Project Highlight: workspace-d

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Not so long ago, Jan Jurzitza sat down at his keyboard intent on writing a D plugin for Atom, his text editor of choice at the time. Then came disappointment.

“I was pretty unhappy with their API,” he says.

Visual Studio Code was released a short time after. He decided to give it a go and “instantly fell in love with it”. His Atom plugin was pushed aside and he started work on a new plugin for VS Code called code-d.

However, I did not want to maintain the same functionality in two plugins for two different text editors, so I thought that making a program which contains most of the plugin logic, like starting and calling dcd, dscanner, dfmt, etc., would be beneficial and would also help with including D support in more editors and IDEs in the future.

For the uninitiated, DCD (the D Completion Daemon), DScanner, and Dfmt are D-oriented tools for plugin developers, all maintained by Brian Schott. They are, respectively, a client-server based auto-completion program, a source code analyzer, and a code formatter. A number of IDE and text editor plugins employ them directly.

So Jan started work on his new tool and named it workspace-d.

With workspace-d I want to make it simple for plugin developers to integrate D functionality into their editor of choice. workspace-d is designed to work both as a standalone program through stdio and as a D library. Once I ported most of the code from my Atom extension to workspace-d, I could simply spawn it as a subprocess in code-d, which I got working with it quite quickly.

In addition to porting his Atom plugin to use workspace-d, he also created one for Sublime Text. Currently, he’s not devoting any time to either and is looking for someone else to take over maintenance of one or both. Anyone interested might start by submitting pull requests. Aside from workspace-d itself, Jan’s focus is on code-d.

He’s recently been working on version 2.0 of workspace-d, with a focus on streamlining the way it handles requests.

Using traits, templates, and CTFE (Compile-Time Function Execution), basically all D compile time magic, I was able to make an automatic wrapper for the functions for version 2.0. Basically, when a request like {"cmd":"hello"} comes in, it runs the D function hello with its default arguments. If the arguments don’t match, it responds with an error. This system automatically binds function arguments to JSON values and generates a response from the return value.

To deserialize the JSON requests, he’s using painlessjson, a third-party library available in the DUB package registry.

It works really great and I can really recommend it for some simple and easy conversions between D types and JSON. This change really cleaned up all the code and made it possible to use workspace-d as a library.

He’s also working on a new project, serve-d, that works with Microsoft’s Language Server Protocol.

serve-d is an alternative for the workspace-d command line I/O for those who prefer JSON RPC over my custom binary/JSON mix. It’s fiber based and uses workspace-d as a library, which results in really clean code. There’s an alpha version of the implementation on github already, both the server and a new branch on code-d. With the Language Server Protocol, I’m hoping for easier integration in other editors. The concept is basically the same as workspace-d’s command line interface, but, because Microsoft is such a big company, I’m hoping that more editors by big developers are going to implement this protocol.

Building and installing workspace-d should go pretty smoothly on Linux or OS X, but it’s currently a little bumpy on Windows. Because of an issue Jan has yet to resolve, it can only be built on Windows with LDC.

The auto completion didn’t work for some people on Windows because it got stuck in the std.process.execute function when creating a pipe to write to. I couldn’t find any way to reproduce it in a standalone program so I couldn’t file a bug either. So what we did to avoid this issue in the short term was to simply disallow compilation on Windows using DMD. It works just fine when compiled with LDC.

Jan’s primarily a Linux user (he doesn’t own a Mac and only runs Windows in a VM). He credits GitHub user @Andrepuel for getting it operational on OS X, and @aka-demik for finding the issue on Windows and verifying that it compiles with LDC. He’ll be grateful to anyone who can help fully resolve the Windows/DMD issue once and for all.

If you’re looking to develop a D plugin for your favorite editor, consider taking advantage of the work Jan as already done with workspace-d to save yourself some effort. And VS Code users can put it to use via code-d to get code completion and more. Visit its VS Code marketplace page to read reviews and installation instructions.

