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I have some big mysql databases with data for calculations and some parts where I need to get data from external websites.
I used python to do the whole thing until now, but what shall I say: its not a speedster.
Now I'm thinking about mixing Python with C++ using Boost::Python and Python C API.
The question I've got now is: what is the better way to get some speed.
Shall I extend python with some c++ code or shall I embedd python code into a c++ programm?
I will get fore sure some speed increment using c++ code for the calculating parts and I think that calling the Python interpreter inside of an C-application will not be better, because the python interpreter will run the whole time. And I must wrap things python-libraries like mysqldb or urllib3 to have a nice way to work inside c++.
So what whould you suggest is the better way to go: extending or embedding?
( I love the python language, but I'm also familiar with c++ and respect it for speed )
Update:
So I switched some parts from python to c++ and used multi threading (real one) in my c modules and my programm now needs instead of 7 hours 30 minutes :))))
In principle, I agree with the first two answers. Anything coming from disk or across a network connection is likely to be a bigger bottleneck than the application.
All the research of the last 50 years indicates that people often have inaccurate intuition about system performance issues. So IMHO, you really need to gather some evidence, by measuring what is actually happening, then chose a solution based on that evidence.
To try to confirm what is causing the slow performance, measure the system and user time of your application (e.g time python prog.py), and measure the load on the machine.
It the application is maxing-out the CPU, and most of that time is spent in the application (user time), then there may be a case for using a more effective technology for the application.
But if the CPU is not maxed, or the application spends most of its time in the system (system time), and not in the application (user time), then it is unlikely that changing the application programming technology will help significantly. (This is an example of Amdahl's Law http://en.wikipedia.org/wiki/Amdahl%27s_law)
You may also need to measure the performance of your database server, and maybe network connection, to identify the source of the bottle neck, but start with the easiest part.
In my opinion, in your case it makes no sense to embed Python in C++, while the reverse could be beneficial.
In most of programs, the performance problems are very localized, which means that you should rewrite the problematic code in C++ only where it makes sense, leaving Python for the rest.
This gives you the best of both world: the speed of C++ where you need it, the ease of use and flexibility of Python everywhere else. What is also great is that you can do this process step by step, replacing the slow code paths by the by, leaving you always with the whole application in an usable (and testable!) state.
The reverse wouldn't make sense: you'd have to rewrite almost all the code, sacrificing the flexibility of the Python structure.
Still, as always when talking about performance, before acting measure: if your bottleneck is not CPU/memory bound switching to C++ isn't likely to produce much advantages.
I have embedded Lua in a C++ application. I need to be able to kill rogue (i.e. badly written scripts) from hogging resources.
I know I will not be able to cater for EVERY type of condition that causes a script to run indefinitely, so for now, I am only looking at the straightforward Lua side (i.e. scripting side problems).
I also know that this question has been asked (in various guises) here on SO. Probably the reason why it is constantly being re-asked is that as yet, no one has provided a few lines of code to show how the timeout (for the simple cases like the one I described above), may actually be implemented in working code - rather than talking in generalities, about how it may be implemented.
If anyone has actually implemented this type of functionality in a C++ with embedded Lua application, I (as well as many other people - I'm sure), will be very grateful for a little snippet that shows:
How a timeout can be set (in the C++ side) before running a Lua script
How to raise the timeout event/error (C++ /Lua?)
How to handle the error event/exception (C++ side)
Such a snippet (even pseudocode) would be VERY, VERY useful indeed
You need to address this with a combination of techniques. First, you need to establish a suitable sandbox for the untrusted scripts, with an environment that provides only those global variables and functions that are safe and needed. Second, you need to provide for limitations on memory and CPU usage. Third, you need to explicitly refuse to load pre-compiled bytecode from untrusted sources.
The first point is straightforward to address. There is a fair amount of discussion of sandboxing Lua available at the Lua users wiki, on the mailing list, and here at SO. You are almost certainly already doing this part if you are aware that some scripts are more trusted than others.
The second point is question you are asking. I'll come back to that in a moment.
The third point has been discussed at the mailing list, but may not have been made very clearly in other media. It has turned out that there are a number of vulnerabilities in the Lua core that are difficult or impossible to address, but which depend on "incorrect" bytecode to exercise. That is, they cannot be exercised from Lua source code, only from pre-compiled and carefully patched byte code. It is straightforward to write a loader that refuses to load any binary bytecode at all.
