Tag: programming

++it18 – Zero Allocation and No Type Erasure Futures

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This talk by Vittorio Romeo tackles a subject that could go ignored if your programming style doesn’t feel the need for parallelism (or if it does, pthreads satisfies such a need). Futures have been introduced in C++ with 2011 ISO standard, they hardly can be deemed as novelty.

I’ve been hit by futures some 4 years ago when confronted with a large code base in Scala that exploited futures and actors. So, at least to save my ego, there is nothing wrong if you don’t know what a future is :-).

In brief a future is a way to model a concurrent operation. The future can be either in execution or succeeded with a result value, or failed with an error. As user, you request an async operation and you get a future of the expected result. You can set callbacks on the completion, or combine several futures together either by chaining or by synchronize them. As you may imagine futures vastly simplify the handling of async computation by hiding the synchronization and communication details and by providing a common and clear usage pattern.

So, no excuse, if you need to do async programming, you’d better know this tool.

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++it18 – Time Travel Debug

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This talk presented a very interesting object – the debugger the could go backward.  But I don’t want to steal the scene, since Paolo Severini from Microsoft explained everything quite well in his talk. Here you can find the slides. Nedless to say errors are mine.

Everyone knows that debugging is twice as hard as writing the code in the first place. So if you’re as clever as you can be when you write it, how long will it take to debug it?

Debugging could be both hard and time consuming. Bugs can be difficult to replicate, especially those involving memory corruption, race conditions, or those that happen randomly or intermittently.

More often than not, it can be hard to spot the cause from logs or crash dumps. Also step by step debugging may require several restarts.

When doing step-by-step debugging is quite common something like:

step into, step over, step over, step over, step ov.. @!# I should have stepped into!.

The more information we can get from debugging tools the better. A tool that could tell “what just happened” would be very very useful.

Reverse debugging is such a tool. The idea is to record state by state everything happens in the CPU that’s executing the code and then the reverse debugger allows the programmer to play back and forth to spot the problem.

First ideas of such a tool dates back to 70s [NdM. I keep noticing that most of the computer science has been invented back in the 60-70s years. They just lacked the CPU resource for practical applications].

There are several tools for reverse debugging native code:

In this speech I will present TTD.

TTD is the reverse debugger for Windows. It is fully integrated with WinDbg. TTD can be used for all user-mode processes, for multithread and multicore

In order to use TTD, you need to set WinDbg to record data. This option needs to be explicitly enabled since has two main drawbacks – first all the program execution is slowed down and then quite a large amount of data is produced. You can expect anywhere between 5 and 50 Mb/s.

After recording has started, debugging works as expected, plus you can step back and forth and also continue backward or forward.

Take note that TTD works only in the latest versions fo Windows 10.

Interestingly you can perform queries on data collected during execution. Every line in the database matches an assembly instruction executed. So you can query how many times some instruction has been executed, how many exceptions have been thrown and so on.

[NdM: A working demo follows, unfortunately the time is quite constrained. A sample program to recursively compute the byte size of file in a directory tree is presented. The program just crashes when executed. So it is executed in WinDbg with TTD enabled. The execution halts as usual in the middle of exceptionland where nothing can be understood unless you speak advanced assembly. By pressing back a couple of time the debugger, going backwards, reaches the last function executed, the one who crashed. At this point Paolo places a breakpoint at the beginning of the function and – with a good implementation of the WOW effect – runs the program backwards. The execution back-reaches the function beginning and can be step forward to better understand why it crashed]

The presentation has been very interesting to show the tool capabilities. I tried an evaluation of UndoDB back in 2006, but it was as uncomfortable as it could be since it had to be used from command line. WinDbg+TTD is light years ahead in terms of usability.

Also I found very interesting the data query feature. Having the db of the program execution states allows the programmer to get a number of important statistics and events without the need of adding instrumentation or specific breakpoints.

The drawback is the execution time, but I guess that without a specific hardware support this is the price we have to pay still for some time in the future.

++it 2018 – Coroutines – coming soon… ?

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I really enjoyed this talk and not only because the speaker is an old friend on mine. I met Alberto back in the Ubisoft days and he was and still is one of the best C++ programmers I know and for sure the only friend who submitted a proposal to the standard committee that got approved. On the other hand I have no friends who submitted a proposal to the standard committee that was refused. So all my friends who submitted… well I’m digressing.

This talk was in Italian, that is welcome, but… weird. Usually all talks are in English even for Italian conference. Anyway here’s my summary, as usual errors and mistakes are mine. You can find Alberto’s slides here.

Coming Soon to a Compiler Near You – Coroutines

Alberto is a C++ enthusiast programmer since 1990. In this hour he’s going to talk about coroutines, their history and their relation with C++ and C++ Technical Specifications (TS).

A suspend and resume point is a point in the instruction sequence where the execution can be suspended, other stuff is executed and then the execution is resumed from there.

The most common form of suspend and resume point is a subroutine call. The execution is transferred from the caller to the callee and when the callee is done, the execution is transferred back to the caller.

Coroutines are a generalized form of suspension and resume points, that allow you to manage where you want to suspend, transfer control and resume, without be constrained in the caller/callee format.

When is this pattern useful?

Continue reading “++it 2018 – Coroutines – coming soon… ?”

++it 2018 – Key Notes

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Saturday 23 June 2018, was a sunny day in Milan. The ideal day to close yourself in Milano Bicocca University with many equally nerdy fellow programmers to attend the Italian C++ conference.

