blog.well-typed.com

As I’m sure you remember, as part of the Haskell OpenSPARC project, Sun has donated a SPARC Enterprise T5120 Server to the Haskell.org community.

Björn took delivery of the server a month or so ago and now that the IT folk at Chalmers are back from holiday it’s installed in the server room. It’s just been given a public IP and I’ve been able to log in for the first time. We’ve now got to get software configured and get ghc working, so don’t ask for accounts just yet.

Björn took some photos as he was getting it set up:

So that’s what 32GB of memory looks like! Under that little heatsink is the T2 CPU with its 8 cores, each core multiplexing 8 threads.

It’s a 1U form factor so it uses lots of little fans which you can see at the left and clear plastic ducts to channel the airflow over the memory and the CPU heatsink. In the bottom right you can see the dual power supplies.

Remember, if you want to hack on this project for three months, the closing deadline for applications is Friday the 5th of September.

I should make it clear that although I am a consultant with Well-Typed and also the coordinator for the OpenSPARC project, the project is really nothing to do with Well-Typed. It’s a joint project between Sun Microsystems and the Haskell.org community. I’m wearing my community hat for this one.

Actually, make is really pretty good. Well, GNU make is pretty good. When used correctly it can rebuild large projects in the minimum number of steps, in parallel.

So what is wrong with it? I’m not complaining about the simple annoyances like insane use of tabs or being based on two inflexible programming languages (shell script and make macros). No, it’s got two fundamental problems: it cannot enforce correct dependency tracking and its dependency language is insufficiently expressive for common tasks.

It is possible to write incorrect make rules

It’s far too easy to write incorrect rules — rules where the named dependencies and targets do not match up with the action:

foo : bar
	cat bar baz > foo

So obviously that’s a totally contrived example and is easy to spot that it has an untracked dependency on baz, however it gets much harder with more complex rules and where the programs you call read or write files that you did not know about.

So the problem is that make cannot check that you’ve written a bogus rule and yet if you do your makefile is broken. What makes it worse is that it is not immediately obvious that the makefile is broken. It will work initially but you’ll end up not trusting parallel make or running into quirks that are solved by make clean. If you have to use make clean your makefile is broken.

The reason it cannot check for correctness of rules is of course because the actions are opaque blobs of shell script. The decision to use shell script made it quicker to implement make and users were already familiar with the language but the cost is that makefiles are more or less impossible to check automatically.

Dynamic dependencies cannot be expressed sanely in make

Make takes the view that there is a single massive static dependency graph. The single and massive aspects are not a problem but the static one is. The problem is that in many cases you do actually need to run some build actions before you know all the dependencies of each file in a project. The canonical example is running a tool to discover the dependencies of .hs or .c files.

GNU make has a clever extension which makes it possible to express this kind of dependency in simple cases. It lets one include deps.mk. That reads in the deps.mk file and extends the dependency graph with all the rules found in deps.mk. The clever thing is that deps.mk itself can depend on other things and can have a rule to generate it. For the example of dependencies of .hs or .c files, this lets us write a rule to call the compiler with a flag to dump make-format dependencies into a file. This works quite nicely, in this simple case.

The problem with doing include deps.mk is that the file can specify any rules at all. It can add edges between any existing nodes in make’s dependency graph. That means that make cannot be confident that it knows everything unless it has got deps.mk up to date and included it. So before anything else can be done, make has to update deps.mk, even if it turns out in the end that the deps given in the include file are irrelevant to the task at hand.

In practise we do not need to be able to add edges willy nilly all over the place and there is a great benefit to being more disciplined. Suppose for the sake of argument that for each .hs file I have a .deps file listing the files that the .hs file depends on. Now if I am trying to update foo.hs then I only need to look at the suffix rule for .hs files and the rules in foo.deps. I know that I do not need to look in bar.deps (unless I discover from foo.deps that foo.hs depends on bar.hi). This is a massive saving. It means I do not have to bring all the .deps files up to date. That also means I do not have to bring the corresponding .hs files up to date. That also means I do not have to run any pre-processors or code generators for those .hs files. That also means I do not have to compile the code generators.