With those points out of the way, that leaves the question of a denial of service attack either through CPU consumption, memory consumption, or both. First, the bad news. There are no perfect techniques to prevent this. That said, one of the most reliable approaches is to push the Lua interpreter into a separate process and use your platform's security and quota features to limit the capabilities of that process. In the worst case, the run-away process can be killed, with no harm done to the main application. That technique is used by recent versions of Firefox to contain the side-effects of bugs in plugins, so it isn't necessarily as crazy an idea as it sounds.
One interesting complete example is the Lua Live Demo. This is a web page where you can enter Lua sample code, execute it on the server, and see the results. Since the scripts can be entered anonymously from anywhere, they are clearly untrusted. This web application appears to be as secure as can be asked for. Its source kit is available for download from one of the authors of Lua.
Snippet is not a proper use of terminology for what an implementation of this functionality would entail, and that is why you have not seen one. You could use debug hooks to provide callbacks during execution of Lua code. However, interrupting that process after a timeout is non-trivial and dependent upon your specific architecture.
You could consider using a longjmp to a jump buffer set just prior to the lua_call or lua_pcall after catching a time out in a luaHook. Then close that Lua context and handle the exception. The timeout could be implemented numerous ways and you likely already have something in mind that is used elsewhere in your project.
The best way to accomplish this task is to run the interpreter in a separate process. Then use the provided operating system facilities to control the child process. Please refer to RBerteig's excellent answer for more information on that approach.
A very naive and simple, but all-lua, method of doing it, is
-- Limit may be in the millions range depending on your needs
setfenv(code,sandbox)
pcall (function() debug.sethook(
function() error ("Timeout!") end,"", limit)
code()
debug.sethook()
end);
I expect you can achieve the same through the C API.
However, there's a good number of problems with this method. Set the limit too low, and it can't do its job. Too high, and it's not really effective. (Can the chunk get run repeatedly?) Allow the code to call a function that blocks for a significant amount of time, and the above is meaningless. Allow it to do any kind of pcall, and it can trap the error on its own. And whatever other problems I haven't thought of yet. Here I'm also plain ignoring the warnings against using the debug library for anything (besides debugging).
Thus, if you want it reliable, you should probably go with RB's solution.
I expect it will work quite well against accidental infinite loops, the kind that beginning lua programmers are so fond of :P
For memory overuse, you could do the same with a function checking for increases in collectgarbage("count") at far smaller intervals; you'd have to merge them to get both.
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It is said that Blitz++ provides near-Fortran performance.
Does Fortran actually tend to be faster than regular C++ for equivalent tasks?
What about other HL languages of exceptional runtime performance? I've heard of a few languages suprassing C++ for certain tasks... Objective Caml, Java, D...
I guess GC can make much code faster, because it removes the need for excessive copying around the stack? (assuming the code is not written for performance)
I am asking out of curiosity -- I always assumed C++ is pretty much unbeatable barring expert ASM coding.
Fortran is faster and almost always better than C++ for purely numerical code. There are many reasons why Fortran is faster. It is the oldest compiled language (a lot of knowledge in optimizing compilers). It is still THE language for numerical computations, so many compiler vendors make a living of selling optimized compilers. There are also other, more technical reasons. Fortran (well, at least Fortran77) does not have pointers, and thus, does not have the aliasing problems, which plague the C/C++ languages in that domain. Many high performance libraries are still coded in Fortran, with a long (> 30 years) history. Neither C or C++ have any good array constructs (C is too low level, C++ has as many array libraries as compilers on the planet, which are all incompatible with each other, thus preventing a pool of well tested, fast code).
Whether fortran is faster than c++ is a matter of discussion. Some say yes, some say no; I won't go into that. It depends on the compiler, the architecture you're running it on, the implementation of the algorithm ... etc.
