Happy New Year from the D Language Foundation

Posted on

Ali Çehreli uses D professionally at Weka.io, is the author of Programming in D, and is frequently found in the D Learn forum with ready answers to questions on using the language. He also is an officer of the D Language Foundation.

Happy 2017!

2016 was filled with many great things happening for the D community:

All of that was achieved by you through your direct contributions or the donations that you’ve made.

We look forward to another great year filled with many cool things happening in the D world. We can’t wait to see your work on D in 2017, some of which we hope to hear about at DConf 2017. 😀

Big Performance Improvement for std.regex

Posted on

Dmitry Olshansky has been a frequent contributor to the D programming language. Perhaps his best known work is his overhaul of the std.regex module, which he architected as part of Google Summer of Code 2011. In this post, he describes an algorithmic optimization he implemented this past summer that resulted in a big performance win.

Optimizing std.regex has been my favorite pastime, but it has gotten harder over the years. It eventually became clear that micro-optimizing the engine’s state copy routine, or trying to avoid that extra write, wasn’t going to cut it anymore. To move further, I needed a new algorithmic improvement. This is how the so-called “Bit-NFA” came to be implemented. Developed in May of this year, it has come a long way to land in the main repository.

Before going into details, a short overview of the engine is called for. A user-specified pattern given to the engine first goes through a compilation process, where it gets transformed into a bytecode program, along with a bunch of lookup tables and auxiliary data-structures. Bytecode implies a VM, not unlike, say, the Java VM, but far simpler and more specific. In fact, there are two of them in std.regex, one that evaluates execution of threads in a backtracking manner and another one which evaluates all threads in lock-step, resolving any duplicates along the way.

Now, running a full blown VM, even a tiny one, on each character of input doesn’t sound all that high-performance. That’s why there is an extra trick, a kickstart engine (it should probably be called a “sidekick engine”), which is a dumb approximation of the full engine. It is run over the input first. When it spots something that looks like a match, the full engine is run to check it. The only requirement is that it can have no false negatives. That is, it has to detect as positive all matches of the regex pattern. This kickstart engine is the central piece of today’s post.

Historically, I intended for there to be a lot of different kickstart engines, ranging from a simple ‘memchr the first byte of the pattern’ to a Boyer-Moore search on the prefix of a pattern. But during the gory days of GSOC 2011, a simple solution came first and out-shadowed all others: the Shift Or algorithm.

Basically, it is an NFA (Nondeterministic Finite Automation), where each state is a bit in a word. Shifting this word advances all the states. Masking removes those that don’t match the current character. Importantly, shifting also places 0 as the first bit, indicating the active state.

With these two insights, the whole process of searching for a string becomes shifting + OR-masking the bits. The last point is checking for the successful match – one of the bits is in the finish state.

Looking at this marvelous construction, it’s tempting to try and overcome its limitation – the straight-forward execution of states. So let’s introduce some control flow by denoting some bits as jumps. To carry out a jump, we just need to map every combination of jump bits to the mask of the resulting positions. A basic hashmap could serve us well in this regard. Then the cycle becomes:

  1. Shift the word
  2. Capture control flow bits
  3. Lookup control flow table
  4. Mask AND with control flow bits
  5. Check for finish state(s) bits
  6. Mask OR with match filter table

In the end, we execute the whole engine with nothing more than a hash-map lookup, a table lookup, and a bit of bitwise operations. This is the essence of what I call the Bit-NFA engine.

Of course, there are some tricky bits, such as properly mapping bytecodes to bits. Then there comes Unicode.. oh gosh. The trick to Unicode, though, is having a fast path for ASCII < 0x80 and the rest. For ASCII, we just go with a simple table. Unicode is a two-staged variation of it. Two-staging the table let’s us coalesce identical pages, saving space for the whole 21 bits of the code point range.

Overall, the picture really is worth a thousand words. Here is how the new kickstart engine stacks up.

This now only leaves us to optimize the VMs further. The proven technique is JIT-ing the bytecode and is what top engines are doing. Still, I’m glad there are notable tricks to speed up regex execution in general without pulling out this heavy handed weapon.

