# A quirky solution to HackerRank’s “Team Formation” problem

As a preface, I’d like to say that this article isn’t intended to help people cheat at the problem, but is posted for the intellectual exercise contained within. Hopefully the folks in and around the HackerRank community won’t mind.

Lately I’ve been having some fun with the challenges on HackerRank. By all accounts, the general (and consensual) solution/s to the Team Formation problem involve some combination of data structures, sorting algorithms and such.

That’s all well and good, but having hit my head many times as a child, something in the back of my mind was nagging away at me, insisting that there existed a more specialized solution. As is a recurring theme of this blog – and indeed my life – I could not resist its call. Fortunately, this one actually paid off.

The algorithm I subsequently developed does not (in theory) depend on any particular data structure nor require sorting; it satisfies the condition of being “greedy“; (potentially) has a time complexity of $O(n)$, requiring only 1-2 loops; and for added lolz, can be made stable and even online if necessary.*

* While this is all possible, in some cases it’s not practical; in one particular implementation I did have to use std::sort. I’ll elaborate more on this at the end of the post.

### The problem (briefly)

The objective of the Team Formation challenge is to accept a list of integers representing skill values for members of a team – and to organise those members such that a) for any one member with skill $x$, either that member is the lowest skilled member of the team or there exists another member of the team with the skill $x-1$ – and b) the members are organised into as few teams as possible, with members being spread as evenly as possible across every team. To pass the test, one must output the size (number of members) of the smallest team.

For example, given the set $\{ 4, 5, 2, 3, -4, -3, -5 \}$, the two teams that can be formed are $\{ -5, -4, -3 \}$ and $\{ 2, 3, 4, 5\}$. The smallest team is $\{ -5, -4, -3 \}$ and so the correct output for this test case is $3$. (Note that it would be erroneous to organise this input into the teams $\{ -5 \}$$\{ -4, -3 \}$ and $\{ 2, 3, 4, 5\}$, for example.)

### Abstractions

Imagine a grid. If you’re struggling, below is a visual to help you along:

A graph, similar in style to those pioneered by William Playfair in the late 1700s.

For an example input of $\{ 51, 47, 45, 46, 46, 47, 48, 47, 50, 47, 47, 49, 51 \}$, the correct team formation can be visualised as:

If another value $48$ were to be added, it would travel down the x axis towards 0 to the next free slot, resulting in:

This works because there is a “free” $47$ at coordinates $(3, 47)$. If this were not the case, the new value $48$ would be placed at the end of the row – at $(5, 48)$. Below is another example, using an additional value of $50$.

You get the idea by now.

Following a set of rules for how to handle various local conditions when a new member is added, it’s possible to organise and – if implemented properly – effortlessly reorganise the members into their most appropriate structure. You can even track the size of each team on the fly.

### The implementation

For the same reason that I’ve not gone into the algorithm in much depth (i.e. not wanting to help out the dirty cheaters too much), I won’t post any code here. However, I did write an implementation which passed the HackerRank test cases.

Following that, I decided to grab a few of the other top scoring solutions along with some of the larger test cases and benchmark them locally against my algorithm using Very Sleepy. The numbers are listed below (lower is better):

User Average time
surwdkgo 0.494
visanr 0.275
Kostroma 0.182
Meeeeee! 0.166

As you can see, I outperformed the competition – which was a reassuring conclusion to this little experiment, having spent far more time and energy than was warranted.

This algorithm doesn’t require use of any particular data structure or sorting algorithm; you can just use the integer value of the skill level $x$ and perform local operations on $x-1$ and $x+1$ and such.

Under certain conditions (e.g. access to large memory or some assurance that $x$ will be within a certain range) this means you can perform all necessary operations using a regular unsorted integer array. However, as the conditions of the challenge state that $x$ may be as large as $10^9$ – which is far too big for the test scenario – that leaves two options: use another lookup method (e.g. hash tables) or sort the array.

In my first attempt I did use an unordered_map, but lookups created a bottleneck so I tried out the sorting approach, which significantly improved performance.

Even after all of this, there’s still a voice in my head telling me that an even better algorithm exists, but… I must move on. I’ve satisfied my curiosity (mostly). And I have things to draw.

# Fuck it

For a while now (and by “while” I mean 5+ years) I’ve been wanting to create a web comic. By some miracle I have taken the first step towards that goal and set up a Tumblr blog to host said strips.