Don’t Fear the Reaper

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D, like many other programming languages in active use today, comes with a garbage collector out of the box. There are many types of software that can be written without worrying at all about the GC, taking full advantage of its benefits. But the GC does have drawbacks, and there are certainly scenarios in which garbage collection is undesirable. For those situations, the language provides the means to temporarily disable it, or even avoid it completely.

In order to maximize the positive impacts of garbage collection and minimize any negative, it’s necessary to have a good grounding in how the GC works in D. A good place to start is the Garbage Collection page at, which outlines the rationale for D’s GC and provides some tips on working with it. This post is the first in a series intended to expand on the information provided on that page.

This time, we’ll look at the very basics, focusing on the language features that can trigger GC allocations. Future posts will introduce ways to disable the GC when necessary, as well as idioms useful in dealing with its nondeterministic nature (e.g. managing resources in the destructors of GC-managed objects).

The first thing to understand about D’s garbage collector is that it only runs during allocation, and only if there is no memory available to allocate. It isn’t sitting in the background, periodically scanning the heap and collecting garbage. This knowledge is fundamental to writing code that makes efficient use of GC-managed memory. Take the following example:

void main() {
    int[] ints;
    foreach(i; 0..100) {
        ints ~= i;

This declares a dynamic array of int, then uses D’s append operator to append the numbers 0 to 99 in a foreach range loop. What isn’t obvious to the untrained eye is that the append operator makes use of the GC heap to allocate space for the values it adds to the array.

DRuntime’s array implementation isn’t a dumb one. In the example, there aren’t going to be one hundred allocations, one for each value. When more memory is needed, the implementation will allocate more space than is requested. In this particular case, it’s possible to determine how many allocations are actually made by making use of the capacity property of D’s dynamic arrays and slices. This returns the total number of elements the array can hold before an allocation is necessary.

void main() {
    import std.stdio : writefln;
    int[] ints;
    size_t before, after;
    foreach(i; 0..100) {
        before = ints.capacity;
        ints ~= i;
        after = ints.capacity;
        if(before != after) {
            writefln("Before: %s After: %s",
                before, after);

Executing this when compiled with DMD 2.073.2 shows the message is printed six times, meaning there were six total GC allocations in the loop. That means there were six opportunities for the GC to collect garbage. In this small example, it almost certainly didn’t. If this loop were part of a larger program, with GC allocations throughout, it very well might.

On a side note, it’s informative to examine the values of of before and after. Doing so shows a sequence of 0, 3, 7, 15, 31, 63, and 127. So by the end, ints contains 100 values and has space for appending 27 more before the next allocation, which, extrapolating from the values in the sequence, should be 255. That’s an implementation detail of DRuntime, though, and could be tweaked or changed in any release. For more details on how arrays and slices are managed by the GC, take a look at Steven Schveighoffer’s excellent article on the topic.

So, six allocations, six opportunities for the GC to initiate one of its pauses of unpredictable length even in that simple little loop. In the general case, that could become an issue depending on if the loop is in a hot part of code and how much total memory is allocated from the GC heap. But even then, it’s not necessarily a reason to disable the GC in that part of the code.

Even with languages that don’t come with a stock GC out of the box, like C and C++, most programmers learn at some point that it’s better for overall performance to allocate as much as possible up front and minimize allocations in the inner loops. It’s one of the many types of premature optimization that are not actually the root of all evil, something we tend to call best practice. Given that D’s GC only runs when memory is allocated, the same strategy can be applied as a simple way to mitigate any potential impact it could have on performance. Here’s one way to rewrite the example:

void main() {
    int[] ints = new int[](100);
    foreach(i; 0..100) {
        ints[i] = i;

Now we’ve gone from six allocations down to one. The only opportunity the GC has to run is before the inner loop. This actually allocates space for at least 100 values and initializes them all to 0 before entering the loop. The array will have a length of 100 after new, but will almost certainly have additional capacity.

