C++17: The two line visitor explained

If you have ever used an “idiomatic” C++ variant datatype like Boost.Variant or the new C++17 std::variant, you probably wished you could assemble a visitor to dispatch on the type by assembling a couple of lambda expressions like this:

auto my_visitor = visitor{
  [&](int value) { /* ... */ },
  [&](std::string const& value) { /* ... */ },
};

The code in question

While reading through the code for lager I stumbled upon a curious way to to make this happen. And it is just two lines of code! Wow, that is cool. A comment in the code indicated that the code was copied from cppreference.com where I quickly found the source on the page for std::visit, albeit with a different name:

template<class... Ts> struct overloaded : Ts... { using Ts::operator()...; };
template<class... Ts> overloaded(Ts...) -> overloaded<Ts...>;

Multiple inheritance to the rescue

Lambda expressions in C++ are just syntactic sugar for callables, pretty much like a struct with an operator(). As such, you can derive from them. which is what the first line does.
It uses variadic templates and multiple inheritance to assemble the types of the lambdas into one type. Without the content in the struct body, an instantiation with our example would be roughly equivalent to this:

struct int_visitor {
  void operator()(int value)
  {/* ... */}
};

struct string_visitor {
  void operator()(std::string const& value)
  {/* ... */}
};

struct visitor : int_visitor, string_visitor {
};

Using all of it

Now this cannot yet be called, as overload resolution (by design) does not work across different types. Hence the using in the structs body. It pulls the operator() implementations into the visitor type where overload resolution can work across all of them.
With it, our hypothetical instantiation becomes:

struct visitor : int_visitor, string_visitor {
  using int_visitor::operator();
  using string_visitor::operator();
};

Now an instance of that type can actually be called with both our types, which is what the interface for, e.g. std::visit demands.

Don’t go without a guide

The second line intruiged me. It looks a bit like a function declaration but that is not what it is. The fact that I had to ask in the (very helpful!) C++ slack made me realize that I did not keep up with the new features in C++17 as much as I would have liked.
This is, in fact, a class template argument deducation (CTAD) guide. It is a new feature in C++17 that allows you do deduce template arguments for a type based on constructor parameters. In a way, it supercedes the Object Generator idiom of old.
The syntax is really quite straight-forward. Given a list of constructor parameter types, resolve to a specific template instance based on those.

Constructing

The last piece of the puzzle is how the visitor gets initialized. The real advantage of using lambdas instead of writing the struct yourself is that you can capture variables from your context. Therefore, you cannot just default-initialize most lambdas – you need to transport its values, its bound context.
In our example, this uses another new C++17 feature: extended aggregate initialization. Aggregate initialization is how you initialized structs way back in C with curly-brackets. Previously, it was forbidden to do this with structs that have a base class. The C++17 extension now lifts this restriction, thus making it possible to initialize this visitor with curly brackets,.

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Integrating catch2 with CMake and Jenkins

A few years back, we posted an article on how to get CMake, googletest and jenkins to play nicely with each other. Since then, Phil Nash’s catch testing library has emerged as arguably the most popular thing to write your C++ tests in. I’m going to show how to setup a small sample project that integrates catch2, CMake and Jenkins nicely.

Project structure

Here is the project structure we will be using in our example. It is a simple library that implements left-pad: A utility function to expand a string to a minimum length by adding a filler character to the left.

├── CMakeLists.txt
├── source
│   ├── CMakeLists.txt
│   ├── string_utils.cpp
│   └── string_utils.h
├── externals
│   └── catch2
│       └── catch.hpp
└── tests
    ├── CMakeLists.txt
    ├── main.cpp
    └── string_utils.test.cpp

As you can see, the code is organized in three subfolders: source, externals and tests. source contains your production code. In a real world scenario, you’d probably have a couple of libraries and executables in additional subfolders in this folder.

The source folder

set(TARGET_NAME string_utils)

add_library(${TARGET_NAME}
  string_utils.cpp
  string_utils.h)

target_include_directories(${TARGET_NAME}
  INTERFACE ./)

install(TARGETS ${TARGET_NAME}
  ARCHIVE DESTINATION lib/)

The library is added to the install target because that’s what we typically do with our artifacts.

I use externals as a place for libraries that go into the projects VCS. In this case, that is just the catch2 single-header distribution.

