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Overview of the ecosystem

Disclaimer

This section is adapted from Gabriella Gonzalez' State of the Ecosystem, which is licensed here.

Some of the text has been cropped or modified.

Contributors
  • Aaron Levin
  • Alois Cochard
  • Ben Kovach
  • Benno Fünfstück
  • Carlo Hamalainen
  • Chris Allen
  • Curtis Gagliardi
  • Deech
  • David Howlett
  • David Johnson
  • Edward Cho
  • Greg Weber
  • Gregor Uhlenheuer
  • Juan Pedro Villa Isaza
  • Kazu Yamamoto
  • Kevin Cantu
  • Kirill Zaborsky
  • Liam O'Connor-Davis
  • Luke Randall
  • Marcio Klepacz
  • Mitchell Rosen
  • Nicolas Kaiser
  • Oliver Charles
  • Pierre Radermecker
  • Rodrigo B. de Oliveira
  • Stephen Diehl
  • Tim Docker
  • Tran Ma
  • Yuriy Syrovetskiy
  • @bburdette
  • @co-dan
  • @ExternalReality
  • @GetContented
  • @psibi
  • @newswim

Legend

🏆 = Best in class: the best experience in any language

🥈 = Mature: suitable for most programmers

🌱 = Immature: acceptable for early-adopters

⛔ = Undeveloped

🏆 Compilers

Notable libraries:

Commentary

Haskell originated in academia, and most languages of academic origin (such as the ML family of languages) excel at compiler-related tasks for obvious reasons. As a result the language has a rich ecosystem of libraries dedicated to compiler-related tasks, such as parsing, pretty-printing, unification, bound variables, syntax tree manipulations, and optimization.

Some compilers written in Haskell:

  • Elm
  • Purescript
  • Idris
  • Agda
  • Pugs (the first Perl 6 implementation)
  • ghc (self-hosting)
  • frege (very similar to Haskell, also self-hosting)
  • hython (a Python3 interpreter written in Haskell)
  • Lasca (a small Scala-like language with global type inference and optional dynamic mode on LLVM backend)
  • verve - Functional language with object-oriented support
  • sixten - Haskell/Idris-style language with a focus on precise and efficient memory layout
  • carp - An efficient, statically typed Lisp with ownership tracking.
  • unison - A purely functional distributed programming language with algebraic effects.
  • oden (no longer in active development)

Educational resources:

Success stories:

🏆 Generic programming

This refers to automatically deriving algorithms for custom data types. Haskell is uniquely good at this:

🏆 Single-machine Concurrency

Notable libraries:

  • stm - Software transactional memory
  • unagi-chan - High performance channels
  • async - Futures library
  • streamly - A streaming library offering high performance concurrency
Commentary

Haskell's concurrency runtime performs as well or better than other mainstream languages and is significantly easier to use due to the runtime support for software-transactional memory.

The best explanation of Haskell's threading module is the documentation in Control.Concurrent:

Concurrency is "lightweight", which means that both thread creation and context switching overheads are extremely low. Scheduling of Haskell threads is done internally in the Haskell runtime system, and doesn't make use of any operating system-supplied thread packages.

In Haskell, all I/O is non-blocking by default, so for example a web server will just spawn one lightweight thread per connection and each thread can be written in an ordinary synchronous style instead of nested callbacks like in Node.js.

The best way to explain the performance of Haskell's threaded runtime is to give hard numbers:

  • The Haskell thread scheduler can easily handle millions of threads
  • Each thread requires 1 kb of memory, so the hard limitation to thread count is memory (1 GB per million threads).
  • Haskell channel overhead for the standard library (using TQueue) is on the order of one microsecond per message and degrades linearly with increasing contention
  • Haskell channel overhead using the unagi-chan library is on the order of 100 nanoseconds (even under contention)
  • Haskell's MVar (a low-level concurrency communication primitive) requires 10-20 ns to add or remove values (roughly on par with acquiring or releasing a lock in other languages)

Haskell also provides software-transactional memory, which allows programmers build composable and atomic memory transactions. You can compose transactions together in multiple ways to build larger transactions:

