Mid-April Update: Thanks for the thoughts everyone! The post quickly grew to >10,000 views and reached #10 on Hacker News for a while. I continue to enjoy the language for a multitude of applications from basic scripting to ‘hard work’ tasks.

I  love to play around with new computer programming languages. Even though I spend most of my time in industry tested standards for their respective applications (e.g. Java, C, C++, Python, Javascript, …), I think there are a lot of good reasons–especially these days–to learn and experiment with new languages. The impact of modern language development isn’t limited to a cottage industry of computer scientists and programmers. Take the growing Scala language as an example.  Twitter transitioned from a framework primarily using Ruby to Scala to scale their service and to maintain a programming model they desired. I also believe we are finally beginning to see languages that are elegant, expressive, and, importantly, fast. For example, these days, a company using two industry standards like Python and C++ might do ‘heavy lifting’ in C++ and write a lot of ‘high level’ wrapper code and scripts in a language like Python. Why not use just one language? Why is Python ‘slow’ for some tasks and C++ ‘unpleasant’ for scripting tasks? A good language should be expressive enough to elegantly express domain-specific tasks while allowing the programmer to make the things that need to be fast, fast.

Why the competition may not quite fit the bill

I could just list out great Nimrod features and say: ‘consider it’, but I don’t think that these features are very useful without some explanation of why these features provide an overall better experience than other compelling languages.  When it comes to picking  a programming language that attempts a speed-elegance unification, there are a lot of choices. The five on the ‘short list’ that I discuss in this post are:

• Scala
• Rust
• Julia
• C++11 and beyond
• Nimrod

There are other options that I could put on this list like Haskell or Go, and I have my reasons for picking the 5 above, but I don’t want to discuss them right now.  What I would like to do is convince you that Nimrod is a particularly nice language to consider since the design decisions they made, to me, result in an elegant, expressive, and fast language (though I  understand people have different syntactic preferences).   These are my initial thoughts after nearly three weeks of coding a lot in Nimrod. I am writing this because I think the language needs to get more attention than it has, and it deserves to be taken seriously as a worthy competitor to the other four mentioned above.

Of course this is not a super-detailed comparison, but overall, I hope I provide some  reasons you may want to consider Nimrod over these other very nice languages. They all have stuff to offer over Nimrod and vice-versa. And there are also a number of overlapping features.  Ideally I would like to have a highly expressive, fast language that is what I call “K&R-memorable” which basically means that it approximately as easy to understand it as it is to understand C (all you do is read K&R and you’re good).

C++11 has really brought C++ a long way. Coding with it results in a lot less boiler-plate code and it did a reasonable job of incorporating higher-order functions and handy value-semantic preserving features such as move semantics.  However, there’s still a lot of boiler plate (e.g. it’s 2014 and I’m still writing header files separate from source because compilation time with header-only files is too slow?), and now I need to implement more operators for classes to preserve value semantics (don’t forget to implement the move assignment operator!). So C++11 is nice and incorporates some modern features, esp. because it works with all other C code, but it’s much too complex, and I think, far less elegant than the other alternatives.

Scala and Rust are both very interesting languages (in general, simpler to understand than the totality of C++11). I have had a good deal of experience with Scala and have played with Rust for a couple of minor tasks. Both languages implement traits. To me, traits are a far more elegant way of adding similar functionality to different objects when compared with multiple inheritance. But my experience with Scala has shown me that while it is easy to use libraries, it is harder to design them in the midst of a complex graph of how objects and traits are related to one another. I spent a lot of time engineering the types to be just right, which is great, but it was also frustrating and I felt that the safety I desire at compile time would be more easily achieved without such a complex system.  I will discuss some design decisions made by Nimrod below that I think result in less time spent on type nitpicking and more time spent on getting the ‘job done right’ with reasonable safety features.   Rust provides more built-in memory safety, which is great, but understanding how it all works and the ‘conversations’ with the compiler after coding can be frustrating. Believe it or not, sometimes hard guarantees with types/traits and memory are not worth the pain when programming (i.e. the programmer effort required, mental model and syntactic complexity).  I think this is precisely why the adoption of a slow dyamically duck-typed language like Python has been so successful. They’re ‘easy’ to program in.  I think Nimrod is a happier medium.

