I read a lot about unordered_map not being very fast but I wonder what's the best alternative to do this:
I need to buffer calculation results for a function of an integer argument. I don't know ahead of time what range or interval will be requested. Storing in a vector with maximal resolution would cost way too much memory.
So I'm using
unordered_map<unsigned long, pair<T, long>>
Where the key is the argument of the function to be computed, the first of the pair the result of the computation of type T, and the second of the pair a version information for that computation.
Only if the unordered_map does not contain the element or it contains it but the version is outdated, the computation is carried out and then added to the unordered_map. The lookup function looks something like this:
template<typename T> class BufferClass{
long MyVersion;
unordered_map<unsigned long, pair<T,long>> Buffer;
public:
BufferClass(): MyVersion{1} {};
T* GetIfValid(unsigned long index)
{
if (!Buffer.count(index)) return nullptr;
pair <T,long> &x{Buffer.at(index)};
if (x.second!=MyVersion) return nullptr;
return &x.first;
}
/* ...Functions to set elements...*/
}
As you can see, I combined element validity check and retrieval in one function, so that I only need one lookup for both.
The profiler shows most of the computation time is used up in the hash function __constrain_hash related to unordered_map.
What would be the fastest way to store and retrieve values like that? The list of stored indices is expected to be non-continuous (there will be a lot of "holes") and first and last index are also mostly unknown.
T will generally be a "small" data type (like double or complex).
Thanks!
Martin
In your code, there could be two hash lookup in one query, one invoked in count() and the other invoked in at(). It is redundant, use unordered_map::find instead, see here.
Sample code:
const auto iter = Buffer.find(index);
if(iter != Buffer.end()) //Found something, so the return value is not end()
{
return &(iter->first);
}
else return nullptr;
In my opinion, unordered_map is slow but not that slow, for 99.9% usage is fast enough. You may want to check whether you call this function (unnecessarily) too many times. Using other fast implementation is not free, it could bloat your code base, harm your application's compatibility with different host systems or so on. If you think std::unordered_map is unreasonably slow, it is almost always because you got somewhere wrong in your work. (either your estimation or your code implementation)
BTW, another thing to mention: You said T is a small data type right? then return its value instead of pointer to it, it is faster and safer.
One thing that strikes me as odd about your implementation is the following two lines:
if (!Buffer.count(index)) return nullptr;
pair <T,long> &x{Buffer.at(index)};
This code is checking if the key exists, then throws away the result and searches for the same key again with bounds checking to boot. I think you'll find searching once with std::unordered_map<unsigned long, std::pair<T, long>>::find and reusing the result to be preferable:
auto it = Buffer.find(index);
if (it == Buffer.end()) return nullptr;
auto& x = *it;
Related
Background: I'm new to C++. I have a std::map and am trying to search for elements by key.
Problem: Performance. The map::find() function slows down when the map gets big.
Preferred approach: I often know roughly where in the map the element should be; I can provide a [first,last) range to search in. This range is always small w.r.t. the number of elements in the map. I'm interested in writing a short binary search utility function with boundary hinting.
Attempt: I stole the below function from https://en.cppreference.com/w/cpp/algorithm/lower_bound and did some rough benchmarks. This function seems to be much slower than map::find() for maps large and small, regardless of the size or position of the range hint provided. I replaced the comparison statements (it->first < value) with a comparison of random ints and the slowdown appeared to resolve, so I think the slowdown may be caused by the dereferencing of it->first.
Question: Is the dereferencing the issue? Or is there some kind of unnecessary copy/move action going on? I think I remember reading that maps don't store their element nodes sequentially in memory, so am I just getting a bunch of cache misses? What is the likely cause of the slowdown, and how would I go about fixing it?
/* #param first Iterator pointing to the first element of the map to search.
* #param distance Number of map elements in the range to search.
* #param key Map key to search for. NOTE: Type validation is not a concern just yet.