I discovered this conference, now in its 3rd edition, by chance, skimming through my neverending queue of unread tweets. A friend of mine and C++ guru twitted about this, so I bookmarked the website and as soon as this year edition was announced I was one of the first to register.

Also it is worth noting that this conference is free (as in beer), lasts a full day and includes the lunch. Talks are interesting and the conference is organized in two tracks with a single starting keynote and a single closing speech.

Although free, there are famous guests – last year Bartosz Milewski – quite a famous functional programmer and category algebra expert – held a speech about C++ and functional programming; this year the keynote was by Peter Sommerlad — C++ expert and member of the C++ ISO committee.

As usual my good intentions are to write and publish about each single talk I attended to.

Comparison with other conferences I attended, such as Scala Days, Scala Italy and Lambda World, is natural, although not always fair – ++it is completely free, while others come to a price (not light in case of Scala Days). I couldn’t fail to notice that age on average was older here than in the functional world. I won’t try an interpretation, but there could be more than one.

But, enough with babbling, let’s start with the keynote (based on my notes taken during the talk, errors or misunderstandings are just mine). You can find Peter Somerlad’s slides here. My comments at the end.

A – B – C : Agile, BMW, C++

In this talk prof. Somerlad basically tells about his professional career, era by era, providing a short list of lessons learned for each period.

He started programming on a HP pocket calculator. This device had some 50 steps of program instruction and no graphics whatsoever. Also TI calculators were more widespread at time, so he had to translate programs from one programming language to another one. Although very similar (they were all stack based) each calculator had some peculiarities.

One program he developed for fun was a ballistic simulation – define a force and a direction to fire a bullet that has to avoid a wall and hit a target. As said, the pocket gizmo has no graphics, but years later, the very same idea was re-instantiated with some graphics and sounds to become… Angry Birds.

Lessons learned:

  • Mentally execute code – there was no debugger;
  • Using the stack;
  • Reading foreign code (to translate from TI to HP);
  • Porting code;
  • Old simple ideas reamain useful.

The first home computer was a ZX Spectrum – an 8bits computer with rubbery keyboard that connected to a TV set and used a cassette recorder as mass storage. Each key was decorated with several keyword beside the standard letter and symbol you normally find on a key. When pressing a key, depending on the context, one of such a keyword appeared on the screen – automatically typed for you. Context was determined by pressing two shift keys. The correct combo produced the required effect.

At school he worked on a mainframe, that had a curious assembly language. Notable one instruction, by exploiting an indirection bit, could be used to create an infinite loop. He and another one student tried the instruction and the mainframe froze. After few seconds an angry System Administrator entered the room, asking them to quit what they were doing.

Lessons learned:

  • Basic, Z80 assembly and machine code, Pascal;
  • Keyboard shortcuts and meta layout;
  • Patience (loading from tape takes forever);
  • Dealing with errors from hardware.

In 1985 he landed his first job where he had to write a kind of customer management application on an Apple IIe. Now the software had to be professional and one requirement was that a cat could walk on the keyboard without causing a crash of the application. At times no commoditized databases were available and the few db engines available were very expensive. Since the Apple IIe had floppy discs, they may have to merge the two databases on two discs. The solution is to employ a merge sort, so he took the book from Niklaus Wirth “Data+Functions=Algorithms” and copied the merge sort implementation. Unfortunately the merge sort algorithm on the book was not stable, so Peter had to rewrite it from scratch.

Implementing the DB was quite a nightmare for many technical constraints – computer memory, mass storage size and computing power. The PC was an IBM with MS-DOS 1.x. This version had no directories. The Pascal language had no dynamic strings – they had to be statically sized.

As computer technology progressed he switched to a 386 compatible and turned to minix. During this period, Peter used PMate – an editor that sported macros, so that statement frames could be automatically generated.

Lessons learned:

  • do not trust programming books and commercial libraries;
  • before putting code in your book, always compile and test it;
  • programming should be supported by fast and powerful editor;
  • you should not need to type all of your code;
  • generic data driven solution make code simpler
  • automate tests.

1987 C and Unix courses

During this period Peter started teaching classes about C and Unix. In this classes shell, system programming and C were taught. Also he had to teach how to unlearn things to Cobol Programmers.

In 1988 he wrote some C guidelines – some are still actual today:

  • a function is too long if it does not fit in a 80×24 terminal; functions with longer code tells a failure in teaching software engineering.
  • a function should not take more than 3-5 parameters, if you need more, then you have to logically group them in struct. Windows API had function with 7 or more parameters, that why Peter swore never to program this OS.
  • Everything is an int, unless it is a pointer or a double. (The reason why we need to use the typename keyword today is a legacy of this choice made by K&R).
  • Program abstract data types using opaque structs and pointers.
  • Do not use scanf() ever in production code, use sscanf() instead and check its result.

Lessons learned:

  • K&R C, Unix, sh, awk, sed, vi, nroff, make, lex, yacc,…
  • You learn the most by teaching.
  • One can program in fortran/basic/cobol in any language.
  • abstraction, layering, cohesion.
  • the power of plain text, regexp, pipelines and scripting. Searching log files for what went wrong.

In 1989 Peter graduated. His master thesis was about a Modula-2 frontend for the Amsterdam Compiler Kit. In this year, also the first experiments with C++ using the Zortech C++ compiler. Writing code with this environment was quite intensive since any error in the code caused the whole machine to die. These were days before Protected Memory.