Where fortran does have a big advantage over C is the time it takes you to implement those algorithms. And that makes it extremely well suited for any kind of numerical computing. I'll state just a few obvious advantages over C:
1-based array indexing (tremendously helpful when implementing larger models, and you don't have to think about it, but just FORmula TRANslate
has a power operator (**) (God, whose idea was that a power function will do ? Instead of an operator?!)
it has, I'd say the best support for multidimensional arrays of all the languages in the current market (and it doesn't seem that's gonna change so soon) - A(1,2) just like in math
not to mention avoiding the loops - A=B*C multiplies the arrays (almost like matlab syntax with compiled speed)
it has parallelism features built into the language (check the new standard on this one)
very easily connectible with languages like C, python, so you can make your heavy duty calculations in fortran, while .. whatever ... in the language of your choice, if you feel so inclined
completely backward compatible (since whole F77 is a subset of F90) so you have whole century of coding at your disposal
very very portable (this might not work for some compiler extensions, but in general it works like a charm)
problem oriented solving community (since fortran users are usually not cs, but math, phy, engineers ... people with no programming, but rather problem solving experience whose knowledge about your problem can be very helpful)
Can't think of anything else off the top of my head right now, so this will have to do.
What Blitz++ is competing against is not so much the Fortran language, but the man-centuries of work going into Fortran math libraries. To some extent the language helps: an older language has had a lot more time to get optimizing compilers (and , let's face it, C++ is one of the most complex languages). On the other hand, high level C++ libraries like Blitz++ and uBLAS allows you to state your intentions more clearly than relatively low-level Fortran code, and allows for whole new classes of compile-time optimizations.
However, using any library effectively all the time requires developers to be well acquainted with the language, the library and the mathematics. You can usually get faster code by improving any one of the three...
FORTAN is typically faster than C++ for array processing because of the different ways the languages implement arrays - FORTRAN doesn't allow aliasing of array elements, whereas C++ does. This makes the FORTRAN compilers job easier. Also, FORTRAN has many very mature mathematical libraries which have been worked on for nearly 50 years - C++ has not been around that long!
This will depend a lot on the compiler, programmers, whether it has gc and can vary too much. If it is compiled directly to machine code then expect to have better performance than interpreted most of the time but there is a finite amount of optimization possible before you have asm speed anyway.
If someone said fortran was slightly faster would you code a new project in that anyway?
the thing with c++ is that it is very close to the hardware level. In fact, you can program at the hardware level (via assembly blocks). In general, c++ compilers do a pretty good job at optimisations (for a huge speed boost, enable "Link Time Code Generation" to allow the inlining of functions between different cpp files), but if you know the hardware and have the know-how, you can write a few functions in assembly that work even faster (though sometimes, you just can't beat the compiler).
You can also implement you're own memory managers (which is something a lot of other high level languages don't allow), thus you can customize them for your specific task (maybe most allocations will be 32 bytes or less, then you can just have a giant list of 32-byte buffers that you can allocate/deallocate in O(1) time). I believe that c++ CAN beat any other language, as long as you fully understand the compiler and the hardware that you are using. The majority of it comes down to what algorithms you use more than anything else.
You must be using some odd managed XML parser as you load this page then. :)
We continously profile code and the gain is consistently (and this is not naive C++, it is just modern C++ with boos). It consistensly paves any CLR implementation by at least 2x and often by 5x or more. A bit better than Java days when it was around 20x times faster but you can still find good instances and simply eliminate all the System.Object bloat and clearly beat it to a pulp.
One thing managed devs don't get is that the hardware architecture is against any scaling of VM and object root aproaches. You have to see it to believe it, hang on, fire up a browser and go to a 'thin' VM like Silverlight. You'll be schocked how slow and CPU hungry it is.
Two, kick of a database app for any performance, yes managed vs native db.
It's usually the algorithm not the language that determines the performance ballpark that you will end up in.
Within that ballpark, optimising compilers can usually produce better code than most assembly coders.
Premature optimisation is the root of all evil
This may be the "common knowledge" that everyone can parrot, but I submit that's probably because it's correct. I await concrete evidence to the contrary.
D can sometimes be faster than C++ in practical applications, largely because the presence of garbage collection helps avoid the overhead of RAII and reference counting when using smart pointers. For programs that allocate large amounts of small objects with non-trivial lifecycles, garbage collection can be faster than C++-style memory management. Also, D's builtin arrays allow the compiler to perform better optimizations in some cases than C++'s STL vector, which the compiler doesn't understand. Furthermore, D2 supports immutable data and pure function annotations, which recent versions of DMD2 optimize based on. Walter Bright, D's creator, wrote a JavaScript interpreter in both D and C++, and according to him, the D version is faster.
C# is much faster than C++ - in C# I can write an XML parser and data processor in a tenth the time it takes me to write it C++.
Oh, did you mean execution speed?
