Tag Archives: Programming

When you can’t (and shouldn’t) unit test

I’m a unit test aficionado, and, as such, have attempted to unit test what really shouldn’t be. It’s common to get excited by a new hammer and then seeing nails everywhere, and unit testing can get out of hand (cough! mocks! cough!).

I still believe that the best tests are free from side-effects, deterministic and fast. What’s important to me isn’t whether or not this fits someone’s definition of what a unit test is, but that these attributes enable the absence of slow and/or flaky tests. There is however another class of tests that are the bane of my existence: brittle tests. These are the ones that break when you change the production code despite your app/library still working as intended. Sometimes, insisting on unit tests means they break for no reason.

Let’s say we’re writing a new build system. Let’s also say that said build system works like CMake does and spits out build files for other build systems such as ninja or make. Our unit test fan comes along and writes a test like this:

assert make_output == "all: foo\nfoo: foo.c\n\tgcc -o foo foo.c"

I believe this to be a bad test, and the reason why is that it’s checking the implementation instead of the behaviour of the production code. Consider what happens when the implementation is changed without affecting behaviour:

all: foo\nfoo: foo.c\n\tgcc -o $@ $<

The behaviour is the same as before: any time `foo.c` is changed, `foo` will get recompiled. The implementation not only isn’t the same, it’s arguably better now, and yet the assertion in the test would fail. I think we can all agree that the ROI for this test is negative if this is all it takes to break it.

The orthodox unit test approach to situations like these is to mock the service in question, except most people don’t get the memo that you should only mock code you own. We don’t control GNU make, so we shouldn’t be doing that. It’s impossible to copy make exactly in a mock/stub/etc. and it’s foolish to even try. We (mostly) don’t care about the string that our code outputs, we care that make interprets that string with the correct semantics.

My conclusion is that I shouldn’t even try to write unit tests for code like this. Integration tests exist for a reason.

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The joys of translating C++’s std::function to D

I wrote a program to translate C headers to D. Translating C was actually more challenging than I thought; I even got to learn things I didn’t know about the language even though I’ve known it for 24 years. The problems that I encountered were all minor though, and to the extent of my knowledge have all been resolved (modulo bugs).

C++ is a much larger language, so the effort should be considerably more. I didn’t expect it to be as hard as it’s been however, and in this blog I want to talk about how “interesting” it was to translate C++11’s std::function by hand.

The first issue for most languages would be that it relies on template specialisation:

template<typename>
class function;  // doesn't have a definition anywhere

template<typename R, typename... Args>
class function<R(Args...)> { /* ... */ }

This is a way of constraining the std::function template to only accept function types. Perhaps surprisingly to some, the C++ syntax for the type of a function that takes two ints and returns a double is double(int, int). I doubt most people see this outside of C++ standard library templates. If it’s still confusing, think of double(int, int) as the type that is obtained by deferencing a pointer of type double(*)(int, int).

D is, as far as I know, the only other language other than C++ to support partial template specialisation. There are however two immediate problems:

  • function is a keyword in D
  • There is no D syntax for a function type

I can mitigate the name issue by calling the symbol function_ instead; however, this will affect name mangling, meaning nothing will actually link. D does have pragma(mangle) to tell the compiler how to mangle symbols, but std::function is a template; it doesn’t have any mangling until it’s instantiated. Let’s worry about that later and call the template function_ for now.

The second issue can be worked around:

// C++: `using funPtr = double(*)(int, int);`
alias funPtr = double function(int, int);
// C++: `using funType = double(int, int);`
alias funType = typeof(*funPtr.init);

As in C++, the function type is the type one gets from deferencing a function pointer. Unlike C++, currently there’s no syntax to write it directly. First attempt:

// helper to automate getting an alias to a function type
template FunctionType(R, Args...) {
    alias ptr = R function(Args);
    alias FunctionType = typeof(*ptr.init);
}

struct function_(T);
struct function_(T: FunctionType!(R, Args), R, Args...) { }

This doesn’t work, probably due to this bug preventing the helper template FunctionType from working as intended. Let’s forget the template constraint:

extern(C++, "std") {
    struct function_(T) {
        import std.traits: ReturnType, Parameters;
        alias R = ReturnType!T;
        alias Args = Parameters!T;
        // In C++: `R operator()(Args) const`;
        R opCall(Args) const;
    }
}

void main() {
    alias funPtr = double function(double);
    alias funType = typeof(*funPtr.init);
    function_!funType f;
    double result = f(3.3);
}

This compiles but it doesn’t link: there’s an undefined reference to std::function_::operator()(double) const. Looking at the symbols in the object files using nm, we see that g++ emitted _ZNKSt8functionIFddEEclEd but dmd is trying to link to _ZNKSt9function_IFddEEclEd. As expected, name mangling issues related to renaming the symbol.