For those who are interested, the URL is http://newphalls.tumblr.com/. I’ll probably also be pestering my Twitter followers with updates – so there’s the option of following me there

My intention is that the cartoons will feature a number of topics, from gaming to world events. This is all assuming I actually post anything further in the future (I will try, I promise).

/

# Programming as art?

Not too long ago, admiring the craftsmanship of the Kritonios Crown got me thinking about the various ways in which people have taken a particular skill – such as painting, sculpting, smithy, writing, etc – and turned it into art.

While plenty has been said about computer programming having artistic undertones, I began to wonder whether it would be possible to create a work of art in the truest sense of the word, through the medium of code.

First, I had to define some parameters for what counts as art. The essence of “what is art” has forever been elusive and is hotly debated. However, I settled on a condensed criteria that I believe the majority of people would be happy to accept. They are:

1. Expression; purpose and meaning.
2. Evocation; provoking an emotional response.
3. Imagination; creativity and innovation.

With this, I began thinking about a way that I could turn code into art.

#### Expression and evocation

I knew immediately that I wanted to pay homage to the miracle of existence, life and the universe. I hoped originally to inspire in the viewer’s mind the same emotions of reverence and confusion that I feel when I look out to space or consider the intricacies of nature – and I feared that some (or indeed many) may feel nothing at all when they look at my piece. If that were the case, for those people, the art would be a failure.

But then I considered that some people do feel nothing but apathy and indifference when they look at nature (presumably in between watching reality TV shows and listening to Beyonce, but that’s just stereotyping). In its own way then, even indifference towards my art could be taken as another accurate portrayal of my chosen subject.

Ultimately, I decided to drop the expectation of provoking any one particular thought or emotion, as long as one or the other occurred at all. I embraced the subjectivity of my art, just as the meaning of existence is itself subjective.

#### Imagination

Having settled on what I wanted to say and how I wanted people to feel, I needed to plan the execution. I decided that I wanted to write some code which would mystify even my fellow programmers, but at the same time, I felt like obfuscation would be a cheap copout.

Even the choice of language was assigned meaning. Spoiler alert: I chose PHP because the history of PHP itself is a tale of haphazard guesswork and random mutations that may or may not be beneficial, much like biological evolution. (Not language bashing; just some innocent humour.)

#### The result

The end result can be seen in this Gist.

I title this piece, “Microcosm.” Recall that this code is not merely a script that has been fed through a code obfuscator; the code stands as-is – and, like the universe it hopes to represent, has mechanics and implications.

Thank you and good night. Much love.

# Web development in Julia: A progress report (Warning: Contains benchmarks)

Continuing my quest to explore the idea of using Julia for web development, I wanted to address some of my own questions around performance and implementation. My two biggest concerns were:

1. Should Julia web pages be served by a Julia HTTP server (such as HttpServer.jl) – or would it be better to have Julia work with existing software such as Apache and nginx?
2. How would Julia perform on the web compared to the competition?

### Addressing the HTTP server question

After some consideration, my personal conclusion is that a server implemented in Julia would be another codebase that would need to be maintained; would mean missing out on tools available to existing server software, such as .htaccess, modules and SDKs; and would ultimately feel like reinventing the wheel. I feel it would be more sensible to leverage existing software that already has active development and has been tried and tested in the wild.

Following from this, I knew that my primary performance concern should be the interface between the server and Julia. In my previous posts, I was using Apache and running Julia via CGI. CGI is slow enough, but a known fact of Julia is that the binary is somewhat slow to start due to internal processes/compilation. I figured that FastCGI would be the next best option – and as there are no existing solutions (except for an incomplete FastCGI library), I set about creating a FastCGI process manager for Julia.

FYI: I’ve decided to release all of my web-Julia-related code under the GitHub organisation Jaylle, which can be found at https://github.com/Jaylle. Currently only the FPM and CGI module are available, but in future that’s where I’ll add the web framework and whatever else gets developed.

I plan to elaborate on the process manager more in a future post, but in short there are two parts:

• The FastCGI server / process manager (coded in C). This accepts requests and manages and delegates to the workers.
• The worker (coded in Julia). This listens for TCP connections from the FPM, accepts a bunch of commands and then runs the requested Julia page/code.

