A Dyalog workflow. Or two.

I don’t know how many of you have ever met Dijkstra, but you probably know that arrogance in computer science is measured in nano-Dijkstras. –Alan Kay

For the majority of the examples we’ll encounter in this book, you can just type them directly into the Dyalog IDE, or TryAPL, and when you understand the point made, scrap them. But as you progress, sooner or later you will want to save your work to disk.

If you have a similar background to me, you’ll expect to be able to write your code using an editor of your choice, arranging it into files containing logically related routines, which you then put into a source code revision management system, like git, perhaps triggering a build and test pipeline on every commit via something like jenkins or circle-ci. This is most likely in the neighbourhood of your current workflow, regardless of which language or platform you’re using, from ADA to Zig.

Before we get into the practicalities of how you can do this and more, we’ll need a slight diversion into the history of APL and Dyalog in particular [Kromb&Hui2020]. Firstly, APL predates all these workflows and build tools. That in itself isn’t an issue: so does C. But APL wasn’t really conceived as a software engineering tool. During its heyday, middle management and CFOs were slinging APL to generate forecasts and reports, and they loved APL as it allowed them to essentially perform tasks that up until then had been in the realm of specialist “batch processing” on the company mainframe. It essentially occupied the niche which is now dominated by Microsoft Excel.

This influenced all APL vendors. Commercial APL systems are full application stacks that make it seductively convenient to package up your work as binary workspaces that can be easily distributed to other APL users without ever having to “leave” the confines of the stack itself. In terms of Dyalog, its tooling and development philosophy supports this world view, as that is what its customers expect: professional Dyalog developers working for billion-dollar financial institutions will be of the opinion that you can pry their binary workspaces from their cold, dead hands. You can build fully-fledged, GUI-driven applications integrating deeply with code bases written in .NET.

Yet, for you, the newcomer, this approach will probably feel a bit alien. Dyalog obviously understand this, and they’re walking the tightrope of both delivering what their current paying, long-term customers need, but also a keen eye on what new users and potential customers might be looking to. Morten Kromberg, Dyalog CTO, wrote a blog post recently, ostensibly about the APL Orchard chat room, but also touching on some of Dyalog’s challenges when dealing with this dichotomy.

Fortunately, and for the avoidance of any doubt, you can absolutely work with modern Dyalog (I’m using version 18.0) in a pretty similar way to what you expect, but it requires a little bit of setup to get right, and is not quite there “out of the box” at the time of writing. However, at the time of reading some of the kinks may well have been ironed out: better support for Dyalog as a scripting engine, and working with code-as-text is a priority for Dyalog right now.

So let’s take a brief look at workspaces and then at the ]LINK tool that allows you to keep your code in text files that can be versioned.

Other resources:

Saving and loading workspaces

If you open RIDE, you’ll see no “Save as..” in the menu, and your OS’s default key binding for “Save” does nothing. You might have figured out how to enter multi-line functions, and how to “fix” (somewhere between “save” and “compile”) them. Yet, when you “fix” a function, where does it go?

At no point were you asked for a file name. The Excel analogy might help to clarify. Think of a cell containing a formula in Excel – you’re not expecting to “save” the formula out to a separate file. Instead, the formula becomes an integral part of the sheet or workbook. We can think of Dyalog workspaces as analoguous to Excel workbooks: a snapshot of values and logic to manipulate them. It has the nice feature that if you distribute a workspace, you can be sure that users of that workspace sees exactly what you intended: it’s an exact snapshot.

Let’s try this out. In a new RIDE session, let’s create a few functions and variables, like so: workspace1

To save the workspace into a workspace file, type

)save myworkspace
Cannot perform operation with trace/edit windows open.

Ooops, that didn’t work. Dyalog can only save a workspace if you close down all edit windows first. So let’s do that, and once you’ve saved your workspace, close down the session and reopen a fresh one.

To get back your workspace, type

)load myworkspace


We can use the commands )vars to see the defined variables, and )fns to show the names of any functions contained in the workspace. As we can see, they’re all present and correct. If you find yourself using a set of utility functions over and over, they might be usefully combined into a workspace that you can then load up as a unit with a simple )load command. As you might expect, the workspace concept is deeply engrained in Dyalog and it comes with rich, sophisticated, thoroughly battle-tested functionality – all of which is overkill for our needs right now. If you want to explore this further, Legrand’s book, Mastering Dyalog APL covers it extensively.


Also in Dyalog v18.1, a new feature called dyalogscript is present. For me, this is a bit of a workflow game changer. dyalogscript allows you to execute a text file containing APL code, top to bottom, with function definitions and all, from the command-line, following standard un×x conventions. This means that with this approach you can now use Dyalog for ‘quick and dirty’ scripts where you otherwise would have reached for Python, and that you can easily share code as files, or via a github gist and have other people run it. For example, I created the following file:


and can run it from the command line:


Jupyter workflow

As I’ve mentioned before, this book is composed of a set of Jupyter notebooks running the Dyalog kernel. APL is a pretty sharp tool for data analysis and ad-hoc modeling and experimentation, and as such fits neatly into the expectations and workflows that gave rise to Jupyter in the first place. The way I have ended up working with Dyalog APL is 75% Jupyter, 20% exploration and debugging in RIDE and 5% LINK for stuff I really think I might reuse.

Here’s a Dyalog webinar introducing the Jupyter kernel.