vector is the first choice in many situations because random access is O(1), as there are not many containers that are fast enough, or at least O(log(n)).
My issue with vector being that vector<>::erase() is O(n), map<>::erase() is faster and is a better container.
An alternative would be to use an object pool, but it is not a standard container, and implementations might vary depending on use, so I'm not very keen on using something I don't really understand or know a lot about.
It seems map is a very good alternative to vector<> when there is often-occurring deletions, but I wanted to know if there are better alternatives to it.
So is there a container that is both fast with random access and deletion?
Is there an usual way to make an object pool?
What alternative to C++ vector when it comes to fast deletion?
Erasing the last element of a vector (i.e. pop operation) has constant complexity, so if you don't need to keep your sequence ordered, then an efficient solution is to swap the target element with the last one, and pop it.
A linked list has constant complexity deletion that maintains the order of the sequence, but indexed lookup is linear (i.e not random access).
The (unordered) map sure has both asymptotically efficient lookup and erase, but you won't get the same behaviour as a vector would have. If you create an index -> element map, and remove element from index i, then there will be a gap between i - 1 and i + 1, while the vector would shift the elements at indices greater than i left.
The indexable skip list has logarithmic (on average; worst case is linear) lookup and deletion. However, there is no implementation of it in the standard library.
Related
Language: C++
One thing I can do is allocate a vector of size n and store all data
and then sort it using sort(begin(),end()). Else, I can keep putting
the data in a map or set which are ordered itself so I don't have to
sort afterwards. But in this case inserting an element may be more
costly due to rearrangements(I guess).
So which is the optimal choice for minimal time for a wide range of n(no. of objects)
It depends on the situation.
map and set are usually red-black trees, they should do a lot of work to be balanced, or the operation on it will be very slow. And it doesn't support random access. so if you only want to sort one time, you shouldn't use them.
However, if you want to continue insert elements into the container and keep order, map and set will take O(logN) time, while the sorted vector is O(N). The latter is much slower, so if you want frequently insert and delete, you should use map or set.
The difference between the 2 is noticable!
Using a set, you get O(log(N)) complexity for each element you insert. So by result you get O(N log(N)), which is the complexity of an insertion sort.
Adding everything in a vector is of complexity O(1), and sorting it will be O(N log(N)) since C++11 (before it, std::sort have O(N log(N)) on average.).
Once sorted, you could use binary_search to have the same complexity as in a set.
The API of using a vector as set ain't the friendly, although it does give nice performance benefits. This off course is only useful when you can do a bulk insert of data or when the amount of lookups is much larger than the manipulations of the content. Algorithmsable to sort on partially sorted vector, when you have to extend later on.
Finally, one has to remark that you don't have the same guarantees of iterator invalidation.
So, why are vectors better? Cache locality!
A vector has all data in a single memory block, hence the processor can do prefetching while for a set, the memory is scattered around the place requireing the data to find the next address. This makes vector a better set implementation than std::set for large data when you can live with the limitations.
To give you an idea, on the codebase I'm working on, we have several set and map implementations based on vectors which have their own narratives to function in. (For example: no erase or no operator[])
What is the most efficient way of adding non-repeated elements into STL container and what kind of container is the fastest? I have a large amount of data and I'm afraid each time I try to check if it is a new element or not, it takes a lot of time. I hope map be very fast.
// 1- Map
map<int, int> Map;
...
if(Map.find(Element)!=Map.end()) Map[Element]=ID;
// 2-Vector
vector<int> Vec;
...
if(find(Vec.begin(), Vec.end(), Element)!=Vec.end()) Vec.push_back(Element);
// 3-Set
// Edit: I made a mistake: set::find is O(LogN) not O(N)
Both set and map has O(log(N)) performance for looking up keys. vector has O(N).
The difference between set and map, as far as you should be concerned, is whether you need to associate a key with a value, or just store a value directly. If you need the former, use a map, if you need the latter, use a set.
In both cases, you should just use insert() instead of doing a find().
The reason is insert() will insert the value into the container if and only if the container does not already contain that value (in the case of map, if the container does not contain that key). This might look like
Map.insert(std::make_pair(Element, ID));
for a map or
Set.insert(Element);
for a set.