This way, there’s always a pre-loaded version of Julia in memory, circumventing any startup concerns (unless a worker crashes, of course).

### Some early benchmarks

Now that the FPM is in a usable prerelease state, I wanted to see how it could perform compared to the alternatives. In this case, I chose PHP (obvious) and Python. I chose Python because the name often crops up in Julia discussions and there’s a FastCGI module available for it.

To run these tests, I used the Apache ab tool from my Windows machine. The server is a cheap 1-core VPS running CentOS 6 64-bit.

In all tests, the server software used was nginx. For the languages, I used PHP-FPM for PHP, Web.py for Python and the Jaylle FPM for Julia.

The individual tests are superficial and the results anecdotal, but I just wanted something to give me an idea of how my FPM performed by comparison. To elaborate:

• Basic output: Printed “Hello, [name]” – with [name] taken from the query string (?name=…)
• Looped arithmetic: Adding and outputting numbers in a loop with 7000 iterations.
• Looped method calls: Calling arithmetic-performing methods from within a loop with 7000 iterations.

Below is a table of the results. The numbers shown are requests per second; higher is better.

 Basic output Looped arithmetic Looped method calls PHP 28.17 11.29 10.92 Web.py (Python) 24.61 7.92 7.25 Jaylle (Julia) 24.85 5.27 5.12

The only thing that I can say from these results is that I’m comforted seeing that my FPM’s performance isn’t obviously terrible compared to the others, but that there’s probably some work that does need to be done to at least get it up to the same level as Python, if not PHP.

In other news, I’ve realised (4 years late) that all the cool people use Twitter now. I therefore have started actively using my account. I can’t promise that following me will improve your quality of life, but feel free to give it a chance: @phollocks

Coming soon: FPM documentation + writeup (as soon as I’m comfortable enough to tag a release).

# Revisiting emulated OOP behaviour and multiple dispatch in Julia

In an earlier post, I explored one approach to emulating bundling functionality with the data on which it operates, akin to object methods in OOP languages such as C# and PHP. A comment posted by Matthew Browne questioned whether this approach was compatible with Julia’s multiple dispatch.

This is something I thought about at the time of writing the original article, but I had assumed it wouldn’t be possible due to the way in which the anonymous functions are assigned to variables i.e. assigning one definition would overwrite the previous. However, Matthew’s question prompted me to reconsider – and after some brief experimentation and some small alterations, I found that there is indeed a way to maintain compatibility with multiple dispatch.

Below is an updated example type definition:

type MDTest
method::Function

function MDTest()
this = new()

function TestFunction(input::String)
println(input)
end

function TestFunction(input::Int64)
println(input * 10)
end

this.method = TestFunction

return this
end
end

The theory is basically the same, with the constructor assigning the methods to their respective fields within the type. The difference is in how the functions are defined and assigned.

On lines 7 and 11, methods are defined with different argument types. These methods could be defined outside of the type definition without error, but defining them within the constructor has the advantage of not polluting the global scope.

On line 15, the function is assigned to its field using some slightly different syntax, which allows both methods to be called.

With this, the example code below:

test = MDTest()

test.method("String")

test.method(5)

Produces the output:

String
50

Another advantage to this approach is the absence of anonymous functions – which, according to benchmarks and GitHub issues, have significantly worse performance compared to named functions.

# Julia variable gotchas

As is typical for many languages, assigning one variable to another in Julia does not create a copy of the variable data, but rather a reference to the existing data. However, I learned the hard way whilst working on the CGI module that Julia does not currently support a copy-on-write mechanism for collections.

Take the example code below:

n = [ 1, 2, 3 ]

m = n

As expected, m becomes a reference to the collection referenced by n. Working with any number of mainstream languages, one might expect a copy to be made of the data referenced by n if either n or m is modified, for example:

n = [ 1, 2, 3 ]

m = n

push!(n, 4)

# Expect n = [ 1, 2, 3, 4 ] and m = [ 1, 2, 3 ]

This is not the case for Julia. When the array pointed to by n is modified, m maintains its reference to that same array, giving both a value of [ 1, 2, 3, 4 ].

### Problems in the wild

I encountered this quirk when working with binary data and UTF-8 strings.

n = Uint8[ 0x32, 0x33, 0x34, 0x61 ]

m = utf8(n)

empty!(n)

Having created a string using the utf8 function, I wanted to empty the original byte array to free those resources. After a few minutes of trying to figure out how a bounds error had crept in to my app, I narrowed it down to this deletion of the byte array.

