Crit-bit trees for Haskell
This is the first purely functional implementation of crit-bit
trees that I'm aware of.
A crit-bit tree is a key/value container that allows efficient lookups
and ordered traversal for data that can be represented as a string of
This package exists in part with education in mind:
The core data structures are simple.
The core algorithms are easy to grasp.
I have intentionally structured the source to be easy to follow and
Originally, I deliberately left the package incomplete. (It has
since been substantially fleshed out.) Ever thought to yourself,
"I'd write a bit of Haskell if only I had a project to work on"?
Well, here's your chance! I will set aside time to review your code
and answer what questions I can.
Education aside, crit-bit trees offer some interesting features
compared to other key/value container types in Haskell.
For some operations, they are much faster than
Data.Map from the
containers package, while for others, they are slower.
Data.HashMap, you get about the same lookup
performance, but also some features that a hash-based structure
can't provide: prefix-based search, efficient neighbour lookup,
Of course crit-bit trees have some downsides, too. For example,
building a tree from randomly ordered inputs is somewhat slow, and of
course the set of usable key types is small (only types that can be
interpreted as bitstrings "for free").
Compared to the most easily findable crit-bit tree code you'll come
across that's written in C, the core
of this library has a lot less accidental complexity, and so may be
easier to understand. It also handles arbitrary binary data that will
cause the C library to go wrong.
How to contribute
I've purposely published this package in an incomplete state, and I'd
like your help to round it out. In return, you get to learn a little
Haskell, have your code reviewed by someone who wants to see you
succeed, and contribute to a rather nifty library.
Do you need any prior experience with Haskell to get started? No! All
you need is curiosity and the ability to learn from context. Oh, and a
My aim with this library is drop-in API compatibility with the widely
library, which has two happy consequences:
There are lots of functions to write!
In almost every case, you'll find a pre-existing function in
containers that (from a user's perspective) does exactly what its
counterparts in this library ought to do.
If you want to contribute or play around, please use the most modern
version of the Haskell Platform.
Once you have the Platform installed, there are just a few more steps.
Set up your local database of known open source Haskell packages.
Both the new
cabal command and
cabal-dev will install to
$HOME/.cabal/bin, so put that directory at the front of your shell's
search path before you continue.
git clone git://github.com/bos/critbit
Set up a sandbox.
The first time through, you may need to download and install a ton of
dependencies, so hang in there.
cabal sandbox init
cabal install \
-j flag above tells
cabal to use all of your CPUs, so even the
initial build shouldn't take more than a few minutes.
To actually build:
Running the test suite
Once you've built the code, you can run the entire test suite fairly
quickly. This takes about 30 seconds on my oldish 8-core Mac laptop:
dist/build/tests/tests +RTS -N
+RTS -N above tells GHC's runtime system to use all available
If you're feeling impatient, run a subset of the test suite:
dist/build/tests/tests -t properties/map/bytestring +RTS -N
And if you want to explore, the
tests program accepts a
option. Try it out.
It is just as easy to benchmark stuff as to test it.
First, you need a dictionary. If your system doesn't have a file named
/usr/share/dict/words, you can download a dictionary
If you've downloaded a dictionary, tell the benchmark
suite where to find it by setting the
WORDS environment variable.
You can then run benchmarks and generate a report. For instance, this
runs every benchmark that begins with
dist/build/benchmarks/benchmarks -o lookup.html \
lookup.html file in your browser. Here's an
of what to expect.
tests, run the
benchmarks program with
--help if you
want to do some exploring.
What your code should look like
Okay, so you've bought into this idea, and would like to try writing a
patch. How to begin?
I've generally tried to write commits with a view to being readable,
so there are examples you can follow.
For instance, here's the commit where I added the
commit follows a simple pattern:
Naturally, you'll follow the prevailing coding and formatting style.
If you forget, I'll be sad and offer you only a terse "fix your
formatting" review, and then you'll be sad too.
What your commits should look like
Please follow the guidelines below, as they make it easier to review
your pull request and deal with your commits afterwards.
One logical idea per commit! If you want to add five functions,
that's fine, but please spread them across five commits.
Do not reorganize or refactor unrelated code in a commit whose
purpose is to add new code.
When you add a new function, add its tests and benchmarks in the
Do not add trailing whitespace. Follow the same formatting
and naming conventions as you already see in the code around you.
Keep your maximum line length at 80 columns for everything except
lines of example code in documentation.
(If you can't follow the guidelines, there's a good chance I'll ask
you to fix your commits and resubmit them.)