There’s an alternative to new for arrays: the reserve function:

void main() {
    int[] ints;
    foreach(i; 0..100) {
        ints ~= i;

This allocates memory for at least 100 values, but the array is still empty (its length property will return 0) when it returns, so nothing is default initialized. Given that the loop only appends 100 values, it’s guaranteed not to allocate.

In addition to new and reserve, it’s possible to call GC.malloc directly for explicit allocation.

import core.memory;
void* intsPtr = GC.malloc(int.sizeof * 100);
auto ints = (cast(int*)intsPtr)[0 .. 100];

Array literals will usually allocate.

auto ints = [0, 1, 2];

This is also true when an array literal enum is used.

enum intsLiteral = [0, 1, 2];
auto ints1 = intsLiteral;
auto ints2 = intsLiteral;

An enum exists only at compile time and has no memory address. Its name is a synonym for its value. Everywhere you use one, it’s like copying and pasting its value in place of its name. Both ints1 and ints2 trigger allocations exactly as if they were declared like so:

auto ints1 = [0, 1, 2];
auto ints2 = [0, 1, 2];

Array literals do not allocate when the target is a static array. Also, string literals (strings in D are arrays under the hood) are an exception to the rule.

int[3] noAlloc1 = [0, 1, 2];
auto noAlloc2 = "No Allocation!";

The concatenate operator will always allocate:

auto a1 = [0, 1, 2];
auto a2 = [3, 4, 5];
auto a3 = a1 ~ a2;

D’s associative arrays have their own allocation strategy, but you can expect them to allocate when items are added and potentially when removed. They also expose two properties, keys and values, which both allocate arrays and fill them with copies of the respective items. When its desirable to modify the underlying associative array during iteration, or when the items need to be sorted or otherwise manipulated independently of the associative array, these properties are just what the doctor ordered. Otherwise, they’re needless allocations that put undue pressure on the GC.

When the GC does run, the total amount of memory it needs to scan is going to determine how long it takes. The smaller, the better. Avoiding unnecessary allocations isn’t going to hurt anyone and is another good mitigation strategy. D’s associative arrays provide three properties that help do just that: byKey, byValue, and byKeyValue. These each return forward ranges that can be iterated lazily. They do not allocate because they actually refer to the items in the associative array, so it should not be modified while iterating them. For more on ranges, see the chapters titled Ranges and More Ranges in Ali Çehreli’s Programming in D.

Closures, which are delegates or function literals that need to carry around a pointer to the local stack frame, may also allocate. The last allocating language feature listed on the Garbage Collection page is the assertion. An assertion will allocate when it fails because it needs to throw an AssertError, which is part of D’s class-based exception hierarchy (we’ll look at how classes interact with the GC in a future post).

Then there’s Phobos, D’s standard library. Once upon a time, much of Phobos was implemented with little concern for GC allocations, making it difficult to use in situations where they were undesirable. However, a massive effort was initiated
to make it more conservative in its GC usage. Some functions were made to work with lazy ranges, others were rewritten to take buffers supplied by the caller, and some were reimplemented to avoid unnecessary allocations internally. The result is a standard library more amenable to GC-free code (though there are still probably some corners of the library that have not yet been renovated — PRs welcome).

Now that we’ve seen the basics of using the GC, the next post in this series will look at the tools the language and the compiler provide for turning the GC off and making sure specific sections of code are GC-free.

Thanks to Guillaume Piolat and Steven Schveighoffer for their help with this article.

Editable and Runnable Doc Examples on

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Sebastian Wilzbach was a GSoC student for the D Language Foundation in 2016 and has since become a regular contributor to Phobos, D’s standard library, and

This article explains the steps that were needed to have editable and runnable examples in the documentation on First, let’s begin with the building blocks.

