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Koka: a function-oriented language with effect inference

Note: Koka v2 is a research language that currently under heavy development with the new evidence translation and C backend -- various features may be lacking, documentation may be outdated, not all tests will run, and there may be bugs. Use the branch v1-master to use the older stable Koka v1 with the Javascript backend.

Koka is a strongly typed, strict functional language which tracks the (side) effects of every function in its type. Koka syntax is Javascript/C like, the evaluation is strict like OCaml/C, and the type- and effect system is Haskell like, where pure and effectful computations are distinguished. The precise effect typing gives Koka rock-solid semantics backed by well-studied category theory, which makes Koka particularly easy to reason about for both humans and compilers. (Given the importance of effect typing, the name Koka was derived from the Japanese word for effective (Kōka, 効果)).

A function without any effect is called total and corresponds to mathematically total functions -- a good place to be. Then we have effects for partial functions that can raise exceptions, as exn, and potentially non-terminating functions as div (divergent). The combination of exn and div is called pure as that corresponds to Haskell's notion of purity. On top of that we find mutability (as st) up to full non-deterministic side effects in io.

Koka also has full support for algebraic effect handlers. This enables powerful control-flow abstraction that allows users to define compositional control-flow abstractions as a library; this includes advanced abstractions like exceptions, iterators, async-await concurrency, ambient state, backtracking parser combinators, probablistic programming, Bayesian machine learning, etc. Algebraic effect handlers subsume (free) monads, and are compositional without needing lifting or monad transformers.

Recent work on evidence translation and Perceus precise compiler guided reference counting enable Koka to compile directly to plain C code without needing a garbage collector or runtime system. Initial performance benchmarks are promising (see below), and it is our goal to generally fall within a factor 2× of C++ performance without needing manual memory management.

For more background information, see:

Enjoy, Daan Leijen

Special thanks to:

  • Ningning Xie: for her work on the theory and practice of evidence translation for algebraic effect handlers [6].
  • Alex Reinking: for the ongoing work on the Perceus reference counting analysis.
  • And all previous interns working on earlier versions of Koka: Daniel Hillerström, Jonathan Brachthäuser, Niki Vazou, Ross Tate, and Edsko de Vries.

Main branches:

  • master: latest stable version.
  • dev: current development branch -- submit PR's to this branch.
  • v1-master: last stable version of Koka v1: this is Koka with the Javascript (and C#) backend which does not use evidence translation. This version still supports more features (like named handlers and std/async) and should compile examples from published papers.

Installing the compiler

At this point there are no binary releases of Koka and you need to build the compiler yourself. Fortunately, Koka has few dependencies and should build without problems on most common platforms, e.g. Windows (including WSL), macOS X, and Unix.

The following programs are required to build Koka:

  • Stack to run the Haskell compiler.
    (use > curl -sSL | sh on Unix and macOS X)
  • CMake to compile the generated C files.
    (use > sudo apt-get install cmake on Ubuntu, > brew install cmake on macOS X).
  • Optional: The Ninja build system for faster build times.
    (required on Windows, use > sudo apt-get install ninja-build on Ubuntu, > brew install ninja on macOS X).
  • Optional: the NodeJS runtime if using the Javascript backend.

Building Koka (note the --recursive flag):

> git clone --recursive
> cd koka
> stack build

You can also use stack build --fast to build a debug version of the compiler. You can invoke the compiler now as: (this takes a while as it needs to build the core libraries as well)

> stack exec koka -- -c test/algeff/common
compile: test/algeff/common.kk
loading: std/core
loading: std/core/types
loading: std/core/hnd
check  : test/algeff/common
cmake --build "out\Debug\cbuild" --target test_algeff_common
[5/5] Linking C executable test_algeff_common.exe
compiled: out\Debug\test_algeff_common.exe

and run the resulting executable:

> out\Debug\test_algeff_common.exe
Hello there, there

If you leave out the -c flag, Koka will execute the compiled program automatically. The -O2 flag builds an optimized program. Let's try it on a functional implementation of balanced insertion in a red-black tree balanced (rbtree.kk)