You might think this is a contrived situation but it is not. The Gtk2Hs build system use GNU make (non-recursive of course). It has lots of .chs.pp files which get pre-processed with cpp and then c2hs to generate a .hs file. For each .chs file we look for all the imports and generate a .deps file which gets included into the top level makefile. So just to calculate all the dependencies we actually have to two about two thirds of the build. That’s because we first have to build c2hs and two other code generators and then run cpp and c2hs on a hundred or so files, just so we can generate all the .hs files and find their dependencies. This means that from a clean state I cannot build just one particular .hs file because the very makefile itself depends on all the other .hs files. Amusingly, just to make clean, make has to build half the project. Obviously that is insane so we have to hack it using includes that are conditional on the make target. That’s not a technique that scales however.

In any non-trivial build system it is necessary to interleave build actions with discovering dependencies. Classic make ignores this fact completely. GNU make notes it but forces us to do all the actions to discover the full dependency graph up front before we can get round to useful work. A better build system would be built around the notion of interleaving graph exploration with graph reduction. Saizan’s make-like framework is built around this principle.

Could we do better?

Looking around at previous work, like the classic recursive make considered harmful or the Vesta or Maak build systems, one lesson comes up again and again: track dependencies absolutely precisely or you lose.

I am convinced that the solution to the first problem is not to specify the dependencies and targets separately from the action. They should be specified together so that there is simply no way to express an untracked dependency. This is also the approach taken by Vesta and Maak.

If we have a bunch of primitive actions that correctly declare their dependencies and ways of combining actions that remembers dependencies then we should never end up with untracked dependencies. For example consider the above example again. Lets suppose we have two primitives:

readFile  :: FilePath -> Rule String
writeFile :: FilePath -> Rule (String -> ())

Clearly one has a single dependency and the other a single target. Lets write that contrived rule again:

rule = write <*> read
  where
    write = writeFile “foo”
    read  = (++) <$> readFile “bar” <*> readFile “baz”

So the intuition here is that applicative composition gives us static rule-graph composition. As for rules with dynamic dependencies, this corresponds to monadic composition:

(<**>) :: Rule a -> Rule (a -> b) -> Rule b
(>>=)  :: Rule a -> (a -> Rule b) -> Rule b

This lets us express the cases where very structure of the dependency graph depends on a value calculated from another part of the graph.

With static composition we can look at the resulting rule and ’see’ down both branches of the composition to discover the graph structure without running any actions. With dynamic composition we can look at the left branch statically but the right branch requires the value produced by the left before we can even discover its structure.

So we should use static composition where possible because it tells us more about the graph which gives us more opportunities for parallelism and for caching intermediate results to avoid needless recompilation. Where necessary we can use dynamic dependencies but we should not over-use them or our dependency ‘graph’ would just become one long chain.

So, that’s the idea. I should make clear: there is no corresponding implementation! Unlike my vaporware, Saizan has a working implementation of a slightly lower level API as part of his GSoC project. We have talked about putting a higher level API on top though it’s not completely clear how to do that yet.

There are also undoubtedly connections to FRP that could do with further exploration. I mean it is quite clearly an FRP style problem, but an ugly grungy one where many parts work by side effects in the file system.

In a previous post I explained the the dreaded diamond dependency problem. In this post I’ll look at a solution.

Ever seen an error like when building a package?

Couldn't match expected type
  `bytestring-0.9.0.4:Data.ByteString.ByteString'
  against inferred type `ByteString'

Or when configuring a package?

Warning: This package indirectly depends on multiple versions
of the same package. This is highly likely to cause a compile failure.
package pureMD5-0.1.2 requires bytestring-0.9.0.4
package binary-0.4.1 requires bytestring-0.9.1.0
package cabal2arch-0.3 requires bytestring-0.9.1.0

These failures were pretty common.

So the good news is that the latest release does solve the problem. It contains a completely new package dependency resolution algorithm based on constraint solving.

The problem space is this: we’ve got a bunch of packages, say A, B, C, D.

