I use C++, say i want to store 40 usernames, I will simply use an array. However, if I want to store 40000 usernames is this still a good idea in terms of search speed? Which data structure should I use to improve this speed?
You need to specify what the insertion and removal requirements are. Do things need to be removed and inserted at random points in the sequence?
Also, why the requirement to search sequentially? Are you doing searches that aren't suitable for a hash table lookup?
At the moment I'd suggest a deque or a list. Often it's best to choose a container with the interface that makes for the simplest implementation for your algorithm and then only change the choice if the performance is inadequate and an alternative provides the necessary speedup.
A vector has two principle advantages, there is no per-object memory overhead, although vectors will over-allocate to prevent frequent copying and objects are stored contiguously so sequential access tends to be fast. These are also its disadvantages. Growing vectors require reallocation and copying, and insertion and removal from anywhere other than the end of the vector also require copying. Contiguous storage can produce problems for vectors with large numbers of objects or large objects as the contiguous storage requirements can be hard to satisfy even with only mild memory fragmentation.
A list doesn't require contigous storage but list nodes usually have a per-object overhead of two pointers (in most implementation). This can be significant in list of very small objects (e.g. in a list of pointers, each node is 3x the size of the data item). Insertion and removal from the middle of a list is very cheap though and list nodes never need to me moved in memory once created.
A deque uses chunked storage, so it has a low per-object overhead similar to a vector, but doesn't require contiguous storage over the whole container so doesn't have the same problem with fragmented memory spaces. It is often a very good choice for collections and is often overlooked.
As a rule of thumb, prefer vector to list or, diety forbid, C-style array.
After the vector is filled, make sure it is properly ordered using the sort algorithm. You can then search for a particular record using either find, binary_search or lower_bound. (You don't need to sort to use find.)
Seriously unless you are in a resource constrained environment (embedded platform, phone, or other). Use a std::map, save the effort of doing sorting or searching and let the container take care of everything. This will possibly be a sorted tree structure, probably balance (e.g. Red-Black), which means you will get good searching performance. Unless the size of you data is close to the size of one or two pointers, the memory overhead of whatever data structure you pick is negligable. You Graphics Card probably has more memory that you are going to use up for the data you are think about.
As others said there is very little good reason to use vanilla array, if you don't want to use a map use std::vector or std::list depending on whether you need insert/delete data (=>list) or not (=>vector)
Also consider if you really need all that data in memory, how about putting it on disk via sqlite. Or even use sqlite for in memory access. It all depends on what you need to do with your data.
std::vector and std::list seem good for this task. You can use an array if you know the maximum number of records beforehands.
If you need only sequentially search and storage, then list is the proper container.
Also, vector wouldn't be a bad choice.
Related
I would like some advice on which STL container best meets the following needs:
The collection is relatively short-lived.
The collection contains pointers.
Elements are added only at the end. The order of elements must be maintained.
The number of elements is unknown and may vary from hundreds to millions. The number is known only after the final element is added.
I may iterate over the elements several times.
After all elements are added, I need to sort the collection, based on the objects the pointers refer to.
After sorting, I may iterate over the elements several more times.
After that, the collection will be destroyed.
Thread safety is not required.
Here are my thoughts:
list: Requires a separate allocation for each element. More expensive traversal.
vector: Need to be reallocated as the collection grows. Best sort and traversal performance.
deque: Fewer allocations than list and fewer reallocations than vector. I don't know about behavior with respect to sort.
I am currently using list. The flowchart at In which scenario do I use a particular STL container? leads me to deque.
My knowledge of STL is old; I don't know about container types that have been added since 2003, so maybe there's something well-suited that I've never heard of.
std::vector<T*> will be the winner based on the points discussed.
Don't be afraid of the resizing that will need to occur--just reserve() a reasonable amount (say 500 if many of your collections will be around there).
Sorting performance with vector<T*> will also be very good.
Allocation and deallocation of each T will be important. Pay attention to this. For example you may want to allocate thousands of Ts at a time, to reduce the memory allocation overhead (and make it faster to deallocate everything at the end). This is known as an "arena" or "pool". You can probably store 32-bit relative pointers into the arena, saving half the pointer storage space.
And of course, if T is small you might consider storing it by value instead of by pointer.