> stack exec koka -- -O2 -c test/bench/koka/rbtree32.kk
cmake --build "out/RelWithDebInfo/cbuild" --target test_bench_koka_rbtree32
[15/15] Linking C executable test_bench_koka_rbtree32
compiled: out/RelWithDebInfo/test_bench_koka_rbtree32

> time out/RelWithDebInfo/test_bench_koka_rbtree32
real    0m1.132s

We can compare this against an in-place updating C++ implementation using stl::map (rbtree.cpp) (which uses the GNU RBTree implementation internally):

> g++ --std=c++17 -o cpp_rbtree -O3 test/bench/cpp/rbtree.cpp
> time ./cpp_rbtree
real    0m1.096s

The close performance to C++ here is a result of Perceus automatically tranforming the fast path of the pure functional rebalancing to use mostly in-place updates, closely mimicking the imperative rebalancing code of the hand optimized C++ library.

Without giving any input files, the interpreter runs by default:

> stack exec koka

The Atom text editor is recommended to edit Koka programs. You can install support for Koka programs using

> jake atom

(or use jake sublime) for the Sublime editor). If node is not installed, you can also copy the grammar files manually from the support/atom directory to ~/.atom/packages/language-koka.

Running the interactive compiler

After running the plain stack exec koka command, the Koka interactive environment will start:

 _          _           ____
| |        | |         |__  \
| | __ ___ | | __ __ _  __) |
| |/ // _ \| |/ // _` || ___/ welcome to the koka interpreter
|   <| (_) |   <| (_| ||____| version 2.0.0-alpha, Aug 23 2020, libc 64-bit
|_|\_\\___/|_|\_\\__,_|       type :? for help

loading: std/core
loading: std/core/types
loading: std/core/hnd


Now you can test some expressions:

> println("hi koka")
loading: std/core
loading: std/core/types
loading: std/core/hnd
check  : interactive
cmake --build "out\Debug\cbuild" --target interactive
[2/2] Linking C executable interactive.exe
compiled: out\Debug\interactive.exe

hi koka

> :t "hi"

> :t println("hi")
console ()

Or load a demo:

> :l test/medium/fibonacci
compile: test/medium/fibonacci.kk
loading: std/core
loading: std/core/types
loading: std/core/hnd
check  : test/medium/fibonacci

> main()
cmake --build "out/Debug/cbuild" --target interactive
[2/2] Linking C executable interactive
compiled: out/Debug/interactive

The 10000th fibonacci number is 33644764876431783266621612005107543310302148460680063906564769974680081442166662368155595513633734025582065332680836159373734790483865268263040892463056431887354544369559827491606602099884183933864652731300088830269235673613135117579297437854413752130520504347701602264758318906527890855154366159582987279682987510631200575428783453215515103870818298969791613127856265033195487140214287532698187962046936097879900350962302291026368131493195275630227837628441540360584402572114334961180023091208287046088923962328835461505776583271252546093591128203925285393434620904245248929403901706233888991085841065183173360437470737908552631764325733993712871937587746897479926305837065742830161637408969178426378624212835258112820516370298089332099905707920064367426202389783111470054074998459250360633560933883831923386783056136435351892133279732908133732642652633989763922723407882928177953580570993691049175470808931841056146322338217465637321248226383092103297701648054726243842374862411453093812206564914032751086643394517512161526545361333111314042436854805106765843493523836959653428071768775328348234345557366719731392746273629108210679280784718035329131176778924659089938635459327894523777674406192240337638674004021330343297496902028328145933418826817683893072003634795623117103101291953169794607632737589253530772552375943788434504067715555779056450443016640119462580972216729758615026968443146952034614932291105970676243268515992834709891284706740862008587135016260312071903172086094081298321581077282076353186624611278245537208532365305775956430072517744315051539600905168603220349163222640885248852433158051534849622434848299380905070483482449327453732624567755879089187190803662058009594743150052402532709746995318770724376825907419939632265984147498193609285223945039707165443156421328157688908058783183404917434556270520223564846495196112460268313970975069382648706613264507665074611512677522748621598642530711298441182622661057163515069260029861704945425047491378115154139941550671256271197133252763631939606902895650288268608362241082050562430701794976171121233066073310059947366875

And quit the interpreter:

> :q

Before the effect one believes in different causes than one does after the effect.
 -- Friedrich Nietzsche

Algebraic effect handlers

A novel feature of Koka is a compiled and typed implementation of algebraic effect handlers (described in detail in [3]). In the interactive environment, you can load various demo files with algebraic effects which are located in the test/algeff directory.