  • You can sequence two transactions to build a larger atomic transaction
  • You can combine two transactions using alternation, falling back on the second transaction if the first one fails
  • Transactions can retry, rolling back their state and sleeping until one of their dependencies changes in order to avoid wasteful polling

A few other languages provide software-transactional memory, but Haskell's implementation has two main advantages over other implementations:

  • The type system enforces that transactions only permit reversible memory modifications. This guarantees at compile time that all transactions can be safely rolled back.
  • Haskell's STM runtime takes advantage of enforced purity to improve the efficiency of transactions, retries, and alternation.

Haskell is also the only language that supports both software transactional memory and non-blocking I/O.

Educational resources:

Success Stories:

🏆 Parsing / Pretty-printing

Parsing libraries:

  • megaparsec - Modern, actively maintained fork of parsec
  • attoparsec - Extremely fast backtracking parser
  • Earley - Earley parsing embedded within the Haskell language. Parses all context-free grammars, even ambiguous ones, with no need to left factor. Returns all valid parses.
  • trifecta - Best error messages (clang-style)
  • parsers - Interface compatible with attoparsec, parsec and trifecta which lets you easily switch between them. People commonly use this library to begin with trifecta or parsec (for better error messages) then switch to attoparsec when done for performance
  • alex / happy - Like lexx / yacc but with Haskell integration
Commentary

Haskell parsing is extremely powerful. Recursive descent parser combinators are far-and-away the most popular parsing paradigm within the Haskell ecosystem, so much so that people use them even in place of regular expressions.

If you're not sure what library to pick, we generally recommend the megaparsec library as a default well-rounded choice because it strikes a decent balance between ease-of-use, performance, good error messages, and small dependencies.

attoparsec deserves special mention as an extremely fast backtracking parsing library. The speed and simplicity of this library will blow you away. The main deficiency of attoparsec is the poor error messages.

The pretty-printing front is also excellent. Academic researchers just really love writing pretty-printing libraries in Haskell for some reason.

Pretty-printing libraries:

Educational resources:

Success Stories:

🥈 Server-side web programming

Notable libraries:

  • aeson - Parsing and generation of JSON
  • warp / wai - the low-level server and API that all server libraries share, with the exception of snap
  • scotty - A beginner-friendly server framework analogous to Ruby's Sinatra
  • spock - Lighter than the "enterprise" frameworks, but more featureful than scotty (type-safe routing, sessions, conn pooling, csrf protection, authentication, etc)
  • yesod / yesod-* / snap / snap-* / happstack-server / happstack-* - "Enterprise" server frameworks with all the bells and whistles
  • ihp - batteries-included web framework with a friendly and helpful community. The best choice when getting started with haskell.
  • servant / servant-* - Library for type-safe REST servers and clients that might blow your mind
  • graphql-api - Implement a GraphQL API
  • websockets - Standalone websockets client and server
  • authenticate / authenticate-* - Shared authentication libraries
  • ekg / ekg-* - Haskell service monitoring
  • stm - Software-transactional memory
  • lucid - Haskell DSL for building HTML
  • mustache / karver - Templating libraries
Commentary

The main features in this category that Haskell brings to the table are:

  • Server stability
  • Performance
  • Ease of concurrent programming
  • Excellent support for web standards

The strong type system and polished runtime greatly improve server stability and simplify maintenance. This is the greatest differentiator of Haskell from other backend languages, because it significantly reduces the total-cost-of-ownership. You should expect that you can maintain Haskell-based services with significantly fewer programmers than other languages, even when compared to other statically typed languages.

The greatest weakness of server stability is space leaks. The most common solution that I know of is to use ekg (a process monitor) to examine a server's memory stability before deploying to production. The second most common solution is to learn to detect and prevent space leaks with experience, which is not as hard as people think.

Haskell's performance is excellent and currently comparable to Java. Both languages give roughly the same performance in beginner or expert hands, although for different reasons.