We’ve got a bunch of versions of of those packages, let’s say:

• A-1.0, A-1.1
• B-1.0
• C-1.0, C-2.0.
• D-1.0, D-1.1

Each package version has dependencies on other packages along with version constraints, for example A-1.0 might need C >= 1 and A-1.1 might need C >= 2. Then given a goal like A >= 1, we have to pick exactly one version of package A and one version of C and so on recursively until we have picked a package version to satisfy each dependency. So even if we can get to a package by following two different dependency paths, we must end up picking the same version. That’s what it means to avoids inconsistent dependencies. Of course the version we pick for a package has to satisfy the version constraints of all the packages that depend on it.

The problem space can actually get extremely large. In theory each choice about a package version is independent. In the above example supposing we have to pick a version of A, B, C and D then we have two choices for A, one choice for B and two choices for C and D which gives us 2*1*2*2 combinations overall. It is clear that we cannot explore the entire space of combinations because it’s exponential in size. That may not seem important for this example with just 4 packages but there are over 700 packages on hackage and we’d like to be able to install most of them at once. We need to apply some smarts.

Note that this is actually an NP-complete problem. It’s possible to encode 3-SAT into the package dependency problem. Fortunately most real instances are fairly easy so we need not give up hope.

Ultimately we have to make choices between versions of a package and we have to end up with a consistent set of choices (or fail). It makes sense therefore to keep track of which versions of which packages are still valid choices. We can make the problem easier if we can eliminate choices that we know would lead to impossible or inconsistent solutions. We can propagate information both up and down the dependency graph to help us eliminate choices.

For example supposing we looked at D-1.0 and D-1.1 and we discovered that D-1.0 could not possibly be built on our operating system but D-1.1 could. If later on we discover we need some version of D then we only have one possibility so we can pick it right away. More interestingly, it might also allow us to eliminate a choice higher up. Supposing for the sake of argument that C-1.0 needed D-1.0 and would not work with D-1.1 then by eliminating D-1.0 we can also eliminate C-1.0.

In the downwards direction, consider the effect of picking A-1.1 rather than A-1.0. We said earlier that A-1.0 needs C >= 1 and A-1.1 might need C >= 2. So making this choice for A eliminates C-1.0.

One class of strategies is to make a series of firm choices and try to build up to a consistent solution. If we do this with no backtracking then we will always run in polynomial time, but there will always be cases where we cannot find solutions even though a solution exists. The chance of us finding a solution in this kind of strategy depends on the order in which we make the choices.

The algorithm

The strategy I’ve adopted is to make package choices in a top down order. It is a kind of mixture between breadth first and depth first search. We have a set of packages that we know we will need and a set of specific package versions we’ve already picked. We also maintain a set of constraints on all the packages. This lets us know what versions are available for each package that we’ve yet to decide about.

In each round we pick a package and choose one version of it and update our set of constraints and if the package we picked depends on a new package then we add that to the pending package set. When there is a package we know we need and there is only one version left then we pick it. That’s the easy case.

The hard case is when all remaining packages have at least two versions we could pick. Here we just have to pick something and hope for the best. First of all, we have to decide which package we’re going to make a choice for. A good heuristic here is to decide on packages higher up the dependency graph earlier since their dependencies impose constraints on later choices. We can do this by caching the depth first numbering for each package. Once we’ve decided on which package to go for then we have to decide which version to pick. The most sensible thing is to pick the highest version, however there is a complication with installed packages that I’ll come back to later.

Results

So this algorithm produces pretty good results in most cases. It always runs in polynomial time and because of the way it gathers constraints it is guaranteed that if it finds a solution that it will be a valid consistent solution. The old dep solver which would very often come up with inconsistent solutions. Remember: consistent solutions avoid the diamond dependency problem.

Another good thing to gathering constraints is that when we fail we have some information about why we failed because we know which additional constraint made the constraint set inconsistent. We can translate that into a human readable error message, though not necessarily a particularly brilliant one.

By manually adding additional constraints we can also guide the solver to find solutions it would not otherwise find, or to avoid failure. In a sense this is just a hack to work around the fact that the solver isn’t even better, but in the mean time it’s pretty handy.

You can play around and get a sense of what the algorithm is doing if you run cabal install --dry-run -v pkg1 pkg2 etc and look at the intermediate output and the final solution.