> :f test/algeff/common

where :f forces a recompile (versus :l which avoids a recompile if possible). Use the :? command to get an overview of all commands. After loading the common demo, we can run it directly from the interpreter:

> :f test/algeff/common
loading: test/algeff/common
loading: std/core
loading: std/core/types
loading: std/core/hnd

> :t test2    
() -> console ()

> test2()
loading: std/core
loading: std/core/types
loading: std/core/hnd
loading: test/algeff/common
check  : interactive
cmake --build "out/Debug/cbuild" --target interactive
[2/2] Linking C executable interactive
compiled: out/Debug/interactive

Hello there, there

Some interesting demos are:

  • common.kk: Various examples from the paper "Algebraic Effects for Functional Programming" [3]. Shows how to implement common control-flow abstractions like exceptions, state, iterators, ambiguity, and asynchronous programming.

  • nim.kk: Various examples from the paper "Liberating effects with rows and handlers" [1].


There is a standard benchmark suite. It is still basic but more benchmarks with effect handlers are coming. We only test on Linux and the benchmarks need gcc, ghc (should be there already), ocamlopt (use sudo apt-get install ocaml), and swiftc in the path. (On Linux (or WSL) the Swift compiler can be downloaded here and the benchmarks expect switfc to be installed at /opt/swift/bin). The benchmarks are build using:

> cd test/bench
> mkdir build
> cd build
> cmake .. -DCMAKE_BUILD_TYPE=Release
> cmake --build .

We can then run all benchmarks as:

> ctest .

Or only run benchmarks for one language with -L <lang>:

> ctest -L koka

Or run specific benchmarks using -R <regex>, like the symbolic derivative benchmark:

> ctest -R deriv      
Test project /home/daan/dev/koka/test/bench/build
    Start  4: hs-deriv
1/4 Test  #4: hs-deriv .........................   Passed    2.29 sec
    Start 10: kk-deriv
2/4 Test #10: kk-deriv .........................   Passed    1.25 sec
    Start 19: ml-deriv
3/4 Test #19: ml-deriv .........................   Passed    1.73 sec
    Start 25: sw-deriv
4/4 Test #25: sw-deriv .........................   Passed    2.88 sec

100% tests passed, 0 tests failed out of 4


To run tests, use stack:

> stack test                                              # All tests
> stack test --test-arguments="--match /parc/"            # One category
> stack test --test-arguments="--mode new"                # Create output files
> stack test --test-arguments="--mode update"             # Update output files
> stack test --test-arguments="--match /parc/ --mode new" # Combined



On Windows, Koka's C backend can compile with the Stack-supplied MinGW compiler. However, the MinGW runtime libraries are not added to the PATH by default. In this case, you can prefix any command with stack exec (not just those that Stack itself built). For example, to use Intel VTune to profile a Koka program:

stack exec "C:\Program Files (x86)\IntelSWTools\VTune Profiler 2020\bin64\vtune-gui.exe"

More on Evidence Translation and Perceus

Koka compiles directly to plain C code without needing a garbage collector or runtime system. There are two crucial ingredients to make this possible: evidence translation and Perceus.

Evidence translation

As described in the paper Effect Handlers, Evidently, Xie et al. [6] show how to translate algebraic effect handlers at compilation time down to pure lambda calculus where all control flow is explicit again. This is done by Koka to remove any dependence on runtime mechanisms like split-stacks (as in Multi-core OCaml) or C stack copying [7]. Moreover, as the evidence for each handler is passed down to the call site, all tail-resumptive operations can be executed in-place without needing to do an expensive yield- and resume. This makes the cost of tail-resumptive operations on effects comparable to a virtual method call.


Even a pure core intermediate language with explicit control flow is not yet good enough to compile to C directly: without manual memory management functional languages still need a (tracing) garbage collector (like OCaml or Haskell). A well performing concurrent generational garbage collector is very hard to build and is invasive as it needs to be able to scan the roots and stack. Even the best garbage collectors still suffer from unpredictable latencies (especially with large live sets) and tend to require (much) more memory than achievable with manual memory management (as with C/C++ and Rust).

