Total Recall

I learned programming computers before I had the internet. While technically I am a Millennial, unlike most younger Millennial programmers I had a completely different experience than they did learning how to program computers. Programming became an obsession for me as a child when I accidentally pressed the wrong button on my keyboard playing Gorilla.BAS and seeing screens of hieroglyphs cross my eyes. It was written in QBasic and I didn’t know that Bill Gates wrote it with Neil Konzen. I didn’t even know who Bill Gates was.

I asked my best friend Dave what it was. He went to a computer summer camp that year and I thought he might have some idea. He said “oh, that’s code”. We spent some time together and he showed me what an variable was, what an “if” statement was, and how to get input and print text. My first program was a choose-your-own adventure book. I was hooked!

My only resource during this time was the library. My mother would take me and I would get stacks of books. My software development process looked like this.

  1. Make a guess about how something worked.
  2. Write some code.
  3. Run the code.
  4. It failed, look up what the function does in the reference.
  5. Repeat.

The process of looking up how things worked in a reference was so tedious that I was forced to guess before I expended the serious effort. Eventually I remembered how things worked and would rarely have to search a reference to get something done.

This process of trying to remember and guessing is called Active Recall. It is an incredibly efficient way of developing long-term memory. This is why I felt as time went on I would rarely have to consult a reference. This was especially fortunate for me since my references were library books and I would have to return them! Not only did it improve long term memory, but I had to be endlessly curious to solve my problems.

This is a completely different experience than what younger programmers have. With google at your finger tips, there seems to be no reason to try and guess. With StackOverflow the distance between you having a problem and there being an answer is very short. Looking things up is not tedious anymore.

Their development process looks like this.

  1. Open up a browser tab and type in some text related to what you are doing.
  2. Click on the StackOverflow link and read if someone else is trying to accomplish the same thing.
  3. Look at the answers.
  4. Copy and paste the solution and change it so that your code compiles.
  5. Repeat.

This new way is called Passive Review. Which as you can guess is not very efficient in cultivating long term memory. There is little reason left to guess and develop that muscle. There is also very little reason to be creative and come up with a solution, when you can simply search for one very quickly.

The June 2017 volume of the Communications of the ACM has a brilliant article called The Debugging Mind-Set which links Active Recall with the cultivation of the Incremental Theory of intelligence and Passive Review with the Entity Theory.

People with the Entity Theory mindset believe intelligence and ability is fixed at birth and you either got it or you don’t. People with the The Incremental Theory mindset believe intelligence and ability are not fixed and can improve with effort.

I squarely have the Incremental Theory mindset and I believe this mindset promotes growth and creativity and above all what I like to call “The Fighting Spirit”, which I believe is central to the hacker mindset. I also believe the way I learned how to program had a huge influence over me having this style to begin with. I had to constantly guess and be curious, trying things before looking up solutions in tedious references and manuals.

The question I would love to ask you dear reader is this, is this new era of software development, where you don’t have to remember things anymore but use a different set of skills to find, instead of develop, solutions to problems. Is this new era promoting a generation of Entity Theorists? And has the hacker spirit died with it?

We are Not Children

I am not a woman/man child. I don’t need games and toys to be motivated to do my work. I don’t need sugar snacks and soda pop. I take my work seriously and find inherent joy in it.

Companies often use hackathons to “motivate” employees. This only acknowledges that they believe the daily work is boring and that we are not hacking every day. Just like “happy hour” is unlike all other miserable hours, “hack-athons” are unlike all other miserable “life-athons”.

They bring snacks, pizza, and beer. On top of thinking we need play time, they feel that isn’t enough. They turn the hackathon into a competition. Who has the best ideas? Who played the best?

Programming is already inherently play. I don’t need play time. I am trying to solve real problems for real people in real communities. Part of that process is programming, and in it, there exists an infinite space of play.

I am a professional programmer. I take myself seriously and so should you.

The Fox in the Henhouse


I’m writing a new time series DB in C++ called Henhouse and this is a story about a bug and my heroic battle with it. Actually, it’s not really a story about a bug, but more of a way of finding bugs and preventing them. I hope to convince you that there are better ways of writing code that don’t involve slogging with unit tests.

Freakin Lasers!

So I ran a randomized test that hammered my new precious DB. It dumped random data into it and did random queries. I started it and came back several hours later to find this…

!! require failed
!! expr: (_metadata->size >= 170) [_metadata->size = 171, _max_items = 170]
!! func: size
!! file: /home/maxim/src/max/henhouse/src/db/../util/mapped_vector.hpp (63)

./henhouse(_ZN8henhouse4util5traceERSo+0x35) [0x68dbea]
./henhouse(_ZN8henhouse4util5raiseEPKc+0x81) [0x68dd32]
./henhouse(_ZN8henhouse4util6raise2ImiEEvPKcS3_iS3_S3_S3_RKT_S3_RKT0_+0x2b8) [0x67923f]


See that?! That is a bug right there!

You see, I am writing the DB in a style of programming called Design by Contract. My code is littered with contracts and whenever one of them fails, the program prints a helpful message with a stack trace and then violently aborts. No remorse. Want a guaranteed way to have a fairly bug free system? Make sure the system can detect bugs, and then can find your family and send them down river.