The tests folder

I typically mirror the filename and path of the unit under test and add some extra tag, in this case the .test. You should really not need headers here. The corresponding CMakeLists.txt looks like this:

set(UNIT_TEST_LIST
  string_utils)

foreach(NAME IN LISTS UNIT_TEST_LIST)
  list(APPEND UNIT_TEST_SOURCE_LIST
    ${NAME}.test.cpp)
endforeach()

set(TARGET_NAME tests)

add_executable(${TARGET_NAME}
  main.cpp
  ${UNIT_TEST_SOURCE_LIST})

target_link_libraries(${TARGET_NAME}
  PUBLIC string_utils)

target_include_directories(${TARGET_NAME}
  PUBLIC ../externals/catch2/)

add_test(
  NAME ${TARGET_NAME}
  COMMAND ${TARGET_NAME} -o report.xml -r junit)

The list and the loop help me to list the tests without duplicating the .test tag everywhere. Note that there’s also a main.cpp included which only defines the catch’s main function:

#define CATCH_CONFIG_MAIN
#include <catch.hpp>

The add_test call at the bottom tells CTest (CMake’s bundled test-runner) how to run catch. The “-o” switch commands catch to direct its output to a file, report.xml. The “-r” switch sets the report mode to JUnit format. We will need both to integrate with Jenkins.

The top-level folder

The CMakeLists.txt in the top-level folder needs to call enable_testing() for our setup. Other than that, it just directs to the subfolders via add_subdirectory().

Jenkins

Now all that is needed is to setup Jenkins accordingly. Setup jenkins to get your code, add a “CMake Build” build-step. Hit “Add build tool invocation” and check “Use cmake” to let cmake handle the invocation of your build tool (e.g. make). You also specify the target here, which is typically “install” or “package” via the “–target” switch.

Now you add another step that runs the tests via CTest. Add another Build Step, this time “CMake/CPack/CTest Execution” and pick CTest. The one quirk with this is that it will let the build fail when CTest returns a non-zero exit code – which it does when any tests fail. Usually, you want the build to become unstable and not failed if that happens. Hence set “1-65535” in the “Ignore exit codes” input.

The final step is to let jenkins use the report.xml that we had CTest generate so it can generate the test result charts and tables. To do that, add the post-build action: “Publish JUnit test result report” and point it to tests/report.xml.

Done!

That’s it. Now you got your CI running nice catch tests. The code for this example is available on our github.

Keeping connections alive with libcurl

libcurl is quite a comfortable option to transfer files across a variety of network protocols, e.g. HTTP, FTP and SFTP.

It’s really easy to get started: downloading a single file via http or ftp takes only a couple of lines.

Drip, drip..

But as with most powerful abstractions, it is a bit leaky. While it does an excellent job of hiding such steps as name resolution and authentication, these steps still “leak out” by increasing the overall run-time.

In our case, we had five dozen FTP servers and we needed to repeatedly download small files from all of them. To make matters worse, we only had a small time window of 200ms for each transfer.

Now FTP is not the most simple protocol. Essentially, it requires the client to establish a TCP control connection, that it uses negotiate a second data connection and initiate file transfers.

This initial setup phase needs a lot of back and forth between server and client. Naturally, this is quite slow. Ideally, you would want to do the connection setup once and keep both the control and the data connection open for subsequent transfers.

libcurl does not explicitly expose the concept of an active connection. Hence you cannot explicitly tell the library not to disconnect it. In a naive implementation, you would download multiple files by simply creating an easy session object for each file transfer:

for (auto file : FILE_LIST)
{
  std::vector<uint8_t> buffer;
  auto curl = curl_easy_init();
  if (!curl)
    return -1;
  auto url = (SERVER+file);
  curl_easy_setopt(curl, CURLOPT_URL,
    url.c_str());
  curl_easy_setopt(curl, CURLOPT_WRITEFUNCTION,
    appendToVector);
  curl_easy_setopt(curl, CURLOPT_WRITEDATA,
    &buffer);
  if (curl_easy_perform(curl) != CURLE_OK)
    return -1;

  process(buffer);
  curl_easy_cleanup(curl);
}

That does indeed reset the connection for every single file.

Re-use!

