What is to implement a custom string type in f# for interning strings. i have to read large csv files into memory. Given most of the columns are categorical, values are repeating and it makes sense to create new string first time it is encountered and only refer to it on subsequent occurrences to save memory.
In c# I do this by creating a global intern pool (concurrent dict) and before setting a value, lookup the dictionary if it already exists. if it exists, just point to the string already in the dictionary. if not, add it to the dictionary and set the value to the string just added to dictionary.
New to f# and wondering what is the best way to do this in f#. will be using the new string type in records named tuples etc and it will have to work with concurrent processes.
Edit:
String.Intern uses the Intern Pool. My understanding is, it is not very efficient for large pools and is not garbage collected i.e. any/all interned strings will remain in intern pool for lifetime of the app. Imagine a an application where you read a file, perform some operations and write data. Using Intern Pool solution will probably work. Now imagine you have to do the same 100 times and the strings in each file have little in common. If the memory is allocated on heap, after processing each file, we can force garbage collector to clear unnecessary strings.
I should have mentioned I could not really figure out how to do the C# approach in F# (other than implementing a C# type and using it in F#)
Memorisation pattern is slightly different from what I am looking for? We are not caching calculated results - we are ensuring each string object is created no more than once and all subsequent creations of same string are just references to the original. Using a dictionary to do this is a one way and using String.Intern is other.
sorry if is am missing something obvious here.
I have a few things to say, so I'll post them as an answer.
First, I guess String.Intern works just as well in F# as in C#.
let x = "abc"
let y = StringBuilder("a").Append("bc").ToString()
printfn "1 : %A" (LanguagePrimitives.PhysicalEquality x y) // false
let y2 = String.Intern y
printfn "2 : %A" (LanguagePrimitives.PhysicalEquality x y2) // true
Second, are you using a dictionary in combination with String.Intern in your C# solution? If so, why not just do s = String.Intern(s); after the string is ready following input from file?
To create a type for use in your business domain to handle string deduplication in general is a very bad idea. You don't want your business domain polluted by that kind of low level stuff.
As for rolling your own. I did that some years ago, probably to avoid that problem you mentioned with the strings not being garbage collected, but I never tested if that actually was a problem.
It might be a good idea to use a dictionary (or something) for each column (or type of column) where the same values are likely to repeat in great numbers. (This is pretty much what you said already.)
It makes sense to only keep these dictionaries live while you read the information from file, and stuff it into internal data structures. You might be thinking that you need the dictionaries for subsequent reads, but I am not so sure about that.
The important thing is to deduplicate the great majority of strings, and not necessarily every single duplicate. Because of this you can greatly simplify the solution as indicated. You most probably have nothing to gain by overcomplicating your solution to squeeze out the last fraction of memory savings.
Releasing the dictionaries after the file is read and structures filled, will have the advantage of not holding on to strings when they are no longer really needed. And of course you save memory by not holding onto the dictionaries.
I see no need to handle concurrency issues in the implementation here. String.Intern must necessarily be immune to concurrency issues. If you roll your own with the design suggested, you would not use it concurrently. Each file being read would have its own set of dictionaries for its columns.
Related
I have to pick a type for a sequence of floats with 16K elements. The values will be updated frequently, potentially many times a second.
I've read the wiki page on arrays. Here are the conclusions I've drawn so far. (Please correct me if any of them are mistaken.)
IArrays would be unacceptably slow in this case, because they'd be copied on every change. With 16K floats in the array, that's 64KB of memory copied each time.
IOArrays could do the trick, as they can be modified without copying all the data. In my particular use case, doing all updates in the IO monad isn't a problem at all. But they're boxed, which means extra overhead, and that could add up with 16K elements.
IOUArrays seem like the perfect fit. Like IOArrays, they don't require a full copy on each change. But unlike IOArrays, they're unboxed, meaning they're basically the Haskell equivalent of a C array of floats. I realize they're strict. But I don't see that being an issue, because my application would never need to access anything less than the entire array.
Am I right to look to IOUArrays for this?
Also, suppose I later want to read or write the array from multiple threads. Will I have backed myself into a corner with IOUArrays? Or is the choice of IOUArrays totally orthogonal to the problem of concurrency? (I'm not yet familiar with the concurrency primitives in Haskell and how they interact with the IO monad.)
A good rule of thumb is that you should almost always use the vector library instead of arrays. In this case, you can use mutable vectors from the Data.Vector.Mutable module.