Lessons Learned:

  • Compilers and AST with polymorphic nodes (polymorphism via union);
  • Virtual Machine Languages (p-code, em-code). Java bytecode is very close to em-code;
  • more RAM and CPU power make compilation much faster;
  • ADT works even within a compile;
  • lex and yacc can be used in practice.

Conscript at Bundenswehr (the German Army). Peter worked in the Computer Center, on systems for Radar detection and analysis. This was quite a travel back in time. The computer was an 18 bits with core memory, switches to input bits into the main memory and tape for mass storage.

Also Peter found another computer history milestone – the VT100 terminal. This terminal defined what ANSI escape codes are today.

The language used for programming the system was Jovial a mix of pascal and algol that was later replaced by Ada.

Lessons learned

  • Computer and software history can be interesting;
  • Even hardware can be used beyond its expected lifetime;
  • source control is important (and copying floppy disc is not source control)

In 1990 he landed a job at Siemens and started working in OO programming environment. Here he worked with Erich Gamma and Andrè Weinard [NdM: I tried to look up this but haven’t found anything relevant, maybe I got the name wrong].

The system Peter was working at was a framework for C++ written in C++, named OOPU, intended to replace TOROS_OBER a C macro based OO system used for car diagnostic computer. Unfortunately OOPU was killed by internal politics. The reason was that a single programming environment had to be used across several business units, and other business units didn’t want to pay for it.

In this context Peter wrote an object inspector, named OBER, that showed the relations among objects (this was before DDD was invented). From architectural point of view this application sported an UI separated from the backend.

In 1991 Peter wrote the first C++ guidelines, although some are still applicable, many are obsolete because the language evolved and things can be done better.

1993 was the year of the PLoP (Pattern Languages of Programming), this was still before the Gang of Four Book. In this conferences many opportunities wer to learn and understand architecture by trying to explain patterns.

1995 is the year of GoF. The most important design pattern are:

Do not use the singleton since C++ has no fixed sequence of initialization. If you need singleton, think about design first. If you need globals, just use globals [NdM, I understand that singleton has some problems, but I don’t see what is the alternatives – globals lacks of encapsulation].

In PLoPs and EuroPLoP, patterns had a community, where knowledge was shared, and it offerd an environment to try out ides. Discussing how stuff has to be written and why.

With other participant of the PLoP/EuroPLoP, Peter wrote the book POSA – Pattern Oriented Software Architecture. This is less famous than Design Pattern by the Gang of Four, but tackled the same matter.

Layers vs. Tiers -Layers are different level of abstraction , you can swap a layer implementation with another; while in tiers, functionality spread on multiple tiers, all the the same level of abstraction employing different technologies.

Using layers the team can specialize, while using tiers the team needs to know everything.

Good abstraction is a skill. Too many layers are a problem if layers do nothing but pass values.

Lessons learned:

  • A prophet has no honor in his own country – get out to be heard;
  • good patterns require several rewrites;
  • Sharing teaches you to become a better author;
  • Get a good copy editor, when publishing;
  • Network to learn – attend and speak at conferences;
  • most important part of a conference are the breaks.

In 1997 he moved to Switzerland to take the job of Erich Gamma, more precisely to work on a small server framework www.coast-project.org. While in charge Peter extended and improved the library and developed C++ Unit Test and test automation in doing so. He delivered high-quality solutions using Agile Software development and Wiki wiki web.

In these years Peter came to some interesting conclusions about high quality software. First high quality leads to better developer and better retainment of those developer. Unfortunately high quality leads to canceled maintenance contracts. If nothing goes wrong, the customer doesn’t see the point in paying for something that is never used.

Also, counter intuitively, high quality leads to less business opportunities. Since the contacts between customer and developer are minimal and usually don’t escalate to top level management. So the developer has less chance to meet and know the decision makers of the customer company.

Also CUTE – the test environment – has been created to replace macro-ish test.

Lessons learned

  • to advance you need to take risks;
  • do not be shy to use your network;
  • if you can not change your organization change your organization;
  • leading a team and evolving it is gratifying
  • producing software with a superior quality might be bad for business
  • working software lives longer than intended.

1998 and Agile Development. It became evident that following rituals is not adhering to values. You can do all the scrum rituals, but without committing to its values. When doing agile the most important things are:

  • Unit Tests and TDD;
  • Refactoring and Simple Design;
  • Rhythm and feedback, i.e. thinking about what you do and what you achieve;
  • Open and honest communication.

Lessons Learned

  • be self sufficient, but accept and appreciate help;
  • as a patient you are responsible for yourself.

Since 2004, Peter started teaching agile and continued teaching C++, concurrent and network programming. Also started working on IDE and tools, with the goal to eliminate bad software.

In 2005 many people thought that refactoring tool for C++ was impossible to write. Peter worked to prove them wrong, and Cevelop is the result.

[NdM: The talk ran out of time about now.]

My comments

Despite being sympathetic with Peter, since we shared so much in terms of computer history, programming and software engineering, I have to admit that his talk didn’t leave me with much. Also I fail to see the lack of some self-celebration. Maybe the most interesting thought was that quality software leads to less business. The rest of the talk was enjoyable, but I couldn’t see the connection with C++ or the future (or the history) of this language.

Despicable People

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Every place I worked had a different culture. I found this interesting and, when the conditions are proper, very entertaining. The workplace culture is something that can strongly motivate you, something that makes you feel part of something unique that’s happening right here and now.

When I worked at Ubisoft I collected all the jargon we used in a dictionary.