Even then, if you take the time from the first line of code written to the end of the first execution of the code, C# is still probably faster than C++.
This is a very interesting article about converting a C++ program to C# and the effort required to make the C++ faster than the C#.
So, if you take development speed into account, almost anything beats C++.
OK, to address tht OP's runtime only performance requirement: It's not the langauge, it's the implementation of the language that determines the runtime performance. I could write a C++ compiler that produces the slowest code imaginable, but it's still C++. It is also theoretically possible to write a compiler for Java that targets IA32 instructions rather than the Java VM byte codes, giving a runtime speed boost.
The performance of your code will depend on the fit between the strengths of the language and the requirements of the code. For example, a program that does lots of memory allocation / deallocation will perform badly in a naive C++ program (i.e. use the default memory allocator) since the C++ memory allocation strategy is too generalised, whereas C#'s GC based allocator can perform better (as the above link shows). String manipulation is slow in C++ but quick in languages like php, perl, etc.
It all depends on the compiler, take for example the Stalin Scheme compiler, it beats almost all languages in the Debian micro benchmark suite, but do they mention anything about compile times?
No, I suspect (I have not used Stalin before) compiling for benchmarks (iow all optimizations at maximum effort levels) takes a jolly long time for anything but the smallest pieces of code.
if the code is not written for performance then C# is faster than C++.
A necessary disclaimer: All benchmarks are evil.
Here's benchmarks that in favour of C++.
The above two links show that we can find cases where C++ is faster than C# and vice versa.
Performance of a compiled language is a useless concept: What's important is the quality of the compiler, ie what optimizations it is able to apply. For example, often - but not always - the Intel C++ compiler produces better performing code than g++. So how do you measure the performance of C++?
Where language semantics come in is how easy it is for the programmer to get the compiler to create optimal output. For example, it's often easier to parallelize Fortran code than C code, which is why Fortran is still heavily used for high-performance computation (eg climate simulations).
As the question and some of the answers mentioned assembler: the same is true here, it's just another compiled language and thus not inherently 'faster'. The difference between assembler and other languages is that the programmer - who ideally has absolute knowledge about the program - is responsible for all of the optimizations instead of delegating some of them to the 'dumb' compiler.
Eg function calls in assembler may use registers to pass arguments and don't need to create unnecessary stack frames, but a good compiler can do this as well (think inlining or fastcall). The downside of using assembler is that better performing algorithms are harder to implement (think linear search vs. binary seach, hashtable lookup, ...).
Doing much better than C++ is mostly going to be about making the compiler understand what the programmer means. An example of this might be an instance where a compiler of any language infers that a region of code is independent of its inputs and just computes the result value at compile time.
Another example of this is how C# produces some very high performance code simply because the compiler knows what particular incantations 'mean' and can cleverly use the implementation that produces the highest performance, where a transliteration of the same program into C++ results in needless alloc/delete cycles (hidden by templates) because the compiler is handling the general case instead of the particular case this piece of code is giving.
A final example might be in the Brook/Cuda adaptations of C designed for exotic hardware that isn't so exotic anymore. The language supports the exact primitives (kernel functions) that map to the non von-neuman hardware being compiled for.
Is that why you are using a managed browser? Because it is faster. Or managed OS because it is faster. Nah, hang on, it is the SQL database.. Wait, it must be the game you are playing. Stop, there must be a piece of numerical code Java adn Csharp frankly are useless with. BTW, you have to check what your VM is written it to slag the root language and say it is slow.
What a misconecption, but hey show me a fast managed app so we can all have a laugh. VS? OpenOffice?
Ahh... The good old question - which compiler makes faster code?
It only matters in code that actually spends much time at the bottom of the call stack, i.e. hot spots that don't contain function calls, such as matrix inversion, etc.
(Implied by 1) It only matters in code the compiler actually sees. If your program counter spends all its time in 3rd-party libraries you don't build, it doesn't matter.
In code where it does matter, it all comes down to which compiler makes better ASM, and that's largely a function of how smartly or stupidly the source code is written.
With all these variables, it's hard to distinguish between good compilers.
However, as was said, if you've got a lot of Fortran code to compile, don't re-write it.
How would you go about converting a reasonably large (>300K), fairly mature C codebase to C++?
