I saw this article:
https://itnext.io/comparing-darts-loops-which-is-the-fastest-731a03ad42a2
It says that ".map" is slow with benchmark result
But I don't understand why slower than while/for loop
How does it work in low level?
I think it's because .map is called an unnamed method like this (_){ }
Can you explain that in detail?
Its because mapping an array will create a copy of each value than modify the original array.
Since a while/for loop does not copy the values but rather just accesses them using their index, it is a lot faster.
Can you explain that in detail?
It's like saying "I don't understand why hitchhiking on the back of a construction truck is so much slower than taking the high speed train to my destination".
The only detail that is important is that map is not a loop. map() internally probably uses a loop of some kind.
This person is misusing a method call that is meant for something else, just because a side-effect of that call when combining it with a call materializing the iterable, like toList(), is that it loops through the iterable given. It doesn't even have the side effect on it's own.
Stop reading "tutorials" or "tips" of people misusing language features. map() is not a loop. If you need a loop, use a loop. The same goes for the ternary operator. It's not an if, if you need an if, use it.
Use language features for what they are meant, stop misusing language features because their side-effect does what you want and then wondering why they don't work as well as the feature actually meant for it.
Sorry if this seems a bit ranty, but I have seen countless examples by now. I don't know where it comes from. My personal guess is "internet tutorials". Because everybody can write one. Please don't read them. Read a good book. It was written by professionals, proofread, edited, and checked. Internet tutorials are free, written by random people and about worth as much as they cost.
Related
I'm having hard time understanding what value effect systems, like ZIO or Cats Effect.
It does not make code readable, e.g.:
val wrappedB = for {
a <- getA() // : ZIO[R, E, A]
b <- getB(a) // : ZIO[R, E, B]
} yield b
is no more readable to me than:
val a = getA() // : A
val b = getB(a) // : B
I could even argue, that the latter is more straight forward, because calling a function executes it, instead of just creating an effect or execution pipeline.
Delayed execution does not sound convincing, because all examples I've encountered so far are just executing the pipeline right away anyways. Being able to execute effects in parallel or multiple time can be achieved in simpler ways IMHO, e.g. C# has Parallel.ForEach
Composability. Functions can be composed without using effects, e.g. by plain composition.
Pure functional methods. In the end the pure instructions will be executed, so it seems like it's just pretending DB access is pure. It does not help to reason, because while construction of the instructions is pure, executing them is not.
I may be missing something or just downplaying the benefits above or maybe benefits are bigger in certain situations (e.g. complex domain).
What are the biggest selling points to use effect systems?
Because it makes it easy to deal with side effects. From your example:
a <- getA() // ZIO[R, E, A] (doesn't have to be ZIO btw)
val a = getA(): A
The first getA accounts in the effect and the possibility of returning an error, a side effect. This would be like getting an A from some db where the said A may not exist or that you lack permission to access it. The second getA would be like a simple def getA = "A".
How do we put these methods together ? What if one throws an error ? Should we still proceed to the next method or just quit it ? What if one blocks your thread ?
Hopefully that addresses your second point about composability. To quickly address the rest:
Delayed execution. There are probably two reasons for this. The first is you actually don't want to accidentally start an execution. Or just because you write it it starts right away. This breaks what the cool guys refer to as referential transparency. The second is concurrent execution requires a thread pool or execution context. Normally we want to have a centralized place where we can fine tune it for the whole app. And when building a library we can't provide it ourselves. It's the users who provide it. In fact we can also defer the effect. All you do is define how the effect should behave and the users can use ZIO, Monix, etc, it's totally up to them.
Purity. Technically speaking wrapping a process in a pure effect doesn't necessarily mean the underlying process actually uses it. Only the implementation knows if it's really used or not. What we can do is lift it to make it compatible with the composition.
what makes programming with ZIO or Cats great is when it comes to concurrent programming. They are also other reasons but this one is IMHO where I got the "Ah Ah! Now I got it".
Try to write a program that monitor the content of several folders and for each files added to the folders parse their content but not more than 4 files at the same time. (Like the example in the video "What Java developpers could learn from ZIO" By Adam Fraser on youtube https://www.youtube.com/watch?v=wxpkMojvz24 .