We could manually add a pragma(mangle) to tell D how to mangle the operator for the double(double) template instantiation, but that solution doesn’t scale. CTFE (constexpr if you speak C++ but not D) to the rescue!

// snip - as before
pragma(mangle, opCall.mangleof.fixMangling)
R opCall(Args) const;

// (elsewhere at file scope)
string fixMangling(string str) {
    import std.array: replace;
    return str.replace("9function_", "8function");
}

What’s going on here is an abuse of D’s compile-time power. The .mangleof property is a compile-time string that tells us how a symbol is going to be mangled. We pass this string to the fixMangling function which is evaluated at compile-time and fed back to the compiler telling it what symbol name to actually use. Notice that function_ is still a template, meaning .mangleof has a different value for each instantiation. It’s… almost magical. Hacky, but magical.

The final code compiles and links. Actually creating a valid std::function<double(double)> from D code is left as an exercise to the reader.

 

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#include C headers in D code

I’ll lead with a file:

// stdlib.dpp
#include <stdio.h>
#include <stdlib.h>

void main() {
    printf("Hello world\n".ptr);

    enum numInts = 4;
    auto ints = cast(int*) malloc(int.sizeof * numInts);
    scope(exit) free(ints);

    foreach(int i; 0 .. numInts) {
        ints[i] = i;
        printf("ints[%d]: %d ".ptr, i, ints[i]);
    }

    printf("\n".ptr);
}

The keen eye will notice that, except for the two include directives, the file is just plain D code. Let’s build and run it:

% d++ stdlib.dpp
% ./stdlib
Hello world
ints[0]: 0 ints[1]: 1 ints[2]: 2 ints[3]: 3

Wait, what just happened?

You just saw a D file directly #include two headers from the C standard library and call two functions from them, which was then compiled and run. And it worked!

Why? I mean, just… why?

I’ve argued before that #include is C++’s killer feature. Interfacing to existing C or C++ libraries is, for me, C++’s only remaining use case. You include the relevant headers, and off you go. No bindings, no nonsense, it just works. As a D fan, I envied that. So this is my attempt to eliminate that “last” (again, for me, reasonable people may disagree) use case where one would reach for C++ as the weapon of choice.

There’s a reason C++ became popular. Upgrading to it from C was a decision with essentially 0 risk.  I wanted that “just works” feature for D.

How?

d++ is a compiler wrapper. By default it uses dmd to compile the D code, but that’s configurable through the –compiler option. But dmd can’t compile code with #include directives in it (the lexer won’t even like it!), so what gives?

d++ will go through a .dpp file, and upon encountering an #include directive it expands it in-place, similarly to what would happen with a C or C++ compiler. Differently from clang or gcc however, the header file can’t just be inserted in, since the syntax of the declarations is in a different language. So it uses libclang to parse the header, and translates all of the declarations on the fly. This is trickier than it sounds since C and C++ allow for things that aren’t valid in D.

There’s one piece of the usability puzzle that’s missing from that story: the preprocessor. C header files have declarations but also macros, and some of those are necessary to use the library as it was intended. One can try and emulate this with CTFE functions in D, and sometimes it works. But I don’t want “sometimes”, I want guarantees, and the only way to do that is… to use the C preprocessor.

Blasphemy, I know. But since worse is better, d++ redefines all macros in the included header file so they’re available for use by the D program. It then runs the C preprocessor on the result of expanding all the #include directives, and the final result is a regular D file that can be compiled by dmd.

What next?

Bug fixing and C++ support. I won’t be happy until this works:

#include <vector>
void main() {
    auto v = std.vector!int();
    v.push_back(42);
}

Code or it didn’t happen

I almost forgot: https://github.com/atilaneves/dpp.