The kind of C I have in mind is split into files roughly corresponding to modules (i.e. less granular than a typical OO class-based decomposition), using internal linkage in lieu private functions and data, and external linkage for public functions and data. Global variables are used extensively for communication between the modules. There is a very extensive integration test suite available, but no unit (i.e. module) level tests.
I have in mind a general strategy:
Compile everything in C++'s C subset and get that working.
Convert modules into huge classes, so that all the cross-references are scoped by a class name, but leaving all functions and data as static members, and get that working.
Convert huge classes into instances with appropriate constructors and initialized cross-references; replace static member accesses with indirect accesses as appropriate; and get that working.
Now, approach the project as an ill-factored OO application, and write unit tests where dependencies are tractable, and decompose into separate classes where they are not; the goal here would be to move from one working program to another at each transformation.
Obviously, this would be quite a bit of work. Are there any case studies / war stories out there on this kind of translation? Alternative strategies? Other useful advice?
Note 1: the program is a compiler, and probably millions of other programs rely on its behaviour not changing, so wholesale rewriting is pretty much not an option.
Note 2: the source is nearly 20 years old, and has perhaps 30% code churn (lines modified + added / previous total lines) per year. It is heavily maintained and extended, in other words. Thus, one of the goals would be to increase mantainability.
[For the sake of the question, assume that translation into C++ is mandatory, and that leaving it in C is not an option. The point of adding this condition is to weed out the "leave it in C" answers.]
Having just started on pretty much the same thing a few months ago (on a ten-year-old commercial project, originally written with the "C++ is nothing but C with smart structs" philosophy), I would suggest using the same strategy you'd use to eat an elephant: take it one bite at a time. :-)
As much as possible, split it up into stages that can be done with minimal effects on other parts. Building a facade system, as Federico Ramponi suggested, is a good start -- once everything has a C++ facade and is communicating through it, you can change the internals of the modules with fair certainty that they can't affect anything outside them.
We already had a partial C++ interface system in place (due to previous smaller refactoring efforts), so this approach wasn't difficult in our case. Once we had everything communicating as C++ objects (which took a few weeks, working on a completely separate source-code branch and integrating all changes to the main branch as they were approved), it was very seldom that we couldn't compile a totally working version before we left for the day.
The change-over isn't complete yet -- we've paused twice for interim releases (we aim for a point-release every few weeks), but it's well on the way, and no customer has complained about any problems. Our QA people have only found one problem that I recall, too. :-)
What about:
Compiling everything in C++'s C subset and get that working, and
Implementing a set of facades leaving the C code unaltered?
Why is "translation into C++ mandatory"? You can wrap the C code without the pain of converting it into huge classes and so on.
Your application has lots of folks working on it, and a need to not-be-broken.
If you are serious about large scale conversion to an OO style, what
you need is massive transformation tools to automate the work.
The basic idea is to designate groups of data as classes, and then
get the tool to refactor the code to move that data into classes,
move functions on just that data into those classes,
and revise all accesses to that data to calls on the classes.
You can do an automated preanalysis to form statistic clusters to get some ideas,
but you'll still need an applicaiton aware engineer to decide what
data elements should be grouped.
A tool that is capable of doing this task is our DMS Software Reengineering
Toolkit.
DMS has strong C parsers for reading your code, captures the C code
as compiler abstract syntax trees, (and unlike a conventional compiler)
can compute flow analyses across your entire 300K SLOC.
DMS has a C++ front end that can be used as the "back" end;
one writes transformations that map C syntax to C++ syntax.
A major C++ reengineering task on a large avionics system gives
some idea of what using DMS for this kind of activity is like.
See technical papers at
www.semdesigns.com/Products/DMS/DMSToolkit.html,
specifically
Re-engineering C++ Component Models Via Automatic Program Transformation
This process is not for the faint of heart. But than anybody
that would consider manual refactoring of a large application
is already not afraid of hard work.
Yes, I'm associated with the company, being its chief architect.
I would write C++ classes over the C interface. Not touching the C code will decrease the chance of messing up and quicken the process significantly.
Once you have your C++ interface up; then it is a trivial task of copy+pasting the code into your classes. As you mentioned - during this step it is vital to do unit testing.
GCC is currently in midtransition to C++ from C. They started by moving everything into the common subset of C and C++, obviously. As they did so, they added warnings to GCC for everything they found, found under -Wc++-compat. That should get you on the first part of your journey.