Unit testing in D

One of D’s coolest features is its unittest block, which allows the insertion of testable code anywhere in a program. It has become idiomatic for a function to be followed directly by its tests. For example, let’s consider a simple function add which is accompanied by two tests:

auto add(int a, int b)
    return a + b;

    assert(2.add(2) == 4);
    assert(3.add(4) == 7);

By default, all unittest blocks will be ignored by the compiler. Specifying -unittest on the compiler’s command line will cause the unit tests to be included in the compiled binary. Combined with -main, tests in D can be directly executed with:

rdmd -main -unittest add.d

If a unittest block is annotated with embedded documentation, a D documentation generator can also display the tests as examples in the generated documentation. The DMD compiler ships with a built-in documentation generator (DDoc), which can be run with the -D flag, so executing:

dmd -D -main add.d

would yield the documentation of the add function above with its tests displayed as examples, as demonstrated here:

Please note that the documentation on is generated with DDoc. However, in case you don’t like DDoc, there are several other options.

Executing code on the web

Frequent readers of the D Blog might remember Damian Ziemba’s DPaste – an online compiler for the D Programming language. In 2012, he made the examples on the front page of D’s website runnable via his service. Back in those old days, the website of the D Programming language looked like this:

As a matter of fact, until 2015, communication with DPaste was done in XML.

Putting things together

So D has a unit test system that allows placing executable unit tests next to the functions they test, the tests can also be rendered as examples in the generated documentation, and there exists a service, in the form of DPaste, that allows D code to be executed on the web. The only thing missing was to link them together to produce interactive documentation for a compiled language.

There was one big caveat that needed to be addressed before that could happen. While D’s test suite, which is run on ten different build machines, ensures that all unit tests compile & run without errors, an extracted test might contain symbols that were imported at module scope and thus wouldn’t be runnable on A unittest block can only be completely independent of the module in which it is declared if all of its symbols are imported locally in the test’s scope. The solution was rather simple: extract all tests from Phobos, then compile and execute them separately to ensure that a user won’t hit a “missing import” error on Thanks to D’s ultra-fast frontend, this step takes less than a minute on a typical machine in single-core build mode.

Moreover, to prevent any regressions, this has been integrated into Phobos’s test suite and is run for every PR via CircleCi. As Phobos has extensive coverage with unit tests, we started this transition with a blacklist and, step-by-step, removed modules for which all extracted tests compile. With continuous checking in place, we were certain that none of the exposed unit tests would throw any errors when executed in the documentation, so we could do the flip and replace the syntax-highlighted unit test examples with an interactive code editor.

Going one step further

With this setup in place, hitting the “Run” button would merely show the users “All tests passed”. While that’s always good feedback, it conveys less information than is usually desirable.

Documentation that supports runnable examples tends to send any output to stdout. This allows the reader to take the example and modify it as needed while still seeing useful output about the modifications. So, for example, instead of using assertions to validate the output of a function, which is idiomatic in D unit tests and examples:

assert(myFun() == 4);

Other documentation usually prints to stdout and shows the expected output in a comment. In D, that would look like this:

writeln(myFun()); // 4

I initially tried to do such a transformation with regular expressions, but I was quickly bitten by the complexity of a context-free language. So I made another attempt using Brian Schott’s libdparse, a library to parse and lex D source code. libdparse allows one to traverse the abstract syntax tree (AST) of a D source file. During the traversal of the AST, the transformation tool can rewrite all AssertExpressions into writeln calls, similar to the way other documentation displays examples. To speak in the vocabulary of compiler devs: we are lowering AssertExpressions into the more humanly digestible writeln calls!

Once the AST has been traversed and modified, it needs to be transformed into source code again. This led to improvements in libdparse’s formatting capabilities (1, 2).

The future

As of now, there are still a small number of functions in Phobos that don’t have a nice public example that is runnable on Tooling to check for this has recently been activated in Phobos. So now you can use this tool (make -f posix.mak has_public_example) to find functions lacking public tests and remove those modules from the blacklist.

Another target for improvement is DPaste. For example, it currently doesn’t cache incoming requests, which could improve the performance of executed examples on However, due to the fast compilation speed of the DMD compiler, this “slow-down” isn’t noticeable and is more of a perfectionist wish.

I hope you enjoy the new “Run” button on the documentation and have as much fun playing with it as I do. Click here to get started.