GSoC Report: std.experimental.xml

Posted on

Lodovico Giaretta is currently pursuing a Bachelor Degree in Computer Science at the University of Trento, Italy. He participated in Google Summer of Code 2016, working on a new XML module for D’s standard library, Phobos.

GSoC-icon-192I started coding in high school with Pascal. I immediately fell in love with programming, so I started studying it by myself and learned both Java and C++. But when I was using Java, I was missing the powerful metaprogramming facilities and the low level features of C++. When I was using C++, I was missing the simplicity and usability of Java. So I started looking for a language that “filled the gap” between these two worlds. After looking into many languages, I finally found D. Despite being more geared towards C++, D provides a very high level of productivity, as correct code is easier to read and write. As an example, I was programming in D for several months before I was bitten by a segfault for the first time. It easily became one of my favorite languages.

The apparent lack of libraries, my lack of time, and the need to use other languages for university projects made me forget D for some time, at least until someone told me about Google Summer of Code. When I discovered that the D Foundation was participating, I immediately decided to take part and found that there was the need for a new XML library. So I contacted Craig Dillabaugh and Robert Schadek and started to plan my adventure. I want to take this occasion to thank them for their great continuous support, and the entire community for their feedback and help.

This was my first public codebase and my first contribution to a big open source project, so I didn’t really know anything about project management. The advice about this field from my mentor Robert has been fundamental for my success; he helped me improve my workflow, keep my efforts focused towards the goal, and set up correctness tests and performance benchmarks. Without his help, I would never have been able to reach this point.

The first thing to do when writing a library is to pick a set of principles that will guide development. This choice is what will give the library its peculiar shape, and by having a look around one finds that there are XML libraries that want to be minimal in terms of codebase size, or very small in terms of binary size, or fully featured and 101% adherent to the specification. For std.experimental.xml, I decided to focus on genericity and extensibility. The processing is divided in many small, quite simple stages with well-defined interfaces implemented by templated components. The result is a pipeline that is fully customizable; you can add or substitute components anywhere, and add custom validation steps and custom error handlers.

From an XML library, a programmer expects different high level constructs: a SAX parser, a DOM parser, a DOM writer and maybe some extensions like XPath. He also expects to be able to process different kinds of input and, for std.experimental.xml, to “hack in” his own logic in the process. This requires a simple, yet very flexible, intermediate representation, which is produced by the parsing stage and can be easily manipulated, validated, and transformed into whatever high-level construct is needed. For this, I chose a concept called Cursor, a pointer inside an XML document, which can be queried for properties of a given XML node or advanced to a subsequent one. It’s akin to Java’s StAX (Streaming API for XML), from which I took inspiration. In std.experimental.xml, all validations and transformations are implemented as chains of Cursors, which are then usually processed by a SAX parser or a DOM builder, but can also be used directly in user code, providing more control and speed.

Talking about speed, which in XML processing can be very important, I have to admit that I didn’t spend much time on optimization, leaving a lot of space for future performance improvements. Yet, the library is fast enough to guarantee that, for big files (where performance matters), an SSD (Solid State Drive) is needed to move the bottleneck from the fetching to the processing of the data. Being this is an extensible and configurable library, the user can choose his tradeoffs with fine granularity, trading input validation and higher level constructs for speed at will.

To conclude, the GSoC is finished, but the library is not. Although most parts are there, some bits are still missing. As a new university semester has started, time is becoming a rare and valuable resource, but I’ll do my best to finish the work in a short time so that Phobos can finally have a modern XML library to be proud of. I also have a plan to add more advanced functionality, like XML Schemas and XPath, but I don’t know when I’ll manage to work on that, as it is quite a lot to do.