My current job is not different. During the years, recurring rants and complains made my friend Francesco and I create a list of despicable types of people. You know when you see something in the code that it is not just right or some approach to the problem that you consider plainly wrong? That’s the base idea. Then take into account other colleagues that have strong ideas about what is Holy and what is Evil and you get lot of jokes only you and your team can understand.

So the list has a split nature, half a satire against Absolute Truth and half a complain against those who write code for a living, but are not Real Programmers. That’s how this ever groving list of wrongdoers came together.

Here are our current list. It is very nerdy and most pun and jokes are likely difficult to catch even if you are a seasoned programmer, so I will offer a decoding table at the end of the post. Ready? Let the rant begin?

Despicable types of people (in no particular order)

  1. those who don’t do a memset at the beginning of each function
  2. those who define nonsense operators
  3. those who start counting from 1
  4. [C++] those who pass references without const modifier even when they do not change the argument
  5. [C++] those who don’t place const in the signature of methods that don’t change the status of the instance
  6. [C++] those who use delete without [] to free an array
  7. those who don’t program in C
  8. those who don’t use Windows XP
  9. those who use the very same logging message in several lines of their code
  10. [C++] those who do delete this with no reason
  11. those who don’t know Z80 assembly
  12. those who don’t remove every warning from their code
  13. those who check in an if condition if a boolean variable is == true in languages where booleans can only be true or false
  14. those who name variables and functions in a language but English
  15. those who use DBs
  16. those who create animations using .obj files
  17. those who punctuate randomly
  18. those who pronounce acronyms half in Italian and half in English
  19. those who write programs that exceed 48k RAM.

So here is a little analysis/explanation

1 This means “to perform a memset of the variables on the stack, so that everything is initialized in a function before doing actual work”. Therefore this belongs to a sort of self defensive programming approach – trust no one, yourself included (after all who is going to use the variables freshly created on the stack?).

  1. It is aimed mostly at languages where you can redefine operators – C++ and Scala. In Scala you can define every sequence of symbols as a custom operator. Usually this is utterly confusing for beginners and everyone that is not fluent with the library the defines such operators. Personally I find DSL a messy confusion that brings no real advantage for at least two reason – first you know the host language but you need to learn the DSL that has different syntax/semantic and then the DSL forces the “host language” but has to comply with it and this results in odd looking syntax, misleading punctuation or phrasing. I much prefer a traditional library with a possibly well defined interface to Vulcan gibberish such as :::, |–>, ~>.

  2. How many times have you found code where the programmer started counting from 1, even if the language was C? This is annoying because there are very good reasons to start from 0… but someone prefers to ignore them and sticks with medieval culture lacking the concept of zero.

4,5. In C++ (and to a lesser extent in C) you can enforce a type checking strategy called const-correctness. Basically this strategy marks data that is expected to stay constant through-out some operation. This helps in writing better code because the user programmer can make stronger assumptions. Unfortunately you can’t use non const-correct code from const-correct code (and possibly this is why Java has no const-correctness concept) so the lazy programmer prefers to ignore the whole issue.

  1. This is a plain bug, because delete new int[10] is undefined behavior as per C++ standard. Nonetheless poorly written (and tested) code has such patterns.

7, 8 and 15 are basically exaggerations of what some people with Absolute Truth assert. Taken out of their contexts these sentences sound even more absurd, but the meaning is “simple and tested systems tend to be more reliable than complex and new ones”.

  1. Logging is helpful to get insights on the sequence of operations inside a running software. If you replicate the very same text then it is quite hard to figure out what the executed sequence was. This is plain dumb.

  2. Deleting this in C++ can be colorfully depicted as cutting the branch on which you are sitting. This is something that usually you don’t want to do. If you find yourself needing this it is likely that you are approaching the problem from a wrong perspective. Seldom this operation is correct.

  3. Z80 CPU was a popular microprocessor back in the 80s. Programming in Z80 assembly was easy, surely easier than programming the 6502, another popular micro of the same years. This point is another overstatement that refers back to a Golden Age where everything was proper and sound.

  4. Compiler warning, most of the times, are just picky annoyance, not much different than a crooked picture on the wall. Sometimes they are actually errors – the intention of the programmer was not caught by the lines he wrote and some subtleties were detected and reported by the compiler. This is one of the reasons why you want zero warnings when compiling your code. The other reason is that if your output space is cluttered by harmless warnings hardly you will notice the dangerous one.

  5. When a programmer is insecure of the language she/he is programming in, the first thing that she/he should do is to study the language rather than writing code with it. Adding redundant or useless structures (such as this – comparing with true) is a clear sign of doubt and uncertainty and usually it appears in code with below average quality.

  6. As many programmers I like tidy and well sorted out thing (even if my desk could tell a different story to the casual observer). Programming languages (at least most of the commonly used ones) have English keywords. So, yes the official reason is that your code should be read internationally, the real reason is that mixed language code looks really ugly.

  7. See 7.

  8. Once upon a time you made animations by playing different images (frames) one after another at a given speed. This worked fine, but the production was labor intensive. Obj files contains static 3D model, so in order to play them you need to perform a lot of data transfer to the graphics adapter. New techniques based on morphing and skeletal deformations arose in the years so that animations can be created with less data production and transferring, and – more important – can be optimized by the 3D accelerator. Making an animation with objs means that you miss where the bottlenecks in the graphics pipeline are, together with a good part of the last decades in 3D technology.