The biggest test I’ve tried so far is trying to install all of hackage simultaniously. It takes several seconds to find a solution, which is not too bad. I had to discard about 30 packages to arrive at a consistent set. Adding any one of the discarded ones back leads to inconsitent dependencies, for example conflicts over which version of HaXml is required. There are also many packages that simply cannot ever be installed because they have missing dependencies.

Installed packages

So what about installed packages? I mentioned that there’s a complication there. We often have to decide between picking an already installed version of a package and the available one from hackage (even if they are the same version). If we pick the installed version then we immediately pick up a lot of precise constraints, because the installed package depends on exact versions. That is often enough to prevent us from finding a solution. Indeed in the diamond dependency examples I gave previously it was clear that to find a consistent solution we would have to rebuild some existing installed package. So we cannot default to preferring installed packages. A simple observation is that the available package always has looser constraints than the installed version, so it’s always safe to pick the available version over the installed version. We can always ‘improve’ our solution at the end if we find the constraints we end up with are consistent with the constraints of the installed version. And that is what we do. There is a further issue however. If ultimately we would like the option to use the installed version then we had better pick the same version of the available package and not just the highest available version. What we do is allow a per-package preference for the highest available version or the highest available version that has a corresponding installed version. For the ‘install’ command we prefer the latest version for the packages explicitly given and the installed version for all others. For the ‘upgrade’ command we prefer the latest version everywhere.

That seems to work pretty well, though I’ve found people do get somewhat confused when cabal-install decides to re-install a package they already have. What is not clear to them is that it is rebuilding a package against different versions of its dependencies. Probably the thing to do is to make that clearer in the output of cabal install --dry-run.

Improvements

There are still plenty of improvements that could be made. The algorithm currently does not deal well with installing packages for older compilers because it finds out too late that a package it choose could only work with a newer version of the base library. The solution there is some bottom up constraint propagation. We should check at the beginning that each package version could possibly be installed with the current compiler and if not eliminate it. There are still real cases where we end up with an avoidable conflict. For example we might have two packages that need HaXml, lets say Foo needs HaXml =1.13.* while there are two versions of Bar, one that works with HaXml =1.13.* and a later version that needs HaXml >= 1.16. If we end up picking Bar before Foo then we would default to the later version of HaXml but then fail when we find Foo needs the earlier version. This is not solved by the ordering heuristic because Foo and Bar do not depend on each other. This is an example where we need either more look ahead, backtracking or a much more sophisticated algorithm in general.

The right long term approach I think is a proper complete constraint solving algorithm.

Cabal-1.4 and cabal-install-0.5 are now released!

If you were already using a pre-release of cabal-install then you can just:
$ cabal update
$ cabal install cabal-install

The Cabal library

There are no huge new features in the Cabal library itself. The main new feature is that is supports a significantly improved version of cabal-install. Cabal-1.4 does have a number of incremental improvements and lots of bug fixes. You can see the full list in the changelog.

So the most exciting thing is the release of cabal-install…

New features in cabal-install

Command line improvements

The most immediately noticeable thing is that the command line interface now has all the commands that runhaskell Setup.hs has. Of course it still has the features to download and install packages from hackage. It also gained an upload command. So it now provides a command line interface to the whole Cabal/Hackage system. There’s no need to use runhaskell Setup.hs ever again. There is also bash command line completion support included which I find is a great time saver.

Installing and upgrading

The next big thing is that it includes a new package dependency resolution system that finds correct and sensible solutions more of the time and has better default behaviour. The new behaviour should be similar to other package managers that people are used to.

For example, suppose you’ve got xmonad-0.5 installed and version 0.7 is the latest on hackage, then

$ cabal install xmonad

will upgrade to xmonad-0.7.

The behaviour of install is to upgrade as little as possible to satisfy your request, but sometimes you want to upgrade all the dependencies too. Supposing now that we have xmonad-0.7 installed, but we’re still using X11-1.4.1 and the latest version on hackage is X11-1.4.2, then

$ cabal upgrade xmonad

will install X11-1.4.2 and re-install xmonad-0.7, this time built against the newer X11-1.4.2.

So in general, the install command will install the latest version of things but will try and use any existing installed versions of dependencies while the upgrade command will also try to use the latest versions of all dependencies. As a special case, cabal upgrade on its own will try to upgrade all the packages that you have installed.