Digging deeper into the Julia source, the utf8 function is just an alias for a conversion function.

utf8(x) = convert(UTF8String, x)
...
convert(::Type{UTF8String}, a::Array{Uint8,1}) = is_valid_utf8(a) ? UTF8String(a) : ...

You can see here that passing an array of Uint8 bytes to utf8() creates an instance of UTF8String with the Uint8 array as its data. The type definition for UTF8String is:

immutable UTF8String <: String
data::Array{Uint8,1}
end

As was covered above, the UTF8String’s data field will be only a reference to the collection passed to the utf8 function. If that collection is modified in any way at any point during the program’s runtime, so too will be the returned string.

### In closing

It seems that the solution at this time is to explicitly use the copy or deepcopy functions, where copies of data are required by the program logic.

The issue is explored in this Google Groups thread. If I’ve understood correctly, the gist of it is that Julia makes this sacrifice for the sake of performance. As this is a feature wanted by many, there’s a possibility of it being implemented in a later version of the language.

# Capturing output in Julia

In a previous blog post I pondered whether it may be possible to redirect STDERR to an IOBuffer so that the output can be handled in a controlled way e.g. written to a log file. It turns out that it’s not quite that simple, but capturing output can still be achieved easily with a few more lines of code.

The noteworthy functions here are the redirect_std* family of functions. These functions redirect their respective handles to a new pipe and return a read and write handle for said pipe.

### Capturing output

Below is an example of capturing output to STDOUT.

print("Test")
print("ing")

close(outWrite)

At the end of execution, the variable “data” will contain the string ‘Testing’.

(As seen on line 6, it’s advisable to close the write handle before trying to read from the pipe, as this will ensure that any buffered writes are flushed and available for reading.)

If you need to write to the original output stream after redirecting and capturing data, you’ll first need to create a copy of the original handle and restore it later. For example:

originalSTDOUT = STDOUT

print("Test")
print("ing")

close(outWrite)

redirect_stdout(originalSTDOUT)

print(data)

Line 1 is where the original handle is copied; line 14 is where it’s restored. The print on line 16 will therefore write to the original STDOUT (e.g. console window, browser, etc) instead of the outWrite pipe.

### Capturing errors

This technique is particularly useful when applied to STDERR, as it can be used to write errors to a log file. The process is the same, but instead using functions applicable to STDERR. Below is a slightly different example:

atexit(function ()
close(errorWrite)

logfile = open("errors.log", "a")
write(logfile, errors)
close(logfile)
end)

atexit registers a function to be called when the program execution ends for whatever reason (fatal error, user called the quit function, etc). The code between lines 4 and 8 is similar to the STDOUT example – and code as been added at lines 10 to 12 to log the captured error(s) to a file.

# Understanding object-oriented programming in Julia – Inheritance (part 2)

In part 1, I explored the concept of objects from the perspective of the Julia language. In this article, I will be looking into Julia’s implementation of object inheritance i.e. the inheritance of behaviour and properties.

### Classes / types

As was covered in part 1, the closest thing to a “class” or “object” in Julia is a type – which may contain fields (properties), but there is no support for including methods.

There are a number of different types, but the two most noteworthy for this article are abstract and concrete types.

Concrete types have a name, a list of fields and a constructor. Concrete types can be instantiated and used to store and manipulate information. These types are closest in behaviour to the traditional “classes” of object-oriented programming.

Abstract types have only a name and no other properties or behaviour. The purpose of abstract types seems to be as a way to indicate relationships between concrete types and can be thought of as being most similar to OOP interfaces than abstract classes.

(There also exist immutable types, which are essentially constants)

### Inheritance

In Julia, all types are only able to “inherit” (i.e. be a subtype of) abstract types. This means that you could for example have an abstract type Letter which is inherited by concrete types A and B, but this would serve no other purpose than indicating that A and B are letters.

Having read the documentation and various contributor comments on the help forums, it appears that Julia was never intended to and will never support inheritance of either data or behaviour e.g. concrete types inheriting or extending other concrete types. The consensus appears to be that delegation is the preferred solution.