I mean this in ZIO is really easy to write :)
The all idea behind the fact that you combine data structure (A ZIO is a data structure) in order to make bigger data structure is so easy to understand that I would not want to code without it for complex problems :)
The two examples are not comparable since an error in the first statement will mark as faulty the value equal to the objectified sequence in the first form while it will halt the whole program in the second. The second form shall then be a function definition to properly encapsulate the two statements, followed by an affectation of the result of its call.
But more than that, in order to completely mimic the first form, some additional code has to be written, to catch exceptions and build a true faulty result, while all these things are made for free by ZIO...
I think that the ability to cleanly propagate the error state between successive statements is the real value of the ZIO approach. Any composite ZIO program fragment is then fully composable itself.
That's the main benefit of any workflow based approach, anyway.
It is this modularity which gives to effect handling its real value.
Since an effect is an action which structurally may produce errors, handling effects like this is an excellent way to handle errors in a composable way. In fact, handling effects consists in handling errors !
If I have a function call that returns true or false as the condition of an if statement, do I have to worry about Swift forking my code for efficiency?
For example,
if (resourceIsAvailable()) { //execute code }
If checking for the resource is computationally expensive, will Xcode wait or attempt to continue on with the code?
Is this worth using a completion handler?
What if the resource check must make a database call?
Good question.
First off... can a function be used? Absolutely.
Second... should it be used?
A lot of that depends on the implementation of the function. If the function is known (to the person who wrote it) to take a long time to complete then I would expect that person to deal with that accordingly.
Thankfully with a lot of iOS things like that are taken out of the hands of the developer (mostly). CoreData and Network requests normally come with a completion handler. So any function that uses them would also need to be async and have a completion handler.
There is no fixed rule for this. My best advice would be...
If you can see the implementation of the function then try to work out what it’s doing.
If you can’t then give it a go. You could even use the time profiler in Xcode profiler to determine how long it is taking to complete.
The worst that could happen is you find it is slow and then change it for something else.
For example, if I implement some simple object caching, which method is faster?
1. return isset($cache[$cls]) ? $cache[$cls] : $cache[$cls] = new $cls;
2. return #$cache[$cls] ?: $cache[$cls] = new $cls;
I read somewhere # takes significant time to execute (and I wonder why), especially when warnings/notices are actually being issued and suppressed. isset() on the other hand means an extra hash lookup. So which is better and why?
I do want to keep E_NOTICE on globally, both on dev and production servers.
I wouldn't worry about which method is FASTER. That is a micro-optimization. I would worry more about which is more readable code and better coding practice.
I would certainly prefer your first option over the second, as your intent is much clearer. Also, best to keep away edge condition problems by always explicitly testing variables to make sure you are getting what you are expecting to get. For example, what if the class stored in $cache[$cls] is not of type $cls?
Personally, if I typically would not expect the index on $cache to be unset, then I would also put error handling in there rather than using ternary operations. If I could reasonably expect that that index would be unset on a regular basis, then I would make class $cls behave as a singleton and have your code be something like
return $cls::get_instance();
The isset() approach is better. It is code that explicitly states the index may be undefined. Suppressing the error is sloppy coding.
According to this article 10 Performance Tips to Speed Up PHP, warnings take additional execution time and also claims the # operator is "expensive."
Cleaning up warnings and errors beforehand can also keep you from
using # error suppression, which is expensive.
Additionally, the # will not suppress the errors with respect to custom error handlers:
http://www.php.net/manual/en/language.operators.errorcontrol.php
If you have set a custom error handler function with
set_error_handler() then it will still get called, but this custom
error handler can (and should) call error_reporting() which will
return 0 when the call that triggered the error was preceded by an #.
If the track_errors feature is enabled, any error message generated by
the expression will be saved in the variable $php_errormsg. This
variable will be overwritten on each error, so check early if you want
to use it.
# temporarily changes the error_reporting state, that's why it is said to take time.
If you expect a certain value, the first thing to do to validate it, is to check that it is defined. If you have notices, it's probably because you're missing something. Using isset() is, in my opinion, a good practice.
I ran timing tests for both cases, using hash keys of various lengths, also using various hit/miss ratios for the hash table, plus with and without E_NOTICE.