 

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Operator overloading is a good thing (TM)

Brains are weird things. I used to be a private maths tutor, and I always found it amazing how a little change in notation could sometimes manage to completely confuse a student. Notation itself seems to me to be a major impediment for the majority of people to like or be good at maths. I had fun sometimes replacing the x in an equation with a drawing of an apple to try and get the point across that the actual name (or shape!) of a variable didn’t matter, that it was just standing in for something else.

Programmers are more often than not mathematically inclined, and yet a similar phenomenon seems to occur with the “shape” of certain functions, i.e. operators. For reasons that make us much sense to me as x confusing maths students, the fact that a function has a name that has non-alphanumeric characters in them make them particularly weird. So weird that programmers shouldn’t be allowed to defined functions with those names, only the language designers. That’s always a problem for me – languages that don’t give you the same power as the designers are Blub as far as I’m concerned. But every now and again I see a blost post touting the advantages of some language or other, listing the lack of operator overloading as a bonus.

I don’t even understand the common arguments against operator overloading. One is that somehow “a + b” is now confusing, because it’s not clear what the code does. How is that different from having to read the documentation/implementation of “a.add(b)”? If it’s C++ and “a + b” shows up, anyone who doesn’t read it as “a.operator+(b)” or “operator+(a, b)” with built-in implementations of operator+ for integers and floating point numbers needs to brush up on their C++. And then there’s the fact that that particular operator is overloaded anyway, even in C – the compiler emits different instructions for floats and integers, and its behaviour even depends on the signedness of ints.

Then there’s the complaint that one could make operator+ do something stupid like subtract. Because, you know, this is totally impossible:

int add(int i, int j) {
    return i - j;}

Some would say that operator overloading is limited in applicability since only numerical objects and matrices really need them. But used with care, it might just make sense:

auto path = "foo" / "bar" / "baz";

Or in the C++ ranges by Eric Niebler:

using namespace ranges;
int sum = accumulate(view::ints(1)
                   | view::transform([](int i){return i*i;})
                   | view::take(10), 0);

I’d say both of those previous examples are not only readable, but more readable due to use of operator overloading. As I’ve learned however, readability is in the eye of the beholder.

All in all, it confuses me when I hear/read that lacking operator overloading makes a language simpler. It’s just allowing functions to have “special” names and special syntax to call them (or in Haskell, not even that). Why would the names of functions make code so hard to read for some people? I guess you’d have to ask my old maths students.

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API clarity with types

API design is hard. Really hard. It’s one of the reasons I like TDD – it forces you to use the API as a regular client and it usually comes out all the better for it. At a previous job we’d design APIs as C headers, review them without implementation and call it done. Not one of those didn’t have to change as soon as we tried implementing them.

The Win32 API is rife with examples of what not to do: functions with 12 parameters aren’t uncommon. Another API no-no is several parameters of the same type – which means which? This is ok:

auto p = Point(2, 3);

It’s obvious that 2 is the x coordinate and 3 is y. But:

foo("foo", "bar", "baz", "quux", true);

Sure, the actual strings passed don’t help – but what does true mean in this context? Languages like Python get around this by naming arguments at the call site, but that’s not a feature of most curly brace/semicolon languages.

I semi-recently forked and extended the D wrapper for nanomsg. The original C API copies the Berkely sockets API, for reasons I don’t quite understand. That means that a socket must be created, then bound or connect to another socket. In an OOP-ish language we’d like to just have a contructor deal with that for us. Unfortunately, there’s no way to disambiguate if we want to connect to an address or bind to it – in both cases a string is passed. My first attempt was to follow in Java’s footsteps and use static methods for creation (simplified for the blog post):

struct NanoSocket {
    static NanoSocket createBound(string uri) { /* ... */ }
    static NanoSocket createConnected(string uri) { /* ... */ }
    private this() { /* ... */ } // constructor
}

I never did feel comfortable: object creation shouldn’t look *weird*. But I think Haskell has forever changed by brain, so types to the rescue:

struct NanoSocket {
    this(ConnectTo connectTo) { /* ... */ }
    this(BindTo bindTo) { /* ... */ }
}

struct ConnectTo {
    string uri;
}

struct BindTo {
    string uri;
}

I encountered something similar when I implemented a method on NanoSocket called trySend. It takes two durations: a total time to try for, and an interval to wait to try again. Most people would write it like so:

void trySend(ubyte[] data, 
             Duration totalDuration, 
             Duration retryDuration);

At the call site clients might get confused about which order the durations are in. I think this is much better, since there’s no way to get it wrong:

void trySend(ubyte[] data, 
             TotalDuration totalDuration, 
             RetryDuration retryDuration);

struct TotalDuration {
    Duration duration;
}

struct RetryDuration {
    Duration duration;
}

What do you think?