I began to look for a way to emulate the behaviour of concrete inheritance. As is often the case, the solution is an abomination. Still, for academic purposes…

### Forcing inheritance of properties and behaviour

My plan was to find a way to merge two types together. I decided to create a method that, when two “Inheritable” objects are added together (i.e. “a + b”), will produce a new type containing the fields and data from both objects.

As of Julia 0.3, there’s no way to add field definitions to a type after the type has been declared. This meant I needed to create a new type declaration at runtime – which requires the use of an eval.

Sidenote: As if that’s not enough of a performance hit, the code here must first parse a text string containing the code for the type definition, which creates an Expr object that eval can then execute; it would therefore be possible to optimise the below code by directly manipulating an Expr’s args array, rather than relying on the parse function – but this is beyond my current Julia skillZ.

Below is a big chunk of code, which I’ll elaborate on:

abstract Inheritable

+(a::Inheritable, b::Inheritable) = (function (a::Inheritable, b::Inheritable)
properties = Dict{String, Any}()

for property in names(a)
propertyName = string(property)

properties[propertyName] = (propertyName, string(fieldtype(a, property)))
else
(fieldName, fieldType) = properties[propertyName]
properties[propertyName] = (fieldName, "Any")
end
end

for property in names(b)
propertyName = string(property)

properties[propertyName] = (propertyName, string(fieldtype(b, property)))
else
(fieldName, fieldType) = properties[propertyName]
properties[propertyName] = (fieldName, "Any")
end
end

fieldCode = ""

for property in values(properties)
(fieldName, fieldType) = property
fieldCode = fieldCode * fieldName * "::" * fieldType * "\n"
end

randomTypeName = "An" * randstring(16);

typeCode = "type " * randomTypeName * " <: Inheritable " * fieldCode * " function " * randomTypeName * "() return new () end end"

eval(parse(typeCode))

randomTypeName = symbol(randomTypeName)

c = @eval begin
\$randomTypeName()
end

for property in names(a)
try
c.(property) = a.(property)
catch
c
end
end

for property in names(b)
try
c.(property) = b.(property)
catch
c
end
end

return c
end)(a, b)

This code contains an abstract type (“Inheritable”) and a method to handle the addition of one Inheritable object to another – the result being both objects merged together to form another Inheritable object.

The first two loops go through each object and create a record of their properties and the properties’ types. You’ll notice that if both objects have a property with the same name, the type is changed to Any to eliminate any conflicts between types e.g. one object accepting an integer and another accepting a float. In practice this is likely to cause a lot of headaches due to Julia’s multiple dispatch, but just roll with it.

Immediately after that, the properties are written into a string of Julia code as field definitions. A random name is generated for the new pseudotype, with care taken to ensure that the first letter is alphabetic (numbers will cause a parse error). The code for the type is then compiled together into a final string, parsed, evaluated and executed.

A second eval calls the pseudotype’s constructor, creating an incomplete instance of the new type.

Two more loops then iterate over the objects being merged together, assigning their current values to the properties on the new type. The resulting object is then returned.

Below is an example of two types making use of this emulated inheritance behaviour:

type A <: Inheritable
whoAmI::Function
uniqueFunctionA::Function

function A()
instance = new()

instance.whoAmI = function ()
println("I am object A")
end

instance.uniqueFunctionA = function ()
println("Function unique to A")
end

return instance
end
end

type B <: Inheritable
whoAmI::Function
uniqueFunctionB::Function

function B()
instance = new()

instance.whoAmI = function ()
println("I am object B")
end

instance.uniqueFunctionB = function ()
println("Function unique to B")
end

return A() + instance
end
end

Type A is a standard type declaration, using the same emulated method bundling from part 1. Type B extends type A in the constructor, by returning an instance of the merged pseudotype instead of an instance of type B. That code is:

return A() + instance

The below code is an example of using these two objects:

a = A()
a.whoAmI()

b = B()
b.whoAmI()

b.uniqueFunctionA()
b.uniqueFunctionB()

Which produces the output:

I am object A
I am object B
Function unique to A
Function unique to B

### Issues remain unsolved

Even the above hack-around doesn’t solve the problem of visibility. The code carries an increased performance penalty to run, is less intuitive and not particularly elegant. At this stage, I don’t think Julia is suited to the same approaches and design patterns used in languages like PHP and C#. Is that a good or bad thing? In part 3 I’ll try doing things the “Julia way” and report back with any benefits or limitations I encounter.

