I was thinking about C++ for embedded environments recently and wondered how much of today’s Modern C++ was recommended for use. I found an excellent article by Dominic Herity on the subject:
Even better, Dominic linked to some presentation materials Scott has published electronically on the subject. I’ve long been a fan of Scott’s writing and wasn’t aware of this electronic book, so I’m reading that next.
Filed under C++, Programming, Tech Book
September’s C++ London Meetup featured two talks: Type Punning by Timur Doumler and Counting Nanoseconds by David Gross of Optiver. Optiver were the generous sponsors of this event, hosted at SkillsMatter near Moorgate.
Optiver are based in Amsterdam and operate in the High-Frequency Trading sphere. Naturally, they need code to be fast in order to take trades more quickly than their competitors, but the benefits of fast code also extend to minimising energy costs in data centres and extending battery life on portable devices for other applications. David Gross spoke about micro-benchmarking – boiling code down to very small numbers of instructions to aid comparisons and improve performance. The caveat was that it’s always vital to measure performance in production using real data as well as doing this offline, low-level analysis against potted test cases. David warning against relying on the high-resolution clock which can be problematic (may not be steady and may not have the resolution you need). He often uses Intel Time Stamp Counter (TSC), although that’s not the original intention of that metric.
Timur is fascinated by the ability to take an object and treat it as another type in C++. Yet, in many cases, this results in undefined behaviour. For example:
class Gizmo {
public:
virtual void print(){ std::cout << "Gizmo\n"; }
};
class Widget {
public:
virtual void print(){ std::cout << "Widget\n"; }
};
Gizmo g;
Widget* w = (Widget*)(&g);
w->print();
The behaviour of this code is undefined, because Widget and Gizmo are unrelated, although both have a virtual print function, so the virtual function table is the same. The standard says that you can’t call a method on a reinterpret-cast’d object – you can only cast it back to the actual type of the object. But on most compilers tried by Timur, the code runs and prints “Gizmo”. He referenced the CppCon 2018 talk, Undefined Behaviour is Not an Error.
Other examples concerned how to share memory across multiple data types, such as treating 4 bytes as either a char array or as a float. Even using a union can result in undefined behaviour, because only one member is ‘alive’ at a time. In order to write portable code, you must consider aliasing rules, object lifetime rules and alignment rules. In same cases, you can use C++17’s std::memcpy to ensure alignment (but only if the types are the same size and trivially copyable). std::bit_cast is coming with C++20. And in C++23, there’s a proposal for std::start_lifetime_as(char[]) to implicitly create objects, for example when reading from a buffer.
The video for this talk is available on SkillsCast.
Filed under C++, C++ Code, Meetup, Programming
I was lucky to get a place at this month’s C++ London meet up, having a limited number of seats and being hosted by Smarkets at their offices in St Katherine’s Dock. Smarkets are an online betting exchange – and they’re currently hiring!
This evening was started by Alex Schmolck, presenting his work configuring the development and build systems for Smarkets. He’s experimented with Vagrant and Docker, but is now an advocate of Nix. He admits that Docker is initially more productive, but Nix has the edge for its efficiency and speed.
The main talk (see slides) was given by Mateusz Pusz, showing a series of examples to demonstrating how a body of code can evolve and improve significantly with the features introduced by C++11, C++14, C++17 and soon C++20.
For example, implementing functions with variable numbers of parameters – such as for populating a container of items for a testing library. With C++98, this might have led to many overloads with increasing numbers of arguments (but only ever handling up to some hand-coded limit). With C++11 onwards, you could use variadic templates, handling any number of parameters. And with C++17 onwards, you could use fold expressions to simplify the code further (no need to the ‘base class’ template overload).
Another interesting example was the evolution of Compile-Time Dispatch. Whereas even C++ 11requires hand-rolling overloads on a hierarchy of tag classes, post C++17, you can use constexpr to organise the code within a single method.
Filed under C++, Meetup, Programming
This C++ Meetup, held at Skillsmatter, was split into two talks.
The first by Ervin Bosenbacher was Modern C++ Smart Pointers in C++ 17, 20 & Beyond. It served as a good introduction to smart pointers and motivation for using them, particularly for developers new to C++ (or those who had not yet started to use C++11). The talk covered:
A good point was that use of std::shared_ptr means that other parts of the codebase can modify an object that you’re sharing – so synchronisation primitives (such as a std::mutex) are needed to ensure access to the underlying resource is thread-safe. This is often overlooked because the reference counting *is* thread-safe. In C++20, we will get atomic smart pointers to help with this.