However, libcurl can actually keep the connection open as part of a connection re-use mechanism in the session object. This is documented with the function curl_easy_perform. If you simply hoist the easy session object out of the loop, it will no longer disconnect between file transfers:

auto curl = curl_easy_init();
if (!curl)
  return -1;

for (auto file : FILE_LIST)
{
  std::vector<uint8_t> buffer;
  auto url = (SERVER+file);
  curl_easy_setopt(curl, CURLOPT_URL, 
    url.c_str());
  curl_easy_setopt(curl, CURLOPT_WRITEFUNCTION, 
    appendToVector);
  curl_easy_setopt(curl, CURLOPT_WRITEDATA, 
    &buffer);
  if (curl_easy_perform(curl) != CURLE_OK)
    return -1;

  process(buffer);
}
curl_easy_cleanup(curl);

libcurl will now cache the active connection in the session object, provided the files are actually on the same server. This improved the download timings of our bulk transfers from 130ms-260ms down to 30ms-40ms, quite the enormous gain. The timings now fit into our 200ms time window comfortably.

Evolution of programming languages

Programming languages evolve over time. They get new language features and their standard library is extended. Sounds great, doesn’t it? We all know not going forward means your go backward.

But I observe very different approaches looking at several programming ecosystems we are using.

Featuritis

Java and especially C# added more and more “me too” features release after release making relatively lean languages quite complex multi-paradigm languages. They started object oriented and added generics, functional programming features and declarative programming (LINQ in C#) and different UI toolkits (AWT, Swing, JavaFx in Java; Winforms, WPF in C#) to the mix.

Often the new language features add their own set of quirks because they are an afterthought and not carefully enough designed.

For me, this lack of focus makes said language less attractive than more current approaches like Kotlin or Go.

In addition, deprecation often has no effect (see Java) where 20 year old code and style still works which increases the burden further . While it is great from a business perspektive in that your effort to maintain compatibility is low it does not help your code base. Different styles and old ways of doing something tend to remain forever.

Revolution

In Grails (I know, it is not a programming language, but I has its own ecosystem) we see more of a revolution. The core concept as a full stack framework stays the same but significant components are changed quite rapidly. We have seen many changes in technology like jetty to tomcat, ivy to maven, selenium-rc to geb, gant to gradle and the list goes on.

This causes many, sometimes subtle, changes in behaviour that are a real pain when maintaining larger applications over many years.

Framework updates are often a time-consuming hassle but if you can afford it your code base benefits and will eventually become cleaner.

Clean(er) evolution

I really like the evolution in C++. It was relatively slow – many will argue too slow – in the past but it has picked up pace in the last few years. The goal is clearly stated and only features that support it make it in:

  • Make C++ a better language for systems programming and library building
  • Make C++ easier to teach and learn
  • Zero-Cost abstractions
  • better Tool-support

If you cannot make it zero-cost your chances are slim to get your feature in…

C at its core did not change much at all and remained focused on its merits. The upgrades mostly contained convenience features like line comments, additional data type definitions and multithreading.

Honest evolution – breaking backwards compatibility

In Python we have something I would call “honest evolution”. Python 3 introduced some breaking changes with the goal of a cleaner and more consistent language. Python 2 and 3 are incompatible so the distinction in the version number is fair. I like this approach of moving forward as it clearly communicates what is happening and gets rid of the sins in the past.

The downside is that many systems still come with both, a Python 2 and a Python 3 interpreter and accompanying libraries. Fortunately there are some options and tools for your code to mitigate some of the incompatibilities, like the __future__ module and python-six.

At some point in the future (expected in 2020) there will only support for Python 3. So start making the switch.

A Tale of Two Languages

Recently, I presented my mysteriously titled talk “A Tale of Two Languages” at our local C++ user group. Before the talk, I was not really sure whether it would resonate with my audience. But it did, and helped to engage people in a healthy discussion about how to use C++.

Essentially, the talk was about how I am using two different modes or dialects of C++ to write and maintain applications. The title suggests two languages – and it sure can be thought of that way, but for now I’m using the word “modes” to distinguish it from the term programming languages.

You write the application in one mode, while keeping the style relatively easy. In the other mode, you make sure that you can write easy and efficient code in the other, while leveraging the full power of C++.

I call the first application mode and the second library mode.

Library mode

As I said before, this the power mode. One of C++’s design paradigms is self-extension. You are extending the language from the language itself. It’s a very powerful mechanism, the same one that drives the standard library. It’s also why C++ does not have the need for a built-in string type.

