Lambda the Ultimate

Generics of a Higher Kind. Adriaan Moors, Frank Piessens, and Martin Odersky.

With Java 5 and C# 2.0, first-order parametric polymorphism was introduced in mainstream object-oriented programming languages under the name of generics. Although the first-order variant of generics is very useful, it also imposes some restrictions: it is possible to abstract over a type, but the resulting type constructor cannot be abstracted over. This can lead to code duplication. We removed this restriction in Scala, by allowing type constructors as type parameters and abstract types. This paper presents the design and implementation of the resulting type constructor polymorphism. It combines type constructor polymorphism with implicit parameters to yield constructs similar to, and at times more expressive than, Haskell’s constructor type classes. The paper also studies interactions with other object-oriented language constructs, and discusses the gains in expressiveness.

Many readers will already be aware that Scala has added support for higher-kinded generics, related to Haskell's type constructor classes. I believe Scala is the first language to provide this capability in an OO "generics" framework. This ECOOP submission presents this work, with many practical examples.

(Consider this penance for my last post...)

OCaml Light: a formal semantics for a substantial subset of the Objective Caml language.

OCaml light is a formal semantics for a substantial subset of the Objective Caml language. It is written in Ott, and it comprises a small-step operational semantics and a syntactic, non-algorithmic type system. A type soundness theorem has been proved and mechanized using the HOL-4 proof assistant, thereby ensuring that the proof is free from errors. To ensure that the operational semantics accurately models Objective Caml, an executable version of the semantics has been created (and proved equivalent in HOL to the original, relational version) and tested on a number of small test cases.

Note that while we have tried to make the semantics accurate, we are not part of the OCaml development team - this is in no sense a normative specification of the implemented language.

From a team including Peter Sewell (Acute, HashCaml, Ott).

I continue to believe that things are heating up nicely in mechanized metatheory, which, in the multicore/multiprocessor world in which we now live, is extremely good news.

Metaprogramming with Traits, Aaron Turon and John Reppy. ECOOP 2007

In many domains, classes have highly regular internal structure. For example, so-called business objects often contain boilerplate code for mapping database fields to class members. The boilerplate code must be repeated per-field for every class, because existing mechanisms for constructing classes do not provide a way to capture and reuse such member-level structure. As a result, programmers often resort to ad ho code generation. This paper presents a lightweight mechanism for specifying and reusing member-level structure in Java programs. The proposal is based on a modest extension to traits that we have termed trait-based metaprogramming. Although the semantics of the mechanism are straightforward, its type theory is difficult to reconcile with nominal subtyping. We achieve reconciliation by introducing a hybrid structural/nominal type system that extends Java's type system. The paper includes a formal calculus defined by translation to Featherweight Generic Java.

This paper explains how to scratch an itch I've had for a long, long time -- uniformly generating groups of fields and methods, including computation of the field/method names. Something like this would be quite useful in an ML-like language's module system, too.

Pardus Linux is a case study of functional Python. It's a Linux distribution built from semi-scratch, the main focii being package management and init subsystems - places where C and shell script make poor sense. A funded group has finally tackled these issues.

A package management software deals a lot with sets, lists, and dependency graphs....We have extensively used functional operators (map, filter, reduce) and list comprehensions, even metaclasses are used in a few places.

Someone nudge Guido. Scheme or Oz might have been the better choice, but give them credit. They admit frankly to social acceptance issues.

Combining Total and Ad Hoc Extensible Pattern Matching in a Lightweight Language Extension. Don Syme, Gregory Neverov and James Margetson.

Pattern matching of algebraic data types (ADTs) is a standard feature in typed functional programming languages but it is well known that it interacts poorly with abstraction. While several partial solutions to this problem have been proposed, few have been implemented or used. This paper describes an extension to the .NET language F# called ``Active Patterns'', which supports pattern matching over abstract representations of generic heterogeneous data such as XML and term structures, including where these are represented via object models in other .NET languages. Our design is the first to incorporate both ad hoc pattern matching functions for partial decompositions and ``views'' for total decompositions, and yet remains a simple and lightweight extension. We give a description of the language extension along with numerous motivating examples. Finally we describe how this feature would interact with other reasonable and related language extensions: existential types quantified at data discrimination tags, GADTs, and monadic generalizations of pattern matching.

Quite related to the recent discussions of the relationships between libraries, frameworks and language features.

A Real-World Use of Lift

Well, lift is actually being used in production. I converted a Rails app to lift and it was a very interesting experience...

