I want to know which data-structures are more efficient for iterating through their elements between std::set, std::map and std::unordered_set, std::unordered_map.
I searched through SO and I found this question. The answers either propose to copy the elements in a std::vector or to use Boost.Container, which IMHO don't answer my question.
My purpose is to keep in a container a big number of unique elements, that most of the time I want to iterate through them. Insertions and extractions are more rare. I want to avoid std::vector in combination with std::unique.
Lets consider set vs unordered_set.
The main difference here is the 'nature' of the iteration, that is the traversal of the set will give you the elements in order while traversing a range in an unordered set will give you a bunch of values in no particular order.
Suppose you want to traverse a range [it1, it2]. If we exclude the lookup time that's needed to find elements it1 and it2 there can be no direct mapping from one case to another since the elements in between are not guarrandeed to be the same even if you've used the same elements to construct the container.
There are cases however where something like this has meaning when e.g. you want to traverse a fixed number of elements (regardless of what they are) or when you need to traverse the whole container. In such cases you need to consider implementation mechanics :
Sets are usually implemented like Red–black trees (a form of binary search trees). Like all binary search trees allow efficient in-order traversal (LRR: left root right) of their elements. That is to traverse you pay the cost of pointer chasing (just like traversing a list).
Unordered sets on the other hand are hash tables and to my knowledge the STL implementation uses hashing with chaining. That means (in a very very high level) that what's used for the structure is a (contiguous) buffer where each element is the head of a chain (list) that contains the elements. The way the elements are layed out across those chains (buckets) and across the buffer will affect the traversal time, however you'll be chasing pointers once again jumping through differents lists as well this time. I don't think it'll vary significantly from the tree case but won't be any better for sure.
In any case micro tuning and benchmarking will give you the answer for your particular application.
The difference does not lie between the ordering or lack of one but in the backing container. If it's a contiguous memory it should be fast to iterate over, due to simple implementation of iterator and cache friendliness.
Unordered containers are usually stored as a vector of vectors (or a similar thing), while ordered containers are implemented using trees, but it is left for implementation after all. This would suggest that iterating over unordered version should be waster. However this is left for implementation after all, and I saw implementations (which bent rules a little to be fair) with different behaviour.
Generally speaking, container performance is quite a complex topic and usually has to be tested in actual application to get reliable answer. There is plenty on implemention-defined stuff that might affect the performance. I'd go with hash_set if I had to go in blind. Copying into a vector might also turn out a good option.
EDIT: As #TonyD said in it's comment, there is a rule, that disallows invalidating iterators during addition of element when the max_load_factor() is not exceeded, this practically rules out backing containers which are contiguous in memory.
Thus, copying everything into a vector seems like even more reasonable option. If you need to remove duplicates, a feasible option might be to use http://en.cppreference.com/w/cpp/algorithm/sort and have dupes easily ignored. I have heard that using vector and sort to have a sorted array (or vector) is quite often a used option in case of need for a container that needs to be sorter and is being iterated over more often than modified.
iterate from fastest to slowest should be : set > map > unordered_set > unordered_map;
set is a little lighter than map, and they are ordered with binary tree rule, so should be faster than unordered_ containers.
Related
I'm doing a little research about searching and sorting algorithms in the Standard library. I couldn't find something about those questions. I hope someone can help me out. You can also send me links if you know some.
Does the searching behavior change if the data is not sorted compared to one which is sorted?
How can I know if it is better to use std::sort() on a vector instead of maybe to copy the vector to an already sorted set? That is just an example. I hoped to find some explanations on the web which ways are the best for searching or sorting, but I didn't.
How can I adapt the behavior of the searching and sorting algorithms to make it more efficient?
Does the searching behavior change if the data is not sorted compared
to one which is sorted?
Depends. If you access your data in a vector/array by position, there's no performance improvement, and there's no need for sorting neither.
Searching can be done linearly, binary, keys, and by hash function.
For small (I guess something below a few dozens of items) and contiguous containers (e.g. a vector) linear search can be the fastest, just because of cache-friendly memory layout.
Binary search has O(log N) complexity which is likely the best you can get... I'm thinking in Information theory. It requires that you sort previously the container. It's useful for frequetly searches in the same container.