This power is a bit of a double-edged sword. On the one hand, it allows you to adapt the language specifically for your needs, for example with application specific value types. For a 3D application, a well designed 2d vector or point type will make your code easier and probably faster. On the other hand, a badly designed type on this level will haunt your application for years to come. I have seen both.

That’s a simple example though. More powerful primitives, such as domain-specific-language like constructs also belong into this mode. In general, things in this mode are less discoverable and less maintainable, but they strive to improve discoverability, efficiency and maintainability on the other side. As a consequence, this code needs more stringent documentation and specification.

Application mode

This is the mode that you use to write the majority of your application. Application mode is all about agility and leveraging opportunities. You intentionally restrict yourself in order to keep your development speed up. Simplicity trumps most other qualities in this mode. If you need another quality to be the defining factor, for example because you need some code to be a little more complex in order to run faster, you should put it into library mode instead.

Unlike code in library mode, this code needs to speak for itself. Therefore, documentation is usually nothing but a duplication.

One important aspect is that this code should be devoid of all subtleties.

Parameter passing and its consequences

That last bit is especially uncommon in C++, where most decisions are really a catch-22. Hence the resulting code hints at the struggles endured while writing it.

For example, to write an efficient function in C++, you need to decide whether to pass each parameter by value, or by reference, or by a pointer. The decision on which to use depends largely on your implementation, i.e. what you are doing with the parameter after it was passed to the function. That usually couples your implementation too tightly to its interface and degrades programmer productivity by giving too many options.

Using a shared-ownership smart-pointer such as std::shared_ptr by default is a good middle ground here. It does the right thing most of the time and is not to far off at most other times. Many other mainstream languages, such as python, go this route. Some frameworks, such as Qt, use that semantic as well.
Like const-correctness, passing all parameters in a std::shared_ptr is viral. Object thus passed need to be created on a the stack, preferably with std::make_shared. You will also store those smart pointers in other objects, so shared_ptr will have quite a lot of screen space. Therefore I usually make an alias:

template using Ptr = std::shared_ptr;

If it’s going to be the default, it should not clutter your code. Since objects are transported in a Ptr by default, they usually do not even need a copy constructor or other “value-like” semantics. These objects are less about maintaining invariants, and more about implementing abstract interfaces and bundling functionality in maintainable chunks. I usually use boost::noncopyable to mark them, though Herb Sutter’s Metaclasses proposal could make this even nicer.
Note that you can still promote them to value types in library mode, should the need arise. But they will become more costly to maintain.

Other simplifications

There are plenty of other things to avoid in application mode. Writing templated types makes your code inherently non-local and dependent on a type that can be anywhere. Note that instantiation of templates from library mode is fine – at that point, all the facts are known.

Another thing that makes your code non-local, and therefore unfitting for application mode, is overloading. Especially in the presence of ADL. For example, which functions are in your actual overload set depends on which headers you include and which using-directives and declarations are active. Sometimes, that is desirable. But not in application mode.

Resolution

Since using this “two modes” approach, I have found that my productivity is much higher – even in older code that went through a lot of evolution. The code does not actually get a lot slower, even with all the smart pointers. In fact, I am sure that I could only optimize a few cases because the design in application mode is a lot more flexible, and the structure more visible.

Do most language make false promises?

Some years ago I stumbled over this interesting article about C being the most effective of programming language and one making the least false promises. Essentially Damien Katz argues that the simplicity of C and its flaws lead to simple, fast and easy to reason about code.

C is the total package. It is the only language that’s highly productive, extremely fast, has great tooling everywhere, a large community, a highly professional culture, and is truly honest about its tradeoffs.

-Damien Katz about the C Programming language

I am Java developer most of the time but I also have reasonable experience in C, C++, C#, Groovy and Python and some other languages to a lesser extent. Damien’s article really made me think for quite some time about the languages I have been using. I think he is right in many aspects and has really good points about the tools and communities around the languages.

After quite some thought I do not completely agree with him.

My take on C

At a time I really liked the simplicity of C. I wrote gtk2hack in my spare time as an exercise and definitely see interoperability and a quick “build, run, debug”-cycle as big wins for C. On the other hand I think while it has a place in hardware and systems programming many other applications have completely different requirements.