How to Write @trusted Code in D

Posted on

Steven Schveighoffer is the creator and maintainer of the dcollections and iopipe libraries. He was the primary instigator of D’s inout feature and the architect of a major rewrite of the language’s built-in arrays. He also authored the oft-recommended introductory article on the latter.

d6In computer programming, there is a concept of memory-safe code, which is guaranteed at some level not to cause memory corruption issues. The ultimate holy grail of memory safety is to be able to mechanically verify you will not corrupt memory no matter what. This would provide immunity from attacks via buffer overflows and the like. The D language provides a definition of memory safety that allows quite a bit of useful code, but conservatively forbids things that are sketchy. In practice, the compiler is not omnipotent, and it lacks the context that we humans are so good at seeing (most of the time), so there is often the need to allow otherwise risky behavior. Because the compiler is very rigid on memory safety, we need the equivalent of a cast to say “yes, I know this is normally forbidden, but I’m guaranteeing that it is fine”. That tool is called @trusted.

Because it’s very difficult to explain why @trusted code might be incorrect without first discussing memory safety and D’s @safe mechanism, I’ll go over that first.

What is Memory Safe Code?

The easiest way to explain what is safe, is to examine what results in unsafe code. There are generally 3 main ways to create a safety violation in a statically-typed language:

  1. Write or read from a buffer outside the valid segment of memory that you have access to.
  2. Cast some value to a type that allows you to treat a piece of memory that is not a pointer as a pointer.
  3. Use a pointer that is dangling, or no longer valid.

The first item is quite simple to achieve in D:

auto buf = new int[1]; 
buf[2] = 1;

With default bounds checks on, this results in an exception at runtime, even in code that is not checked for safety. But D allows circumventing this by accessing the pointer of the array:

buf.ptr[2] = 1;

For an example of the second, all that is needed is a cast:

*cast(int*)(0xdeadbeef) = 5;

And the third is relatively simple as well:

auto buf = new int[1];
auto buf2 = buf;
delete buf;  // sets buf to null
buf2[0] = 5; // but not buf2.

Dangling pointers also frequently manifest by pointing at stack data that is no longer in use (or is being used for a different reason). It’s very simple to achieve:

int[] foo()
    int[4] buf;
    int[] result = buf[];
    return result;

So simply put, safe code avoids doing things that could potentially result in memory corruption. To that end, we must follow some rules that prohibit such behavior.

Note: dereferencing a null pointer in user-space is not considered a memory safety issue in D. Why not? Because this triggers a hardware exception, and generally does not leave the program in an undefined state with corrupted memory. It simply aborts the program. This may seem undesirable to the user or the programmer, but it’s perfectly fine in terms of preventing exploits. There are potential memory issues possible with null pointers, if one has a null pointer to a very large memory space. But for safe D, this requires an unusually large struct to even begin to worry about it. In the eyes of the D language, instrumenting all pointer dereferences to check for null is not worth the performance degradation for these rare situations.

D’s @safe rules

D provides the @safe attribute that tags a function to be mechanically checked by the compiler to follow rules that should prevent all possible memory safety problems. Of course, there are cases where developers need to make exceptions in order to get some meaningful work done.

The following rules are geared to prevent issues like the ones discussed above (listed in the spec here).

  1. Changing a raw pointer value is not allowed. If @safe D code has a pointer, it has access only to the value pointed at, no others. This includes indexing a pointer.
  2. Casting pointers to any type other than void* is not allowed. Casting from any non-pointer type to a pointer type is not allowed. All other casts are OK (e.g. casting from float to int) as long as they are valid. Casting a dynamic array to a void[] is also allowed.
  3. Unions that have pointer types that overlap other types cannot be accessed. This is similar to rules 1 and 2 above.
  4. Accessing an element in or taking a slice from a dynamic array must be either proven safe by the compiler, or incur a bounds check during runtime. This even happens in release mode, when bounds checks are normally omitted (note: dmd’s option -boundscheck=off will override this, so use with extreme caution).
  5. In normal D, you can create a dynamic array from a pointer by slicing the pointer. In @safe D, this is not allowed, since the compiler has no idea how much space you actually have available via that pointer.
  6. Taking a pointer to a local variable or function parameter (variables that are stored on the stack) or taking a pointer to a reference parameter are forbidden. An exception is slicing a local static array, including the function foo above. This is a known issue.
  7. Explicit casting between immutable and mutable types that are or contain references is not allowed. Casting value-types between immutable and mutable can be done implicitly and is perfectly fine.
  8. Explicit casting between thread-local and shared types that are or contain references is not allowed. Again, casting value-types is fine (and can be done implicitly).
  9. The inline assembler feature of D is not allowed in @safe code.
  10. Catching thrown objects that are not derived from class Exception is not allowed.
  11. In D, all variables are default initialized. However, this can be changed to uninitialized by using a void initializer:
    int *s = void;

