskell.bayern/dates.html If you plan to join, please add yourself to this dudle so we can reserve enough seats! It is OK to add yourself to the dudle anonymously or pseudonymously. https://dudle.inf.tu-dresden.de/haskell-munich-jul-2016/ Everybody is welcome! cu,
Categories: Offsite Discussion

good choice for random number generator ?

haskell-cafe - Mon, 07/25/2016 - 12:48am
There's quite a few. Many are very old. I would have like to use vector-random and got this : Data/Vector/Random/Mersenne.hs:33:18: Could not find module ‘Data.Vector.Fusion.Stream’ It is a member of the hidden package ‘vector-0.10.12.3< at >vecto_1COyUuV1LrA1IjYnWfJnbs’. Perhaps you need to add ‘vector’ to the build-depends in your .cabal file. Use -v to see a list of the files searched for. Data/Vector/Random/Mersenne.hs:35:18: Could not find module ‘Data.Vector.Fusion.Stream.Size’ It is a member of the hidden package ‘vector-0.10.12.3< at >vecto_1COyUuV1LrA1IjYnWfJnbs’. Perhaps you need to add ‘vector’ to the build-depends in your .cabal file. Use -v to see a list of the files searched for. I wouldn't be opposed to fixing it, but I'm wondering at this point there isn't a better package to use instead. I'm looking for both integer and floating point random numbers, uniform and gaussian. gsl-random looks promising as does mwc-random. any other suggestio
Categories: Offsite Discussion

How do I debug this RTS segfault?

haskell-cafe - Sun, 07/24/2016 - 6:50pm
Hello, I have run into this RTS bug recently. In short, when executing multiple consequtive forks, after 500-600 or so the process is terminated by SIGSEGV. I know this kind of thing is totally artificial, but still. The problem I have is that I can't get any meaningful backtrace in gdb. For example, for threaded RTS I get this (gdb) bt #0 0x0000000000560d63 in base_GHCziEventziThread_ensureIOManagerIsRunning1_info () Backtrace stopped: Cannot access memory at address 0x7fffff7fcea0 For non-threaded RTS I get this (gdb) bt #0 0x00000000007138c9 in stg_makeStablePtrzh () Backtrace stopped: Cannot access memory at address 0x7fffff7fc720 Build command: ghc --make -O2 -g -fforce-recomp fork.hs Add threaded if needed. I was able to reproduce this bug with both GHC 7.10.3 and todays HEAD with the code below. With best regards. _______________________________________________ Haskell-Cafe mailing list To (un)subscribe, modify options or view archives go to: http://mail.haskell.org/cgi-bin/mailman/
Categories: Offsite Discussion