Project Highlight: vibe.d

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Since the day Sönke Ludwig first announced vibe.d on the D Forums, the project has been a big hit in the D community. It’s the exclusive subject of one book, has a chapter of its own in another, and has been proven in production both commercially and otherwise. As so many projects do, it all started out of frustration.

I was dissatisfied with existing network web libraries (in particular with Node.js, which was the new big thing back then, because it was also built on an asynchronous I/O model). D 2.0 gained cross platform fiber support through the integration of DRuntime, which seemed like a perfect opportunity to avoid the shortcomings of Node.js’s programming model (“callback hell”). Together with D’s strong type checking and the high performance of natively compiled applications this had the ideal preconditions for creating a network framework.

From the initial release, work progressed on adding web and REST interface generators (vibe.web.web and, respectively).

This was made possible by D’s advanced meta programming facilities, string mixins and compile-time reflection in particular. The eventual addition of user-defined attributes to the language enabled some important advances later on, such as the recently added authorization framework.

Vibe.d is at its core an I/O and concurrency framework that makes heavy use of fibers which run in a quasi-parallel framework.

Every time an operation (e.g. reading from a socket) needs to wait, the fiber yields execution, so another fiber can run instead. Each fiber uses up very little memory compared to a full thread and switching between fibers is very cheap. This enables highly scalable applications that behave like normal multithreaded applications (save for the low-level issues associated with real multithreading).

At a higher level, it can serve as a web framework for backend development and provides functionality for protocols like HTTP and SMTP, database connectivity, and the parsing of data formats. A number of third-party packages that extend or complement vibe.d can be found in the DUB repository (Sönke is also the creator and maintainer of DUB, the D build tool and package manager).

Big changes are currently afoot with the project. Beginning with the release of vibe.d 0.7.27 in February 2016, work began on splitting the monolithic project into independent DUB packages. One goal is to make it possible to use one vibe.d component without pulling them all in, reducing build times in the process.

Another goal is to employ modern D idioms where possible and to improve memory usage and performance as far as possible. It is surprising how much D evolved in just the short amount of time that vibe.d has been alive!

Diet-NG, vibe.d’s template engine based on Jade, was the first to be granted independence. It went through a complete rewrite that adds a strong test suite, makes use of D’s ranges where possible, provides a more flexible API, and eliminates dependencies on other vibe.d packages. Now he’s working on the core package.

The vibe-core package encapsulates the whole event and fiber logic, including I/O, tasks, concurrency primitives and general operating system abstraction. The original design was based heavily on classes and interfaces and had a very high level operating system abstraction layer, resulting in several downsides. For example, there was a dependence on the GC and virtual function calls could be an issue on certain platforms. One of the main goals was to minimize performance overhead in the new implementation.

As part of his experimentation with different API idioms and slimming down the code base, he produced the eventcore library.

The API follows a proactor pattern, meaning that a callback gets invoked whenever a certain asynchronous operation finishes. This is in contrast to the reactor pattern that is exposed by the non-blocking Posix APIs. It was chosen mainly so that asynchronous I/O APIs, such as Windows overlapped I/O and POSIX AIO, could be supported.

On top of eventcore, the fiber logic was implemented in a completely general way, using a central fiber scheduler and a generic asyncAwait function. This means that lots of corner cases are handled in a much more robust way now and that improvements in all areas can be made much faster and with fewer chances of breaking anything.

Next on the list for independence is the HTTP package. Sönke plans to completely rebuild the package from the ground up, adding HTTP/2 support and making it possible to enable allocation-free request/response processing.

Other obvious candidates are MongoDB and Redis clients, JSON and BSON support, the serialization framework, and the Markdown parser. The goal for each of these packages is always to take a look at the code and employ modern D idioms during the process.

If you’ve never taken D for a spin, vibe.d is a fun playground in which to do so. It’s not difficult to get up and running, and easier still if you have experience with such frameworks in other languages. It’s also ready to put into production in its current state, despite the leading zero in the version number. As Sönke makes progress on breaking it up into separate packages, it will almost certainly become an even more integral part of the growing D community.