With Koka we took a new approach based on reference counting. The usual wisdom is that reference counting does not perform well due to various factors but in Koka we believe we can do better: 1) we know that all inductive and co-inductive datatypes are never cyclic so we can identify potential cycle introducing datatypes statically (like mutable references, and these are not so often used in mostly functional Koka), 2) again due to the strict type system we can statically track which values may become shared across threads and avoid expensive atomic operations for the majority of operations, and finally 3) due to the explicit control-flow we can do deep analysis on variable life times. In particular, we use aggressive static analysis to insert precise reference count instructions where memory is freed as soon as it is no longer live (and in particular, we do not hold on to memory based on lexical scope as in almost all reference counting implementations in the wild, like Swift, Python, C++ shared_ptr etc).

Perceus stands for Precise automatic reference counting with reuse and specialization: the reuse component transform functional style pattern matches into in-place update when possible, while specialization specialize the reference counting based on the call sites and removes most rc operations in the fast path. For example, a simple map function:

fun map( xs : list<a>, f : a -> e b ) : e list<b> {
  match(xs) {
    Cons(x,xx) -> Cons( f(x), map(xx,f) )
    Nil        -> Nil

will update the list in place (reusing the Cons nodes that are matched) if the list happens to be not shared (and makes a copy otherwise). This dynamically adjust the program from in-place update to persistence and is the main reason why it can approach the performance of hand-optimized C++ on the red-black tree benchmark.

Talk and paper are coming soon...

Things to do

The following is the immediate todo list to be completed in the coming months:

  • Port all libray modules, in particular std/text/regex (using PCRE), std/os/file, and std/async (using libuv).
  • Run the full test suite again.
  • Remove dependency on Jakefile and use stack only.
  • Support local state inside a handler without needing mask<local>.
  • Support named effect handlers again.
  • Improve syntax for ambient values, functions, and control.
  • Improve syntax for applications (now disallows whitespace between the function and arguments).
  • Run the Bayesian machine learning program with large parameters.
  • Tune code generation better; the output is still too large.

And future projects:

  • Implement inline specialization where functions like map, fold etc get specialized for the function with which they are called.
  • Various standard optimizations like case-of-case, join points, case-of-known constructor, etc.
  • Borrowing analysis for Perceus.
  • Known reference count specialization.
  • Improve compilation of local state to use local variables directly (in C).
  • Package management of Koka modules.
  • Functions with a pattern match in the argument.

Contact me if you are interested in tackling some of these :-)


  1. Daniel Hillerström, and Sam Lindley. “Liberating Effects with Rows and Handlers.” In Proceedings of the 1st International Workshop on Type-Driven Development, 15--27. TyDe 2016. Nara, Japan. 2016. doi:10.1145/2976022.2976033.

  2. Daan Leijen. “Koka: Programming with Row Polymorphic Effect Types.” In Mathematically Structured Functional Programming 2014. EPTCS. Mar. 2014. arXiv:1406.2061.

  3. Daan Leijen. Algebraic Effects for Functional Programming. MSR-TR-2016-29. Microsoft Research. Aug. 2016. Extended version of [4].

  4. Daan Leijen. “Type Directed Compilation of Row-Typed Algebraic Effects.” In Proceedings of Principles of Programming Languages (POPL’17). Paris, France. Jan. 2017.

  5. Nicolas Wu, Tom Schrijvers, and Ralf Hinze. “Effect Handlers in Scope.” In Proceedings of the 2014 ACM SIGPLAN Symposium on Haskell, 1--12. Haskell ’14. ACM, New York, NY, USA. 2014. doi:10.1145/2633357.2633358

  6. Ningning Xie, Jonathan Brachthäuser, Daniel Hillerström, Philipp Schuster, Daan Leijen. “Effect Handlers, Evidently” The 25th ACM SIGPLAN International Conference on Functional Programming (ICFP), August 2020. doi:10.1145/3408981, pdf

  7. Ningning Xie and Daan Leijen. “Effect Handlers in Haskell, Evidently” The 13th ACM SIGPLAN International Haskell Symposium, August 2020. pdf

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