Julia’s motivation comes from two places. The language resembles typical scientific programming syntax (ala Matlab and Pylab) that executes fast when compiled, and offers extensive and intuitive metaprogramming capabilities since it is homoiconic like Lisp. (And the scientist in me really likes the IJulia notebook feature that they have apparently worked quickly to develop.) I will show some examples below on how Nimrod offers a powerful and elegant metaprogramming environment without necessarily being homoiconic.      My only real concern with Julia is  lower-level systems programming. Forced garbage collection can be a game-changer here, and I’m not sure I think its choice of being ‘largely’ dynamically typed is a good one in this setting either.   Providing a library developer some level of type annotation and type class restriction can be useful for engineering purposes and more helpful when dealing with compile-time errors.    I work in the area of computational biology and I am left wondering: is Julia the right language to build the fastest read aligners, gene expression estimators, etc.? These tools are often written in C/C++, so Julia code would have to beat that!  A similar sentiment applies to Scala: it’s dependence on the JVM has actually resulted in very poor performance in even a simple multicore application, in my experience.

Quick start with Nimrod

OK, so you should read the tutorial and eventually the manual on the web site to get a quick start and get to know the language better, but I’ll tell you how I started using it: as a scripting language. I know this isn’t the best for ‘performance’ testing, but any language that has this ‘unification’ quality should be equally good at scripting as it is for high-performance applications. Here is a simple example:

import os

proc shell(cmd: string) =
    if os.execShellCmd(cmd) != 0:
       raise newException(EOS, cmd & "returned non-zero error code")

proc fexists(fname: string) : bool =
    try: discard Open(fname)
    except EIO: return false
    return true

const fromScratch = false

shell "clear"

if fromScratch:
    echo "Removing cached files and log"
    shell "rm -rf nimcache log.txt"
    echo "All output in log.txt"
    echo "Compiling ..."
    shell "g++ -fPIC -O3 -shared -std=c++11 bla.so bla.cpp 2>&1 >> log.txt"

# More of the pipeline, e.g.

if not fexists("blah.txt"): createBlah()
else: useBlah()

This, to me is a very clean way to do basic shell scripting while having the power of a full programming language.

Nimrod avoids ‘over-objectifying’

OK, so that was relatively straightforward. Here is a simple example of how to create a matrix type (partially inspired from a stackoverflow post):

type Matrix[T] = object
    nrows, ncols: int
    data: seq[T]

proc index(A: Matrix, r,c: int): int {.inline.} =
    if r<0 or r>A.nrows-1 or c<0 or c>A.ncols-1:
        raise newException(EInvalidIndex, "matrix index out of range")
    result = r*A.ncols+c

proc alloc(A: var Matrix, nrows,ncols: int) {.inline.} =
    ## Allocate space for a m x n matrix
    A.nrows = nrows
    A.ncols = ncols
    newSeq(A.data, nrows*ncols)

proc `[]`(A: Matrix, r,c: int): Matrix.T =
    ## Return the element at A[r,c]
    result = A.data[A.index(r,c)]

proc `[]=`(A: var Matrix, r,c: int, val: Matrix.T) =
    ## Sets A[r,c] to val
    A.data[A.index(r,c)] = val

iterator elements(A: Matrix): tuple[i:int, j:int, x:Matrix.T] =
    ## Iterates through matrix elements row-wise
    for i in 0 .. <A.nrows:
        for j in 0 .. <A.ncols:
            yield (i,j,A[i,j])

proc `$`(A: Matrix) : string =
    ## String representation of matrix
    result = ""
    for i in 0 .. <A.nrows:
        for j in 0 .. <A.ncols:
            result.add($A[i,j] & " ")
        result.add("\n")

The first thing to notice is that a matrix is an object type that contains data and its number of rows and columns. All the methods take a matrix as the first argument. This matrix is generic on any type Matrix.T. An alternative syntax where ‘[T]’ comes after a procedure name may also be used. Nimrod uses a uniform call syntax that implies these two calls are equivalent:

A.alloc(nr,nc): ...
alloc(A,nr,nc)

Notice that ‘elements’ is an iterator. This is a very efficient iterator called an ‘inline’ iterator. You can read more about this in the tutorial and manual. The `$` operator before a variable is the standard ‘to string’ operator. This allows you to do:

echo A

and a matrix will be printed out.