  9. Code punctuation is used to separate stuff. Keeping a consistent punctuation style (i.e. relative position between spaces, parenthesis, commas and the likes) is a good habit, is good manner to those who will read it and shows that you care for your code. And if you don’t care about your code, then you don’t care about your work, and don’t care about who is going to read your code, then you are despicable.

  10. This is much like 14. Italian programmers (but I guess that this is shared by all non English native programmers), while speaking, mix Italian and English words in the same sentence. This is fine because quite often technical documentation is written in English and for many terms there are no Italian equivalents. Pronouncing an acronym half in Italian and half in English is something that tells about the English knowledge of the speaker and his/her desire to show off with unfamiliar buzzwords.

  11. 48K RAM was the memory size of the ZX Spectrum a popular home computer back in the 80s. This is much on the line of point 7, 8 and 15 – a simple system tends to be more reliable than a complex one. There are also some implications about how lazy programmers are that are no longer willing to spend time in doing their homework (optimization) and waste memory and CPU for no good reason.

There are basically two main interesting ideas: the love for the code, meaning the care for your craft, and that simpler is better. I am not sure I actually heard the sentence “this code hasn’t received enough love” referring to a ugly or messy source file, but that’s the care idea. What I wrote above is true – if you don’t care for the details, why should you care for less evident characteristics?

Simpler is better, though put a little dramatically on the list, is not a new concept. KISS principle is also called. No sane programmer is going to oppose this, on the other hand it has to be balanced with development time. Programmers that uses more than 48k are not lazy, but have deadlines to hit.

The list is over, but we’re still looking for wrongdoers to add. Have you any suggestion?

button down -> light on

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Picture this project – you have a battery operated gizmo with LEDs that has to light up at a given time and light off at another one. Reality is a bit more complex than this, but not that much.

So what could possibly go wrong? How much could cost the development of its firmware?

The difficult may seem on par with “press button – light on”.

As usual devil is in the details.

Let’s take just a component from the whole. If it is battery operated you need to assess quite precisely the charge of the battery, after all – it is expected to do one thing and it is expected to do it reliably. The battery charger chip (fuel gauge) comes with a 100+ pages datasheet. You may dig out of internet a pseudo driver for this chip only to realize that it uses undocumented registers. Eventually you get some support from the producer and get a similar, but different data-sheet, and a initialization procedure leaflet that is close, but doesn’t match exactly neither of the two documents.

Now may think that a battery is a straightforward component and that the voltage you measure to its connectors are proportional to the charge left in the battery. In fact it is not like that, because the voltage changes according to temperature, humidity and the speed with which you drain the energy. So the voltage may fall and then rise even if you don’t recharge the battery.

To cope with this non-linear behavior the fuel-gauge chip keeps track of energy in and energy out and performs some battery profile learning to provide reliable charge status and sound predictions about residual discharge time and charging time. The chip feature an impressive number of battery specific parameters.

Since the chip is powered by the battery itself (or by the charger) and doesn’t feature persistent storage, you have to take care of reading learned parameters, store into some persistent memory, and rewrite them back at the next reboot.

Btw, this is just a part of the system which is described by some 2000 pages programming manuals specific to the components used in this project.

In a past post, I commented an embedded muse article about comparing firmware/software project cost to the alternatives. The original article basically stated that for many projects there is no viable mechanical or electrical alternative.

This specific project could have an electromechanical alternative – possibly designing a clock with some gears, dials and a mechanical dimmer could be feasible. I am not so sure that the cost would be that cheaper. But the point I’d like to make now is that basically you can’t have an electro-mechanical product today – it is not what the customer wants. Even if the main function is the same, the consumer expects the battery charge meter, the PC connection, the interaction. That’s something we take for granted.

Even the cheapest gizmo from China has the battery indicator!

And this is the source of distortion, we (and our internal customer who requested the project) have a hard time in getting that regardless of how inexpensive the consumer device is, that feature may have taken programmer-years to be developed.

This distortion is dangerous in two ways – first it leads you to underestimate the cost and allocate an insufficient budget; second and worse it leads you to think that this is a simple project and can be done by low-cost hobbyists. Eventually you get asked to fix it, but when you realize how bad the shape of the code (and the electronics) is, you are already overbudget with furious customers waving pitchforks, torches and clubs knocking at your door.

Lambda World 2017 – Unconference – Category Theory Crash Course

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Here I am in Cadiz to attend 2017 edition of Lambda World. Cadiz is really a nice frame for this conference and the weather is so good that it seems to be back at vacation time instead then in mid-Autumn.

Anyway, on this morning I attended the “unconference”, a sort of informal prelude to the real conference. After the first, planned talk, people could register to present their talk and other attendants could vote their preferred talk. This yield two interesting talk – one on declarative testing and the other one on category theory. Here is what I learned from the last one, by Rùnar Bjarnason.

First, this is algebra stuff, really. Not the kind of ‘a+b’ you do in mid schools, but the high order stuff you do later when you study groups, monoids and the likes.

How does these concepts applies to practical programming still eludes me, but I sense that something is there waiting for my brain to grasp it.

Basically an abstract algebra of function wants to answer to the following questions – what functions do? And what can we say about them? Here we go, all mistakes, errors, inconsistencies are just mine, blame me not Rùnar.

A Category is defined by objects and arrows and how we can compose those arrows.

If you consider language types as objects then functions can be thought as arrows from an object to another object:

Some types: A, B, C.
Some functions f, g.

f: A => B
g: B => C

a composite functions is formed by invoking a function on the result of another one:

g compose f = (x => g(f(x)))

As long as types line up we can concatenate function composition.