Discussing std::shared_ptr with a friend, I also learnt about a secret constructor on std::shared_ptr to share the resource control block and keep a parent pointer alive.
The second talk was Arno Schödl on Error Handling. He described how Think Cell grade errors into different levels, each with a clearly defined error handling strategy. The aim is to minimise coding and testing overhead whilst maximising the ability to capture and debug error conditions.
Filed under C++, Meetup, Programming
This evening of talks was split into a set of Lightning Talks in the first half followed by a discussion about the Standard Library in the second half.
Part 1 – Lightning Talks
Richard Chandler – ACCU 2018 Trip Report – much on C++14 and C++17 standards to catch up on as well as introduction to Kotlin and a number of talks on diversity and inclusivity.
Phil Nash – East End Story
Phil dived into the controversial East Const v West Const debate
void f( T const& t ) // East const
void f( const T& ) // West const
i.e. should you put the const before or after the type. This topic was introduced by Dan Saks in 2011 – Simplifying const Syntax, Dr Dobbs and revived by Jon Kalb in A Foolish Inconsistency.
“Const applies to what is on its left, unless there is nothing to the left, in which case it applies to what is on the right.” But using east const format, it always applies to the left! West const reads more naturally from left to right, e.g. “const integer” – the East const argument is that you would always read declarations inside out and right to left!
char const * const pc = str // pc is a const pointer to a const char, reading right to left
Developers are already accustomed to using East const for const member functions (must put const on the ‘east’ side). And there are other examples of going East:
// moving the return type further east as a result of c++11
auto someFunc( int i ) -> std::string;
// you have to put the return type on the east side
auto lambda = [] -> double { return 0; }
Other considerations: Swift/Haskell have trailing return type; putting auto at the start of list of function definitions, the start of the names line up; C++ standard NL.26 says use conventional const notation, endorses West Const notation, but is itself inconsistent!
Phil is now using trailing return types everywhere in production code – see his blog, East End Functions (LevelOfIndirection.com)
Stewart Becker – A Fool’s Consistency, the rebuttal (Jonathan Muller’s talk)
East const gives more consistency? If 83% of people write const T (i.e. West const), you need to be consistent with everyone else in your codebase.
Const pointer to const fallacy – with modern c++, use const std::string_view instead. Can use std::as_const(x) to get the const cast.
Right-to-left fallacy – but we read loops etc from left to right, and ‘const’ is an adjective which goes before the noun in English.
Clockwise-Spiral rule for function pointers.
const east west const – you are allowed to write const in both the East and West positions!!
Part 2 – Tuppence More on Standard Algorithms – Iakov Sergeev
Iakov believes the standard algorithms should make code easier to implement and read, but are forgotten in practice. He raised issues with inconsistencies and the learning curve to use STL (output iterator, predicates, modifying input sequences, could take both begin/end iterators).
Historically, the predicates were hard to implement where you had to supply a functor to call algorithms. Now, with lambdas, it’s much easier and localised to call algorithms.
The audience felt that Eric Niebler’s work on the Ranges TS should help a lot with adoption of standard algorithms.
Filed under C++, Meetup, Programming
C++ has supported lambda functions since C++11, and post-C++14 even supported generic lambdas. C++17 adds a quirky feature to enable ‘copy capture’ of ‘this’. Here’s an example of it in action:
struct MyStruct
{
auto lambda_with_this() // Before C++17, copies this pointer
{
auto f = [this]{ return value; };
return f;
}
auto lambda_with_star_this() // C++17 - takes a local copy of *this
{
auto f = [*this]{ return value; };
return f;
}
int value;
};
TEST( Cpp17, lambda_capture_this )
{
MyStruct s{ 42 };
auto f = s.lambda_with_this();
s.value = 101;
EXPECT_EQ( 101, f() );
}
TEST( Cpp17, lambda_capture_star_this )
{
MyStruct s{ 42 };
auto f = s.lambda_with_star_this();
s.value = 101;
EXPECT_EQ( 42, f() );
}
Notice that in the second case, we capture a copy of our object – so the lambda returns the value held at the point of capture (42) rather than the value when it is called (101). This can be very important if ‘this’ has been destroyed between the creation of the lambda and the time at which it’s called.
Now, C++14 also supported generalised lambda capture, which meant you could (re-)name variables when capturing (and provided a neat way to capture-by-move):
auto f = [tmp = *this]{ return tmp.value; };
But the C++17 code is more concise. See this useful StackOverflow post for more discussion.