I'm using a std::map to store about 20 million entries. If they were stored without any container overhead, it would take approximately 650MB of memory. However, since they are stored using std::map, it uses up about 15GB of memory (i.e. too much).
The reason I am using an std::map is because I need to find keys that are equal to/larger/smaller than x. This is why something like sparsehash wouldn't work (since, using that, I cannot find keys by comparison).
Is there an alternative to using std::map (or ordered maps in general) that would result in less memory usage?
EDIT: Writing performance is much more important than reading performance. It will probably only read ~10 entries, but I don't know which entries it will read.
One alternative would be to use flat_map from Boost.Containers: that supports the same interface as std::map, but is backed by a sorted contiguous array (think std::vector) instead of a tree. Or hand-roll your own solution based on the same idea.
Its performance characteristic is of course different, due to the different back-end. It's up to you to evaluate whether it's usable in your case.
Are you writing on-the-fly or one time before the lookup is done? If the later is the case, you shouldn't need a map, you could use std::vector and one-time sort.
You could just insert everything unsorted to the vector, sort one-time after everything is there (O(N * log N) as well as std::map, but much better performance characteristics) and then lookup in the sorted array (O(logN) as the std::map).
And especially if you know the number of elements before reading and could reserve the vector size upfront, that could work pretty well. Or at least if you know some "upper bound" to reserve perhaps slightly more than actually needed but avoid the reallocations.
Given your requirements:
Insertion needs to be quick
There are many elements to read
Read-back can be slow
You only read back data once
I'd consider typedef std::pair<uint64, thirty_six_byte_struct> element; and populate a std::list<element>. That will be hard to beat in terms of performance.
For reading back, I'd simply traverse the linked list, checking at every point if you need one of those elements. That's a O(N) traversal but as you say, you'll only do that once.
Turns out the issue wasn't std::map.
I realized was using 3 separate maps to represent various parts of the same data, and after slimming it down to 1, the difference in memory was entirely negligible.
Looking at the code a little more, I realized code I had written to free a really expensive struct (per element of the map) didn't actually work.
Fixing that part, it now uses <1GB of memory, as it should! :)
TL;DR: std::map's overhead is entirely negligible for this. The issue was my own.
I'm looking for ideas to implement a templatized sequence container data structure which can beat the performance of std::vector in as many features as possible and potentially perform much faster. It should support the following:
Finding the minimum element (and returning it's index)
Insertion at any index
Removal at any index
Accessing and updating any element by index (via operator[])
What would be some good ways to implement such a structure in C++?
You generally be pretty sure that the STL implementations of all containers tend to be very good at the range of tasks they were designed for. That is to say, you're unlikely to be able to build a container that is as robust as std::vector and quicker for all applications. However, generally speaking, it is almost always possible to beat a generic tool when optimizing for a specific application.
First, let's think about what a vector actually is. You can think of it as a pointer to a c-style array, except that its elements are stored on the heap. Unlike a c array, it also provides a bunch of methods that make it a little bit more convenient to manipulate. But like a c-array, all of it's data is stored contiguously in memory, so lookups are extremely cheap, but changing its size may require the entire array to be shifted elsewhere in memory to make room for the new elements.
Here are some ideas for how you could do each of the things you're asking for better than a vanilla std::vector:
Finding the minimum element: Search is typically O(N) for many containers, and certainly for a vector (because you need to iterate through all elements to find the lowest). You can make it O(1), or very close to free, by simply keeping the smallest element at all times, and only updating it when the container is changed.
Insertion at any index: If your elements are small and there are not many, I wouldn't bother tinkering here, just do what the vector does and keep elements contiguously next to each other to keep lookups quick. If you have large elements, store pointers to the elements instead of the elements themselves (boost's stable vector will do this for you). Keep in mind that this make lookup more expensive, because you now need to dereference the pointer, so whether you want to do this will depend on your application. If you know the number of elements you are going to insert, std::vector provides the reserve method which preallocates some memory for you, but what it doesn't do is allow you to decide how the size of the allocated memory grows. So if your application warrants lots of push_back operations without enough information to intelligently call reserve, you might be able to beat the standard std::vector implementation by tailoring the growth function of your container to your particular needs. Another option is using a linked list (e.g. std::list), which will beat an std::vector in insertions for larger containers. However, the cost here is that lookup (see 4.) will now become vastly slower (O(N) instead of O(1) for vectors), so you're unlikely to want to go down this path unless you plan to do more insertions/erasures than lookups.
