Rubbish Interview Questions

I came across this photo in my archive from quite a few years ago. I was taking one of those awful online C++ tests as an initial pre-interview screener. I took a photo of this question as an example of the standard of questions this particular company was asking.

What do you think the answer is?

When you’ve gone through a series of timed questions like this, you start to second-guess the person who wrote it.

Clearly the variable “o” is a stack-based variable, a pointer, so will be destroyed at the end of its scope. So technically the answer is “a”.

But I have a feeling the person that wrote this dubious question actually means to ask about what o points to, especially as it’s a memory leak (unless the mysterious bar() function deletes what o points to). I remember being in a quandary about this one… do you answer entirely correctly? Or do you try to answer the question you think is being asked?

Obviously in a face-to-face or telephone interview one could discuss this. I’ve always preferred, when being the interviewER, to sit down with someone in a real-world situation, give them access to Stack Overflow and Google (which, as we all know, are essential tools of the trade these days), and ask them to explain their thought processes.

If you’re turning down potential candidates on the strength of questions like the above, I’d argue you’re doing it wrong.

Getting Started with Boost MultiIndex

Note: this is an introduction to / overview of Boost MultiIndex. It is not intended to be in-depth.

So. Containers. In the words of luminaries in the C++ field such as Chandler Caruth, Scott Meyers and plenty of others, reach first for std::vector!

99% of the time this container, out of all the choices you have in the STL, is the right choice. The next most obvious is std::map if you need to “index” access to data: for example if you store 1,000,000 phone numbers and you want to quickly find one given a name, a std:map will give you “indexed” access – you look up your data in O(log n) time (or potentially quicker if you’re using a std::unordered_map) as opposed to O(n) time for an unsorted std::vector. (Obviously these are theoretical complexities – see a previous post which advocates actual benchmarking, as processor cache and branch prediction can have a huge effect).
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Big O – Theory vs Practice

I saw a question online recently which basically asked whether big-O notation always holds true in practice. For example, in theory, searching through a vector for an item should be O(n) whereas searching a map should be O(log n). Right?

Let’s imagine we create a vector with 100,000 random integers in it. We then sort the vector, and run two different searches on it. The first search starts at the beginning, compares the element against the value we’re searching for, and simply keeps incrementing until we find it. A naive search you might say. Without resorting to std:: algorithms, how might you improve it?

A worthy idea might be a recursive binary, or bisection, search. We might, for example, make 15 bisections to find a value in the upper quadrant of our range, compared to, say 75,000 comparisons in the brute force approach.

That’s fairly safe to say that the binary search is going to be much faster, right?

Not necessarily. See the results below:

100000 random numbers generated, inserted, and sorted in a vector:
 0.018477s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (54.1%)

Linear search iterations: = 75086, time taken:
 0.000734s wall, 0.000000s user + 0.000000s system = 0.000000s CPU (n/a%)

Binary search iterations: 17, time taken:
 0.001327s wall, 0.000000s user + 0.000000s system = 0.000000s CPU (n/a%)

Are you surprised to see that the binary search took (very) slightly longer than the brute force approach?

Welcome to the world of modern processors. What’s going on there is a healthy dose of branch prediction and plenty of valid processor cache hits which actually makes the brute force approach viable.

I guess the moral of this story is, as always, don’t optimise prematurely – because you might actually be making things worse! Always profile your hot spots and work empirically rather than on what you think you know about algorithm efficiency.

The code used here is reproduced below – it should compile on VS 2015, clang and g++ without issue. You’ll need Boost for the timers. You may see wildly different timings depending on optimisation levels and other factors :)

#include <iostream>
#include <vector>
#include <random>
#include <algorithm>
#include <string>

#include "boost/timer/timer.hpp"

const int MAX_VAL = 100'000;

template <typename T>
T LinearSearch(typename std::vector<T>::iterator begin,
    typename std::vector<T>::iterator end, T value)
{
    // While not ostensibly efficient, branch prediction and CPU cache for the
    // contiguous vector data should make this go like greased lightning :)
    size_t counter{0};
    while (begin < end)
    {
        if (*begin >= value)
        {
            std::cout << "\nLinear search iterations: = " << counter;
            return *begin;
        }
        ++begin;
        ++counter;
    }
    return 0;
}

template <typename T>
T BinarySearch(typename std::vector<T>::iterator begin,
    typename std::vector<T>::iterator end, T value)
{
    // The return value is not exact... just interested in getting close to the
    // value.
    static size_t counter{0};
    size_t mid = (end - begin) / 2;
    ++counter;
    if (begin >= end)
    {
        std::cout << "\nBinary search iterations: " << counter;
        return *begin;
    }
    if (*(begin + mid) < value)
    {
        return BinarySearch(begin + mid + 1, end, value);
    }
    else if (*(begin + mid) > value)
    {
        return BinarySearch(begin, begin + mid - 1, value);
    }
    else
    {
        std::cout << "\nBinary search iterations: " << counter;
        return value;
    }
}

int main()
{
    // Fill a vector with MAX_VAL random numbers between 0 - MAX_VAL
    std::vector<unsigned int> vec;
    vec.reserve(MAX_VAL);

