ᚣ The Rune Programming Language

A faster, safer, and more productive systems programming language

This is not an officially supported Google product.

NOTE: Rune is an unfinished language. Feel free to kick tires and evaluate the cool new security and efficiency features of Rune, but, for now, it is not recommended for any production use case.

• ᚣ The Rune Programming Language
• What is Rune?
• Rune in Action
  • Dedicated "secret" types
  • A "DB-embedded" language: relational data-structures and optimal memory-efficiency
    • How does this work?
• Installation
  • Compiling the Rune compiler:

What is Rune?

Rune is a Python-inspired efficient systems programming language designed to interact well with C and C++ libraries. Rune has many security features such as memory safety, and constant-time processing of secrets. Rune aims to be faster than C++ for most memory-intensive applications, due to its Structure-of-Array (SoA) memory management.

It provides many of its features by deeply integrating features similar to the "DataDraw" tool into the primitives and constructs of the language. DataDraw is a code-generation tool that generates highly-optimized C code which outperforms e.g., the C++ STL given a declarative description of data-structures and relationships between them. For more information, see the DataDraw 3.0 Manual.

Additional documentation:

• Rune Overview
• Rune for Python programmers
• DataDraw 3.0 Manual
• DataDraw GitHub repository

Rune in Action

Let's take a look at two examples that showcase many of the unique features of Rune:

1. A simple example of a secret type, and how it can be used to protect against timing attacks.
2. A more complex example of a data-structure with nullable, self-referential recursive references.
   • (This can present footguns in many languages, but it is trivial in Rune)

Dedicated "secret" types

Consider the following example for treatment of secrets:

// Check the MAC (message authentication code) for a message.  A MAC is derived
// from a hash over the `macSecret` and message.  It ensures the message has not
// been modified, and was sent by someone who knows `macSecret`.
func checkMac(macSecret: secret(string), message: string, mac: string) -> bool {
    computedMac = computeMac(macSecret, message)
    return mac == computedMac
}

func computeMac(macSecret: string, message: string) -> string {
  // A popular MAC algorithm.
  return hmacSha256(macSecret, message)
}

Can you see the potential security flaw? In most languages, an attacker with accurate timing data can forge a MAC on a message of their choice, causing a server to accept it as genuine.

Assume the attacker can tell how long it takes for mac == computedMac to run. If the first byte of an attacker-chosen mac is wrong for the attacker-chosen message, the loop terminates after just one comparison. With 256 attempts, the attacker can find the first byte of the expected MAC for the attacker-controlled message. Repeating this process, the attacker can forge an entire MAC.

Users of Rune are protected, because the compiler sees that macSecret is secret, and thus the result of hmacSha256 is secret. The string comparison operator, when either operand is secret, will run in constant time, revealing no timing information to the attacker. Care must still be taken in Rune, but many common mistakes like this are detected by the compiler, and either fixed or flagged as an error.

A "DB-embedded" language: relational data-structures and optimal memory-efficiency

As for the speed and safety of Rune's memory management, consider a simple Human class. This can be tricky to model in some languages, yet is trivial in both SQL and Rune.

class Human(self: Human, name: string, mother: Human? = null(self), father: Human? = null(self)) {
  self.name = name

  // The methods "appendMotheredHuman" and "appendFatheredHuman" are generated
  // by code "transformers", a key factor in Rune's performance.
  //
  // We'll learn more about these methods in the next section ("How does this work?").
  if !isnull(mother) {
    mother.appendMotheredHuman(self)
  }
  if !isnull(father) {
    father.appendFatheredHuman(self)
  }

  func printFamilyTree(self, level: u32) {
    for i in range(level) {
      print "    "
    }
    println self.name
    for child in self.motheredHumans() {
      child.printFamilyTree(level + 1)
    }
    for child in self.fatheredHumans() {
      child.printFamilyTree(level + 1)
    }
  }
}

// Relation statements are similar to "Foreign Key" constraints in SQL.
// They are used to define the relationships between data-structures.
//
// The "cascade" keyword means that when a Human is destroyed, all of
// its children are recursively destroyed.
// (In other words, manual invalidation of pointers is not required)
//
// This ability is also provided by code transformers.  See
// builtin/doublylinked.rn
relation DoublyLinked Human:"Mother" Human:"Mothered" cascade
relation DoublyLinked Human:"Father" Human:"Fathered" cascade

// Let's create a family tree.
adam = Human("Adam")
eve = Human("Eve")
cain = Human("Cain", eve, adam)
abel = Human("Abel", eve, adam)
alice = Human("Alice", eve, adam)
bob = Human ("Bob", eve, adam)
malory = Human("Malory", alice, abel)
abel.destroy()
adam.printFamilyTree(0u32)
eve.printFamilyTree(0u32)

When run, this prints:

Adam
    Cain
    Alice
    Bob
Eve
    Cain
    Alice
    Bob

How does this work?