You can consult the return value to determine whether or not an insertion was actually performed.
If you're using C++11, you have two more choices, which are std::unordered_map and std::unordered_set. These both have amortized O(1) performance for insertions and lookups. However, they also require that the key (or value, in the case of set) be hashable, which means you'll need to specialize std::hash<> for your key. Conversely, std::map and std::set require that your key (or value, in the case of set) respond to operator<().
If you're using C++11, you can use std::unordered_set. That would allow you O(1) existence-checking (technically amortized O(1) -- O(n) in the worst case).
std::set would probably be your second choice with O(lg n).
Basically, std::unordered_set is a hash table and std::set is a tree structure (a red black tree in every implementation I've ever seen)1.
Depending on how well your hashes distribute and how many items you have, a std::set might actually be faster. If it's truly performance critical, then as always, you'll want to do benchmarking.
1) Technically speaking, I don't believe either are required to be implemented as a hash table or as a balanced BST. If I remember correctly, the standard just mandates the run time bounds, not the implementation -- it just turns out that those are the only viable implementations that fit the bounds.
You should use a std::set; it is a container designed to hold a single (equivalent) copy of an object and is implemented as a binary search tree. Therefore, it is O(log N), not O(N), in the size of the container.
std::set and std::map often share a large part of their underlying implementation; you should check out your local STL implementation.
Having said all this, complexity is only one measure of performance. You may have better performance using a sorted vector, as it keeps the data local to one another and, therefore, more likely to hit the caches. Cache coherence is a large part of data structure design these days.
Sounds like you want to use a std::set. It's elements are unique, so you don't need to care about uniqueness when adding elements, and a.find(k) (where a is an std::set and k is a value) is defined as being logarithmic in complexity.
if your elements can be hashed for O(1), then better to use an index in a unordered_map or unordered_set (not in a map/set because they use RB tree in implementation which is O(logN) find complexity)
Your examples show a definite pattern:
check if the value is already in container
if not, add the value to the container.
Both of these operation would potentially take some time. First, looking up an element can be done in O(N) time (linear search) if the elements are not arranged in any particular manner (e.g., just a plain std::vector), it could be done in O(logN) time (binary search) if the elements are sorted (e.g., either std::map or std::set), and it could be done in O(1) time if the elements are hashed (e.g., either std::unordered_map or std::unordered_set).
The insertion will be O(1) (amortized) for a plain vector or an unordered container (hash container), although the hash container will be a bit slower. For a sorted container like set or map, you'll have log-time insertions because it needs to look for the place to insert it before inserting it.
So, the conclusion, use std::unordered_set or std::unordered_map (if you need the key-value feature). And you don't need to check before doing the insertion, these are unique-key containers, they don't allow duplicates.
If std::unordered_set / std::unordered_map (from C++11) or std::tr1::unordered_set / std::tr1::unordered_map (since 2007) are not available to you (or any equivalent), then the next best alternative is std::set / std::map.
What would be an efficient implementation for a std::set insert member function? Because the data structure sorts elements based on std::less (operator < needs to be defined for the element type), it is conceptually easy to detect a duplicate.
How does it actually work internally? Does it make use of the red-back tree data structure (a mentioned implementation detail in the book of Josuttis)?
Implementations of the standard data structures may vary...
I have a problem where I am forced to have a (generally speaking) sets of integers which should be unique. The length of the sets varies so I am in need of dynamical data structure (based on my narrow knowledge, this narrows things down to list, set). The elements do not necessarily need to be sorted, but there may be no duplicates. Since the candidate sets always have a lot of duplicates (sets are small, up to 64 elements), will trying to insert duplicates into std::set with the insert member function cause a lot of overhead compared to std::list and another algorithm that may not resort to having the elements sorted?
Additional: the output set has a fixed size of 27 elements. Sorry, I forgot this... this works for a special case of the problem. For other cases, the length is arbitrary (lower than the input set).
If you're creating the entire set all at once, you could try using std::vector to hold the elements, std::sort to sort them, and std::unique to prune out the duplicates.
The complexity of std::set::insert is O(log n), or amortized O(1) if you use the "positional" insert and get the position correct (see e.g. http://cplusplus.com/reference/stl/set/insert/).