The nasty bug above was found in this bit of code.

uint64_t size() const
    REQUIRE_LESS_EQUAL(_metadata->size, _max_items);

    return _metadata->size;

This is a member function which implements a memory mapped vector. Every time you call size on the vector, the contracts are tested.

This Machine Kills Fascist Bugs

Design by Contract is one of those secret weapons nobody teaches you. The term was coined by Bertrand Meyer who created the Eiffel programming language. Eiffel is really the only language that has truly native support for this technique, well, Eiffel and Perl 6. But we all know Perl 6 is from the future, but talking about Perl 6 is for another day.

The idea is that units in your software, like a function, or an object, establish contracts between the caller and the callee. The callee says “I REQUIRE you give me parameters that meet this criteria and I will ENSURE you this result”.


The important bit to understand here is that a RELATIONSHIP is established between pieces of code. Not just an informal relationship, but an executable one! A relationship that can be tested millions of times a second. All day, every day on real user data.

This is substantially different than Unit testing, which ignores relationships between code.

Parallel Monkeys Are Terrible

The bug was introduced when I added support for parallel puts and queries into my DB. As data is put into the DB I have a function that would resize the memory mapped file that looked like this.

void resize(size_t new_size)
    REQUIRE_GREATER_EQUAL(new_size, _data_file>size() + sizeof(data_type));
    const auto old_max = _max_items;

    _items = reinterpret_cast<data_type>(_data_file->data());
    _max_items = _data_file->size() / sizeof(data_type);

    ENSURE_GREATER(_max_items, old_max);

The failure detected that “_max_items” was being stale somewhere, which indicated a copy was being made in another thread.

Amazing! our contract found a multi-threading error!

I solved the bug by a standard method. I created a worker pool and made sure puts and gets for a piece of data always went to the same worker. The worker has a request queue that it pulls from and processes requests one after another. No more chances of a get and a put happening in parallel. The much more tricky solution would have been to use read/write locks, but I know better.

The Fat Bollard

What would our code above look like using unit tests instead? Well first thing, we would not have contracts, because we are TDD people and TDD people don’t know about contracts.

void resize(size_t new_size)
    _items = reinterpret_cast<data_type>(_data_file->data());
    _max_items = _data_file->size() / sizeof(data_type);

What we lose should be immediately obvious. For example, the requirement that new_size should be bigger than the old size. If we enjoy defensive programming, we may further refine the code to look like this.

void resize(size_t new_size)
    if(new_size < _data_file->size() + sizeof(data_type))
        throw runtime_error("new size too small");

    _items = reinterpret_cast<data_type>(_data_file->data());
    _max_items = _data_file->size() / sizeof(data_type);

I get a lot of people asking me “What the difference is between Design by Contract and plain old defensive programming?” I think it should be pretty obvious from above why defensive programming is not the same thing. First with defensive programming, you allow the caller to recover. With the Design by Contract approach there is no remorse. Fail early and fail fast. Second, the caller has no information about what the callee ensures in the result, requiring the caller to check the result.

Being good TDD folks we would actually have written a test first that looks like this.

void test_resize()
    size_t initial_capacity=50
    mapped_vector<int> v{"test_file", initial_capacity};
    for(int a = 0; a < 100; a++)
    EXPECT_EQ(v.size(), 100);

And guess what, this would not have found the parallel bug! Even if we modified the test to use threads to insert into the vector, the bug would still not fail the tests. We would also have to query the size as we insert in parallel for the bug to show up, and guess what, in that query the size would necessarily be unknown since the query and insert are done in parallel. The best you can do is maybe somehow coerce the test to segfault (which it won’t since it is accessing valid memory). Or worse, expose max_items to the outside world muddying the interface.

I tested to see what would happen if I removed the contract. I ran the test and waited a day. And guess what? nothing happened except that the DB put data in the wrong place. In other words, a silent and deadly data corruption.

Not All Tests Are Created Equal

I combined Design by Contract with other powerful tools such as randomized testing and fuzzing. There are many tools for randomized testing such as QuickCheck and for fuzz testing such as afl-fuzz. I decided to write my own tests using Perl 6. For example, here is a test for dumping random data into the DB. Henhouse is compatible with Graphite’s input format.

use v6;

sub MAIN($workers, $a, $z)
    my @keys = "$a".."$z";
    my @ps;

    for (1..$workers) -> $w {
        @ps.push: start { put(@keys); };
    await Promise.allof(@ps);

sub put(@keys)
    my $s =<localhost>, :port<2003>);
        my $c = (0..10).pick;
        my $k = @keys.pick;
        $s.print("{$k} {$c} {}\n");
        sleep 0.01;

Why should I write many manually created unit tests when I can create a test that makes tests! A typical project using unit testing has tens of thousands of hand written tests. Look Ma! I just made a million!

Do This, Be Brave

Design by Contract is a far better weapon to fight bugs and improve code quality than Unit Testing. Design By Contract…

  • Helps you do code reviews by being explicit about assumptions.
  • Tests both input domain and output codomain
  • Tests ranges of values instead of individual points.
  • Tests real data in production vs fake data in development.
  • Always on. Ships with production code.
  • Prevents data corruption and secures code.
  • Failures happen close to the source of the problem.

If you combine this technique with…

  • Randomized Testing
  • Fuzz Testing
  • Acceptance and Behavioral Testing

You will never look back. Stop unit testing, do it, be brave.