If a function links an object to itself (in the example a type to itself), this is the identity function:

identity function: identity[A] : A=>A
identity compose f = f
f compose identity = f

Sometime composition is expressed with a dot. An interesting property is that composition is associative:

h.g.f = (h.g).f = h.(g.f)

(so no need for parenthesis).

In Scala you have a category for functions and types, but also for types and inheritance (subtyping). This category is defined as follows:

Objects: Scala Types: A, B, C…
Arrows: Subtype relations: A <: B, B <: C…
Composition: [ A <: B, B <: C ] => A <: C
Associativity: A <: B <: C (no need for parenthesis)
Identity: A <: A

Another example of category is the the pre-order (<=). Objects are instances of a Type on which <= binary relationship is defined:

Objects a, b, c…
Arrows: a <= b, b <= c
Transitivity: <=
Identity: a <= a

E.g. natural numbers are a category for preorder.

Monoids are categories with one object. This object, by convention, is written as ‘*’. All arrows links * to itself. In a turn of notation I’ll use the symbol |+| for composition and ‘mzero’ for identity.

Objects: *
Arrows: f, g, h: * -> *
associativity
identity: * -> *
mzero |+| a = a
a |+| mzero = a

Object is just an anchor, to hold arrows.

Category of preorders:
A preorder is a set with <=
Objects: all the preorders (Scala integers)
arrows: Homomorphisms on preorders

[NdM: at this point things start to be complicated and I have likely lost the speaker. What follows are likely to be glimpses of his talk… the parts I got]

Monotonic functions.
e.g. string length : _.length is a monotonic function.
Preserves relationship:
x <= y then h(x) <= h(y)

Category of monoids:
Objects: all the monoids
Arrows: monoid homomorphism
h( x |+| y) == h(x) |+| h(y)

Category of categories
Objects: all the categories
Arrows: category homomorphism F
F( f . g ) = F(f) . F(g) e.g. _map( f compose g ) = _.map(f) compose _.map(g)
these are functors.

Given a category C, there is a category of endofunctors on C
endofunctor F[_]
Objects: functors
arrows: functor homomorphism
h: F[A] => G[A]
h compose _.map(f) = _.map(f) compose h

trait ~>[F[_].G[_]] { def apply[A](a: F[A]) : G[A] }

A monad on C is an endofunctor M[_] together with two natural transformations:

pure[A] : A=>M[A]
join[A] : M[M[A]] => M[A]

A monad is a kind of category:
M[M[M[A]]] : can be associated as we want, preserving the result.

A monad is a monoid in an endofunctor category.

[And with this truth the brief talk is over]

Reacting to the movies: reactive systems and the lessons of narrative.

Posted on by max Leave a Comment on Reacting to the movies: reactive systems and the lessons of narrative.

Another Reactive Summit 2016 talk. Speaker was Steve Moore – IBM story strategist. My comments at the end. (When writing about heros I used “she” as pronoun, if you are sensitive to this use sed s/she/she/he/g).

Thinking differently helps you gaining new perspectives on your work. That’s the work for story strategist and this is the idea behind this talk. Finding analogies between reactive systems and movies.

Stories behave like reactive systems you can see three analogies:

  1. hero as scalability,
  2. plot as resilience,
  3. genre as elasticity.

Scalability : heroes are under constant pressure to scale. Scaling is usually achieved via some resource. The best resource a hero has at her disposal is a new understanding of the world.

There are three form of escaping in the scaling up of the hero.

Escape the skin. The hero is trying to escape the fake reality she’s living to reach the real and more complex reality behind. Paranoia and questioning foundation of the system are the mean to achieve this escape. This can be summed by the question: “Are you being paranoid enough”?

Escape stoicisms. The hero lives in a simple world that avoids emotional entanglement. The hero takes more responsibility that she think she can bear. Scalability and generosity go hand in hand. Question: “Are you being generous enough?”

Escape the story. The hero lives in a world she wants to escape. This is different from the deception of the escape the skin (first form). The question is “are you being patient enough?” Sometime you need to take the time to observe and understand.

plotasresilience

The plot directs the hero where is needed by the story. Stories are all about failures – the plot is just a sequence of failures that get the hero further and further away from his goal.

Heroes never ever give up. Likeability is not required by heros.

3 ways the hero fails:

Lure of the invisible. This is a kind of accumulation of something that is somewhat hiding. Can be a log file that is increasing without anyone noticing, or some peril that grows unnoticed over time. “What is the system accumulating?” This is not an issue until it is. This way of failure includes also non technical accumulation such as management attention, customer complaints…

Lure of inclination. The hero would like something, but she has to put away that to pursue his goal. Following inclinations may get the hero in hot water, but in the end brings benefits.  “Can you make the system fragile?” [NdM sorry, it is likely I missed something in the translation]

Lure of independence. The story has a lot of moving parts apparently in an unrelated way. Using micro services the system may be better managed and written by smaller teams. “Could coordination be fun?”

Coordination among the teams is needed.

Genre is elasticity.

Elasticity means that response does not change when workload changes or accidents happen. What defines the genre is a set of rules. Genres are categories of antagonists. An antagonist puts strain on the hero.

Three different kinds of genres:

Human v. Human. The analogy on system is threats posed by human hostile actions. “Can you change the game?”