Where Haskell shines in usability is the runtime support for the following three features:

  • software transactional memory (which differentiates Haskell from Go)
  • lightweight threads that use non-blocking I/O (which differentiates Haskell from the JVM)
  • garbage collection (which differentiates Haskell from Rust)

If you have never tried out Haskell's software transactional memory (STM), we highly recommend giving it a go, since it eliminates a large number of concurrency logic bugs. STM is far and away the most underestimated feature of the Haskell runtime.

Some web sites,services, and projects powered by Haskell:

Success Stories:

Educational resources:

Notable hosting platforms:

🥈 Testing

Notable libraries:

  • QuickCheck - property-based testing
  • doctest - tests embedded directly within documentation
  • free - Haskell's abstract version of "dependency injection"
  • hspec - Testing library analogous to Ruby's RSpec
  • HUnit - Testing library analogous to Java's JUnit
  • tasty - Combination unit / regression / property testing library
  • hedgehog - property-based testing with integrated shrinking
  • HTF - Preprocessor based unit testing with various output formats
Commentary

There are a few places where Haskell is the clear leader among all languages:

  • property-based testing
  • mocking / dependency injection

Haskell's QuickCheck is the gold standard which all other property-based testing libraries are measured against. The reason QuickCheck works so smoothly in Haskell is due to Haskell's type class system and purity. The type class system simplifies automatic generation of random data from the input type of the property test. Purity means that any failing test result can be automatically minimized by rerunning the check on smaller and smaller inputs until QuickCheck identifies the corner case that triggers the failure.

Haskell also supports most testing functionality that you expect from other languages, including:

  • standard package interfaces for testing
  • unit testing libraries
  • test result summaries and visualization

Educational resources:

🥈 Data structures and algorithms

Notable libraries:

Commentary

Haskell primarily uses persistent data structures, meaning that when you "update" a persistent data structure you just create a new data structure and you can keep the old one around (thus the name: persistent). Haskell data structures are immutable, so you don't actually create a deep copy of the data structure when updating; any new structure will reuse as much of the original data structure as possible.

The Notable libraries sections contains links to Haskell collections libraries that are heavily tuned. You should realistically expect these libraries to compete with tuned Java code. However, you should not expect Haskell to match expertly tuned C++ code.

The selection of algorithms is not as broad as in Java or C++ but it is still pretty good and diverse enough to cover the majority of use cases.

🥈 Benchmarking

Notable libraries:

  • criterion
  • gauge offers a similar feature set as criterion but has much fewer dependencies
  • tasty-bench even lighter than gauge with support for comparing benchmarks
Commentary

This boils down exclusively to the criterion library, which was done so well that nobody bothered to write a competing library. Notable criterion features include:

  • Detailed statistical analysis of timing data
  • Beautiful graph output: (Example)
  • High-resolution analysis (accurate down to nanoseconds)
  • Customizable HTML/CSV/JSON output
  • Garbage collection insensitivity

Educational resources:

🥈 Unicode

Notable libraries:

Commentary

Haskell's Unicode support is excellent. Just use the text and text-icu libraries, which provide a high-performance, space-efficient, and easy-to-use API for Unicode-aware text operations.

Note that there is one big catch: the default String type in Haskell is inefficient. You should always use Text whenever possible.

🥈 Stream programming

Notable libraries:

Commentary

Haskell's streaming ecosystem is mature. Probably the biggest issue is that there are too many good choices (and a lot of ecosystem fragmentation as a result), but each of the streaming libraries listed below has a sufficiently rich ecosystem including common streaming tasks like:

  • Network transmissions
  • Compression
  • External process pipes
  • High-performance streaming aggregation
  • Concurrent streams
  • Incremental parsing

Educational resources:

🥈 Serialization

Notable libraries:

Commentary

Haskell's serialization libraries are reasonably efficient and very easy to use. You can easily automatically derive serializers/deserializers for user-defined data types and it's very easy to encode/decode values.

Haskell's serialization does not suffer from any of the gotchas that object-oriented languages deal with (particularly Java/Scala). Haskell data types don't have associated methods or state to deal with so serialization/deserialization is straightforward and obvious. That's also why you can automatically derive correct serializers/deserializers.