The key operations you'll want are read and write which let you mutably read from and write to the mutable vector.
You'll want to benchmark of course (with criterion) or you might be interested in browsing some benchmarks I did e.g. here (if that link works for you; broken for me).
The vector library is a nice interface (crazy understatement) over GHC's more primitive array types which you can get to more directly in the primitive package. As are the things in the standard array package; for instance an IOUArray is essentially a MutableByteArray#.
Unboxed mutable arrays are usually going to be the fastest, but you should compare them in your application to IOArray or the vector equivalent.
My advice would be:
if you probably don't need concurrency first try a mutable unboxed Vector as Gabriel suggests
if you know you will want concurrent updates (and feel a little brave) then first try a MutableArray and then do atomic updates with these functions from the atomic-primops library. If you want fine-grained locking, this is your best choice. Of course concurrent reads will work fine on whatever array you choose.
It should also be theoretically possible to do concurrent updates on a MutableByteArray (equivalent to IOUArray) with those atomic-primops functions too, since a Float should always fit into a word (I think), but you'd have to do some research (or bug Ryan).
Also be aware of potential memory reordering issues when doing concurrency with the atomic-primops stuff, and help convince yourself with lots of tests; this is somewhat uncharted territory.
In my application I'm working with MutableArrays (via the primitive package) shared across threads. I know when individual elements are no longer used and I'd like some way (unsafeMarkGarbage or something) to indicate to the runtime that they can be collected. At least I'd like to experiment with that if such a function or equivalent technique exists.
EDIT, to add a bit more detail: I've got a conceptual "infinite tape" implemented as a linked list of short MutableArray segments, something like:
data Seg a = Seg (MutableArray a) (IORef (Maybe (Seg a)))
I access the tape using a concurrent counter and always know when an element of the tape will no longer be accessed. In certain cases when a thread is descheduled it's possible that entire array segments (both the array and its elements) which could have been GC'd will stick around as their references will persist.
An ideal solution would avoid an additional write (maybe that's silly), avoid another layer of indirection in the array, and allow entire MutableArrays to be collected when all their elements expire.
Weak references do seem to be the most promising sort of mechanism I've seen, but I can't yet see how they can help me here.
I would suggest you store undefined in the positions that you would like to garbage collect.
I have a large amout of objects that all have a filename stored inside. All file names are within a given base directory (let's call it C:\BaseDir\). I am now considering two alternatives:
Store absolute paths in the objects
Store relative paths in the object and store the base path additionally
If I understand Delphi strings correctly the second approach will need much less memory because the base path string is shared - given that I pass the same string field to all the objects like this:
TDataObject.Create (FBasePath, RelFileName);
Is that assumption true? Will there be only one string instance of the base path in memory?
If anybody knows a better way to handle situations like this, feel free to comment on that as well.
Thanks!
You are correct. When you write s1 := s2 with two string variables, there is one string in memory with (at least two) references to it.
You also ask whether trying to reduce the number of strings in memory is a good idea. That depends on how many strings you have in comparison to other memory consuming objects. Only you can really answer that.
As David said, the common string would be shared (unless you use ie UniqueString()).
Having said that, it looks like premature optimisation. If you actually need to work with full paths and never need the dir and filename part separately then you should think about splitting them up only when you really run into memory problems.
Constantly concatenating the base and filename parts could significantly slow down your program and cause memory fragmentation.
I know that memoization seems to be a perennial topic here on the haskell tag on stack overflow, but I think this question has not been asked before.
I'm aware of several different 'off the shelf' memoization libraries for Haskell:
The memo-combinators and memotrie packages, which make use of a beautiful trick involving lazy infinite data structures to achieve memoization in a purely functional way. (As I understand it, the former is slightly more flexible, while the latter is easier to use in simple cases: see this SO answer for discussion.)
The uglymemo package, which uses unsafePerformIO internally but still presents a referentially transparent interface. The use of unsafePerformIO internally results in better performance than the previous two packages. (Off the shelf, its implementation uses comparison-based search data structures, rather than perhaps-slightly-more-efficient hash functions; but I think that if you find and replace Cmp for Hashable and Data.Map for Data.HashMap and add the appropraite imports, you get a hash based version.)
However, I'm not aware of any library that looks answers up based on object identity rather than object value. This can be important, because sometimes the kinds of object which are being used as keys to your memo table (that is, as input to the function being memoized) can be large---so large that fully examining the object to determine whether you've seen it before is itself a slow operation. Slow, and also unnecessary, if you will be applying the memoized function again and again to an object which is stored at a given 'location in memory' 1. (This might happen, for example, if we're memoizing a function which is being called recursively over some large data structure with a lot of structural sharing.) If we've already computed our memoized function on that exact object before, we can already know the answer, even without looking at the object itself!