Human v. Nature. The natural world is challenging the hero. Natural antagonists are simple, unbeatable and unchangeable. These stories are about survival. If you live, you deserve to live. “do you deserve to live?” If the system were in danger do these people would do something to protect it?

Human vs self. Hero is also the antagonist. The hero doesn’t want to push away something that is abundant in his life. This kind of abundance forces the hero to questioning the reality “can you endure abundance?” We need to transform ourselves and the system at the same time.

This is the only way the system has to survive.

This talk was very interesting even if not very practical or even theorical. I like the idea that adding word to vocabulary and concepts to the programmer resources may help to devise better system or to enhance the understanding of the system. It makes food for lateral thinking (lateral food?).

One of the first person we met at the luminaries barbecue the day we arrived, had the following job title “Chief Storyteller”. Maybe this is a sign of times.

A Journey to Modern Apps Through Containers and Microservices

Posted on by max Leave a Comment on A Journey to Modern Apps Through Containers and Microservices

Here I am, warped through jet leg in the sunny Texas. Austin. The reason is to attend the Reactive summit 2016. Two days crammed with interesting speeches by luminaries of the reactive community. I’ll try my best to provide a brief summary of each talk I will attend, reportin faithfully what the speaker said and adding my thoughts. If you know about reactive systems and you read something unreasonable it is likely I missed something in the process.

This talk is by Edward Hwu – mesosphere.

(I’ll leave out the quote about Justin Bibier being the reason for having reactive systems… in part because I missed something).

If you compare the number of Internet users today with the same number a few years ago you will notice that it has increased manyfolds. Almost everyone owns a smartphone and almost everyone use it to interact with Internet. Regardless companies that make business over Internet need to be sure to be able to comply with these large number of interactions in order not to lose customers and business opportunities.

Because of the increase in the number of Internet users an architecture shift was needed: distributed systems are the only way for industry to cope with this growth.

Modern applications that runs websites are made from open source software, VMs have been replaced by containers that have shorter startup time and are lighter to run.

Additionally containers provide the means for elasticity – they can be used just for the time needed for run the service – and allows for fast and frequent releases.

Data services provide connection and persistence.

Mesos is the core component of DC/OS whose purpose is to manage and abstract data center in the same way a traditional OS abstracts from hardware. Where the traditional OS operates on a single computer, DC/OS operates on the whole data center.

Operations such as installing Kafka or Spark on a cluster, with the proper configuration and replica count, is handled with a single click. You can find services ready to install and use on the open universe app store. As of today you can find 40+ apps

Here’s my thought. Well DC/OS seems to be the real shift in distributed systems. My impression is that the era of custom made software to handle the specific aspects of distributed system (such as load balancing, or provisioning) is going the way of the dodo since structured and standard solutions are being developed.

Having the facility to handle the whole datacenter with simple commands is possibly going to revolutionize datacenter in the same way docker revolutionized application deployment. The latter avoiding developer to keep track manually of all the dependencies and providing a sensible installer, the former avoiding manual install on multiple node with configuration twiddling.

Maybe this technology is a bit ahead – I’m saying this because I found no trace of the universe app store on the internet (maybe I didn’t search enough), but for sure is very promising and being open source, free for all, is something that for sure will provide widespread adoption.

Comparisons

Posted on by max 6 Comments on Comparisons

Recently I had to spend some time trying to adapt my imperative/OO background to a piece of code I need to write in functional paradigm. The problem is quite simple and can be described briefly. You have a collection of pairs containing an id and a quantity. When a new pair arrives you have to add the quantity to the pair with the same id in the collection, if exists, otherwise you have to add the pair to the collection.

Functional programming is based on three principles (as seen from an OO programmer) – variables are never modified once assigned, no side-effects (at least, avoid them as much as possible), no loops – work on collections with collective functions. Well maybe I missed something like monad composition, but that’s enough for this post.

Thanks to a coworker I wrote my Scala code that follows all the aforementioned principles and is quite elegant as well. It relies on the “partition” function that transforms (in a functional fashion) a collection into two collections containing the elements of the first one partitioned according to a given criteria. The criteria is the equality of the id so that I find the element with the same id if it exists, or just an empty collection if it doesn’t.

Here’s the code:

    type Collection = List[Data]

    def merge( collection : Collection, data : Data ) : Collection = {
        val result = collection.partition( _.id == data.id )
        result match {
            case (List(),other) =>
                data :: other
            case (List(old),other) =>
                Data( data.id, data.quantity+old.quantity ) :: other
        }
    }

Yes, I could have written more concisely, but that would have been too much write-only for me to be comfortable with.

Once the pleasant feeling of elegance wore off a bit I wondered what is the cost of this approach. Each time you invoke merge the collection is rebuilt and, unless the compile optimizer be very clever, also each list item is cloned and the old one goes to garbage recycling.

Partitioning scans and rebuild, but since I’m using an immutable collection, also adding an item to an existing list causes a new list to be generated.

Performance matters in some 20% of your code, so it could acceptable to sacrifice performance in order to get a higher abstraction level and thus a higher coding speed. But then I wonder what about premature pessimization? Premature pessimization, at least in context where I read the them, means the widespread adoption of idioms that lead to worse performances (the case was for C++ use of pre or post increment operator). Premature pessimization may cause the application to run generally slower and makes more difficult to spot and optimize the cause.

This triggered the question – how is language idiomatic approach impacts on performances?

To answer the question I started coding the same problem in different languages.