# Understanding object-oriented programming in Julia – Objects (part 1)

Disclaimer #1: I’m new to Julia – so it’s possible that I may have missed or misunderstood aspects of the language.

Disclaimer #2: Julia is not necessarily intended to be an object-oriented language or even intended for use in professional/commercial software development.

Disclaimer #3: The OOP terminology used here is for familiarity with PHP and such, but may not be appropriate for Julia.

Coming from working with OOP in languages like PHP, Java and C++, it took me a while to adjust to the Julia approach to the concept of objects. Take the following paragraph from the documentation (relevant parts highlighted in bold):

In mainstream object oriented languages, such as C++, Java, Python and Ruby, composite types also have named functions associated with them, and the combination is called an “object”. In purer object-oriented languages, such as Python and Ruby, all values are objects whether they are composites or not. In less pure object oriented languages, including C++ and Java, some values, such as integers and floating-point values, are not objects, while instances of user-defined composite types are true objects with associated methods. In Julia, all values are objects, but functions are not bundled with the objects they operate on. This is necessary since Julia chooses which method of a function to use by multiple dispatch, meaning that the types of all of a function’s arguments are considered when selecting a method, rather than just the first one (see Methods for more information on methods and dispatch). Thus, it would be inappropriate for functions to “belong” to only their first argument. Organizing methods into function objects rather than having named bags of methods “inside” each object ends up being a highly beneficial aspect of the language design.

What this means is that objects have properties, but no methods. However, it’s possible to define a single function within the object’s type definition that will act as the object’s constructor. If you’re familiar with C, objects in Julia can be thought of as C structs with the addition of a constructor.

Having read through the docs and existing Julia source code, it seems that the way to encapsulate functionality within a Julia object is to assign functions manually – ideally (but not necessarily) from within the constructor – to properties within the object. The same applies to defining default values for properties. As an example, below is the current version of the Response class from my experimental web framework.

type Response
data::String

getContents::Function
getResponse::Function

function Response()
this = new ()

this.data = ""

this.data = this.data * append
end

end

if length(value) > 0
end

end

end

this.getContents = function ()
return this.data
end

this.getResponse = function ()
response = ""

response = this.getHeaders() * "\n" * this.getContents()

return response
end

return this
end
end

To expand on the noteworthy parts:

data::String

getContents::Function
getResponse::Function

These are definitions for the properties of the object. The top two are intended for use as typical data properties, whereas the Function properties are intended to contain the methods for the object, which will be assigned in the constructor. This distinction is arbitrary; Julia itself makes no distinction between these two types.

function Response()
...
end

This is the constructor definition. There are a number of different options for this within Julia’s syntax, but the style used here is the most familiar to me.

Constructors in Julia differ from the PHP-esque style of constructors, which exist within an instance of an object and are executed immediately after the instance has been created. In Julia, constructors are responsible for the actual creation of an instance, as you’ll see below.

this = new ()

This code is where the instance of the object is created and assigned to a “this” variable. My use of “this” here is again only for the sake of familiarity; any valid variable name would work the same.

this.data = ""

Here, default values are assigned to the new instance’s properties.

...
end

This code is an example of assigning a module to the object. Essentially, it’s the same as assigning a value to a property; it’s just that in this case, the value is a callable function.

return this

When the instance has been constructed, it’s returned to the caller.

Instances of the object are then created in user code as below:

response = Response()

Methods can be called as:

### Bells and whistles

As mentioned before, access modifiers appear to be missing from Julia. Therefore, everything is public – and even methods can be overwritten if new functions are assigned to their respective properties. Also, as methods don’t “belong” to the object, it may not be possible to reproduce the behaviour of inheritance as in other languages (getting my head around Julia’s inheritance will be part 2).

It may be too early to say, but at this stage it appears that Julia may be lacking the necessary features for effective use of traditional OOP development patterns – though that doesn’t necessarily mean that the language itself or its implementation of objects is broken.

### Update

Part 2 is now online here.

# Julia and Apache

Julia caught my attention recently. As I understand it, it’s intended to be a fast, all-purpose language, so that we need not have one language for scripting, one for parallel programming, another for mathematics, etc. As someone who works primarily with the web – an area where Julia is reportedly lacking – I decided to see how it would fare in that arena.