The uniform call syntax is a simple way to support a lot of ‘call-chaining’ like behavior commonly seen in object-functional programming and avoids forcing methods to be in objects. As an example, say I have a BigInt, and a little int and I want to be able to support addition of them. In Nimrod, you simply write a procedure that overloads the ‘+’ operator and it works (otherwise you get a compile time error). In a language like Scala, you define what’s called an ‘implicit conversion’ to do this for you. The added idea of an implicit conversion on objects and having to define them so explicitly seems more complex than just overloading the operator.  Note that there are other cases where you would like to use implicit conversions and Nimrod provides this capability. Calls to procedures in Nimrod can be ‘pass by reference’ in C++ world:

proc test(x:var int) =
   x=5

var x = 3
echo x
test(x)
echo x

results in:

3
5

The compiler will chose the appropriate method at compile time to call based on the types in the procedure. Nimrod also supports multiple dispatch.

Nimrod has an intuitive type system

As mentioned above, traits are a nice way of defining components of functionality tied to an object and the compiler will error out if certain traits are required, but missing, for example. I also mentioned that this can lead to complexities in library design and engineering (which may be good or bad depending on your perspective and the outcome).

One feature of Nimrod that’s appealing is that it offers the programmer type classes — the ability to group types into a single type (e.g. define float and int to be of type number), and distinct types — the ability to create two different types corresponding to the same underlying data type (e.g. dollars and euros are both ints in their tutorial example). Similar to type classes, Nimrod also allows constraints on generic types, and support for additional constraints is in the works. So the compiler will provide an error message if a method is not defined for a particular class of types its defined on or if a desired method is missing. Traits appear to be a formalism that could be useful, but might result in a lot of added complexity given the capabilities already provided by type classes and distinct types. Nimrod also supports an effects system which allows for additional compile-time safety checks.

You will want to metaprogram in Nimrod

Nimrod makes it easy to extend the language and the abstract syntax tree to generate the code you want. Say I wanted to do an openMP-like parallel for using Nimrod’s threads over a shared sequence of data. The thread code looks a lot like this:

template parallelFor[T](data:openarray[T], i:expr, numProcs:int, body : stmt) : stmt {.immediate.} =
    let numOpsPerThread = (data.len/numProcs).toInt
    proc fe(j:int) {.thread.} =
        for q in 0 .. numOpsPerThread-1:
            let i = j*numOpsPerThread+q
            body # so something with data[i]
    var thr: array[0..numProcs-1, TThread[int]]
    for j in 0 .. numProcs-1:
        createThread(thr[j], fe, j)
    joinThreads(thr)

But using the template as defined above, I can just do:

parallelFor(sequence, i, numProcs):
    # do something with sequence[i]

Note: this is just a toy example showing how to do some handy metaprogramming using a lower level version of threads.  The developers are working on a much better way of handling threads and parallelism at a higher level. The tutorial defines a debug statement (a variation of it which I use a lot) that shows how you can actually modify the abstract syntax tree.

C code and Memory Allocation

The typical compilation scheme in Nimrod I use is to compile to C code. They introduce a nimcache/ directory with all the C and object code for me to inspect to see how efficient it is compared to what I would write in C. It’s fairly straightforward to link into C code if you must.

The C code Nimrod generates often appears indistinguishable in speed when compared to hand-crafted C code I made in certain examples. Nimrod is much more pleasurable to program in than C, and the compile-time and run-time error messages are far better than C.

Also, I’d like to note that Nimrod allows for manually allocated memory and low-level operations to provide the developer ‘C-like’ control.  In most cases the standard libraries using the GC are appropriate, but in some cases you may want to manage your own data on the heap and Nimrod allows for this.

Young language, helpful community

The Nimrod language is young and has a handful of developers working on making it to a 1.0 release. The Nimrod community has been very helpful to me and I think it has a lot of potential.

I’m writing this post based on my experiences so far. I would really appreciate any feedback if I’m wrong or misrepresented a language I discussed. The post will be modified accordingly with acknowledgement.

Thanks to Rob Patro and the Nimrod community for useful discussions.