I started from my language of choice – C++. In this language it is likely you approach a similar problem by using std::vector. This is the preferred collection and the recommended one. Source is like this:

typedef std::vector<Data> Collection;

void
merge( Collection& collection, Data data )
{
    auto iter = std::find_if( collection.begin(),
                              collection.end(),
                              [&]( Data const& probe ){ return probe.id == data.id; } );
    if( iter == collection.end() )
    {
        collection.push_back( data );
    }
    else
    {
        iter->quantity += data.quantity;
    }
}

Code is slightly longer (consider that in C++ I prefer opening brace on a line alone, while in Scala “they” forced me to have opening braces at the end of the statement line). Having mutable collections doesn’t require to warp your mind around data to find which aggregate function could transform your input data into the desired output – just find what you are looking for and change it. Seems simpler to explain to a child.

Then I turned to Java. I’m not so fond of this language, but it is quite popular and has a comprehensive set of standard libraries that really allow you to tackle every problem confidently. Not sure what a Java programmer would consider idiomatic, so I staid traditional and went for a generic List. The code follows:

    static class Data
    {
        public int id;
        public int quantity;
    }

    private static void merge( List<Data> collection, Data data )
    {
        ListIterator<Data> iterator = collection.listIterator();
        while( iterator.hasNext() )
        {
            Data probe = iterator.next();
            if( probe.id == data.id )
            {
                probe.quantity += data.quantity;
                return;
            }
        }
        collection.add( 0, data );
    }

I’m not sure why the inner class Data needs to be declared static, but it seems that otherwise the instance has a reference to the outer class instance. Anyway – code is decidedly more complex. There is no function similar to C++ find_if nor to Scala partition. The loop is simple, but it offers some chances to add bugs to your code. Anyway explaining the code is straightforward once the iterator concept is clear.

Eventually I wrote a version in C. This language is hampered by the lack of basic standard library – beside some functions on strings and files you have nothing. This could have been fine in the 70s, but today is a serious problem. Yes there are non-standard libraries providing all the needed functionalities, you have plenty of them, gazillions of them, all incompatible. Once you chose one you are locked in… Well clearly C shows the signs of age. So I write my own single linked list implementation:

struct Data
{
    int id;
    int quantity;
};

struct ListNode
{
    struct ListNode* next;
    struct Data data;
};

typedef struct ListNode* List;

void merge( List* list, struct Data data )
{
    for( struct ListNode* scan = *list; scan != NULL; scan = scan->next )
    {
        if( scan->data.id == data.id )
        {
            scan->data.quantity += data.quantity;
            return;
        }
    }
    pushFront( list, data );
}

Note that once cleaned of braces, merge function is shorter in C than in Java! This is a hint that Java is possibly verbose.

I just wrote here the merge function. The rest of the sources is not relevant for this post, but it basically consists in parsing the command line (string to int conversion), getting some random numbers and getting the time. The simplest frameworks for this operation are those based on the JVM. The most complex is C++ – it allows a wide range of configuration (random and time), but I had to look up on internet how to do it and… I am afraid I wouldn’t know how to exploit the greater range of options. Well, in my career as a programmer (some 30+ years counting since when I started coding) I think I never had the need to use a specific random number generator, or some clock different from a “SystemTimeMillis” or Wall Clock Time. I don’t mean that because of this no one should ask for greater flexibility, but that I find daunting that every programmer should pay this price because there is case for using a non default RNG.

Anyway back to my test. In the following table I reported the results.

C++ Scala Java C C++
vector list
time (ms) 170,75 11562,45 2230,05 718,75 710,9
lines 81 35 69 112 81

Times have been taken performing 100000 insertions with max id 10000. The test has been repeated 20 times and the results have been averaged in the table. The difference in timing between C++ and Scala is dramatic – with the first faster about 70 times the latter. Wildly extrapolating you can say that if you code in C++ you need 1/70 of the hardware you need to run Scala… there’s no surprise (still guessing wildly) that IBM backs this one.

Java is about 5 times faster than Scala. I’m told this is more or less expected and possibly it is something you may be willing to pay for higher level.

In the last column I reported the results for a version of the C++ code employing std::list for a more fair comparison (all the other implementations use a list after all). What I didn’t expected was that C++ is faster (even if slightly) than C despite using the same data structure. It is likely because of some template magic.

The other interesting value I wrote in the table is the number of lines (total, not just the merge function) of each implementation. From my studies (that now are quite aged) I remember that some researches reported that the speed of software development (writing, testing and debugging), stated as lines of code per unit of time, is the same regardless of the language. I’m starting having some doubt because my productivity in Scala is quite low if compared with other languages, but … ipse dixit.

Let’s say that you spend 1 for the Scala program, then you would pay 2.31 for C++, 1.97 for Java and 3.20 for C.

Wildly extrapolating again you could draw a formula to decide whether it is better to code in C++ or in Scala. Be H the cost of the CPU and hardware to comfortably run the C++ program. Be C the cost of writing the program in Scala. So the total cost of the project is:

(C++) H+C×2.31

(Scala) 68×H+C

(C++) > (Scala) ⇒ H+C×2.31 > 68×H+C ⇒ C×1.31 >67×H ⇒ C > 51.14×H

That is, you’d better using Scala when the cost of the hardware you want to use will not exceed the cost of Scala development by a factor of 50. If hardware is going to cost more, then you’d better use C++.

Beside of being a wild guess, this also assumes that there is no hardware constraint and that you can easily scale the hardware of the platform.

(Thanks to Andrea for pointing out my mistake in inequality)