    Such usage is not allowed in @safe D. The above pointer would point to random memory and create an obvious dangling pointer.

  12. __gshared variables are static variables that are not properly typed as shared, but are still in global space. Often these are used when interfacing with C code. Accessing such variables is not allowed in @safe D.
  13. Using the ptr property of a dynamic array is forbidden (a new rule that will be released in version 2.072 of the compiler).
  14. Writing to void[] data by means of slice-assigning from another void[] is not allowed (this rule is also new, and will be released in 2.072).
  15. Only @safe functions or those inferred to be @safe can be called.

The need for @trusted

The above rules work well to prevent memory corruption, but they prevent a lot of valid, and actually safe, code. For example, consider a function that wants to use the system call read, which is prototyped like this:

ssize_t read(int fd, void* ptr, size_t nBytes);

For those unfamiliar with this function, it reads data from the given file descriptor, and puts it into the buffer pointed at by ptr and expected to be nBytes bytes long. It returns the number of bytes actually read, or a negative value if an error occurs.

Using this function to read data into a stack-allocated buffer might look like this:

ubyte[128] buf;
auto nread = read(fd, buf.ptr, buf.length);

How is this done inside a @safe function? The main issue with using read in @safe code is that pointers can only pass a single value, in this case a single ubyte. read expects to store more bytes of the buffer. In D, we would normally pass the data to be read as a dynamic array. However, read is not D code, and uses a common C idiom of passing the buffer and length separately, so it cannot be marked @safe. Consider the following call from @safe code:

auto nread = read(fd, buf.ptr, 10_000);

This call is definitely not safe. What is safe in the above read example is only the one call, where the understanding of the read function and calling context assures memory outside the buffer will not be written.

To solve this situation, D provides the @trusted  attribute, which tells the compiler that the code inside the function is assumed to be @safe, but will not be mechanically checked. It’s on you, the developer, to make sure the code is actually @safe.

A function that solves the problem might look like this in D:

auto safeRead(int fd, ubyte[] buf) @trusted
    return read(fd, buf.ptr, buf.length);

Whenever marking an entire function @trusted, consider if code could call this function from any context that would compromise memory safety. If so, this function should not be marked @trusted under any circumstances. Even if the intention is to only call it in safe ways, the compiler will not prevent unsafe usage by others. safeRead should be fine to call from any @safe context, so it’s a great candidate to mark @trusted.

A more liberal API for the safeRead function might take a void[] array as the buffer. However, recall that in @safe code, one can cast any dynamic array to a void[] array — including an array of pointers. Reading file data into an array of pointers could result in an array of dangling pointers. This is why ubyte[] is used instead.

@trusted escapes

A @trusted escape is a single expression that allows @system (the unsafe default in D) calls such as read without exposing the potentially unsafe call to any other part of the program. Instead of writing the safeRead function, the same feat can be accomplished inline within a @safe function:

auto nread = ( () @trusted => read(fd, buf.ptr, buf.length) )();

Let’s take a closer look at this escape to see what is actually happening. D allows declaring a lambda function that evaluates and returns a single expression, with the () => expr syntax. In order to call the lambda function, parentheses are appended to the lambda. However, operator precedence will apply those parentheses to the expression and not the lambda, so the entire lambda must be wrapped in parentheses to clarify the call. And finally, the lambda can be tagged @trusted as shown, so the call is now usable from the @safe context that contains it.

In addition to simple lambdas, whole nested functions or multi-statement lambdas can be used. However, remember that adding a trusted nested function or saving a lambda to a variable exposes the rest of the function to potential safety concerns! Take care not to expose the escape too much because this risks having to manually verify code that should just be mechanically checked.