The underlying mechanism is implementation-dependent. It's often a red-black tree, but this is not mandated. You should look at the source code for your favourite implementation to find out what it's doing.
For small sets, it's possible that e.g. a simple linear search on a vector will be cheaper, due to spatial locality. But the insert itself will require all the following elements to be copied. The only way to know for sure is to profile each option.
When you only have 64 possible values known ahead of time, just take a bit field and flip on the bits for the elements actually seen. That works in n+O(1) steps, and you can't get less than that.
Inserting into a std::set of size m takes O(log(m)) time and comparisons, meaning that using an std::set for this purpose will cost O(n*log(n)) and I wouldn't be surprised if the constant were larger than for simply sorting the input (which requires additional space) and then discarding duplicates.
Doing the same thing with an std::list would take O(n^2) average time, because finding the insertion place in a list needs O(n).
Inserting one element at a time into an std::vector would also take O(n^2) average time – finding the insertion place is doable in O(log(m)), but elements need to me moved to make room. If the number of elements in the final result is much smaller than the input, that drops down to O(n*log(n)), with close to no space overhead.
If you have a C++11 compiler or use boost, you could also use a hash table. I'm not sure about the insertion characteristics, but if the number of elements in the result is small compared to the input size, you'd only need O(n) time – and unlike the bit field, you don't need to know the potential elements or the size of the result a priori (although knowing the size helps, since you can avoid rehashing).
I have the following requirements for a collection of objects:
Dynamic size (in theory unlimited, but in practice a couple of thousand should be more than enough)
Ordered, but allowing reorder and insertion at arbitrary locations.
Allows for deletion
Indexed Access - Random Access
Count
The objects I am storing are not large, a couple of properties and a small array or two (256 booleans)
Is there any built in classes I should know about before I go writing a linked list?
Original answer: That sounds like std::list (a doubly linked list) from the Standard Library.
New answer:
After your change to the specs, a std::vector might work as long as there aren't more than a few thousand elements and not a huge number of insertions and deletions in the middle of the vector. The linear complexity of insertion and deletion in the middle may be outweighed by the low constants on the vector operations. If you are doing a lot of insertions and deletions just at the beginning and end, std::deque might work as well.
-Insertion and Deletion: This is possible for any STL container, but the question is how long it takes to do it. Any linked-list container (list, map, set) will do this in constant time, while array-like containers (vector) will do it in linear time (with constant-amortized allocation).
-Sorting: Considering that you can keep a sorted collection at all times, this isn't much of an issue, any STL container will allow that. For map and set, you don't have to do anything, they already take care of keeping the collection sorted at all times. For vector or list, you have to do that work, i.e. you have to do binary search for the place where the new elements go and insert them there (but STL Algorithms has all the pieces you need for that).
-Resorting: If you need to take the current collection you have sorted with respect to rule A, and resort the collection with respect to rule B, this might be a problem. Containers like map and set are parametrized (as a type) by the sorting rule, this means that to resort it, you would have to extract every element from the original collection and insert them in a new collection with a new sorting rule. However, if you use a vector container, you can just use the STL sort function anytime to resort with whatever rule you like.
-Random Access: You said you needed random access. Personally, my definition of random access means that any element in the collection can be accessed (by index) in constant time. With that definition (which I think is quite standard), any linked-list implementation does not qualify and it leaves you with the only option of using an array-like container (e.g. std::vector).
Conclusion, to have all those properties, it would probably be best to use a std::vector and implement your own sorted insertion and sorted deletion (performing binary search into the vector to find the element to delete or the place to insert the new element). If your objects that you need to store are of significant size, and the data according to which they are sorted (name, ID, etc.) is small, you might consider splitting the problem by holding a unsorted linked-list of objects (with full information) and keeping a sorted vector of keys along with a pointer to the corresponding node in the linked-list (in that case, of course, use std::list for the former, and std::vector for the latter).
BTW, I'm no grand expert with STL containers, so I might have been wrong in the above, just think for yourself. Explore the STL for yourself, I'm sure you will find what you need, or at least all the pieces that you need. Maybe, look at Boost libraries too.
You haven't specified enough of your requirements to select the best container.
Dynamic size (in theory unlimited, but in practice a couple of thousand should be more than enough)
STL containers are designed to grow as needed.