I’ll clarify this early: Julia is still in its infancy. The stable release at the time of writing is version 0.2. It’s likely that difficulties like those I encountered will be ironed out as the project evolves.

I figured (very wrongly) that the quickest and easiest way to get up and running would be to configure Apache to run .jl files as CGI. That didn’t work out the way that I hoped it would – and after an hour of basic debugging, it became clear that I had two choices: 1) use a Julia HTTP server or 2) potentially spend hours of my life figuring out why it wasn’t working – and just maybe find a workaround. If you’re familiar with this blog, you’ll know which option I went for.

### Debugging – Apache

Apache was giving me the “End of script output before headers” 500 error page. The Julia code I was testing was supposed to write logs to a file to confirm that it was running, which worked fine in the console window, but there was no file activity when I loaded the page in my browser, suggesting the program/script wasn’t even being executed.

I ruled out file permissions as a cause. The CGI error log was useless (for reasons which now make sense). I was flying blind – so I tried something else…

### Debugging – CGI proxy

I wrote a C program to be invoked by Apache instead of Julia. The proxy would execute Julia itself, giving me a console window with some I/O so that I could hopefully get a better idea of what was going on. Using this, I was able to identify two issues:

Issue 1: Environment variables.

Julia is hard-coded to access the APPDATA environment variable. This isn’t an issue when running from the console, but the environment created by mod_cgi doesn’t contain this variable. I was able to work around this by having an APPDATA variable be set in either the .htaccess or httpd.conf using SetEnv (see far below).

Issue 2: Julia’s bizarre I/O

I don’t mind admitting that I’m unfamiliar with the I/O library used by Julia. However, given some of the errors I was getting in my console window (inability to open I/O pipe, unsupported stdio handles, etc) and one of the pull requests on the repository, I figured that Julia 0.2 must just be incompatible with the way mod_cgi was trying to redirect the child process’ I/O. It was with this that I decided to try the (potentially unstable) build of Julia 0.3.

### Julia 0.3

At the time of writing, version 0.3 is a prerelease version – which is why I first chose to use 0.2.

Using my CGI proxy, I was able to see that Julia 0.3 requires two more environment variables: HOMEDRIVE and HOMEPATH. These can be set in either the httpd.conf or .htaccess as with APPDATA.

I could also see straight away that 0.3 had a better rapport with I/O handles. After some more experimentation, I felt confident enough to direct mod_cgi to invoke julia-basic.exe directly.

### Putting it all together…

Configure Apache to handle .jl files as CGI scripts and set the required environment variables (APPDATA, HOMEDRIVE, HOMEPATH). Below is an example from my own httpd-vhosts.conf:

<VirtualHost localhost:80>
Options +ExecCGI
SetEnv APPDATA "G:/Dev/appdata"
SetEnv HOMEDRIVE "C:"
SetEnv HOMEPATH "/Users/Phalls"
ScriptLog logs/julia_cgi.log
DocumentRoot "D:/htdocs"
ServerName localhost
</VirtualHost>

Restart Apache (or just reload config if you have that option).

… and that should be all there is to it. As with the likes of Perl and Ruby, the first line of your .jl files should be the path to the Julia binary. I chose to use julia-basic.exe, but I also tried it with julia-readline.exe and it was no different.

Below is some example code:

#!"G:\\Julia3\\bin\\julia-basic"
redirect_stderr(STDOUT)
print("Content-Type: text/html\n\n")
print("<html>
<body>
<b>Hello, world!</b>
</body>
</html>")

(language=”scala” is the closest I could get to Julia for syntax highlighting)

On line 2 (i.e. 1) I redirect STDERR to STDOUT because errors (e.g. syntax errors) sent to STDERR cause Apache to hang and display a 500 error page. Redirecting to STDOUT shows the error message on the page, which is cleaner and more helpful for debugging. It may even be possible to redirect STDERR to something else, such as a stream or an IOBuffer where it can be handled in a more controlled manner.

Line 3 (i.e. 2) just outputs a header. Bear in mind that, with Julia not being strictly CGI-compatible, that becomes your app’s responsibility.

### Next on the “to do” list:

• Compare performance vs Ruby and PHP
• Compare Apache/CGI performance vs HttpServer.jl
• Write basic web framework (despite this)

Estimated delivery date: I’ll get to it when I get to it.

Julia language documentation