For the latter parts, once you actually have everything compiling with a C++ compiler, I would focus on replacing things that have idiomatic C++ counterparts. For example, if you're using lists, maps, sets, bitvectors, hashtables, etc, which are defined using C macros, you will likely gain a lot by moving these to C++. Likewise with OO, you'll likely find benefits where you are already using a C OO idiom (like struct inheritence), and where C++ will afford greater clarity and better type checking on your code.
Your list looks okay except I would suggest reviewing the test suite first and trying to get that as tight as possible before doing any coding.
Let's throw another stupid idea:
Compile everything in C++'s C subset and get that working.
Start with a module, convert it in a huge class, then in an instance, and build a C interface (identical to the one you started from) out of that instance. Let the remaining C code work with that C interface.
Refactor as needed, growing the OO subsystem out of C code one module at a time, and drop parts of the C interface when they become useless.
Probably two things to consider besides how you want to start are on what you want to focus, and where you want to stop.
You state that there is a large code churn, this may be a key to focus your efforts. I suggest you pick the parts of your code where a lot of maintenance is needed, the mature/stable parts are apparently working well enough, so it is better to leave them as they are, except probably for some window dressing with facades etc.
Where you want to stop depends on what the reason is for wanting to convert to C++. This can hardly be a goal in itself. If it is due to some 3rd party dependency, focus your efforts on the interface to that component.
The software I work on is a huge, old code base which has been 'converted' from C to C++ years ago now. I think it was because the GUI was converted to Qt. Even now it still mostly looks like a C program with classes. Breaking the dependencies caused by public data members, and refactoring the huge classes with procedural monster methods into smaller methods and classes never has really taken off, I think for the following reasons:
There is no need to change code that is working and that does not need to be enhanced. Doing so introduces new bugs without adding functionality, and end users don't appreciate that;
It is very, very hard to do refactor reliably. Many pieces of code are so large and also so vital that people hardly dare touching it. We have a fairly extensive suite of functional tests, but sufficient code coverage information is hard to get. As a result, it is difficult to establish whether there are already sufficient tests in place to detect problems during refactoring;
The ROI is difficult to establish. The end user will not benefit from refactoring, so it must be in reduced maintenance cost, which will increase initially because by refactoring you introduce new bugs in mature, i.e. fairly bug-free code. And the refactoring itself will be costly as well ...
NB. I suppose you know the "Working effectively with Legacy code" book?
You mention that your tool is a compiler, and that: "Actually, pattern matching, not just type matching, in the multiple dispatch would be even better".
You might want to take a look at maketea. It provides pattern matching for ASTs, as well as the AST definition from an abstract grammar, and visitors, tranformers, etc.
If you have a small or academic project (say, less than 10,000 lines), a rewrite is probably your best option. You can factor it however you want, and it won't take too much time.
If you have a real-world application, I'd suggest getting it to compile as C++ (which usually means primarily fixing up function prototypes and the like), then work on refactoring and OO wrapping. Of course, I don't subscribe to the philosophy that code needs to be OO structured in order to be acceptable C++ code. I'd do a piece-by-piece conversion, rewriting and refactoring as you need to (for functionality or for incorporating unit testing).
Here's what I would do:
Since the code is 20 years old, scrap down the parser/syntax analyzer and replace it with one of the newer lex/yacc/bison(or anything similar) etc based C++ code, much more maintainable and easier to understand. Faster to develop too if you have a BNF handy.
Once this is retrofitted to the old code, start wrapping modules into classes. Replace global/shared variables with interfaces.
Now what you have will be a compiler in C++ (not quite though).
Draw a class diagram of all the classes in your system, and see how they are communicating.
Draw another one using the same classes and see how they ought to communicate.
Refactor the code to transform the first diagram to the second. (this might be messy and tricky)
Remember to use C++ code for all new code added.
If you have some time left, try replacing data structures one by one to use the more standardized STL or Boost.
I had an idea I was mulling over with some colleagues. None of us knew whether or not it exists currently.
The Basic Premise is to have a system that has 100% uptime but can become more efficient dynamically.
Here is the scenario: * So we hash out a system quickly to a
specified set of interfaces, it has
zero optimizations, yet we are
confident that it is 100% stable
though (dubious, but for the sake of
this scenario please play
along) * We then profile
the original classes, and start to
program replacements for the
bottlenecks.
* The original and the replacement are initiated simultaneously and
synchronized.