Ordered, but allowing reorder and insertion at arbitrary locations.
Allowing reorder? A std::map can't be reordered: you can delete from one std::map and insert into another using a different ordering, but as different template instantiations the two variables will have different types. std::list has a sort() member function [thanks Blastfurnace for pointing this out], particularly efficient for large objects. A std::vector can be resorted easily using the non-member std::sort() function, particularly efficient for tiny objects.
Efficient insertion at arbitrary locations can be done in a map or list, but how will you find those locations? In a list, searching is linear (you must start from somewhere you already know about and scan forwards or backwards element by element). std::map provides efficient searching, as does an already-sorted vector, but inserting into a vector involves shifting (copying) all the subsequent elements to make space: that's a costly operation in the scheme of things.
Allows for deletion
All containers allow for deletion, but you have the exact-same efficiency issues as you do for insertion (i.e. fast for list if you already know the location, fast for map, deletion in vectors is slow, though you can "mark" elements deleted without removing them, e.g. making a string empty, having a boolean flag in a struct).
Indexed Access - Random Access
vector is indexed numerically (but can be binary searched), map by an arbitrary key (but no numerical index). list is not indexed and must be searched linearly from a known element.
Count
std::list provides an O(n) size() function (so that it can provide O(1) splice), but you can easily track the size yourself (assuming you won't splice). Other STL containers already have O(1) time for size().
Conclusions
Consider whether using a std::list will result in lots of inefficient linear searches for the element you need. If not, then a list does give you efficient insertion and deletion. Resorting is good.
A map or hash map will allow quick lookup and easy insertion/deletion, can't be resorted, but you can easily move the data out to another map with another sort criteria (with moderate efficiency.
A vector allows fast searching and in-place resorting, but the worst insert/deletion. It's the fastest for random-access lookup using the element index.
Hey, I right now have a list of a struct that I made, I sort this list everytime I add a new object, using the std::list sort method.
I want to know what would be faster, using a std::multimap for this or std::list,
since I'm iterating the whole list every frame (I am making a game).
I would like to hear your opinion, for what should I use for this incident.
std::multimap will probably be faster, as it is O(log n) per insertion, whereas an insert and sort of the list is O(n log n).
Depending on your usage pattern, you might be better off with sorted vectors. If you insert a whole bunch of items at once and then do a bunch of reads -- i.e. reads and writes aren't interleaved -- then you'll have better performance with vector, std::sort, and std::binary_search.
You might consider using the lower_bound algorithm to find where to insert into your list. http://stdcxx.apache.org/doc/stdlibref/lower-bound.html
Edit: In light of Neil's comment, note that this will work with any sequence container (vector, deque, etc.)
If you do not need Key/Value pairs std::set or std::multiset is probably better than using std::multimap.
Reference for std::set:
http://www.cplusplus.com/reference/stl/set/
Reference for std::multiset:
http://www.cplusplus.com/reference/stl/multiset/
Edit: (seems like it was unclear before)
It is in general better to use a container like std::(multi)set or std:(multi)map than using std::list and sorting it afterwards everytime an element is inserted because std::list does not perform very good in inserting elements in the middle of the container.
Generally speaking, iterating over a container is likely to take about as much time as iterating over another, so if you keep adding to a container and then iterating over it, it's mainly a question of picking a container that avoids constantly having to reallocate memory and inserts the way you want quickly.
Both list and multimap will avoid having to reallocate themselves simply from adding an element (like you could get with a vector), so it's primarily a question of how long it takes to insert. Adding to the end of a list will be O(1) while adding to a multimap will be O(log n). However, the multimap will insert the elements in sorted order, while if you want to have the list be sorted, you're going to have to either sort the list in O(n log n) or insert the element in a sorted manner with something like lower_bound which would be O(n). In either case, it will be far worse (in the worst case at least) to use the list.
Generally, if you're maintaining a container in sorted order and continually adding to it rather than creating it and sorting it once, sets and maps are more efficient since they're designed to be sorted. Of course, as always, if you really care about performance, profiling your specific application and seeing which works better is what you need to do. However, in this case, I'd say that it's almost a guarantee that multimap will be faster (especially if you have very many elements at all).