  • A standardized ABI means nothing to me if I am writing a service with a REST/JSON interface or a standalone GUI application.
  • Portability means nothing to me if the target system(s) are well defined and/or covered by the runtime of choice.
  • Startup times mean nothing to me if the system is only started once every few months and development is still fast because of hot-code replacement or other means.
  • etc.

But I am really missing more powerful abstractions and better error handling or ressource management features. Data structures and memory management are a lot more painful than in other languages. And this is not (only) about garbage collection!

Especially C++ is making big steps in the right direction in the last few years. Each new standard release provides additional features making code more readable and less error prone. With zero cost abstractions at the core of language evolution and the secondary aim of ease of use I really like what will come to C++ in the future. And it has a very professional community, too.

Aims for the C++11 effort:

  • Make C++ a better language for systems programming and library building
  • Make C++ easier to teach and learn

-Bjarne Stroustup, A Tour of C++

What we can learn from C

Instead of looking down at C and pointing at its flaws we should look at its strengths and our own weaknesses/flaws. All languages and environments I have used to date have their own set of annoyances and gotchas.

Java people should try building simple things and having a keen eye on dependencies especially because the eco system is so rich and crowded. Also take care of ressource management – the garbage collector is only half the deal.

Scala and C++ people should take a look at ABI stability and interoperability in general. Their compile times and “build, run, debug”-cycle has much room for improvement to say the least.

C# may look at simplicity instead of wildly adding new features creating a language without opinion. A plethora of ways implementing the same stuff. Either you ban features or you have to know them all to understand code in a larger project.

Conclusion

My personal answer to the title of this blog: Yes, they make false promises. But they have a lot to offer, too.

So do not settle with the status quo of your language environment or code style of choice. Try to maintain an objective perspective and be aware of the weaknesses of the tools you are using. Most platforms improve over time and sometimes you have to re-evaluate your opinion regarding some technology.

I prefer C++ to C for some time now and did not look back yet. But I also constantly try different languages, platforms and frameworks and try to maintain a balanced view. There are often good reasons to choose one over the other for a particular project.

 

C++ modules example

Two weeks back, I blogged about C++ modules, and why we so desperately need them. Now I have actually played with the implementation in Visual Studio 2017, and I want to share my findings.

The Files

My example consists of four files in two “components”, i.e. one library and one executable. The executable only has one file, main.cpp:

import pets;
import std.core;
import std.memory;

int main()
{
  std::unique_ptr<Pet> pet = std::make_unique<Dog>();
  std::cout << "Pet says: "
    << pet->pet() << std::endl;
}

The library consists of three files. First is pet.cpp, which contains the abstract base class for all pets:

import std.core;
module pets.pet;

export class Pet
{
public:
  virtual char const* pet() = 0;
};

Then there is dog.cpp – our only concrete implementation of that base class (yes, I’m not a cat person).

module pets.dog;
import std.core;
import pets.pet;

export class Dog : public Pet
{
public:
  char const* pet() override;
};

char const* Dog::pet()
{
  return "Woof!";
}

Notice they each define their own submodule. Finally, there is interface.cpp, which just cobbles those submodules together into one single “parent” module:

module pets;

export module pets.pet;
export module pets.dog;

You can get the full source code including the CMake setup at our github repository. I was not able to get the standard library path setup automated so far, so you probably want to adjust that.

Discussion

There are no headers at all, which was one of my goals of laying it out like this. I think that alone means an enormous increase in productivity.

The information that was previously in the header files is now in .ifc files that the microsoft compiler generates automatically from the module definitions.
When trying this out, a couple of things stood out to me:

  • Intellisense does not work with the new keywords yet.
  • The way I used it, interface.cpp needs to be compiled after pet.cpp and dog.cpp, so that the appropriate .ifc file exists. Having an order dependency like that within a single library is a new challenge.
  • I was not able to use the standard lib in the library. That would compile, but not link.
  • Not having to duplicate the function declaration feels very strange in C++.
  • There are a lot of paradigm changes required. For example, include paths are a thing of the past – you will need to configure correct module search paths in the future.
  • We will need to get the naming straight: right now, “modules” is already used as a “distinct software component”. The new meaning is similar, but still competes with it. since the granularity is no longer so flexible. I already started using “components” as a new word for the former.

What are your experiences with modules so far? Do you have another way of composing modules? I really like to hear about it! I think the biggest challenge right now is how to use these new possibilities to improve the design of bigger C++ projects.