Implementing such a memoization library involves several subtle issues and doing it properly requires several special pieces of support from the language. Luckily, GHC provides all the special features that we need, and there is a paper by Peyton-Jones, Marlow and Elliott which basically worries about most of these issues for you, explaining how to build a solid implementation. They don't provide all details, but they get close.
The one detail which I can see which one probably ought to worry about, but which they don't worry about, is thread safety---their code is apparently not threadsafe at all.
My question is: does anyone know of a packaged library which does the kind of memoization discussed in the Peyton-Jones, Marlow and Elliott paper, filling in all the details (and preferably filling in proper thread-safety as well)?
Failing that, I guess I will have to code it up myself: does anyone have any ideas of other subtleties (beyond thread safety and the ones discussed in the paper) which the implementer of such a library would do well to bear in mind?
UPDATE
Following #luqui's suggestion below, here's a little more data on the exact problem I face. Let's suppose there's a type:
data Node = Node [Node] [Annotation]
This type can be used to represent a simple kind of rooted DAG in memory, where Nodes are DAG Nodes, the root is just a distinguished Node, and each node is annotated with some Annotations whose internal structure, I think, need not concern us (but if it matters, just ask and I'll be more specific.) If used in this way, note that there may well be significant structural sharing between Nodes in memory---there may be exponentially more paths which lead from the root to a node than there are nodes themselves. I am given a data structure of this form, from an external library with which I must interface; I cannot change the data type.
I have a function
myTransform : Node -> Node
the details of which need not concern us (or at least I think so; but again I can be more specific if needed). It maps nodes to nodes, examining the annotations of the node it is given, and the annotations its immediate children, to come up with a new Node with the same children but possibly different annotations. I wish to write a function
recursiveTransform : Node -> Node
whose output 'looks the same' as the data structure as you would get by doing:
recursiveTransform Node originalChildren annotations =
myTransform Node recursivelyTransformedChildren annotations
where
recursivelyTransformedChildren = map recursiveTransform originalChildren
except that it uses structural sharing in the obvious way so that it doesn't return an exponential data structure, but rather one on the order of the same size as its input.
I appreciate that this would all be easier if say, the Nodes were numbered before I got them, or I could otherwise change the definition of a Node. I can't (easily) do either of these things.
I am also interested in the general question of the existence of a library implementing the functionality I mention quite independently of the particular concrete problem I face right now: I feel like I've had to work around this kind of issue on a few occasions, and it would be nice to slay the dragon once and for all. The fact that SPJ et al felt that it was worth adding not one but three features to GHC to support the existence of libraries of this form suggests that the feature is genuinely useful and can't be worked around in all cases. (BUT I'd still also be very interested in hearing about workarounds which will help in this particular case too: the long term problem is not as urgent as the problem I face right now :-) )
1 Technically, I don't quite mean location in memory, since the garbage collector sometimes moves objects around a bit---what I really mean is 'object identity'. But we can think of this as being roughly the same as our intuitive idea of location in memory.
If you only want to memoize based on object identity, and not equality, you can just use the existing laziness mechanisms built into the language.
For example, if you have a data structure like this
data Foo = Foo { ... }
expensive :: Foo -> Bar
then you can just add the value to be memoized as an extra field and let the laziness take care of the rest for you.
data Foo = Foo { ..., memo :: Bar }
To make it easier to use, add a smart constructor to tie the knot.
makeFoo ... = let foo = Foo { ..., memo = expensive foo } in foo
Though this is somewhat less elegant than using a library, and requires modification of the data type to really be useful, it's a very simple technique and all thread-safety issues are already taken care of for you.
It seems that stable-memo would be just what you needed (although I'm not sure if it can handle multiple threads):
Whereas most memo combinators memoize based on equality, stable-memo does it based on whether the exact same argument has been passed to the function before (that is, is the same argument in memory).
stable-memo only evaluates keys to WHNF.
This can be more suitable for recursive functions over graphs with cycles.
stable-memo doesn't retain the keys it has seen so far, which allows them to be garbage collected if they will no longer be used. Finalizers are put in place to remove the corresponding entries from the memo table if this happens.
Data.StableMemo.Weak provides an alternative set of combinators that also avoid retaining the results of the function, only reusing results if they have not yet been garbage collected.