Another advance in C++17 is that lambdas are implicitly constexpr – so you can now use them in compile-time contexts, like declaration of std::array:
// lambda explicitly constexpr since C++17
auto square = []( auto v ){ return v*v; };
TEST( Cpp17, lambda_implicitly_constexpr )
{
// std::array calls 'square()' at compile time
std::array<int, square(4)> values;
EXPECT_EQ( 16, values.size() );
}
See also the previous C++17 post.
Filed under C++, C++ Code, Programming
Another crowd-pleaser in C++17 is the ability to declared nested namespaces without literally nesting them. In the past, you had to do this, which involves a lot of wasted whitespace:
namespace NestedNamespaces
{
namespace Really
{
namespace Work
{
auto return_int(){ return 42; };
}
}
}
Happily, you can now do this instead:
namespace NestedNamespaces::Really::Work
{
auto return_int(){ return 42; };
}
TEST( Cpp17, nested_namespaces )
{
EXPECT_EQ( 42, NestedNamespaces::Really::Work::return_int() );
}
See also previous C++17 post.
Filed under C++, C++ Code, Programming
This is one of a series of posts on C++17 features – see also previous post on if initialisers.
Structured bindings are a convenient way of handling multiple return values from functions. Whilst F# has been able to do this:
let f() = 42, "Hello, World" // return a pair of values let a, b = f() // assign a and b to the values returned by f
in C++, we’ve had to declare the variables up front and use std::tie to assign values (so not only does this take more lines, we also have to default initialise the variables then throw away the defaults).
auto t = std::make_tuple( 42, "Hello, World" ); int a, b; std::tie( a, b ) = t;
The new structured bindings are much more concise, even if the use of square brackets came as a surprise. Even better, you can use structured bindings with structs and std::array.
int my_int{ 42 };
std::string my_string{ "Hello, World" };
bool my_bool{ true };
auto return_pair()
{
return std::make_pair( my_int, my_string );
}
auto return_tuple()
{
return std::make_tuple( my_int, my_string, my_bool );
}
struct MyStruct
{
int a;
double b;
int c;
static MyStruct Expected;
};
MyStruct MyStruct::Expected = { 1, 2.2, 3 };
auto return_struct()
{
return MyStruct::Expected;
}
auto return_array()
{
return std::array<int,3>{ 1, 2, 3 };
}
auto return_map()
{
return std::map<int, std::string>{ {1, "a"}, {2, "b"}, {3, "c"} };
}
TEST( Cpp17, structured_bindings_for_pair )
{
auto [i, s] = return_pair();
EXPECT_EQ( my_int, i );
EXPECT_EQ( my_string, s );
}
TEST( Cpp17, structured_bindings_for_tuple )
{
auto [i, s, b] = return_tuple();
EXPECT_EQ( my_int, i );
EXPECT_EQ( my_string, s );
EXPECT_EQ( my_bool, b );
}
TEST( Cpp17, structured_bindings_for_struct )
{
auto [i1, d, i2] = return_struct();
EXPECT_EQ( MyStruct::Expected.a, i1 );
EXPECT_EQ( MyStruct::Expected.b, d );
EXPECT_EQ( MyStruct::Expected.c, i2 );
}
TEST( Cpp17, structured_bindings_for_array )
{
auto [i1, i2, i3] = return_array();
EXPECT_EQ( 1, i1 );
EXPECT_EQ( 2, i2 );
EXPECT_EQ( 3, i3 );
}
TEST( Cpp17, structured_bindings_for_iterating_over_map )
{
for ( const auto& [key,value] : return_map() )
{
switch (key)
{
case 1: EXPECT_EQ( "a", value ); break;
case 2: EXPECT_EQ( "b", value ); break;
case 3: EXPECT_EQ( "c", value ); break;
default: break;
};
}
}
For me, the best examples come when combining features – the range-based for loop with structured bindings is a thing of beauty.
See also next C++17 post.
Filed under C++, C++ Code, Programming
I attended a C++17 presentation by Nicolai Josuttis last year, but at the time, my laptop’s compiler didn’t support any of the features to try them out. After a recent update, it turns out that many are now supported, so I’ve written a few unit tests using GTest.