Rules of Thumb for @trusted

The previous examples show that tagging something as @trusted has huge implications. If you are disabling memory safety checks, but allowing any @safe code to call it, then you must be sure that it cannot result in memory corruption. These rules should give guidance on where to put @trusted marks and avoid getting into trouble:

Keep @trusted code small

@trusted code is never mechanically checked for safety, so every line must be reviewed for correctness. For this reason, it’s always advisable to keep the code that is @trusted as small as possible.

Apply @trusted to entire functions when the unsafe calls are leaky

Code that modifies or uses data that @safe code also uses creates the potential for unsafe calls to leak into the mechanically checked portion of a @safe function. This means that portion of the code must be manually reviewed for safety issues. It’s better to mark the whole thing @trusted, as that’s more in line with the truth. This is not a hard and fast rule; for example, the read call from the earlier example is perfectly safe, even though it will affect data that is used later by the function in @safe mode.

A pointer allocated with C’s malloc in the beginning of the function, and free‘d later, could have been copied somewhere in between. In this case, the dangling pointer may violate @safe, even in the mechanically checked part. Instead, try wrapping the entire portion that uses the pointer as @trusted, or even the entire function. Alternatively, use scope guards to guarantee the lifetime of the data until the end of the function.

Never use @trusted on template functions that accept arbitrary types

D is smart enough to infer @safe for template functions that follow the rules. This includes member functions of templated types. Just let the compiler do its job here. To ensure the function is actually @safe in the right contexts, create an @safe unittest  to call it. Marking the function @trusted allows any operator overloads or members that might violate memory safety to be ignored by the safety checker! Some tricky ones to remember are postblit and opCast.

It’s still OK to use @trusted escapes here, but be very careful. Consider especially possible types that contain pointers when thinking about how such a function could be abused. A common mistake is to mark a range function or range usage @trusted. Remember that most ranges are templates, and can be easily inferred as @system when the type being iterated has a @system postblit or constructor/destructor, or is generated from a user-provided lambda.

Use @safe to find the parts you need to mark as @trusted

Sometimes, a template intended to be @safe may not be inferred @safe, and it’s not clear why. In this case, try temporarily marking the template function @safe to see where the compiler complains. That’s where @trusted escapes should be inserted if appropriate.

In some cases, a template is used pervasively, and tagging it as @safe may make too many parts break. Make a copy of the template under a different name that you mark @safe, and change the calls that are to be checked so that they call the alternative template instead.

Consider how the function may be edited in the future

When writing a trusted function, always think about how it could be called with the given API, and ensure that it should be @safe. A good example from above is making sure safeRead cannot accept an array of pointers. However, another possibility for unsafe code to creep in is when someone edits a part of the function later, invalidating the previous verification, and the whole function needs to be rechecked. Insert comments to explain the danger of changing something that would then violate safety. Remember, pull request diffs don’t always show the entire context, including that a long function being edited is @trusted!

Use types to encapsulate @trusted operations with defined lifetimes

Sometimes, a resource is only dangerous to create and/or destroy, but not to use during its lifetime. The dangerous operations can be encapsulated into a type’s constructor and destructor, marked @trusted, which allows @safe code to use the resource in between those calls. This takes a lot of planning and care. At no time can you allow @safe code to ferret out the actual resource so that it can keep a copy past the lifetime of the managing struct! It is essential to make sure the resource is alive as long as @safe code has a reference to it.

For example, a reference-counted type can be perfectly safe, as long as a raw pointer to the payload data is never available. D’s std.typecons.RefCounted cannot be marked @safe, since it uses alias this to devolve to the protected allocated struct in order to function, and any calls into this struct are unaware of the reference counting. One copy of that payload pointer, and then when the struct is free‘d, a dangling pointer is present.

This can’t be @safe!