*/
template<class ForwardIt, class T>
ForwardIt binary_search_map (ForwardIt& first, const int distance, const T& key) {
ForwardIt it = first;
typename std::iterator_traits<ForwardIt>::difference_type count, step;
count = distance;
while (count > 0) {
it = first;
step = count/2;
std::advance(it, step);
if (it->first < value) {
first = ++it;
count -= step + 1;
}
else if (it->first > value)
count = step;
else {
first = it;
break;
}
}
return first;
}
There is a reason that std::map::find() exists. The implementation already does a binary search, as the std::map has a balanced binary tree as implementation.
Your implementation of binary search is much slower because you can't take advantage of that binary tree.
If you want to take the middle of the map, you start with std::advance it takes the first node (which is at the leaf of the tree) and navigates through several pointers towards what you consider to be the middle. Afterwards, you again need to go from one of these leaf nodes to the next. Again following a lot of pointers.
The result: next to a lot more looping, you get a lot of cache misses, especially when the map is large.
If you want to improve the lookups in your map, I would recommend using a different structure. When ordering ain't important, you could use std::unordered_map. When order is important, you could use a sorted std::vector<std::pair<Key, Value>>. In case you have boost available, this already exists in a class called boost::container::flat_map.
I was set a homework challenge as part of an application process (I was rejected, by the way; I wouldn't be writing this otherwise) in which I was to implement the following functions:
// Store a collection of integers
class IntegerCollection {
public:
// Insert one entry with value x
void Insert(int x);
// Erase one entry with value x, if one exists
void Erase(int x);
// Erase all entries, x, from <= x < to
void Erase(int from, int to);
// Return the count of all entries, x, from <= x < to
size_t Count(int from, int to) const;
The functions were then put through a bunch of tests, most of which were trivial. The final test was the real challenge as it performed 500,000 single insertions, 500,000 calls to count and 500,000 single deletions.
The member variables of IntegerCollection were not specified and so I had to choose how to store the integers. Naturally, an STL container seemed like a good idea and keeping it sorted seemed an easy way to keep things efficient.
Here is my code for the four functions using a vector:
// Previous bit of code shown goes here
private:
std::vector<int> integerCollection;
};
void IntegerCollection::Insert(int x) {
/* using lower_bound to find the right place for x to be inserted
keeps the vector sorted and makes life much easier */
auto it = std::lower_bound(integerCollection.begin(), integerCollection.end(), x);
integerCollection.insert(it, x);
}
void IntegerCollection::Erase(int x) {
// find the location of the first element containing x and delete if it exists
auto it = std::find(integerCollection.begin(), integerCollection.end(), x);
if (it != integerCollection.end()) {
integerCollection.erase(it);
}
}
void IntegerCollection::Erase(int from, int to) {
if (integerCollection.empty()) return;
// lower_bound points to the first element of integerCollection >= from/to
auto fromBound = std::lower_bound(integerCollection.begin(), integerCollection.end(), from);
auto toBound = std::lower_bound(integerCollection.begin(), integerCollection.end(), to);
/* std::vector::erase deletes entries between the two pointers
fromBound (included) and toBound (not indcluded) */
integerCollection.erase(fromBound, toBound);
}
size_t IntegerCollection::Count(int from, int to) const {
if (integerCollection.empty()) return 0;
int count = 0;
// lower_bound points to the first element of integerCollection >= from/to
auto fromBound = std::lower_bound(integerCollection.begin(), integerCollection.end(), from);
auto toBound = std::lower_bound(integerCollection.begin(), integerCollection.end(), to);
// increment pointer until fromBound == toBound (we don't count elements of value = to)
while (fromBound != toBound) {
++count; ++fromBound;
}
return count;
}
The company got back to me saying that they wouldn't be moving forward because my choice of container meant the runtime complexity was too high. I also tried using list and deque and compared the runtime. As I expected, I found that list was dreadful and that vector took the edge over deque. So as far as I was concerned I had made the best of a bad situation, but apparently not!
I would like to know what the correct container to use in this situation is? deque only makes sense if I can guarantee insertion or deletion to the ends of the container and list hogs memory. Is there something else that I'm completely overlooking?