The first feature I tried was the if initialiser. This feature looks a bit odd at first, because C++ programmers are so conditioned to seeing if statements containing a single condition. Allowing an initialiser statement as well
if ( initialiser; condition )
means that you can initialise a variable and test it on the same line. It also prevents the variable being used outside the scope of the if statement – this prevents accidental re-use if you subsequently mis-type a variable name.
auto return_int()
{
return 101;
}
TEST( Cpp17, if_initialiser )
{
// NB we can use i in the body of the if or the else
// Also, must have a variable name for the object to live in the whole statement
// (so must name locks taken, even if not used in the body, otherwise it's a temporary).
if ( auto i = return_int(); i < 100 )
{
EXPECT_TRUE( i < 100 );
}
else
{
EXPECT_TRUE( i >= 100 );
}
}
TEST( Cpp17, if_initialiser_with_map_insert)
{
std::map<int, std::string> my_map{ {42, "Hi" } };
if ( auto[it, inserted] = my_map.insert( std::make_pair(42, "Bye" ) ); !inserted )
{
// See also StructuredBindings for iterating over a map
auto& [key,value] = *it; // iterator is pair of key and value
EXPECT_EQ( 42, key );
EXPECT_EQ( "Hi", value );
value = "Bye"; // update the value
EXPECT_EQ( "Bye", my_map[42] );
// key = 43; // compile error! - cannot assign to const key-type
}
}
See also next post on C++17 Structured Bindings.
Filed under C++, C++ Code, Programming
Jason Gorman of CodeManShip gave an interesting talk on code craft this week. The talk began with the business case for Code Craft, based on the cost of changing a line of code. If you compare the number of changes in source code repository to the expense of their development teams, the cost generally increases massively over time – 40X more expensive per line of code change after 8 years for one company. The same hockey-stick chart appears in Kent Beck’s book “Extreme Programming Explained”. Code Craft skills tend to flatten the cost-of-change curve, which is good for the business, and make the code easier to change, which is good for the developers.
Write good unit tests, use version control, do continuous integration, apply design principles, refactor code safely.
Cost – if the cost of a change in design is 1, then the cost in implementation is 6.5, in testing is 15 and in maintenance is 100. Bringing testing forward in the development process gives a massive return on investment. Legacy code can be defined as any code that doesn’t have fast running, automated unit tests that can discover a bug quickly – enabling a cheap fix. You must automate testing and enable the feedback loop to iterate quickly – ideally in minutes or seconds.
Readability – 50%-80% of time spent on a codebase is reading code, not writing it. Making code easier to read and understand has a great pay-off. Code that’s hard to understand is easy to break.
Complexity – attack complexity, try to replace it with the simplest thing, which is less likely to be wrong.
Duplication – never copy/paste code, especially off the internet! You have no idea if it works or even compiles. Making changes to common logic is multiplied if there’s duplicate code. Duplication is the opposite of reuse – generate an abstraction to help improve the design. Duplication can be hard to spot (e.g. customer playing several roles which could have been parameterised).
Ripple Effect – a change to one area of code breaks a much wider circle of code. Cost of change greatly increased – need to have a local effect from a small change to code.
The speaker also coaches teams in the following principles:
1. Shorter feedback loops – automated unit testing. Not possible with manual testing iterations or a manual testing phase. Automated system testing can give good coverage, but is often too slow, can take hours to run. Need to run in minutes or seconds. Ideal chain is Unit Testing -> Integration testing -> System testing. Want most of the testing in first layer, minimal number of smoke tests in the top System layer.
2. Simple Design – the code works, clearly reveals intent, free of duplication, made out of simple parts. Need another pair of eyes to check the code is easy to read. Code review your own code every few minutes.
3. Single responsibility – traditionally, this is taught as classes should only have one reason to change. Or better, look at the number of combinations of the sections of code implemented in a function – it’s more powerful to break functions into smaller parts then use composition to implement the larger function. Smaller components/functions => many more ways to re-use. So give each piece of software a single responsibility.
4. Tell, don’t ask – lots of low-level coupling between objects. Better to have objects sharing smallest possible interface to each other. Don’t pass the whole object – pass the data needed to do the operation. Put the work where the knowledge lives – do the work in the right place so that data and behaviour belong together.
5. Dependency Inversion – you should be able to swap your dependencies. Talk to dependencies as abstractions through interfaces. Eg1 all commands inherit from ICommand and high-level components work on generic interface, not concrete classes. Eg2 VideoRental example, rather than coupling to Imdb, replace with an interface. Better for mocking/testing as well.
Filed under Meetup, Programming
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