Here now and then, as a programmer, you have a hard time finding a good name for something. That thingy thing that eludes your wit and skill to name things. And that’s bad because it is hard to reason about unnamed entities. Symbols are deeply embedded in our brainwork and the right word has quite a power to drive thoughts efficiently and reliably.
Absolutely
As a programmer, you will be quite familiar with the abs function. I thought I was. In fact it was filed in my brain drawer with the absolute mathematical function. It takes a number and provides an always positive or zero result. Right?
Well, mostly. This morning, while typing in the scala REPL:
scala> Math.abs(Int.MinValue) res0: Int = -2147483648
If the free lunch is over, where can we eat for free?
In the first part of this blog post, I wrote about why FP is mandatory for programmers that work on modern software projects. Summing it up, it was like this – modern hardware scales up by adding cores, therefore, if software wants to scale up and well, then it has to do it by going concurrent, and FP is a good choice to deal with concurrent programming issues.
In this post, I’ll write about some differences between traditional and FP programming and I’ll present one more reason about why FP is a good match with parallel programming.
You can write bad code in any language. That’s a fact. So there’s no magic in functional programming to turn a lame programmer in a brilliant one. On the other hand, you can write pretty good code in the functional paradigm by sticking with the tools provided by functional languages.
So, let’s have a look at this FP thing… First, it is evident that this is not your parent’s FP – map, filter, fold… Just a few of the most recurring spells you’d see in a Scala source and none of them was in the notes I jotted down during my University courses. Even looking at my LISP textbook I couldn’t find any reference to the map operation. (Just to help those with no FP background – map (transform in C++) is an operation that applies a transformation to each item in a collection. In other words, if you have a function that turns apples into pears, then – using map – you can turn your array of apples into an array of pears.)
Read moreIf the free lunch is over, where can we eat for free?
Lambda World 2018
Back in the XVI century, the Flota de Indias based in Cadiz set out to Americas carrying tools, books, and settlers and carried back goods, from the new world.
In much the same way I went to Cadiz and set off for the brave new world of functional programming with the intent of carrying back news and techniques for my daily job.
Cadiz is an awesome and is a wonderful frame for one the best conference I attended in 2017 and 2018 – Lambda World.
As usual, I took a lot of notes and possibly soon or later they will land here. I plan to prepare a short summary with information about the conference and briefs of the speech I attended, much I did for Scala Italy. In the meantime, I warmly suggest these awesome notes (by Yulia). Stay tuned.
Scala Italy 2018
At the end of September, my employer sent me to the awesome city of Florence to attend the Scala Italy conference. Thanks to this patronage, this is the third year I’m able to be at the conference of the Italian Scala Community.
For the first time this year, the conference lasted two days with two distinct tracks – talks and workshops. The choice of one working and one non-working day can be a bit odd and likely a symptom of some ambivalence about the target audience. To keep the conference for Scala enthusiasts attending in their spare time or to move it to the professional realm, that’s the question. Given that the ticket price wasn’t very light on pockets, maybe half of the decision has already been made.
I dutifully took notes during all the talks so I’d like to prepare some posts with my notes. I noted that after I did so for ++it 2018 (Channels are Useful, not only for Water, Zero Allocation and No Type Erasure Futures, Time Travel Debug, ++it 2018 – Coroutines – coming soon… ?, ++it 2018 – Key Notes) conference organization put all the conference videos online. I’m pretty sure the same is going to happen for Scala Italy 2018 as well. So I’ll wait for your feedbacks before investing the time needed for writing all the blogs.
Is Functional Programming functional to programming?
Some 25 years ago I took my class of advanced programming which included exotic paradigms such as logic and functional programming.
As most of the content of that course, FP went into a seldom-if-ever open drawer during my professional career.
Simply I never saw the needs to use Lisp or had the luxury to afford never changing values.
A few years ago (well not that few – just checked it was 2005) I read on DDJ that the free lunch was over. That has nothing to do with the charitable food bank, but it marked a fundamental twist in the computer performance evolution. Something that would have a strong impact on how software was going to be designed, written and tested.
Until 2005 no matter how your code was badly written and underperforming. You were assured that for free, just waiting for the next hardware iteration, your code would have run faster (bugs included)! This is like deflation in economics – why should I invest my money if keeping them in the matters for a year they increase their value?
Read moreIs Functional Programming functional to programming?
++it18 – Channels are Useful, not only for Water
High performance computing made quite some progress lately. Maybe you heard about the reactive manifesto (you should if you are a reader of this blog ;-)), if not, it is an interesting readying … if nothing else as a conversational icebreaker.
Basically the manifesto preaches for systems more responsive and reliable by reacting to requests and dusting it into smaller requests processed by smaller and autonomous systems. This is nothing new, but quite far from traditional processing where single monolitic application took care of requests from reception to response.
There are several programming patterns useful for implementing a reactive system, channels (and streams which are quite the same thing) are one of them. A channel allows you to define a data flow with inputs, computation units, merge, split and collector of information coming in sequence into your system. Once the dataflow is defined, the data flows into it and results come out from the other end.
In this talk Felix Petriconi describes his implementation of channels for C++.
Protecting you from danger
One of the interesting features of Scala is the trait construct. In Java you have interfaces, a stripped down class that lets you express the requirements for classes that implement it to have a given … interface. Just to keep this from an OO perspective – in a certain system all MovableObjects, must expose an interface to retrieve, say, position, speed and acceleration:
interface MovableObject {
Point getPosition();
Vector getSpeed();
Vector getAcceleration();
}
class Rocket implements MovableObject {
public Point getPosition() {
return m_position;
}
public Vector getSpeed() {
return m_speed;
}
public Vector getAcceleration() {
return m_acceleration;
}
private Point m_position;
private Vector m_speed;
private Vector m_acceleration;
}
In Java the interface has no implementation part, just a list of methods that heir(s) must implement. This is a way to mitigate the lack of multiple inheritance in the Java language – a class can inherit at most from a single base class, but can implement as many interfaces as needed.
Scala traits are different in the sense they can provide an implementation part and fields. Now that I read the wikipedia definition, it looks like that traits are a bit more mathematically sound than interfaces (that’s what you can expect in functional language). Anyway, in plain English, traits are very close to C++ base classes (with some additions that is stuff for another blog post) in the sense that you can both require interface and provide common functionality.
++it18 – Zero Allocation and No Type Erasure Futures
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.
Read more++it18 – Zero Allocation and No Type Erasure Futures
++it18 – Time Travel Debug
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:
- Linux:
- Windows:
- Time Travel Debugging (TTD)
- Java:
- JavaScript
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.