For both command there is a --dry-run flag so you can see what would be installed without actually doing it.

Hugs

Yes, it even works with hugs. That is, cabal-install built by ghc can manage the installation of packages for hugs. In principle cabal-install should be able to be run by hugs but currently the zlib binding is using a function that hugs does not support.

Note that for hugs, Cabal does not know what packages are already installed because there is no equivalent of the package database that ghc has. So that means cabal-install cannot do very sensible installation planning. It should work ok so long as all the dependencies are already installed.

Windows

Yes, it even works on Windows. The one caveat is that cabal-install cannot currently upgrade itself because Windows makes it hard for a process to overwrite its own executable file. It needs more complex trickery with the Win32 API. In the meantime the workaround is to rename the cabal.exe file first eg to cabal-foo.exe, then say cabal-foo install cabal-install.

Build reporting

One feature that made it into this release is build reporting. cabal-install keeps logs of all packages that you install (at least packages from hackage, not local ones). It records a bit of information about each package, in particular whether the outcome was successful or not. You can see these build reports in ~/.cabal/packages/$server/build-reports.log.

The plan in the longer term is to let people upload these build reports to hackage so we can get a wider range of testing data about the packages on hackage.

If you look at real programs you’ll find there is often a lot of code spent just on information plumbing. Plumbing is code that doesn’t do any “real work” but is just concerned with getting the right information from one bit of a program to another, often with some rearranging or impedance matching. It’s not as cool as code with real algorithmic content. So how can we stop plumbing code taking over and obscuring the code that is doing real work?

A nice example is some code in cabal-install for installing a package. Well, I say installing a package but really the actual installing is done by the Cabal library. This code just gathers information from various sources and uses that to decide how to configure a package. There is a little bit of real work mixed in, like unpacking tarballs, but it is mostly plumbing. For example there is the global configure options that apply to each package, per-package flag assignments and dependencies. Then there are various options for controlling how we compile and run the Setup.hs scripts for each package, including what compiler to use and what version of the Cabal library to run them with. There’s also an option to use some kind of sudo style wrapper for the install phase.

The code followed a common pattern of a series of layers, each one calling the layer below. In the cabal install example we have executeInstallPlan that calls installConfiguredPackage, which calls installAvailablePackage which calls installUnpackedPackage which calls setupWrapper (which as the name suggests is a layer on top of something else). It’s a lot of layers. Lets pick out just two examples:

installConfiguredPackage verbosity
    scriptOptions miscOptions configFlags
    (ConfiguredPackage pkg flags deps)
  = installAvailablePackage verbosity
    scriptOptions miscOptions configFlags' pkg
  where
    configFlags' = configFlags {
      configConfigurationsFlags = flags,
      configConstraints = deps
    }

installAvailablePackage verbosity
    scriptOptions miscOptions configFlags
    (AvailablePackage _ pkg LocalPackage)
  = installUnpackedPackage verbosity
      scriptOptions miscOptions configFlags
      pkg Nothing

As you can see, the problem with this style is that each layer gets cluttered with passing all the information that the lower layers need. Bundling related bits of information into tuples or records helps to some extent. In this example you can see it’s been done already; scriptOptions, miscOptions and configFlags are bundles of other parameters. (The name miscOptions is a sure sign that we’re bundling arbitrary things together just to try and reduce the number of parameters we’re passing.)

What is worst though is that we end up with a tightly coupled system. To add a new feature at the bottom layer requires changing all the layers to pipe that information all the way down.

So one nice trick is instead of having each layer directly call the next one down, we parametrise each layer by all the layers below it. For example:

installConfiguredPackage configFlags
    (ConfiguredPackage pkg flags deps)
    installPkg
  = installPkg configFlags' pkg
  where
    configFlags' = configFlags {
      configConfigurationsFlags = flags,
      configConstraints = deps
    }

installAvailablePackage
    (AvailablePackage _ pkg LocalPackage)
    installPkg
  = installPkg pkg Nothing

So we take the next layer as an installPkg parameter, do whatever transform the layer was supposed to be do and call installPkg as the next layer. So all the extra parameters that this layer did not care about are gone. They can be passed directly to installPkg by the caller.