Removal at any index: Similar considerations as for 2.
Accessing and updating any element by index (via operator[]): The only way you can beat std::vector in this regard is by making sure your data is in the cache when you try to access it. This is because lookup for a vector is essentially an array lookup, which is really just some pointer arithmetic and a pointer dereference. If you don't access your vector often you might be able to squeeze out a few clock cycles by using a custom allocator (see boost pools) and placing your pool close to the stack pointer.
I stopped writing mainly because there are dozens of ways in which you could approach this problem.
At the end of the day, this is probably more of an exercise in teaching you that the implementation of std::vector is likely to be extremely efficient for most compilers. All of these suggestions are essentially micro-optimizations (which are the root of all evil), so please don't blindly apply these in important code, as they're highly likely to end up costing you a lot of time and headache.
However, that's not to say you shouldn't tinker and learn for yourself, so by all means go ahead and try to beat it for your application and let us know how you go! Good luck :)
Is there an STL container similar to a list in that elements of lists are not stored contiguously? The size of this container can be up to 1000x1000 elements with each element being a vector containing 36 doubles. This would be a large chunk to store together (like ~200 megabytes). Is there a variant that instead stores pointers to its contents as a separate vector so it would allow for random access. Is there an STL container class for this that already exists or should I just store the pointers manually?
The container I need is actually a constant size so I think implementing it myself wouldnt be too difficult, but I was wondering if an STL container already exists for this. I'd like to avoid a vector because the list is large and the contents will be of medium size. If the vectors in the container don't need to reside next to each other then wouldn't it be better to separate them in a list to prevent running out of memory from fragmentation?
Both deque<array<double, 36>> and vector<vector<double>> would avoid the need for any really huge contiguous allocations.
The vector<vector<double>> is worse in those terms. For the numbers you specify it needs a contiguous allocation of 1000*1000*sizeof(vector<double>), which is low 10s of MB (most likely a vector is the size of 3 pointers). That's rarely a problem on a "proper computer" (desktop or server). The places where it would be a concern for fragmentation reasons (small virtual address space or no virtual addressing at all), you might also have a more fundamental problem that you don't have 300MB-ish of RAM anyway. But you could play extra-safe by avoiding it, since clearly there can exist environments where you could allocate 300MB total but not 12MB contiguously.
There is no std::array in C++03, but there's boost::array or you could easily write a class to represent 36 doubles.
vector<array<double, 36>> suffers worst from fragmentation, it requires a contiguous 250-MB allocation. Personally I don't find it easy to simulate in testing "the worst possible memory fragmentation we will ever face", but I'm not the best tester. That size of block is about where I start feeling a bit uneasy in a 32 bit process, but it will work fine in good conditions.
I highly recommend you to use the std::array class. It is constant sized, it supports random access to all elements, and has implementations of iterator, const_iterator, reverse_iterator, const_reverse_iterator. More about it: http://www.cplusplus.com/reference/stl/array/
It isn't clear what characteristic of std::list<T> you are after exactly. If you want a container whose elements stay put when adding or removing elements, you might want to have a look at std::deque<T>: when adding/removing elements at the front or the back all other element stay at the same location. That is, pointers and references to elements stay valid, unless elements are add or removed in the middle. Iterators get invalid on any insertion or removal. std::deque<T> provides random access.
There is no container directly given random access and support addition/removal at any poistion with the elements staying put. However, as others have pointed out, using a container of pointers provides such an interface. It may be necessary to wrap it to hide the use of pointers.
How large does a collection have to be for std::map to outpace a sorted std::vector >?
I've got a system where I need several thousand associative containers, and std::map seems to carry a lot of overhead in terms of CPU cache. I've heard somewhere that for small collections std::vector can be faster -- but I'm wondering where that line is....
EDIT: I'm talking about 5 items or fewer at a time in a given structure. I'm concerned most with execution time, not storage space. I know that questions like this are inherently platform-specific, but I'm looking for a "rule of thumb" to use.
Billy3
It's not really a question of size, but of usage.
A sorted vector works well when the usage pattern is that you read the data, then you do lookups in the data.