A std::set (and its cousin std::map) uses internally a tree, which makes searching O(log N) complexity too. Useful if you search by keys, instead of some criteria of your items. The drawback is that it's a bit slower on building (always keep sorted) than fill a vector an later sort it.
A hashmap or hashtable uses a function for getting the bucket where the item lies. The complexity is something near to O(1), depending on number of items and the function used (collisions issue).
As you see, selecting a type of container depends on how are you going to handle your data. Choose the one that fits your requirements.
How can I know if it is better to use std::sort() on a vector instead
of maybe to copy the vector to an already sorted set?
std::sort changes the container so the result is, obviously, sorted. If you need the original, unordered, container, then make a copy and sort the copy. Sorting the whole of the container is better that "insert-item-so-container-is-always-sorted" for all items, specially with a vector (many memory reallocations); a set/map filling process may be not that slow.
How can I adapt the behavior of the searching and sorting algorithms
to make it more efficient?
It's not clear to me what you mean. But, "The end justifies the means". Again, choose the container that servers best for your data handling.
Does the searching behavior change if the data is not sorted compared to one which is sorted?
No. It depends on the algorithm you choose. General search std::find is O(n), binary search std::lower_bound is O(log n), but it works only on sorted ranges.
How can I know if it is better to use std::sort() on a vector instead of maybe to copy the vector to an already sorted set? That is just an example. I hoped to find some explanations on the web which ways are the best for searching or sorting, but I didn't.
You can write a benchmark and measure. You can sort an std::vector (without duplicated elements) by copying it into an std::set, which maintains sorted order internally. std::set is typically implemented as a red-black tree and has in general high memory fragmentation in contrast to contiguous std::vector. So it is easy to predict the result. Alexander Stepanov discusses (if I remember correctly) this particular example in his lectures available on YouTube.
so my applications has containers with 100 million and more elements.
I'm on the hunt for a container which behaves - time-wise - better than std::deque (let alone std::vector) with respect to frequent insertions and deletions all over the container ... including near the middle. Access time to the n-th element does not need to be as fast as vector, but should definetely be better than full traversal like in std::list (which has a huge memory overhead per element anyway).
Elements should be treated ordered by index (like vector, deque, list), so std::set or std::unordered_set also do not work well.
Before I sit down and code such a container myself: has anyone seen such a beast already? I'm pretty sure the STL hasn't anything like this, looking to BOOST I did not find something I could use but I may be wrong.
Any hints?
There's a whole STL replacement for big data, in case your app is centric to such data:
STXXL - http://stxxl.sourceforge.net/
edit: I was actually a bit fast to answer. 100 million is not really a large number. E.g., if each element is one byte, you could save it in a 96MiB array. So whether STXXL is any useful, the size of an element should be significantly bigger.
I think you can get the performance characteristics that you want with a skip list:
https://en.wikipedia.org/wiki/Skip_list#Indexable_skiplist
It's the "indexable" part that you're interested in, of course -- you don't actually want the items to be sorted. So some modification is needed that I leave as an exercise.
You might find that 100 million list nodes begins to strain a 32 bit address space, but probably not an issue in 64 bits.
1) If the data is highly sparse, i.e. has lots of zeroes or can be expressed as such, I would highly recommend a data structure that takes advantage of that:
sparselib++ for matrices
sparsehash for hash maps
2) Hash maps should do O(1) for all the operations you describe and the sparsehash implementation I mentioned earlier is particularly space-efficient; it also includes a sparsetable type which is a bit more low-level and can be used in place of an array.
3) If the strict ordering is not that important (it probably is, because you mentioned elements should be treated ordered by index), you can swap the elements you want to erase to the end of the vector and then resize to do removal in O(1). Insertion would just be push_back.
Try a hash map. The STL has several, all with the unordered naming prefix , such as unorderd_map, etc. It has constant time insertion and look up given a good hashing algorithm. With your 'huge' data set the hash map would most likely cover your needs. Making a slight change to the application to cover the differences in the interfaces is trivial.
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.
I have data that is a set of ordered ints
[0] = 12345
[1] = 12346
[2] = 12454
etc.