So in the end we stick all the layers together:

executeInstallPlan installPlan $ \cpkg ->
  installConfiguredPackage configFlags cpkg $
  \configFlags' apkg ->
    installAvailablePackage verbosity apkg $
      installUnpackedPackage verbosity
          scriptOptions miscOptions configFlags'

Unlike before, we can now see all the layers at once rather than just the top layer. So we can pass those extra parameters directly down to the bottom layer. As you can see from the installConfiguredPackage layer, we can modify the values on the way down but we only have to pay in plumbing for the ones we’re using at each layer rather than in every layer.

After the refactoring the code can become more general which has advantages for testing and reuse. For example installConfiguredPackage turns out to be pure and executeInstallPlan can work in any monad where as previously it was tied down to being in the IO monad because the bottom layer was in the IO monad. Not being in IO makes it much easier to use a testing system like QuickCheck and being able to pass a dummy makes it possible to test each layer in isolation.

So obviously the message must be that higher order plumbing is cool!

Well perhaps not, but it can make it a little less interconnected and easier to manage.

One issue that came up several times at the hackathon—especially when talking to the Yi hackers—is the dreaded diamond dependency problem. This happens when you have 4 packages in a diamond shaped dependency graph:

The problem arises when we have package B and C already installed but built against different versions of D and then we try to use both packages B and C together in package A:

This can work ok but only if packages B and C do not expose types defined in D in their interfaces. If they do then package A will not be able to use functions from B and C together because they will not be working with the same type. That is you’ll get a type error.

To pick a concrete example, suppose package D is bytestring and we have both bytestring-0.9.0.1 and 0.9.0.4 installed. Lets say B is utf8-string and C is regex-base. Lets say that package A is the Yi editor program. So the point is, at some place in the code in Yi we want to pass a bytestring produced as the result of UTF-8 decoding as input to one of the regex functions. But this does not work because the functions in the utf8-string package are using the ByteString type from bytestring-0.9.0.1 while the regex functions in the regex package are using the ByteString type from bytestring-0.9.0.4. So we get a type error when we try to compile Yi:

Couldn't match expected type `bytestring-0.9.0.4:Data.ByteString.ByteString'
against inferred type `bytestring-0.9.0.1:Data.ByteString.ByteString'

As far as GHC knows, these two types are totally unrelated!

This is obviously extremely annoying. There is also no easy solution. In this example we’re assuming that packages B and C have already been built, so there is actually no way to sensibly use the two packages together without rebuilding on of them against a different version of package D. In this case the obvious solution is to rebuild B to use the D-1.1 rather than D-1.0. The problem with rebuilding a package of course is it breaks all other packages that have already been built against it. It isn’t clear that you want a package manager to go automatically rebuilding lots of apparently unrelated packages.

In the longer term the best solution would seem to be to do what Nix does. In the above example, instead of replacing package B built against D-1.0 with B built against D-1.1, Nix would add
another instance of B built against D-1.1. So the original instance of B would remain unchanged and nothing would break. It’s the functional approach: we never mutate values (installed packages) we just create new ones and garbage collect old one when they are no longer needed.

In practice it means we have to identify installed packages using some hash of the package and the hashes of all dependent packages. jhc already does this and there are moves afoot to do something similar for GHC, though aimed more at tracking API/ABI changes. For sane source based package management, I think it’s the right direction to take.

I should note that this is not a new problem. You’ve been able to construct this problem ever since ghc started to allow multiple versions of the same package to be installed at once. We are just noticing it a lot more frequently now because we split up the base package and allow those split-out packages to be upgraded.

The current state of play is that Cabal warns of this problem but doesn’t really help you solve it. For the above example we’d get:

$ cabal configure
Configuring A-1.0...
Warning: This package indirectly depends on multiple versions of
the same package. This is highly likely to cause a compile failure.
package B-1.0 requires D-1.0
package C-1.0 requires D-1.1

I am very pleased to be mentoring Andrea Vezzosi (aka Saizan) on a Google Summer of Code project to develop a make-like dependency framework for use in Cabal. I’m really looking forward to working with Saizan on this. It should be a fun and useful project.