* An original is allowed to run to completion: if a replacement hasn´t
completed it is vetoed by the system
as a replacement for the
original.
* A replacement must always return the same value as the original, for a
specified number of times, and for a
specific range of values, before it is
adopted as a replacement for the
original.
* If exception occurs after a replacement is adopted, the system
automatically tries the same operation
with a class which was superseded by
it.
Have you seen a similar concept in practise? Critique Please ...
Below are comments written after the initial question in regards to
posts:
* The system demonstrates a Darwinian approach to system evolution.
* The original and replacement would run in parallel not in series.
* Race-conditions are an inherent issue to multi-threaded apps and I
acknowledge them.
I believe this idea to be an interesting theoretical debate, but not very practical for the following reasons:
To make sure the new version of the code works well, you need to have superb automatic tests, which is a goal that is very hard to achieve and one that many companies fail to develop. You can only go on with implementing the system after such automatic tests are in place.
The whole point of this system is performance tuning, that is - a specific version of the code is replaced by a version that supersedes it in performance. For most applications today, performance is of minor importance. Meaning, the overall performance of most applications is adequate - just think about it, you probably rarely find yourself complaining that "this application is excruciatingly slow", instead you usually find yourself complaining on the lack of specific feature, stability issues, UI issues etc. Even when you do complain about slowness, it's usually an overall slowness of your system and not just a specific applications (there are exceptions, of course).
For applications or modules where performance is a big issue, the way to improve them is usually to identify the bottlenecks, write a new version and test is independently of the system first, using some kind of benchmarking. Benchmarking the new version of the entire application might also be necessary of course, but in general I think this process would only take place a very small number of times (following the 20%-80% rule). Doing this process "manually" in these cases is probably easier and more cost-effective than the described system.
What happens when you add features, fix non-performance related bugs etc.? You don't get any benefit from the system.
Running the two versions in conjunction to compare their performance has far more problems than you might think - not only you might have race conditions, but if the input is not an appropriate benchmark, you might get the wrong result (e.g. if you get loads of small data packets and that is in 90% of the time the input is large data packets). Furthermore, it might just be impossible (for example, if the actual code changes the data, you can't run them in conjunction).
The only "environment" where this sounds useful and actually "a must" is a "genetic" system that generates new versions of the code by itself, but that's a whole different story and not really widely applicable...
A system that runs performance benchmarks while operating is going to be slower than one that doesn't. If the goal is to optimise speed, why wouldn't you benchmark independently and import the fastest routines once they are proven to be faster?
And your idea of starting routines simultaneously could introduce race conditions.
Also, if a goal is to ensure 100% uptime you would not want to introduce untested routines since they might generate uncatchable exceptions.
Perhaps your ideas have merit as a harness for benchmarking rather than an operational system?
Have I seen a similar concept in practice? No. But I'll propose an approach anyway.
It seems like most of your objectives would be meet by some sort of super source control system, which could be implemented with CruiseControl.
CruiseControl can run unit tests to ensure correctness of the new version.
You'd have to write a CruiseControl builder pluggin that would execute the new version of your system against a series of existing benchmarks to ensure that the new version is an improvement.
If the CruiseControl build loop passes, then the new version would be accepted. Such a process would take considerable effort to implement, but I think it feasible. The unit tests and benchmark builder would have to be pretty slick.
I think an Inversion of Control Container like OSGi or Spring could do most of what you are talking about. (dynamic loading by name)
You could build on top of their stuff. Then implement your code to
divide work units into discrete modules / classes (strategy pattern)
identify each module by unique name and associate a capability with it
when a module is requested it is requested by capability and at random one of the modules with that capability is used.
keep performance stats (get system tick before and after execution and store the result)
if an exception occurs mark that module as do not use and log the exception.
If the modules do their work by message passing you can store the message until the operation completes successfully and redo with another module if an exception occurs.
For design ideas for high availability systems, check out Erlang.
I don't think code will learn to be better, by itself. However, some runtime parameters can easily adjust onto optimal values, but that would be just regular programming, right?
About the on-the-fly change, I've shared the wondering and would be building it on top of Lua, or similar dynamic language. One could have parts that are loaded, and if they are replaced, reloaded into use. No rocket science in that, either. If the "old code" is still running, it's perfectly all right, since unlike with DLL's, the file is needed only when reading it in, not while executing code that came from there.
Usefulness? Naa...