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unit-threaded: now an executable library

It’s one of those ideas that seem obvious in retrospect, but somehow only ocurred to me last week. Let me explain.

I wrote a unit testing library in D called unit-threaded. It uses D’s compile-time reflection capabilities so that no test registration is required. You write your tests, they get found automatically and everything is good and nice. Except… you have to list the files you want to reflect on, explicitly. D’s compiler can’t go reading the filesystem for you while it compiles, so a pre-build step of generating the file list was needed. I wrote a program to do it, but for several reasons it wasn’t ideal.

Now, as someone who actually wants people to use my library (and also to make it easier for myself), I had to find a way so that it would be easy to opt-in to unit-threaded. This is especially important since D has built-in unit tests, so the barrier for entry is low (which is a good thing!). While working on a far crazier idea to make it a no-brainer to use unit-threaded, I stumbled across my current solution: run the library as an executable binary.

The secret sauce that makes this work is dub, D’s package manager. It can download dependencies to compile and even run them with “dub run”. That way, a user need not even have to download it. The other dub feature that makes this feasible is that it supports “configurations” in which a package is built differently. And using those, I can have a regular library configuration and an alternative executable one. Since dub run can take a configuration as an argument, unit-threaded can now be run as a program with “dub run unit-threaded -c gen_ut_main”. And when it is, it generates the file that’s needed to make it all work.

So now all a user need to is add a declaration to their project’s dub.json file and “dub test” works as intended, using unit-threaded underneath, with named unit tests and all of them running in threads by default. Happy days.

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The importance of making the test fail

TDD isn’t for everyone. People work in different ways and their brains even more so, and I think I agree with Bertrand Meyer in that whether you write the test first or last, the important thing is that the test gets written. Even for those of us for whom TDD works, it’s not always applicable. It takes experience to know when or not to do it. For me, whenever I’m not sure of exactly I want to do and am doing exploratory work, I reach for a REPL when I can and don’t even think of writing tests. Even then, by the time I’ve figured out what to do I usually write tests straight afterwards. But that’s me.

However, when fixing bugs I think “TDD” (there’s not any design going on, really) should be almost mandatory. Almost, because I thought of a way that works that doesn’t need the test to be written first, but it’s more cumbersome. More on that later.

Tests are code. Code is buggy. Ergo… tests will contain bugs. So can we trust our tests? Yes, and especially so if we’re careful. First of all, tests are usually a lot smaller than the code they test (they should be!). Less code means fewer bugs on average. If that doesn’t give you a sense of security, it shouldn’t. The important thing is making sure that it’s very difficult to introduce simultaneous bugs in the test and production code that cancel each other out. Unless the tests are tightly coupled with the production code, that comes essentially for free.

Writing the test to reproduce a bug is important because we get to see it go from red to green, which is what gives us confidence. I’ve lost count of how many fake greens I’ve had due to tests that weren’t part of the suite, code that wasn’t even compiled, bugs in the test code, and many other reasons. Making it fail is important. Making changes in a different codebase (the production code) and then the test passing means we’ve actually done something. If at any point things don’t work as they should (red -> green) then we’ve made a mistake somewhere. The fix is wrong, the test is buggy, or our understanding of the problem and what causes it might be completely off.

Reproducing the bug accurately also means that we don’t start with the wrong foot. You might think you know what’s causing the bug, but what better way than to write a failing test? Now, it’s true that one can fix the bug first, write the test later and use the VCS to go back in time and do the red/green dance. But to me that sounds like a lot more work.

Whether you write tests before of after the production code, make sure that at least one test fails without the bugfix. Even if by just changing a number in the production code. I get very suspicious when the test suite is green for a while. Nobody writes correct code that often. I know I don’t.

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Haskell actually does change the way you think

Last year I started trying to learn Haskell. There have been many ups and downs, but my only Haskell project so far is on hold while I work on other things. I’m not sure yet if I’d choose to use Haskell in production. The problems I had (and the time it’s taken so far) writing a simple server make me think twice, but that’s a story for another blog post.