We cannot know what would make the company happy. If they reject std::vector without concise reasoning I wouldn't want to work for them anyway. Moreover, we dont really know the precise requirements. Were you asked to provide one reasonably well performing implementation? Did they expect you to squeeze out the last percent of the provided benchmark by profiling a bunch of different implementations?
The latter is probably too much for a homework challenge as part of an application process. If it is the first you can either
roll your own. It is unlikely that the interface you were given can be implemented more efficiently than one of the std containers does... unless your requirements are so specific that you can write something that performs well under that specific benchmark.
std::vector for data locality. See eg here for Bjarne himself advocating std::vector rather than linked lists.
std::set for ease of implementation. It seems like you want the container sorted and the interface you have to implement fits that of std::set quite well.
Let's compare only isertion and erasure assuming the container needs to stay sorted:
operation std::set std::vector
insert log(N) N
erase log(N) N
Note that the log(N) for the binary_search to find the position to insert/erase in the vector can be neglected compared to the N.
Now you have to consider that the asymptotic complexity listed above completely neglects the non-linearity of memory access. In reality data can be far away in memory (std::set) leading to many cache misses or it can be local as with std::vector. The log(N) only wins for huge N. To get an idea of the difference 500000/log(500000) is roughly 26410 while 1000/log(1000) is only ~100.
I would expect std::vector to outperform std::set for considerably small container sizes, but at some point the log(N) wins over cache. The exact location of this turning point depends on many factors and can only reliably determined by profiling and measuring.
Nobody knows which container is MOST efficient for multiple insertions / deletions. That is like asking what is the most fuel-efficient design for a car engine possible. People are always innovating on the car engines. They make more efficient ones all the time. However, I would recommend a splay tree. The time required for a insertion or deletion is a splay tree is not constant. Some insertions take a long time and some take only a very a short time. However, the average time per insertion/deletion is always guaranteed to be be O(log n), where n is the number of items being stored in the splay tree. logarithmic time is extremely efficient. It should be good enough for your purposes.
The first thing that comes to mind is to hash the integer value so single look ups can be done in constant time.
The integer value can be hashed to compute an index in to an array of bools or bits, used to tell if the integer value is in the container or not.
Counting and and deleting large ranges could be sped up from there, by using multiple hash tables for specific integer ranges.
If you had 0x10000 hash tables, that each stored ints from 0 to 0xFFFF and were using 32 bit integers you could then mask and shift the upper half of the int value and use that as an index to find the correct hash table to insert / delete values from.
IntHashTable containers[0x10000];
u_int32 hashIndex = (u_int32)value / 0x10000;
u_int32int valueInTable = (u_int32)value - (hashIndex * 0x10000);
containers[hashIndex].insert(valueInTable);
Count for example could be implemented as so, if each hash table kept count of the number of elements it contained:
indexStart = startRange / 0x10000;
indexEnd = endRange / 0x10000;
int countTotal = 0;
for (int i = indexStart; i<=indexEnd; ++i) {
countTotal += containers[i].count();
}
Not sure if using sorting really is a requirement for removing the range. It might be based on position. Anyway, here is a link with some hints which STL container to use.
In which scenario do I use a particular STL container?
Just FYI.
Vector maybe a good choice, but it does a lot of re allocation, as you know. I prefer deque instead, as it doesn't require big chunk of memory to allocate all items. For such requirement as you had, list probably fit better.
Basic solution for this problem might be std::map<int, int>
where key is the integer you are storing and value is the number of occurences.
Problem with this is that you can not quickly remove/count ranges. In other words complexity is linear.
For quick count you would need to implement your own complete binary tree where you can know the number of nodes between 2 nodes(upper and lower bound node) because you know the size of tree, and you know how many left and right turns you took to upper and lower bound nodes. Note that we are talking about complete binary tree, in general binary tree you can not make this calculation fast.
For quick range remove I do not know how to make it faster than linear.
I read through some posts and "wikis" but still cannot decide what approach is suitable for my problem.
I create a class called Sample which contains a certain number of compounds (lets say this is another class Nuclide) at a certain relative quantity (double).