Then we did some benchmarking. For single request processing, the lift code, running inside Tomcat, ran 4 times faster than the Rails code running inside Mongrel. However, the CPU utilization was less than 5% in the lift version, where it was 100% of 1 CPU (on a dual core machine) for the Rails version. For multiple simultaneous requests being made from multiple machines, we're seeing better than 20x performance of the lift code versus the Rails code with 5 Mongrel instances. Once again, the lift code is not using very much CPU and the Rails code is pegging both CPUs.

In terms of new features, we've been able to add new features to the lift code with fewer defects than with the Rails code. Our Rails code had 70% code coverage. We discovered that anything shy of 95% code coverage with Rails means that type-os turn into runtime failures. We do not have any code coverage metrics for the lift code, but we have seen only 1 defect that's been checked in in the 2 weeks since we started using lift (vs. an average of 1 defect per checkin with the Rails code.)

So, yes, I'm pimping my own framework, and yes, I'm able to do with lift what guys like DHH are able to do with Rails, so the comparison is, in some ways, unfair.

On the other hand, Scala and lift code can be as brief and expressive as Ruby code. lift offers developers amazing productivity gains vs. traditional Java web frameworks, just as Rails does. On the other hand, lift code scales much better than Rails code. lift code is type-safe and the compiler becomes your friend (this does not mean you should not write tests, but it means that your tests can focus on the algorithm rather than making sure there are no type-os in variable and method names.)

I promise that "Dave Pollak" is not a pseudonym for "Paul Snively."

Update: I guess the self-deprecating humor hasn't worked, some 400+ reads later. Although the caveat that Dave offers about trying to objectively compare his own framework with Ruby on Rails is well-taken, I think that this nevertheless is an important marker in applying a very PLT-driven language and framework, Scala and lift, to a very realistic application, especially given that it's a rewrite from a currently-popular language and framework, Ruby and Rails. We admitted proponents of static typing and weird languages are constantly being asked for this sort of thing, and while it's doubtful that this adds anything to the PLT discussion per se—at least until we have a chance to dig into lift and see how Scala's design uniquely supports it—I thought people might find the Scala connection worth commenting on.

Threads in JavaScript? "Over your dead body," says Brendan.

But Neil Mix begs to differ -- they're already there!

Neil's latest blog post presents a cool hack combining JavaScript 1.7's generators with trampolined style to implement very lightweight cooperative threads.

The implementation weighs in at a breathtakingly small 4k.

40 years after the invention of OO, I am ready to appreciate objects quite a bit because I can use them in combination with functional programming. Naturally, I call this mix functional OO programming. (I don’t quite count functional objects in C++ or ‘functors’ in Java, a misnomer BTW, as functional programming.)

Ralf lists several of his papers that apply the notion of functional OO programming. He also shares his wish list for future versions of C#.

Matching Objects With Patterns. Burak Emir, Martin Odersky, and John Williams.

Data in object-oriented programming is organized in a hierarchy of classes. The problem of object-oriented pattern matching is how to explore this hierarchy from the outside. This usually involves classifying objects by their run-time type, accessing their members, or determining some other characteristic of a group of objects. In this paper we compare six different pattern matching techniques: object-oriented decomposition, visitors, type-tests/typecasts, typecase, case classes, and extractors. The techniques are compared on nine criteria related to conciseness, maintainability and performance. The paper introduces case classes and extractors as two new pattern-matching methods and shows that their combination works well for all of the established criteria.

A Core Calculus for Scala Type Checking, is a new paper by the Scala team.

Abstract. We present a minimal core calculus that captures interesting constructs of the Scala programming language: nested classes, abstract types, mixin composition, and path dependent types. We show that the problems of type assignment and subtyping in this calculus are decidable.

The paper revolves around the question of decidability of type checking in Scala. The following quote summarizes the background of this question.

Scala’s approach to component modeling is based on three programming language constructs: modular mixin composition, abstract type members, and explicit self-types. All three have been studied in the vObj calculus. A key concept of the vObj calculus, path-dependent types, is also present in Scala. However, some other constructions of vObj do not correspond to Scala language constructs. In particular, vObj has first-class classes which can be passed around as values, but Scala has not.
First-class classes were essential in establishing an encoding of F<: in vObj, which led to a proof of undecidability of vObj by reduction to the same property in F<:. However, since Scala lacks first-class classes, the undecidability result for the calculus does not imply that type checking for the programming language is undecidable.

Ehud: Given current interest in Scala and its more or less unique (don't want to raise controversy here) position as being both a functional and an OO language, furthermore being much more than a toy language, would it be a good idea to give Scala a place in the Spotlight section?