I need to check whether a value is in the collection in C++, what container will have the lowest complexity upon retrieval? In this case, the data does not grow after initiailization. In C# I would use a dictionary, in c++, I could either use a hash_map or set. If the data were unordered, I would use boost's unordered collections. However, do I have better options since the data is ordered? Thanks
EDIT: The size of the collection is a couple of hundred items
Just to detail a bit over what have already been said.
Sorted Containers
The immutability is extremely important here: std::map and std::set are usually implemented in terms of binary trees (red-black trees for my few versions of the STL) because of the requirements on insertion, retrieval and deletion operation (and notably because of the invalidation of iterators requirements).
However, because of immutability, as you suspected there are other candidates, not the least of them being array-like containers. They have here a few advantages:
minimal overhead (in term of memory)
contiguity of memory, and thus cache locality
Several "Random Access Containers" are available here:
Boost.Array
std::vector
std::deque
So the only thing you actually need to do can be broken done in 2 steps:
push all your values in the container of your choice, then (after all have been inserted) use std::sort on it.
search for the value using std::binary_search, which has O(log(n)) complexity
Because of cache locality, the search will in fact be faster even though the asymptotic behavior is similar.
If you don't want to reinvent the wheel, you can also check Alexandrescu's [AssocVector][1]. Alexandrescu basically ported the std::set and std::map interfaces over a std::vector:
because it's faster for small datasets
because it can be faster for frozen datasets
Unsorted Containers
Actually, if you really don't care about order and your collection is kind of big, then a unordered_set will be faster, especially because integers are so trivial to hash size_t hash_method(int i) { return i; }.
This could work very well... unless you're faced with a collection that somehow causes a lot of collisions, because then unsorted containers will search over the "collisions" list of a given hash in linear time.
Conclusion
Just try the sorted std::vector approach and the boost::unordered_set approach with a "real" dataset (and all optimizations on) and pick whichever gives you the best result.
Unfortunately we can't really help more there, because it heavily depends on the size of the dataset and the repartition of its elements
If the data is in an ordered random-access container (e.g. std::vector, std::deque, or a plain array), then std::binary_search will find whether a value exists in logarithmic time. If you need to find where it is, use std::lower_bound (also logarithmic).
Use a sorted std::vector, and use a std::binary_search to search it.
Your other options would be a hash_map (not in the C++ standard yet but there are other options, e.g. SGI's hash_map and boost::unordered_map), or an std::map.
If you're never adding to your collection, a sorted vector with binary_search will most likely have better performance than a map.
I'd suggest using a std::vector<int> to store them and a std::binary_search or std::lower_bound to retrieve them.
Both std::unordered_set and std::set add significant memory overhead - and even though the unordered_set provides O(1) lookup, the O(logn) binary search will probably outperform it given that the data is stored contiguously (no pointer following, less chance of a page fault etc.) and you don't need to calculate a hash function.
If you already have an ordered array or std::vector<int> or similar container of the data, you can just use std::binary_search to probe each value. No setup time, but each probe will take O(log n) time, where n is the number of ordered ints you've got.
Alternately, you can use some sort of hash, such as boost::unordered_set<int>. This will require some time to set up, and probably more space, but each probe will take O(1) time on the average. (For small n, this O(1) could be more than the previous O(log n). Of course, for small n, the time is negligible anyway.)
There is no point in looking at anything like std::set or std::map, since those offer no advantage over binary search, given that the list of numbers to match will not change after being initialized.
So, the questions are the approximate value of n, and how many times you intend to probe the table. If you aren't going to check many values to see if they're in the ints provided, then setup time is very important, and std::binary_search on the sorted container is the way to go. If you're going to check a lot of values, it may be worth setting up a hash table. If n is large, the hash table will be faster for probing than binary search, and if there's a lot of probes this is the main cost.
So, if the number of ints to compare is reasonably small, or the number of probe values is small, go with the binary search. If the number of ints is large, and the number of probes is large, use the hash table.
I'm fairly new to the STL, so I was wondering whether there are any dynamically sortable containers? At the moment my current thinking is to use a vector in conjunction with the various sort algorithms, but I'm not sure whether there's a more appropriate selection given the (presumably) linear complexity of inserting entries into a sorted vector.