The results were: with error_reporting(E_ALL) the isset() variant was faster than the # by some 20-30%. Platform used: command line PHP 5.4.7 on OS X 10.8.
However, with error_reporting(E_ALL & ~E_NOTICE) the difference was within 1-2% for short hash keys, and up 10% for longer ones (16 chars).
Note that the first variant executes 2 hash table lookups, whereas the variant with # does only one lookup.
Thus, # is inferior in all scenarios and I wonder if there are any plans to optimize it.
I think you have your priorities a little mixed up here.
First of all, if you want to get a real world test of which is faster - load test them. As stated though suppressing will probably be slower.
The problem here is if you have performance issues with regular code, you should be upgrading your hardware, or optimize the grand logic of your code rather than preventing proper execution and error checking.
Suppressing errors to steal the tiniest fraction of a speed gain won't do you any favours in the long run. Especially if you think that this error may keep happening time and time again, and cause your app to run more slowly than if the error was caught and fixed.
I've recently learned Haskell, and am trying to carry the pure functional style over to my other code when possible. An important aspect of this is treating all variables as immutable, i.e. constants. In order to do so, many computations that would be implemented using loops in an imperative style have to be performed using recursion, which typically incurs a memory penalty due to the allocation a new stack frame for each function call. In the special case of a tail call (where the return value of a called function is immediately returned to the callee's caller), however, this penalty can be bypassed by a process called tail call optimization (in one method, this can be done by essentially replacing a call with a jmp after setting up the stack properly). Does MATLAB perform TCO by default, or is there a way to tell it to?
If I define a simple tail-recursive function:
function tailtest(n)
if n==0; feature memstats; return; end
tailtest(n-1);
end
and call it so that it will recurse quite deeply:
set(0,'RecursionLimit',10000);
tailtest(1000);
then it doesn't look as if stack frames are eating a lot of memory. However, if I make it recurse much deeper:
set(0,'RecursionLimit',10000);
tailtest(5000);
then (on my machine, today) MATLAB simply crashes: the process unceremoniously dies.
I don't think this is consistent with MATLAB doing any TCO; the case where a function tail-calls itself, only in one place, with no local variables other than a single argument, is just about as simple as anyone could hope for.
So: No, it appears that MATLAB does not do TCO at all, at least by default. I haven't (so far) looked for options that might enable it. I'd be surprised if there were any.
In cases where we don't blow out the stack, how much does recursion cost? See my comment to Bill Cheatham's answer: it looks like the time overhead is nontrivial but not insane.
... Except that Bill Cheatham deleted his answer after I left that comment. OK. So, I took a simple iterative implementation of the Fibonacci function and a simple tail-recursive one, doing essentially the same computation in both, and timed them both on fib(60). The recursive implementation took about 2.5 times longer to run than the iterative one. Of course the relative overhead will be smaller for functions that do more work than one addition and one subtraction per iteration.
(I also agree with delnan's sentiment: highly-recursive code of the sort that feels natural in Haskell is typically likely to be unidiomatic in MATLAB.)
There is a simple way to check this. Create this function tail_recursion_check:
function r = tail_recursion_check(n)
if n > 1
r = tail_recursion_check(n - 1);
else
error('error');
end
end
and run tail_recursion_check(10), for example. You are going to see a very long stack trace with 10 items that says error at line 3. If there were tail call optimization, you would only see one.
Normally I'm well aware that a consideration like this is premature optimization. Right now I have some event handlers being attached inside a foreach loop. I am wondering if this style might be prone to leaks or inefficient memory use due to closures being created. Is there any validity to this thinking?
closures only apply if your event handlers are anonymous methods (including, but not limited to, lambda expressions). If this is the case, you might have a problem. But it should be okay as long as you remove these event handlers at the proper time.
If you are talking about something like this:
foreach (var item in items)
{
item.SomeEvent += delegate {
// do something
};
}
Then the answer is the performance is not noticeable (in my Monotouch experience anyway) as the compiler simply creates a class with a method the same way the Microsoft C# compiler.
The biggest performance bottlenecks I've encountered in Monotouch have been SQLite related, and parsing DateTimes. Everything else, including complex LINQ statements fly on the 3GS - I'm not sure what magic is performed by the AOT compiler but I would only worry if it creeps up into 0.5 or more seconds to perform the task.