Serialization performance is pretty good. You should expect to serialize data at a rate between 100 Mb/s to 1 Gb/s with careful tuning. Serialization performance still has about 3x-5x room for improvement by multiple independent estimates. See the "Faster binary serialization" link below for details of the ongoing work to improve the serialization speed of existing libraries.

Educational resources:

🥈 IDE support

The Haskell Language Server provides IDE support for editors which support Microsoft's Language Service Protocol (LSP). The easiest of these to use is VSCode, but other choices like vim will work.

The Haskell Language Server is included as part of Haskell's installer, GHCup.

🥈 Support for file formats

Notable libraries:

  • aeson - JSON encoding/decoding
  • cassava / sv- CSV encoding/decoding
  • yaml - YAML encoding/decoding
  • HsYAML - pure Haskell YAML 1.2 parser
  • xml - XML encoding/decoding
  • tomland - TOML encoding/decoding
Commentary

Haskell supports all the common domain-independent serialization formats (i.e. XML/JSON/YAML/CSV). For more exotic formats Haskell won't be as good as, say, Python (which is notorious for supporting a huge number of file formats) but it's so easy to write your own quick and dirty parser in Haskell that this is not much of an issue.

🥈 Logging

  • fast-logger - High-performance multicore logging system
  • hslogger - Logging library analogous to Python's logging library
  • monad-logger - add logging with line numbers to your monad stack. Uses fast-logger under the hood.
  • katip - Structured logging
  • log - Logging system with ElasticSearch, PostgreSQL and stdout sinks.
  • co-log - Composable contravariant comonadic logging library.

🥈 Code formatting

Haskell has tools for automatic code formatting:

  • ormolu - More opinionated formatting tool that uses GHC's own parser
  • fourmolu - like ormolu but with configurability
  • stylish-haskell - Less opinionated code formatting tool that mostly formats imports, language extensions, and data type definitions

🥈 Scripting

Notable libraries:

Commentary

Haskell's biggest advantage as a scripting language is that Haskell is the most widely adopted language that supports global type inference. Many languages support local type inference (such as Rust, Go, Java, C#), which means that function argument types and interfaces must be declared but everything else can be inferred. In Haskell, you can omit everything: all types and interfaces are completely inferred by the compiler (with some caveats, but they are minor).

Global type inference gives Haskell the feel of a scripting language while still providing static assurances of safety. Script type safety matters in particular for enterprise environments where glue scripts running with elevated privileges are one of the weakest points in these software architectures.

The second benefit of Haskell's type safety is ease of script maintenance. Many scripts grow out of control as they accrete arcane requirements and once they begin to exceed 1000 LOC they become difficult to maintain in a dynamically typed language. People rarely budget sufficient time to create a sufficiently extensive test suite that exercises every code path for each and every one of their scripts. Having a strong type system is like getting a large number of auto-generated tests for free that exercise all script code paths. Moreover, the type system is more resilient to refactoring than a test suite.

However, the language is also usable even for simple one-off disposable scripts. These Haskell scripts are comparable in size and simplicity to their equivalent Bash or Python scripts. This lets you easily start small and finish big.

Haskell has one advantage over many dynamic scripting languages, which is that Haskell can be compiled into a native and statically linked binary for distribution to others.

Haskell's scripting libraries are feature complete and provide all the niceties that you would expect from scripting in Python or Ruby, including features such as:

  • rich suite of Unix-like utilities
  • advanced sub-process management
  • POSIX support
  • light-weight idioms for exception safety and automatic resource disposal

Some command-line tools written in Haskell:

Educational resources:

🌱 Data science

Notable libraries:

Commentary

Haskell data science can take advantage of other data science ecosystems via the HaskellR and Sparkle projects. HaskellR is a Haskell-to-R bridge with Jupyter notebook integration, which lets you take advantage of the broad R ecosystem while benefiting from the speed and type safety of Haskell. Sparkle is a Haskell-to-Spark bridge which lets you interface with the Spark subset of the Java/Scala data science ecosystem. However, to get a Mature rating Haskell data science needs to be able to stand alone without depending on other programming language ecosystems.