To clarify "dynamically", I am looking for a container that I can modify the sorting order at runtime - e.g. sort it in an ascending order, then later re-sort in a descending order.
You'll want to look at std::map
std::map<keyType, valueType>
The map is sorted based on the < operator provided for keyType.
Or
std::set<valueType>
Also sorted on the < operator of the template argument, but does not allow duplicate elements.
There's
std::multiset<valueType>
which does the same thing as std::set but allows identical elements.
I highly reccomend "The C++ Standard Library" by Josuttis for more information. It is the most comprehensive overview of the std library, very readable, and chock full of obscure and not-so-obscure information.
Also, as mentioned by 17 of 26, Effective Stl by Meyers is worth a read.
If you know you're going to be sorting on a single value ascending and descending, then set is your friend. Use a reverse iterator when you want to "sort" in the opposite direction.
If your objects are complex and you're going to be sorting in many different ways based on the member fields within the objects, then you're probably better off with using a vector and sort. Try to do your inserts all at once, and then call sort once. If that isn't feasible, then deque may be a better option than the vector for large collections of objects.
I think that if you're interested in that level of optimization, you had better be profiling your code using actual data. (Which is probably the best advice anyone here can give: it may not matter that you call sort after each insert if you're only doing it once in a blue moon.)
It sounds like you want a multi-index container. This allows you to create a container and tell that container the various ways you may want to traverse the items in it. The container then keeps multiple lists of the items, and those lists are updated on each insert/delete.
If you really want to re-sort the container, you can call the std::sort function on any std::deque, std::vector, or even a simple C-style array. That function takes an optional third argument to determine how to sort the contents.
The stl provides no such container. You can define your own, backed by either a set/multiset or a vector, but you are going to have to re-sort every time the sorting function changes by either calling sort (for a vector) or by creating a new collection (for set/multiset).
If you just want to change from increasing sort order to decreasing sort order, you can use the reverse iterator on your container by calling rbegin() and rend() instead of begin() and end(). Both vector and set/multiset are reversible containers, so this would work for either.
std::set is basically a sorted container.
You should definitely use a set/map. Like hazzen says, you get O(log n) insert/find. You won't get this with a sorted vector; you can get O(log n) find using binary search, but insertion is O(n) because inserting (or deleting) an item may cause all existing items in the vector to be shifted.
It's not that simple. In my experience insert/delete is used less often than find. Advantage of sorted vector is that it takes less memory and is more cache-friendly. If happen to have version that is compatible with STL maps (like the one I linked before) it's easy to switch back and forth and use optimal container for every situation.
in theory an associative container (set, multiset, map, multimap) should be your best solution.
In practice it depends by the average number of the elements you are putting in.
for less than 100 elements a vector is probably the best solution due to:
- avoiding continuous allocation-deallocation
- cache friendly due to data locality
these advantages probably will outperform nevertheless continuous sorting.
Obviously it also depends on how many insertion-deletation you have to do. Are you going to do per-frame insertion/deletation?
More generally: are you talking about a performance-critical application?
remember to not prematurely optimize...
The answer is as always it depends.
set and multiset are appropriate for keeping items sorted but are generally optimised for a balanced set of add, remove and fetch. If you have manly lookup operations then a sorted vector may be more appropriate and then use lower_bound to lookup the element.
Also your second requirement of resorting in a different order at runtime will actually mean that set and multiset are not appropriate because the predicate cannot be modified a run time.
I would therefore recommend a sorted vector. But remember to pass the same predicate to lower_bound that you passed to the previous sort as the results will be undefined and most likely wrong if you pass the wrong predicate.
Set and multiset use an underlying binary tree; you can define the <= operator for your own use. These containers keep themselves sorted, so may not be the best choice if you are switching sort parameters. Vectors and lists are probably best if you are going to be resorting quite a bit; in general list has it's own sort (usually a mergesort) and you can use the stl binary search algorithm on vectors. If inserts will dominate, list outperforms vector.
STL maps and sets are both sorted containers.
I second Doug T's book recommendation - the Josuttis STL book is the best I've ever seen as both a learning and reference book.
Effective STL is also an excellent book for learning the inner details of STL and what you should and shouldn't do.
For "STL compatible" sorted vector see A. Alexandrescu's AssocVector from Loki.