The thing is, the whole reason I decided to learn Haskell were the many reports that it made me you think differently. As much as I like D, learning it was easy and essentially I’m using it as a better C++. There are things I routinely do in D that I wouldn’t have thought of or bother in C++ because they’re easier. But it’s not really changed my brain.

I didn’t think Haskell had either, until I started thinking of solutions to problems I was having in D in Haskell ways. I’m currently working on a build system, and since the configuration language is D, it has to be compiled. So I have interesting problems to solve with regards to what runs when: compile-time or run-time. Next thing I know I’m thinking of lazy evaluation, thunks, and the IO monad. Some things aren’t possible to be evaluated at compile-time in D. So I replaced a value with a function that when run (i.e. at run-time) would produce that value. And (modulo current CTFE limitations)… it works! I’m even thinking of making a wrapper type that composes nicely… (sound familiar?)

So, thanks Haskell. You made my head hurt more than anything I’ve tried learning since Physics, but apparently you’ve made me a better programmer.

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To learn BDD with Cucumber, you must first learn BDD with Cucumber.

So I read about Cucumber a while back and was intrigued, but never had time to properly play with it. While writing my MQTT broker, however, I kept getting annoyed at breaking functionality that wasn’t caught by unit tests. The reason being that the internals were fine, the problems I was creating had to do with the actual business of sending packets. But I was busy so I just dealt with it.

A few weeks ago I read a book about BDD with Cucumber and RSpec but for me it was a bit confusing. The reason being that since the step definitions, unit tests and implementation were all written in Ruby, it was hard for me to distinguish which part was what in the whole BDD/TDD concentric cycles. Even then, I went back to that MQTT project and wrote two Cucumber features (it needs a lot more but since it works I stopped there). These were easy enough to get going: essentially the step definitions run the broker in another process, connect to it over TCP and send packets to it, evaluating if the response was the expected one or not. Pretty cool stuff, and it works! It’s what I should have been doing all along.

So then I started thinking about learning BDD (after all, I wrote the features for MQTT afterwards) by using it on a D project. So I investigated how I could call D code from my step definitions. After spending the better part of an afternoon playing with Thrift and binding Ruby to D, I decided that the best way to go about this was to implement the Cucumber wire protocol. That way a server would listen to JSON requests from Cucumber, call D functions and everything would work. Brilliant.

I was in for a surprise though, me who’s used to implementing protocols after reading an RFC or two. Instead of a usual protocol definition all I had to go on was… Cucumber features! How meta. So I’d use Cucumber to know how to implement my Cucumber server. A word to anyone wanting to do this in another language: there’s hardly any documentation on how to implement the wire protocol. Whenever I got lost and/or confused I just looked at the C++ implementation for guidance. It was there that I found a git submodule with all of Cucumber’s features. Basically, you need to implement all of the “core” features first (therefore ensuring that step definitions actually work), and only then do you get to implement the protocol server itself.

So I wanted to be able to write Cucumber step definitions in D so I could learn and apply BDD to my next project. As it turned out, I learned BDD implementing the wire protocol itself. It took a while to get the hang of transitioning from writing a step definition to unit testing but I think I’m there now. There might be a lot more Cucumber in my future. I might also implement the entirety of Cucumber’s features in D as well, I’m not sure yet.

My implementation is here.

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Adding Java and C++ to the MQTT benchmarks or: How I Learned to Stop Worrying and Love the Garbage Collector

This is a followup to my first post, where I compared different MQTT broker implementations written in D, C, Erlang and Go. Then my colleague who wrote the Erlang version decided to write a Java version too, and I felt compelled to do a C+11 implementation. This was only supposed to simply add the results of those two to the benchmarks but unfortunately had problems with the C++ version, which led to the title of this blog post. More on that later. Suffice it to say for now that the C++ results should be taken with a large lump of salt. Results:

loadtest (throughput - bigger is better)
Connections:         500            750            1k
D + vibe.d:          166.9 +/- 1.5  171.1 +/- 3.3  167.9 +/- 1.3
C (Mosquitto):       122.4 +/- 0.4   95.2 +/- 1.3   74.7 +/- 0.4
Erlang:              124.2 +/- 5.9  117.6 +/- 4.6  117.7 +/- 3.2
Go:                  100.1 +/- 0.1   99.3 +/- 0.2   98.8 +/- 0.3
Java:                105.1 +/- 0.5  105.8 +/- 0.3  105.8 +/- 0.5
C++11 + boost::asio: 109.6 +/- 2.0  107.8 +/- 1.1  108.5 +/- 2.6