If you restrict yourself to just the Haskell ecosystem then choices are more limited.

The Haskell analog of Python's NumPy is the hmatrix library, which provides Haskell bindings to BLAS, LAPACK. hmatrix's main limitation is that the API is a bit clunky, but all the tools are there.

Haskell's charting story is okay. Most charting APIs tend to be large, the types are a bit complex, and they have a very large number of dependencies.

Fortunately, Haskell does integrate into IPython so you can use Haskell within an IPython shell or an online notebook. For example, there is an online "IHaskell" notebook that you can use right now located here:

If you want to learn more about how to setup your own IHaskell notebook, visit this project:

The closest thing to Python's pandas is the frames library.

One Haskell library that deserves honorable mention here is the diagrams library which lets you produce complex data visualizations very easily if you want something a little bit fancier than a chart. Check out the diagrams project if you have time:

Areas for improvement:

  • Smooth user experience and integration across all of these libraries
  • Simple types and APIs. The data science programmers I know dislike overly complex or verbose APIs
  • Beautiful data visualizations with very little investment

🌱 Numerical programming

Notable libraries:

Commentary

Haskell's numerical programming story is not ready, but steadily improving.

The biggest issues that the ecosystem faces are:

  • Really clunky matrix library APIs
  • Fickle rewrite-rule-based optimizations

When the optimizations work they are amazing and produce code competitive with C. However, small changes to your code can cause the optimizations to suddenly not trigger and then performance drops off a cliff.

There is one Haskell library that avoids this problem entirely: accelerate generates LLVM and CUDA code at runtime and does not rely on Haskell's optimizer for code generation, which side-steps the problem. accelerate has a large set of supported algorithms that you can find by just checking the library's reverse dependencies:

Success Stories:

Educational Resources:

🌱 Front-end web programming

Notable libraries:

Commentary

This boils down to Haskell's ability to compile to JavaScript and WASM. Upcoming GHC versions will allow compilation to both, but for now the technology is experimental.

There are two Haskell-like languages for front-end programming: elm and purescript. These are both used in production today and have equally active maintainers and communities of their own. purescript in particular is extremely similar to Haskell.

Areas for improvement:

  • lack of clear story for smooth integration with existing JavaScript projects
  • lack of educational resources targeted at non-experts explaining how to translate existing front-end programming idioms to Haskell
  • lack of well-maintained and polished Haskell libraries for front-end programming
  • lack of documentation for ghcjs ecosystem. There's not even a basic tutorial on how to actually use ghcjs

Notable Haskell-to-JavaScript compilers:

🌱 Distributed programming

Notable libraries:

Commentary

For distributed service architectures Haskell is catching up to its peers with service toolkit libraries, but for distributed computation Haskell still lags behind.

There has been a lot of work in replicating Erlang-like functionality in Haskell through the Cloud Haskell project, not just in creating the low-level primitives for code distribution / networking / transport, but also in assembling a Haskell analog of Erlang's OTP. Work on the higher-level libraries seems to have stopped, but the low-level libraries are still good for distributing computation.

Areas for improvement:

  • More analytics libraries needed. Haskell has no analog of scalding or spark. The most we have is just a Haskell wrapper around hadoop
  • A polished consensus library (i.e. a high quality Raft implementation in Haskell) needed.