    {
        boost::timer::auto_cpu_timer tm;
        std::random_device randomDevice;
        std::default_random_engine randomEngine(randomDevice());
        std::uniform_int_distribution<unsigned int> uniform_dist(0, MAX_VAL);
        for (int n = 0; n < MAX_VAL; ++n)
        {
            vec.emplace_back(uniform_dist(randomEngine));
        }
        // Sort the vector
        std::sort(vec.begin(), vec.end());
        std::cout << MAX_VAL
                  << " random numbers generated, inserted, and sorted"
                     " in a vector:" << std::endl;
    }

    {
        boost::timer::auto_cpu_timer tm2;
        LinearSearch(
            vec.begin(), vec.end(), static_cast<unsigned int>(MAX_VAL * 0.75));
        std::cout << ", time taken:" << std::endl;
    }
    {
        boost::timer::auto_cpu_timer tm2;
        BinarySearch(vec.begin(), vec.end() - 1,
            static_cast<unsigned int>(MAX_VAL * 0.75));
        std::cout << ", time taken:" << std::endl;
    }

    return 0;
}

C++ Custom Deleters: unique_ptr vs shared_ptr

C++11 gives us two useful indispensable smart pointers, std::unique_ptr and std::shared_ptr. So much has been written about these that there’s no point me re-hashing anything other than to re-iterate that if you are using “naked” owning pointers in your code these days, you are simply doing it wrong.

I wanted to mention briefly the use of custom deleters with smart pointers. If you haven’t looked at this aspect of smart pointers before, it basically gives us a way to specify what should happen when the smart pointer goes out of scope.
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C#, Mono, and System-Wide Mutexes on Linux

Here’s another little “gotcha” from my experimentations in running C# apps cross-platform. The following code implements a system-wide mutex to ensure that only one instance of the program is running at any one time:

public static void Main(string[] args){
	Mutex mutex = null;
	try{
		mutex = Mutex.OpenExisting("test.martyndavis.com");
		// if we *can* open it we're an ADDITIONAL instance
		Console.WriteLine("Application is already running");
		return;
	} catch(WaitHandleCannotBeOpenedException){
		// if we get in here then the mutex doesn't exist,
		// so we are the only instance running on this machine
        mutex = new System.Threading.Mutex(true, "test.martyndavis.com");
	}
	try{
		Console.WriteLine("Doing stuff... press a key");
        Console.Read();
	} finally{
		// Although mutex implements IDisposable, it doesn't
		// call Release Mutex when being Disposed(),
		// hence usage of try/finally instead of 'using'.
		mutex.ReleaseMutex();
	}
}

Running this on Windows it behaves as one would expect. Run one instance you get “Doing stuff… press a key” output, and, if you try to run a second instance in another DOS box you get “Application is already running”.

On Linux though it’s a different story – each instance runs as if it’s the only copy on the machine.

Doing some digging turns up this note in Mono’s release docs – you can see that shared handles are now turned off by default – you can turn them on by setting the environment variable MONO_ENABLE_SHM, but for the application I’m working on I think I’ll simply avoid mutexes and use something else to ensure one instance – a lock file in the application’s data directory will be simple, and work cross-platform.

Lambda Expressions and Threads in C#

Here’s a small program… what do you think the output will be?

public static void Main(string[] args){
    Thread[] t = new Thread[10];
    for (int tNum = 0; tNum < 10; tNum++) {
        t[tNum] = new Thread(() => {
            Thread.Sleep(new Random().Next(20));
            Console.Write(tNum + " ");
        });
        t[tNum].Start();
    }
    // wait for the threads to finish
    for (int tNum = 0; tNum < 10; tNum++){
        t[tNum].Join();
    }
    Console.WriteLine("\nPress a key...");
    Console.Read();
}

Continue reading “Lambda Expressions and Threads in C#”

Using a GTK# TreeView Widget in C# – Very Simply

I’ve been experimenting with using GTK# and C# to build GUI applications which will run on Linux, Windows and Mac. Monodevelop has a graphical form designer for Gtk# front ends, but it’s not as simple as building WinForms applications under Visual Studio. An example is the TreeView widget – it’s designed from an MVC perspective, with a ListStore or TreeStore object containing the model (or data), various objects collectively representing the view (columns, cells, cell renderers etc) and the controller functions allow you to sort and filter the data. All the building needs to be done in code rather than at design time. This is a lot to wrap your head around if you simply want to display some data in a tabular format, as I did, when I wanted to display the parsed contents of a log file!

To make this easier for me in future, I’ve written a small class which hides just about everything. Read on…

Continue reading “Using a GTK# TreeView Widget in C# – Very Simply”