Thus, something like (pseudo):
class Sample {
map<Nuclide, double>;
}
If I had the nuclides Ba-133, Co-60 and Cs-137 in the sample, I would have to use exactly those names in code to access those nuclides in the map. However, the only thing I need to do, is to iterate through the map to perform calculations (which nuclides they are is of no interest), thus, I will use a for- loop. I want to iterate without paying any attention to the key-names, thus, I would need to use an iterator for the map, am I right?
An alternative would be a vector<pair<Nuclide, double> >
class Sample {
vector<pair<Nuclide, double> >;
}
or simply two independent vectors
Class Sample {
vector<Nuclide>;
vector<double>;
}
while in the last option the link between a nuclide and its quantity would be "meta-information", given by the position in the respective vector only.
Due to my lack of profound experience, I'd ask kindly for suggestions of what approach to choose. I want to have the iteration through all available compounds to be fast and easy and at the same time keep the logical structure of the corresponding keys and values.
PS.: It's possible that the number of compunds in a sample is very low (1 to 5)!
PPS.: Could the last option be modified by some const statements to prevent changes and thus keep the correct order?
If iteration needs to be fast, you don't want std::map<...>: its iteration is a tree-walk which quickly gets bad. std::map<...> is really only reasonable if you have many mutations to the sequence and you need the sequence ordered by the key. If you have mutations but you don't care about the order std::unordered_map<...> is generally a better alternative. Both kinds of maps assume you are looking things up by key, though. From your description I don't really see that to be the case.
std::vector<...> is fast to iterated. It isn't ideal for look-ups, though. If you keep it ordered you can use std::lower_bound() to do a std::map<...>-like look-up (i.e., the complexity is also O(log n)) but the effort of keeping it sorted may make that option too expensive. However, it is an ideal container for keeping a bunch objects together which are iterated.
Whether you want one std::vector<std::pair<...>> or rather two std::vector<...>s depends on your what how the elements are accessed: if both parts of an element are bound to be accessed together, you want a std::vector<std::pair<...>> as that keeps data which is accessed together. On the other hand, if you normally only access one of the two components, using two separate std::vector<...>s will make the iteration faster as more iteration elements fit into a cache-line, especially if they are reasonably small like doubles.
In any case, I'd recommend to not expose the external structure to the outside world and rather provide an interface which lets you change the underlying representation later. That is, to achieve maximum flexibility you don't want to bake the representation into all your code. For example, if you use accessor function objects (property maps in terms of BGL or projections in terms of Eric Niebler's Range Proposal) to access the elements based on an iterator, rather than accessing the elements you can change the internal layout without having to touch any of the algorithms (you'll need to recompile the code, though):
// version using std::vector<std::pair<Nuclide, double> >
// - it would just use std::vector<std::pair<Nuclide, double>::iterator as iterator
auto nuclide_projection = [](Sample::key& key) -> Nuclide& {
return key.first;
}
auto value_projecton = [](Sample::key& key) -> double {
return key.second;
}
// version using two std::vectors:
// - it would use an iterator interface to an integer, yielding a std::size_t for *it
struct nuclide_projector {
std::vector<Nuclide>& nuclides;
auto operator()(std::size_t index) -> Nuclide& { return nuclides[index]; }
};
constexpr nuclide_projector nuclide_projection;
struct value_projector {
std::vector<double>& values;
auto operator()(std::size_t index) -> double& { return values[index]; }
};
constexpr value_projector value_projection;
With one pair these in-place, for example an algorithm simply running over them and printing them could look like this:
template <typename Iterator>
void print(std::ostream& out, Iterator begin, Iterator end) {
for (; begin != end; ++begin) {
out << "nuclide=" << nuclide_projection(*begin) << ' '
<< "value=" << value_projection(*begin) << '\n';
}
}
Both representations are entirely different but the algorithm accessing them is entirely independent. This way it is also easy to try different representations: only the representation and the glue to the algorithms accessing it need to be changed.