There is no type class constraint on the function's argument.
stable-memo will not work for arguments which happen to have the same value but are not the same heap object. This rules out many candidates for memoization, such as the most common example, the naive Fibonacci implementation whose domain is machine Ints; it can still be made to work for some domains, though, such as the lazy naturals.
Ekmett just uploaded a library that handles this and more (produced at HacPhi): http://hackage.haskell.org/package/intern. He assures me that it is thread safe.
Edit: Actually, strictly speaking I realize this does something rather different. But I think you can use it for your purposes. It's really more of a stringtable-atom type interning library that works over arbitrary data structures (including recursive ones). It uses WeakPtrs internally to maintain the table. However, it uses Ints to index the values to avoid structural equality checks, which means packing them into the data type, when what you want are apparently actually StableNames. So I realize this answers a related question, but requires modifying your data type, which you want to avoid...
Once I studied about the advantage of a string being immutable because of something to improve performace in memory.
Can anybody explain this to me? I can't find it on the Internet.
Immutability (for strings or other types) can have numerous advantages:
It makes it easier to reason about the code, since you can make assumptions about variables and arguments that you can't otherwise make.
It simplifies multithreaded programming since reading from a type that cannot change is always safe to do concurrently.
It allows for a reduction of memory usage by allowing identical values to be combined together and referenced from multiple locations. Both Java and C# perform string interning to reduce the memory cost of literal strings embedded in code.
It simplifies the design and implementation of certain algorithms (such as those employing backtracking or value-space partitioning) because previously computed state can be reused later.
Immutability is a foundational principle in many functional programming languages - it allows code to be viewed as a series of transformations from one representation to another, rather than a sequence of mutations.
Immutable strings also help avoid the temptation of using strings as buffers. Many defects in C/C++ programs relate to buffer overrun problems resulting from using naked character arrays to compose or modify string values. Treating strings as a mutable types encourages using types better suited for buffer manipulation (see StringBuilder in .NET or Java).
Consider the alternative. Java has no const qualifier. If String objects were mutable, then any method to which you pass a reference to a string could have the side-effect of modifying the string. Immutable strings eliminate the need for defensive copies, and reduce the risk of program error.
Immutable strings are cheap to copy, because you don't need to copy all the data - just copy a reference or pointer to the data.
Immutable classes of any kind are easier to work with in multiple threads, the only synchronization needed is for destruction.
Perhaps, my answer is outdated, but probably someone will found here a new information.
Why Java String is immutable and why it is good:
you can share a string between threads and be sure no one of them will change the string and confuse another thread
you don’t need a lock. Several threads can work with immutable string without conflicts
if you just received a string, you can be sure no one will change its value after that
you can have many string duplicates – they will be pointed to a single instance, to just one copy. This saves computer memory (RAM)
you can do substring without copying, – by creating a pointer to an existing string’s element. This is why Java substring operation implementation is so fast
immutable strings (objects) are much better suited to use them as key in hash-tables
a) Imagine StringPool facility without making string immutable , its not possible at all because in case of string pool one string object/literal e.g. "Test" has referenced by many reference variables , so if any one of them change the value others will be automatically gets affected i.e. lets say
String A = "Test" and String B = "Test"
Now String B called "Test".toUpperCase() which change the same object into "TEST" , so A will also be "TEST" which is not desirable.
b) Another reason of Why String is immutable in Java is to allow String to cache its hashcode , being immutable String in Java caches its hash code and do not calculate every time we call hashcode method of String, which makes it very fast as hashmap key.
Think of various strings sitting on a common pool. String variables then point to locations in the pool. If u copy a string variable, both the original and the copy shares the same characters. These efficiency of sharing outweighs the inefficiency of string editing by extracting substrings and concatenating.
Fundamentally, if one object or method wishes to pass information to another, there are a few ways it can do it:
It may give a reference to a mutable object which contains the information, and which the recipient promises never to modify.
It may give a reference to an object which contains the data, but whose content it doesn't care about.
It may store the information into a mutable object the intended data recipient knows about (generally one supplied by that data recipient).
It may return a reference to an immutable object containing the information.
Of these methods, #4 is by far the easiest. In many cases, mutable objects are easier to work with than immutable ones, but there's no easy way to share with "untrusted" code the information that's in a mutable object without having to first copy the information to something else. By contrast, information held in an immutable object to which one holds a reference may easily be shared by simply sharing a copy of that reference.