🌱 Standalone GUI applications

Notable libraries:

  • brick - Terminal UI based on vty package
  • threepenny-gui - Framework for local apps that use the web browser as the interface
  • gi-gtk and various other bindings such as GStreamer audio/video - GTK+ (and more generally, GObject) bindings done right (autogenerated using GObject Introspection, hence gi)
  • wx - wxWidgets bindings
  • X11 - X11 bindings
  • hsqml - A Haskell binding for Qt Quick, a cross-platform framework for creating graphical user interfaces.
  • fltkhs - A Haskell binding to FLTK. Easy install/use, cross-platform, self-contained executables.
  • FregeFX - Frege bindings to Java FX (Frege is essentially the Haskell for the JVM)
  • typed-spreadsheet - Library for building composable interactive forms
Commentary

Most Haskell GUI libraries are wrappers around toolkits written in other languages (such as GTK+ or Qt). However, the Haskell bindings to GTK+ have a strongly imperative feel to them. The way you do everything is communicating between callbacks by mutating IORefs. Also, you can't take extensive advantage of Haskell's awesome threading features because the GTK+ runtime is picky about what needs to happen on certain threads.

There still isn't a Haskell binding to a widget toolkit that doesn't have some sort of setup issues with the toolkit.

My impression is that most Haskell programmers interested in applications programming have collectively decided to concentrate their efforts on improving Haskell web applications instead of standalone GUI applications. Honestly, that's probably the right decision in the long run.

Another post that goes into more detail about this topic is this post written by Keera Studios:

Areas for improvement:

  • A GUI toolkit binding that is maintained, comprehensive, and easy to use
  • Polished GUI interface builders

Some example applications:

Educational resources:

🌱 Machine learning

Notable libraries:

  • hasktorch - Haskell bindings to libtorch which is the C++ API for PyTorch
  • ad - Automatic differentiation, used as a substrate for many Haskell machine learning projects
  • backprop - AD for heterogenous types
  • ad-delcont
  • grenade - Machine learning library implemented in Haskell with a BLAS/LAPACK backend and a high-level type-based API
  • tensorflow - Haskell bindings to Google's tensorflow project
  • arrayfire - Haskell bindings to ArrayFire
Commentary

There are two approaches to using machine learning in Haskell:

  • Use a Haskell binding to an implementation in another language
  • Use a machine learning library implemented in Haskell

You will most likely want to check out Haskell bindings to the libtorch library if you are interested in the first approach.

Also, Tweag.io has released Sparkle, a Haskell integration with Spark. This enables the use of MLib from Haskell. MLib is widely used in the industry for machine learning. Sparkle itself is fairly new.

🌱 Game programming

Notable libraries:

  • gloss - Simple graphics and game programming for beginners
  • Yampa - A reactive programming library which has been used to implement games in a reactive style
  • Code World - Similar to gloss, but you can try it in your browser
  • vulkan - Low-level Vulkan bindings
  • gl - Comprehensive OpenGL bindings
  • SDL / SDL-* / sdl2 - Bindings to the SDL library
  • SFML - Bindings to the SFML library
  • quine - Github project with cool 3D demos
  • GPipe - Type-safe OpenGL API that also lets you embed shader code directly within Haskell. See the GPipe wiki to learn more
Commentary

Haskell is a garbage collected language, so Haskell is more appropriate for the scripting / logic layer of a game but not suitable manipulating a large object graph or for implementing a high-performance game engine due to the risk of introducing perceptible pauses due to GC pauses. For simple games you can realistically use Haskell for the entire stack.

Examples of games that could be fully implemented in Haskell:

  • Casual games
  • Turn-based strategy games
  • Adventure games
  • Platform / side-scrolling games
  • First-person shooter

Examples of games that are difficult to implement at all in Haskell:

  • Real-time strategy games
  • MMORPGs

Haskell has SDL, OpenGL, and Vulkan bindings, which are actually quite good, but that's about it. You're on your own from that point onward. There is not a rich ecosystem of higher-level libraries built on top of those bindings. There is some work in this area, but nothing production quality or easy to use.

The primary reason for the immature rating is the difficulty of integrating Haskell with existing game platforms, which often are biased towards a particular language or toolchain. The only game platform where Haskell has no issues is native binaries for desktop games. For the web, you must compile to JavaScript, which is doable. For mobile games on Android you have to cross compile and interface the Haskell logic with Android through JNI + Haskell's foreign function interface. For console games, you have no hope.