Note that Abel and Malory are not listed. This is because we didn't just kill Abel, we destroyed Abel, and this caused all of Abel's children to be recursively destroyed. Also note that Rune now supports null safety. Null safety does not mean null does not exist in the language. It means types by default cannot be null. This can be overridden with <type>? in the type constraint.

Relation statements are similar to columns in SQL tables. A table with a Mother and Father column has two many-to-one relations in a database.

Relation statements give the Rune compiler critical hints for memory optimization. Objects which the compiler can prove are always in cascade-delete relationships do not need to be reference counted. The relation statements also inform the compiler to update Human's destructor to recursively destroy children. Rune programmers never write destructors, removing this footgun from the language.

To understand why Rune's generated SoA code is so efficient, consider the arrays of properties created for the Human example above:

nextFree = [null(Human)]
motherHuman = [null(Human)]
prevHumanMotheredHuman = [null(Human)]
nextHumanMotheredHuman = [null(Human)]
firstMotheredHuman = [null(Human)]
lastMotheredHuman = [null(Human)]
fatherHuman = [null(Human)]
prevHumanFatheredHuman = [null(Human)]
nextHumanFatheredHuman = [null(Human)]
firstFatheredHuman = [null(Human)]
lastFatheredHuman = [null(Human)]
name = [""]

A total of 12 arrays are allocated for the Human class in SoA memory layout. In printFamilyTree, we only access 5 of them. In AoS memory layout, all 12 fields would be loaded into cache during the tree traversal, and all fields would be 64 bits on a 64-bit machine. In Rune, only the string references are 64-bits by default. As a result, Rune loads only 25% as much data into cache during the traversal, improving memory load times, while simultaneously improving cache hit rates.

This is why Rune's binary_trees.rn code already runs faster than any other single-threaded result in the Benchmark Games. (Rune is not yet multi-threaded). The only close competitor is C++, where the author uses the little-known std::pmr::monotonic_buffer_resource class from the <memory_resource> library. Not only is Rune's SoA memory layout faster, but its solution is more generic: we can create/destroy Node objects arbitrarily, unlike the C++ benchmark based on std::pmr::monotonic_buffer_resource. When completed, we expect Rune to win most memory-intensive benchmarks.

Installation

Compiling the Rune compiler:

You'll need 6 dependencies installed to compile Rune:

• Bison (parser generator)
• Flex (lexer generator)
• GNU multi-precision package gmp
• Clang version 10
• Datadraw, an SoA data-structure generator for C
• CTTK, a constant-time big integer arithmetic library The first four can be installed with one command:

$ sudo apt-get install bison flex libgmp-dev clang clang-14

Installing Datadraw requires cloning the source from github.

$ git clone https://github.com/waywardgeek/datadraw.git
$ sudo apt-get install build-essential
$ cd datadraw
$ ./autogen.sh
$ ./configure
$ make
$ sudo make install

Hopefully that all goes well... After dependencies are installed, to build rune:

$ git clone https://github.com/google/rune.git
$ git clone https://github.com/pornin/CTTK.git
$ cp CTTK/inc/cttk.h CTTK
$ cd rune
$ make

CTTK was written by Thomas Pornin. It provides constant-time big-integer arithmetic.

If make succeeds, test the Rune compiler in the rune directory with:

$ ./runtests.sh

Some tests are currently expected to fail, but most should pass. To install rune under /usr/local/rune:

$ sudo make install

Test your installation:

$ echo 'println "Hello, World!"' > hello.rn
$ rune -g hello.rn
$ ./hello

You can debug your binary executable with gdb:

$ gdb ./hello

TODO: add instructions on how to debug the compiler itself, especially the datadraw debug functionality.