Inko progress report: September 2019

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The progress report for September 2019 is here! In September we released version 0.5.0 of Inko, and made more progress towards a self-hosting compiler.

Table of contents

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Inko 0.5.0 released

In September we released version 0.5.0 of Inko, four months after the previous 0.4.1 release. This release was packed with changes related to simplifying the syntax, which in turn makes it easier to port the Ruby compiler to Inko.

For more information about the 0.5.0 release, take a look at the 0.5.0 release post.

New standard library types and methods

As part of the compiler work we added new methods and types to the standard library, and re-organised several standard library modules so they are easier to maintain. Such methods include Path.join for joining file system paths, and Iterator.partition to partition iterators. For example, you can now join paths like so:

import std::fs::path::Path'/tmp').join('foo').to_string # => "/tmp/foo"

Path.join supports both Unix and Windows paths, including absolute Windows paths with drive letters:

import std::fs::path::Path'bar').join('C:\\foo').to_string # => "C:\\foo"

Partitioning an Iterator is also simple:

let pair =, 20, 30).iter.partition do (value) { value >= 20 }

pair.first  # =>, 30)
pair.second # =>

We also introduced the Pair and Triple types. A Pair is a tuple of two values, while a Triple contains three values:

import std::pair::(Pair, Triple), 'foo').first         # => 10, 'foo', 10.5).third # => 10.5

We decided not to support more than three values, as custom types are better suited for these cases. This decision is not unique to Inko, Kotlin made the same decision.

Progress for a self-hosting Inko compiler

In September we also started making serious progress towards a self-hosting compiler. There is a work-in-progress merge request, but since it's pretty light on details we'll try to cover everything interesting here.

Let's start with what we have so far:

  1. Several simple compiler passes, such as a pass used to desugar some parts of the AST.
  2. Types for storing configuration data.
  3. A basic setup for storing type information in a type database.

These are not too interesting to discuss, though I would like to highlight one simple pass. Not because it's an exciting one, but to showcase just how simple some of these passes can be. This pass is the pass called "HoistImports" and hoists all import expressions to the top of the module. The implementation is simple:

import std::compiler::ast::body::Body

object HoistImports {
  def run(body: Body) -> Body {
    let pair = body.children.iter.partition do (node) { node.import? } pair.first.append(pair.second), location: body.location)

This pass just grabs all import expressions (which can only occur at the top-level in a module), partitions the list of nodes, then merges the two arrays so that the imports appear first. There are no tricks or bamboozles here, that's all there is to it.

With the boring stuff out of the way, let's talk about the more interesting aspect of the compiler: the design, how we plan to make it fast, and how this compares to the Ruby compiler.

The Ruby compiler is a multi-pass compiler, and so will be the self-hosting compiler. In case of the Ruby compiler we could probably have benefited from using more passes, as right now there's a bit too much crammed into the passes it has. For the Inko compiler we will be separating work across more passes, though we won't go down the path of writing a nano-pass compiler.

The Ruby compiler we use today to compile Inko source code is a serial compiler, and a pretty slow one at that. Compiling all the standard library tests and modules they import takes just under 4.5 seconds. That's 4.5 seconds to compile 20 000 lines of code, excluding comments and including tests. The standard library itself contains just under 9 000 lines of code. This puts the compiler at a rate of about 4500 lines of code per second. This may sound impressive, but it means large projects would take a long time to compile. GitLab's codebase consists of over 700 000 lines of Ruby code. At 4500 lines per second it would take 155 seconds (2.6 minutes) to compile all the source code. That's long!

With this in mind we knew a serial compiler was not going to cut it. Even a well optimised serial compiler may need a long time to compile large projects. Short compile times are important, so we needed a solution. There are two approaches to solving this problem:

  1. Incremental compilation
  2. Parallel compilation

Incremental compilation means that you save some sort of state that you can use the next time, allowing you to skip files that do not need to be recompiled. Parallel compilation means compiling multiple files in parallel (but not necessarily compiling them incrementally).

For Inko we decided to focus on parallel compilation first, and take a look at incremental compilation in the future. Not because we believe parallel compilation is better, but because implementing just parallel compilation is hard enough already.

Building a parallel compiler brings an interesting question: how are the different threads (or lightweight processes in case of Inko) going to access shared data, such as the modules and types that have been defined thus far? In a language with shared memory, one might use synchronisation for this. In Inko, processes don't share data; they communicate by passing (and copying) messages. The data structures used for storing type information can get large, so copying these around will be expensive and is best avoided.

A naive approach would be to spawn a single process that stores all type information. Processes that compile source code communicate with this process to get type information, check if one type is compatible with another, etc:

Serial type communication

The problem with this approach is that all these processes are limited by how fast this type database process can respond to messages. For a small program this might not matter, but for larger programs this may result in (some) of the work being performed in serial.

Our current idea is to instead use multiple processes called "partitions" (inspired by the partitioning of databases). Each partition only stores type information; compilation is done by separate processes. A separate "registry" process is used to record which partition owns a certain module. To look up a type, a compiler process would request the partition for a given module that is imported, then use that partition for obtaining type information. Once a module is looked up, a compiler process may cache it so it does not need to request it again from the registry. The registry process exists so we don't need to scan over all partition to determine which one owns a module. This would not perform well if the number of partition is large, or when looking up lots of unique modules:

Linear module lookups

Instead of partitions sending (and thus copying) entire type data structures to compiler processes, they send type IDs. A type ID is a simple and lightweight data structure that is cheap to copy, storing only the ID of the type and the ID of the module that owns the type. If needed they may send more complex data structures, but in all cases they will be optimised to make it cheap to copy them.

Compiler processes communicate with these type partitions by sending messages known as "queries" (taken again from databases). A query can be a message such as "Give me the argument type IDs of method X", "Does type X respond to message Y?", etc. The Rust compiler uses a similar approach. Using separate type partitions and queries results in a flow of messages that looks like this:

Parallel type communication

Registry operations are performed in serial, as there is only one registry process. Since these operations are simple (they are just hash lookups), they won't create a bottleneck. Compiler processes will cache these lookups locally, so frequently imported modules only need a single lookup per compiler process. The use of type IDs is similar to entities in an Entity Component System. Compiler processes will use these type IDs and other data obtained from a type partition to perform type checking and inference.

Type checking and inference is done by walking a desugared AST and annotating nodes with their type IDs. We also need to store data such as local variable scoping for closures, but we haven't decided yet on how we will do this. Other passes such as optimisations and code generation will be easier to perform in parallel, as these passes do not mutate any shared data structures.

Our goal is to get the compiler to compile the standard library and all its tests in two seconds. For this to happen, we need to compile the code at 10 000 lines per second. Achieving this goal may prove difficult, so we may have to settle for longer compile times for the first version of the self-hosting compiler. Adding support for incremental compilation in the future will also help cut down compilation times, but we will postpone adding support for incremental compilation until deemed necessary.

Plans for October

In October we will continue working on the compiler, and hope to make serious progress on the type checking and inference passes. Other passes such as code generation will probably have to wait until November, depending on how much progress we make.