pingtest (throughput constrained by latency - bigger is better)
parameters:          400p 20w       200p 200w      100p 400w
D + vibe.d:          50.9 +/- 0.3   38.3 +/- 0.2   20.1 +/- 0.1
C (Mosquitto):       65.4 +/- 4.4   45.2 +/- 0.2   20.0 +/- 0.0
Erlang:              49.1 +/- 0.8   30.9 +/- 0.3   15.6 +/- 0.1
Go:                  45.2 +/- 0.2   27.5 +/- 0.1   16.0 +/- 0.1
Java:                63.9 +/- 0.8   45.7 +/- 0.9   23.9 +/- 0.5
C++11 + boost::asio: 50.8 +/- 0.9   44.2 +/- 0.2   21.5 +/- 0.4

In loadtest the C++ and Java implementations turned out to be in the middle of the pack with comparable performance between the two. Both of them are slightly worse than Erlang and D is still a good distance ahead. In pingtest it gets more interesting: Java mostly matches the previous winner (the C version) and beats it in the last benchmark, so it’s now the clear winner. The C++ version matches both of those in the middle benchmark, does well in the last one but only performs as well as the D version in the first one. A win for Java.

Now about my C++ woes: I brought it on myself a little bit, but the way I approached it was by trying to minimise the amount of work I had to do. After all, writing C++ takes a long while at the best of times so I went and ported it from my D version by translating it by hand. I gleaned a few insights from doing so:

  • Using C++11 made my life a lot easier since it closes the gap with D considerably.  const and immutable became const auto, auto remained the same except when used as a return value, etc.
  • Having also written both C++ and D versions of the serialisation libraries I used as well as the unit-testing ones made things a lot easier, since I used the same concepts and names.
  • I’m glad I took the time to port the unit tests as well. I ended up introducing several bugs in the manual translation.
  • A lot of those bugs were initialisation errors that simply don’t exist in D. Or Java. Or Go. Sigh.
  • I hate headers with a burning passion. Modules should be the top C++17 priority IMHO since there’s zero chance of them making into C++14.
  • I missed slices. A lot. std::vector and std::deque are poor substitutes.
  • Trying to port code written in a garbage collected language and trying to simply introduce std::unique_ptr and std::shared_ptr where appropriate was a massive PITA. I’m not even sure I got it right, more on that below.

The C++ implementation is incomplete and will continue to be like that, since I’m now bored of it, tired, and just want to move on. It’s also buggy. All of the loadtest benchmarks were done with only 1000 messages instead of the values at the top since it crashes if left to run for long enough. I’m not going to debug it because it’s not going to be any fun and nobody is paying me to do it.

It’s not optimised either. I never even bothered to run a profiler. I was going to do it as soon as I fixed all the bugs but I gave up long before that. I know it’s doing excessive copying because copying vectors of bytes around was the easiest way I could get it to compile after copying the D code using slices. It was on my TODO list to remove and replace with iterators, but, as I mentioned, it’s not going to happen.

I reckon a complete version would probably do as well as Java at pingtest but have a hunch that D would probably still win loadtest. This is, of course, pure speculation. So why did I bother to include the C++ results? I thought it would still be interesting and give a rough idea of how it would compare. I wish I had the energy to finish it, but I just wasn’t having fun anymore and I don’t see the point. Writing it from scratch in C++ would have been a better idea, but it definitely would have taken a longer amount of time. It would’ve looked similar to what I have now anyway (I’d still be the author), but I have the feeling it would have fewer bugs. Thinking about memory management from the start is very different from trying to apply smart pointers to an already existing design that depended on a garbage collector.

My conclusion from all of this is that I really don’t want to write C++ again unless I have to. And that for all the misgivings I had about a garbage collector, it saves me time that I would’ve used tracking down memory leaks, double frees and all of those other “fun” activities. And, at least for this exercise, it doesn’t even seem to make a dent in performance. Java was the pingtest winner after all, but its GC is a lot better than D’s. To add insult to C++’s injury, that Java implementation took Patrick a morning to write from scratch, and an afternoon to profile and optimise. It took me days to port an existing working implementation from the closest language there is to C++ and ended up with a crashing binary. It just wasn’t worth the time and effort, but at least now I know that.

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