Units in Julia

I’ve been excited about Julia since before their first release (back then you had to build it from source). Lately I’ve been working on CGP.jl so I’ve been able to really immerse myself in the language and ecosystem.

I have always found the idea of associating physical units with values in a programming language to be interesting and potentially useful. We have languages with very powerful type systems, but we don’t typically have elegant ways of saying “this is a floating point value and it represents a number of centimeters”. These exist, of course, and implementing this kind of thing in an object oriented language is probably a pretty useful learning exercise, but again, it’s really about elegance and simplicity.

Julia has three characteristics that make something like this relatively elegant. First, it has optional static types and multiple dispatch. This should let us write a function that operates on “inches” but not on “centimeters” or “kilograms”. Second, it has functions as operators (and some nice syntactic sugar related to multiplication). This lets us override basic operations like addition. When combined with parametric types, we can even use a single implementation to handle multiple units. Finally, Julia supports rich Lisp-style (more or less) macros, meaning we can easily define a whole bunch of unit types and associated functions with a relatively small amount of code.

It is, perhaps, worth considering how we would do something like this in a functional language that supports pattern matching (since, to me at least, that would be the other obviously “elegant” way of solving the problem. Here’s a stupidly simple example in Elixir (which, by the way, also supports macros).

https://gist.github.com/glesica/51e2fa379e9c0105c302

Operator overloading is possible in Elixir, but the point is really that we can very cleanly implement a function that takes only particular kinds of units by agreeing to pass around tuples of a certain kind. We don’t have this sort of pattern matching in Julia, but we can match on types (multiple dispatch).

So here’s the code in Julia:

https://gist.github.com/glesica/ccb86f4d5ad3eb076d58

Let’s look at it a piece at a time. We’ve got three macros. Let’s take them in order. The first, defunit allows us to add a new unit to the units graph we will create. It requires a name and a parent (or “kind”). The name serves an obvious purpose, the parent is less clear. This macro also defines a shortcut function that lets us convert another unit of the same kind (sharing a parent) to this one. So we can do things like In(x) where x is a Cm value.

Each kind of unit has a base unit. This is the unit through which all conversions that aren’t specifically specified will be done. We can define a base unit for a kind using the defbase macro. For example, in the snippet we define centimeters to be the linear base unit. This means that to define an inches unit we need only provide a conversion to centimeters to be able to convert between inches and any other linear unit (such as feet, in the example). This doesn’t work perfectly since we might end up with significant floating point error or overflow, but it works well enough.

Last of all, we can define a conversion using the defconv macro. We must define a conversion from a unit into the base unit, but we can also define other conversions if we’d like better accuracy.

Next, let’s take a look at this line: *{T <: Unit}(x::Real, ::Type{T}) = T(x). Here we have abused the multiplication operator and made it into a pseudo constructor. Why? Because Julia has some fancy syntactic sugar that lets us write 2x instead of 2 * x. This was added, presumably, to aid in translating mathematical formulas to code. For our purposes, it allows us to write something like 2Cm and have it mean exactly what it looks like it should mean. We need to be careful about operator precedence of course.

Finally, we define a bunch of arithmetic functions / operators. Since all units have more or less the same form, what we're doing here is enforcing the rule that you can only multiply a value of a particular unit by another value of the same unit. This means, then, that something like 2Cm + 4Ft is an error (since who knows what the resulting unit should be?! To make this work we would need to explicitly acknowledge the units by converting one of them like so: 2Cm + Cm(4Ft).

I generally like this solution to the problem. Julia provides all the tools necessary to solve this problem in a fairly elegant manner.

Image credit: Scott Akerman

Dynamic Pools in Go

Recently, I wanted to make use of theĀ pool pattern, which is generally pretty simple in Go. Specifically, however, I wanted to be able to dynamically cap the level of concurrency for any given set of tasks submitted to the pool.

To clarify, let’s say we have a pool that consists of N workers. For any given job A, consisting of tasks a_1, a_2, ldots, a_n, we want no more than k of the n tasks in A to run concurrently, where k leq n and k leq N. My use case is a system to test HTTP resources. Each job might be a specific set of endpoints. I might want to hit some more “gently” than others, hence the need to dynamically cap the level of concurrency.

Having worked with the Erlang ecosystem a bit, I really like the idea of passing messages between independent “processes”. This is a very natural and fairly simple abstraction.

Go is a little different, though. In Erlang you create a process and then pass around its PID, which can be used to send it messages (like having its address). In Go, a goroutine (which we can think of as a process) is decoupled and independent (although shared mutable state is still possible).

In order to communicate between goroutines, Go makes use of channels, which are like pipes or queues and can be one- or two-way. This means that if you want to spawn a goroutine and then pass it messages, you need to give it a reference to the channel you plan to use.

I’ve included a very simple example below (note that there is a race condition in this code, it doesn’t matter because the point is to illustrate how channels work). In this case I have made the channel accessible to the goroutine using a closure, but I could have also passed it into the function.

https://gist.github.com/glesica/96c398c83f2648c6eed9

This basic pattern can be used to construct a goroutine pool. We can spawn several goroutines that listen on a channel until they receive a task, complete it, send the result back through another channel, then start listening again. They’ll stop listening when the channel is “closed”. We can use a Wait Group, which is similar to a semaphore, to make sure we don’t move on until all the workers are finished.

https://gist.github.com/glesica/7db5e0308589dbfe149a

This is great except for the fact that, given a set of tasks as described above, we might execute up to N of them concurrently depending on the workload of our pool. We need a way to group tasks together into what I called “jobs” above.

One solution (there may be others) is to take advantage of the fact that channels in Go are themselves just values, so they can be passed through other channels. Instead of workers that pull jobs from a shared queue, they can pull queues (channels) from a shared queue (channel).

https://gist.github.com/glesica/2c4ddcc5c9e71cba442b

Note that now our jobs channel is a channel of channels of integers. So before we submit the tasks associated with a particular job, we decide how many of the workers may work on these tasks concurrently and we submit the task channel that many times. The we feed the tasks into the task channel and, at most, that many workers receive our tasks.

A couple of caveats are in order. First, it is perfectly possible that fewer than the maximum number of workers will process the tasks if the rest are busy. In this case a worker will grab a task channel that has already been closed and immediately discard it. For this reason, this strategy might not be the best for long-running pools (eventually you could end up with a lot of closed channels in your queue, maybe that causes a problem for you, maybe it doesn’t).

Another thing to note is that each job (group of tasks) now requires its own channel. This might not be great for situations where each job is quite small and there are many jobs.

In any event, you can play around with the code and see for yourself that it works. Change the “1” on line 26 to a “3” and you should notice that the results come back mixed up instead of in order.

Image credit: Thomas Hawk