Congratulations to Saizan and good luck with the project!

The project itself is to start prototyping one of the big features we need for Cabal to become a really excellent build system. At the moment Cabal calls out to ghc --make to do the hard work of figuring out what needs to be rebuilt. This only works for .hs files of course while we need it to work for all preprocessors. More generally there are a lot of things that Cabal does that would benefit from being done in a dependency style — not just limited to running external programs that update files. In addition everyone has multi-core CPUs now and we’d like to do parallel builds.

The Real World Haskell book is now available for pre-order!

I’ve just ordered my copy from Amazon UK for £30.99 and it’s available from Amazon in the US for $49.99.

I’d like to be able to recommend a canonical “learning Haskell” bookshelf. The slot for an introductory book has been filled by the excellent Programming in Haskell (you can read my review). Judging by the material the Real World Haskell book aims to cover, it looks like it should be a pretty good complement, mostly picking up where Programming in Haskell leaves off and covering the language techniques and tools you need to get started writing serious programs.

We ended up with quite a big group of people interested in various aspects of Cabal, cabal-install and Hackage. So I ended up spending much more time talking than hacking. I’m sure that’s a good thing though. Hackathons should be about sharing ideas, brainstorming and experimenting.

I arrived at the hackathon with ideas about three main areas I wanted to get people to work on:

• a make-like dependency framework for the Cabal library
• a package dependency resolver for cabal-install
• cabal-install/hackage build reporting

In fact these are more or less exactly the topics for proposed Google Summer of Code projects, but there’s no harm in starting early and exploring possible solutions.

I expounded a bit on the dependency framework issue though we didn’t end up working on that in the end. It’s actually an interesting research problem. I’ll write about it another time.

Josef Svenningsson got interested in the cabal-install dependency resolver issue. He spent much of the hackathon prototyping a BDD-style approach. Hopefully Josef will post his current ideas to the cabal-devel mailing list in the next week or so.

Thomas Schilling (aka nominolo) worked on the last remaining feature we wanted to get into Cabal-1.4. When configuring a package want to be able to specify additional constraints on the versions of dependent packages. As a developer we might want this just to be able to test building a package against an old rather than the latest version of a dependency. The reason we really want it for Cabal-1.4 though is so that we can use it in cabal-install. In cabal-install we want to work out up front exactly how we will configure a whole set of packages. We need a way to get Cabal to configure a package exactly how we wanted it in our global plan, rather than making some local decision. Thomas got this finished and I’ll be integrating the patches some time soon.

Lennart Kolmodin (with some help from David Waern and myself) spent quite a while hacking on build reporting in cabal-install. The idea here is that we want to get cabal-install to report build success and failure back to the hackage server so we can discover what packages build in what environments. On the client side most of the work involves information plumbing — gathering information from various places within Cabal to include into a build report. We realised that most of the information for a build report is discovered once we’ve decided how we’re going to configure the package. So we spent most time hacking on some code I’d written previously for representing installation plans and extending it to collect information on the outcome of installation. We managed to get it integrated though it still needs more work because our new InstallPlan type has a much more demanding notion of validity than the abstraction it replaces, so the current dependency resolver needs to be updated to produce valid InstallPlans.

Hac4 is now over, everyone has arrived home safely and all the equipment has been returned. 27 people attended, though not everyone could come every day. 10 attendees travelled from several European countries and from the US. People hacked on lots of projects, including Yi, Cabal, ByteString, a new web application interface, QuickCheck, CGI, first-order logic reasoning, Allegro bindings, Haddock, the community server, HAppS-HSP, replacing libgmp in GHC, Squiggle, restricted monads, and more.

We also decided to set up a Haskell Hackathon Steering Committee which will help find hosts for future hackathons and aid the local organizers.

Thanks to sponsorship from Credit Suisse and Galois, we could provide food for everyone during the whole hackathon. This made it possible to keep the group together and keep hacking (some stayed well into the night). The Department of Computer Science and Engineering at Chalmers University of Technology and University of Gothenburg provided space and networking. 

A big thank you to everybody who made this happen by sponsoring, attending and helping out with the organization!