A map works well when the usage pattern involves a more or less arbitrary mixture of modifying the data (adding or deleting items) and doing queries on the data.
The reason for this is fairly simple: a map has higher overhead on an individual lookup (thanks to using linked nodes instead of a monolithic block of storage). An insertion or deletion that maintains order, however, has a complexity of only O(lg N). An insertion or deletion that maintains order in a vector has a complexity of O(N) instead.
There are, of course, various hybrid structures that can be helpful to consider as well. For example, even when data is being updated dynamically, you often start with a big bunch of data, and make a relatively small number of changes at a time to it. In this case, you can load your data into memory into a sorted vector, and keep the (small number of) added objects in a separate vector. Since that second vector is normally quite small, you simply don't bother with sorting it. When/if it gets too big, you sort it and merge it with the main data set.
Edit2: (in response to edit in question). If you're talking about 5 items or fewer, you're probably best off ignoring all of the above. Just leave the data unsorted, and do a linear search. For a collection this small, there's effectively almost no difference between a linear search and a binary search. For a linear search you expect to scan half the items on average, giving ~2.5 comparisons. For a binary search you're talking about log2 N, which (if my math is working this time of the morning) works out to ~2.3 -- too small a difference to care about or notice (in fact, a binary search has enough overhead that it could very easily end up slower).
If you say "outspace" you mean consuming more space (aka memory), then it's very likely that vector will always be more efficient (the underlying implementation is an continous memory array with no othe data, where map is a tree, so every data implies using more space). This however depends on how much the vector reserves extra space for future inserts.
When it is about time (and not space), vector will also always be more effective (doing a dichotomic search). But it will be extreamly bad for adding new elements (or removing them).
So : no simple answer ! Look-up the complexities, think about the uses you are going to do. http://www.cplusplus.com/reference/stl/
The main issue with std::map is an issue of cache, as you pointed.
The sorted vector is a well-known approach: Loki::AssocVector.
For very small datasets, the AssocVector should crush the map despite the copy involved during insertion simply because of cache locality. The AssocVector will also outperform the map for read-only usage. Binary search is more efficient there (less pointers to follow).
For all other uses, you'll need to profile...
There is however an hybrid alternative that you might wish to consider: using the Allocator parameter of the map to restrict the memory area where the items are allocated, thus minimizing the locality reference issue (the root of cache misses).
There is also a paradigm shift that you might consider: do you need sorted items, or fast look-up ?
In C++, the only STL-compliant containers for fast-lookup have been implemented in terms of Sorted Associative Containers for years. However the up-coming C++0x features the long awaited unordered_map which could out perform all the above solutions!
EDIT: Seeing as you're talking about 5 items or fewer:
Sorting involves swapping items. When inserting into std::map, that will only involve pointer swaps. Whether a vector or map will be faster depends on how fast it is to swap two elements.
I suggest you profile your application to figure it out.
If you want a simple and general rule, then you're out of luck - you'll need to consider at least the following factors:
Time
How often do you insert new items compared to how often you lookup?
Can you batch inserts of new items?
How expensive is sorting you vector? Vectors of elements that are expensive to swap become very expensive to sort - vectors of pointers take far less.
Memory
How much overhead per allocation does the allocator you're using have? std::map will perform one allocation per item.
How big are your key/value pairs?
How big are your pointers? (32/64 bit)
How fast does you implementation of std::vector grow? (Popular growth factors are 1.5 and 2)
Past a certain size of container and element, the overhead of allocation and tree pointers will become outweighed by the cost of the unused memory at the end of the vector - but by far the easiest way to find out if and when this occurs is by measuring.
It has to be in the millionth items. And even there ...
I am more thinking here to memory usage and memory accesses. Under hundreds of thousands, take whatever you want, there will be no noticeable difference. CPUs are really fast these days, and the bottleneck is memory latency.
But even with millions of items, if your map<> has been build by inserting elements in random order. When you want to traverse your map (in sorted order) you'll end up jumping around randomly in the memory, stalling the CPU for memory to be available, resulting in poor performance.
On the other side, if your millions of items are in a vector, traversing it is really fast, taking advantage of the CPU memory accesses predictions.
As other have written, it depends on your usage.
Edit: I would more question the way to organize your thousands of associative containers than the containers themselves if they contain only 5 items.