Sometimes, the compiler allows a function to be @safe, or is inferred @safe, and it’s obvious that shouldn’t be allowed. This is caused by one of two things: either a function that is called by the @safe function (or some deeper function) is marked @trusted but allows unsafe calls, or there is a bug or hole in the @safe system. Most of the time, it is the former. @trusted is a very tricky attribute to get correct, as is shown by most of this post. Frequently, developers will mark a function @trusted only thinking of some uses of their function, not realizing the dangers it allows. Even core D developers make this mistake! There can be template functions that are inferred safe because of this, and sometimes it’s difficult to even find the culprit. Even after the root cause is discovered, it’s often difficult to remove the @trusted tag as it will break many users of the function. However, it’s better to break code that is expecting a promise of memory safety than subject it to possible memory exploits. The sooner you can deprecate and remove the tag, the better. Then insert trusted escapes for cases that can be proven safe.

If it does happen to be a hole in the system, please report the issue, or ask questions on the D forums. The D community is generally happy to help, and memory safety is a particular focus for Walter Bright, the creator of the language.

GSoC Report: DStep

Posted on

Wojciech Szęszoł is a Computer Science major at the University of Wrocław. As part of Google Summer of Code 2016, he chose to make improvements to Jacob Carlborg’s DStep, a tool to generate D bindings from C and Objective-C header files.

GSoC-icon-192It was December of last year and I was writing an image processing project for a course at my university. I would normally use Python, but the project required some custom processing, so I wasn’t able to use numpy. And writing the inner loops of image processing algorithms in plain Python isn’t the best idea. So I decided to use D.

I’ve been conscious about the existence of the D language for as long as I can remember, but I’d never convinced myself before to try it out. The first thing I needed to do was to load an image. At the time, I didn’t know that there is a DUB repository containing bindings to image loading libraries, so I started writing bindings to libjpeg by myself. It didn’t end very well, so I thought there should be a tool that will do the job for me. That’s when I found DStep and htod.

Unluckily, the capabilities of DStep weren’t satisfying (mostly the lack of any kind of support for the preprocessor) and htod didn’t run on Linux. I ended up coding my project in C++, but as GSoC (Google Summer of Code) was lurking on the horizon, I decided that I should give it a try with DStep. I began by contacting Craig Dillabaugh (Ed. Note: Craig volunteers to organize GSoC projects using D) to learn if there was any need for developing such a project. It sparked some discussion on the forum, the idea was accepted, and, more importantly, Russel Winder agreed to be the mentor of the project. After some time I needed to prepare and submit an official proposal. There was an interview and fortunately I was accepted.

The first commit I made for DStep is dated to February, 1. It was a proof of concept that C preprocessor definitions can be translated to D using libclang. Then I improved the testing framework by replacing the old Cucumber-based tests with some written in D. I made a few more improvements before the actual GSoC coding period began.

During GSoC, I added support for translation of preprocessor macros (constants and functions). It required implementing a parser for a small part of the C language as the information from libclang was insufficient. I implemented translation of comments, improved formatting of the output code (e.g. made DStep keep the spacing from C sources), fixed most of the issues from the GitHub issue list and ported DStep to Windows. While I was coding I was getting support from Jacob Carlborg. He did a great job by reviewing all of the commits I made. When I didn’t know how to accomplish something with D, I could always count on help on forum.dlang.org.

DStep was the first project of such a size that I coded in D. I enjoyed its modern features, notably the module system, garbage collector, built-in arrays, and powerful templates. I used unittest blocks a lot. It would be nice to have named unit tests, so that they can be run selectively. From the perspective of a newcomer, the lack of consistency and symmetry in some features is troubling, at least before getting used to it. For example there is a built-in hash map and no hash set, some identifiers that should be keywords starts with @ (Ed. Note: see the Attributes documentation), etc. I was very sad when I read that the in keyword is not yet fully implemented. Despite those little issues, the language served me very well overall. I suppose I will use D for my personal toy projects in the future. And for DStep of course. I have some unfinished business with it :).

I would like to encourage all students to take part in future editions of GSoC. And I must say the D Language Foundation is a very good place to do this. I learned a lot during this past summer and it was a very exciting experience.