Areas for improvement:

  • Improve the garbage collector and benchmark performance with large heap sizes
  • Provide higher-level game engines
  • Improve distribution of Haskell games on proprietary game platforms

Educational resources:

🌱 ARM processor support

Commentary

On hobbyist boards like the Raspberry Pi its possible to compile Haskell code with GHC. There are limitations; some libraries have problems on the arm platform, and GHCi only works on newer compilers. Cross compiling doesn't work with template Haskell. Stack and other large projects can take more than 1g of memory to compile.

However, if the Haskell code builds, it runs with respectable performance on these machines.

Arch (Banana Pi)

update 2016-02-25:

  • installed today from pacman, current versions are GHC 7.10.3 and cabal-install 1.22.6.0
  • a compatible version of llvm also installed automatically.
  • GHCi passes hello world test; cabal/GHC compiled a modest project normally.

Raspian (Raspberry Pi, pi2, others)

  • current version: GHC 7.4, cabal-install 1.14
  • GHCi doesn't work.

Debian Jesse (Raspberry Pi 3)

  • works with: ghc-7.10.3 and stack-1.1.2
  • Requires llvm version 3.5.2 or higher. Do not use the llvm-3.5 provided by default in the Jessie package distribution

Arch (Raspberry Pi 2)

  • current version 7.8.2, but llvm is 3.6, which is too new.
  • downgrade packages for llvm not officially available.
  • with llvm downgrade to 3.4, GHC and GHCi work, but problems compiling yesod, scotty.
  • compiler crashes, segfaults, etc.

🌱 Computer Vision

Notable libraries:

Commentary

The largest real world Haskell usage of computer vision is LumiGuide, which powers municipal bicycle detection and guidance systems in Amsterdam. They maintain OpenCV bindings in their haskell-opencv library.

There are some interesting projects which try to tackle computer vision in a purely functional manner. cv-combinators, easyVision, and Zef are some examples.

There are Haskell bindings for OpenCV available via HOpenCV which has bindings for versions up to OpenCV 2.0. A fork maintained by Anthony Cowley has bindings available for versions up to OpenCV 2.4, but it pretty much stops there. Currently, OpenCV 3.0 has been released, and there are no Haskell bindings covering it.

Success Stories:

🌱 Mobile apps

Commentary

This greatly lags behind using languages that are natively supported by the mobile platform (i.e. Java for Android or Objective-C / Swift for iOS).

However, one route is to compile Haskell to a supported language. For example, you can compile Haskell to Java using Eta to port Haskell games to Android.

Educational resources:

🌱 Databases and data stores

Notable libraries:

Commentary

The "Immature" ranking is based on the lack of bindings to commercial databases like Microsoft SQL server and Oracle. So whether or not Haskell is right for you probably depends heavily on whether there are bindings to the specific data store you use.

🌱 Debugging

Educational resources:

Commentary

The main Haskell debugging features are:

  • Memory and performance profiling
  • Stack traces
  • Source-located errors, using the assert function
  • Breakpoints, single-stepping, and tracing within the GHCi REPL
  • Informal printf-style tracing using Debug.Trace
  • ThreadScope

🌱 Hot code loading

Notable libraries:

🌱 Systems / embedded programming

Educational resources:

Commentary

Systems programming here means: programs where speed, memory layout, and latency really matter.

Haskell fares really poorly in this area because:

  • The language is garbage collected, so there are no latency guarantees
  • Executable sizes are large
  • Memory usage is difficult to constrain (thanks to space leaks)
  • Haskell has a large and unavoidable runtime, which means you cannot easily embed Haskell within larger programs
  • You can't easily predict what machine code that Haskell code will compile to

Typically people approach this problem from the opposite direction: they write the low-level parts in C or Rust and then write Haskell bindings to the low-level code.

It's worth noting that there is an alternative approach which is Haskell DSLs that are strongly typed that generate low-level code at runtime. This is the approach championed by the company Galois.

Notable libraries:

  • copilot - Stream DSL that generates C code
  • atom / ivory - DSL for generating embedded programs
  • improve - High-assurance DSL for embedded code that generates C and Ada
Last update: February 21, 2023
Created: February 8, 2023