UPDATED:
I am working on a program whose performance is very critical. I have a vector of structs that are NOT sorted. I need to perform many search operations in this vector. So I decided to cache the vector data into a map like this:
std::map<long, int> myMap;
for (int i = 0; i < myVector.size(); ++i)
{
const Type& theType = myVector[i];
myMap[theType.key] = i;
}
When I search the map, the results of the rest of the program are much faster. However, the remaining bottleneck is the creation of the map itself (it is taking about 0.8 milliseconds on average to insert about 1,500 elements in it). I need to figure out a way to trim this time down. I am simply inserting a long as the key and an int as the value. I don't understand why it is taking this long.
Another idea I had was to create a copy of the vector (can't touch the original one) and somehow perform a faster sort than the std::sort (it takes way too long to sort it).
Edit:
Sorry everyone. I meant to say that I am creating a std::map where the key is a long and the value is an int. The long value is the struct's key value and the int is the index of the corresponding element in the vector.
Also, I did some more debugging and realized that the vector is not sorted at all. It's completely random. So doing something like a stable_sort isn't going to work out.
ANOTHER UPDATE:
Thanks everyone for the responses. I ended up creating a vector of pairs (std::vector of std::pair(long, int)). Then I sorted the vector by the long value. I created a custom comparator that only looked at the first part of the pair. Then I used lower_bound to search for the pair. Here's how I did it all:
typedef std::pair<long,int> Key2VectorIndexPairT;
typedef std::vector<Key2VectorIndexPairT> Key2VectorIndexPairVectorT;
bool Key2VectorIndexPairComparator(const Key2VectorIndexPairT& pair1, const Key2VectorIndexPairT& pair2)
{
return pair1.first < pair2.first;
}
...
Key2VectorIndexPairVectorT sortedVector;
sortedVector.reserve(originalVector.capacity());
// Assume "original" vector contains unsorted elements.
for (int i = 0; i < originalVector.size(); ++i)
{
const TheStruct& theStruct = originalVector[i];
sortedVector.insert(Key2VectorIndexPairT(theStruct.key, i));
}
std::sort(sortedVector.begin(), sortedVector.end(), Key2VectorIndexPairComparator);
...
const long keyToSearchFor = 20;
const Key2VectorIndexPairVectorT::const_iterator cItorKey2VectorIndexPairVector = std::lower_bound(sortedVector.begin(), sortedVector.end(), Key2VectorIndexPairT(keyToSearchFor, 0 /* Provide dummy index value for search */), Key2VectorIndexPairComparator);
if (cItorKey2VectorIndexPairVector->first == keyToSearchFor)
{
const int vectorIndex = cItorKey2VectorIndexPairVector->second;
const TheStruct& theStruct = originalVector[vectorIndex];
// Now do whatever you want...
}
else
{
// Could not find element...
}
This yielded a modest performance gain for me. Before the total time for my calculations were 3.75 milliseconds and now it is down to 2.5 milliseconds.
Both std::map and std::set are built on a binary tree and so adding items does dynamic memory allocation. If your map is largely static (i.e. initialized once at the start and then rarely or never has new items added or removed) you'd probably be better to use a sorted vector and a std::lower_bound to look up items using a binary search.
Maps take a lot of time for two reasons
You need to do a lot of memory allocation for your data storage
You need to perform O(n lg n) comparisons for the sort.
If you are just creating this as one batch, then throwing the whole map out, using a custom pool allocator may be a good idea here - eg, boost's pool_alloc. Custom allocators can also apply optimizations such as not actually deallocating any memory until the map's completely destroyed, etc.
Since your keys are integers, you may want to consider writing your own container based on a radix tree (on the bits of the key) as well. This may give you significantly improved performance, but since there is no STL implementation, you may need to write your own.
If you don't need to sort the data, use a hash table, such as std::unordered_map; these avoid the significant overhead needed for sorting data, and also can reduce the amount of memory allocation needed.
Finally, depending on the overall design of the program, it may be helpful to simply reuse the same map instead of recreating it over and over. Just delete and add keys as needed, rather than building a new vector, then building a new map. Again, this may not be possible in the context of your program, but if it is, it would definitely help you.
I suspect it's the memory management and tree rebalancing that's costing you here.
Obviously profiling may be able to help you pinpoint the issue.
I would suggest as a general idea to just copy the long/int data you need into another vector and since you said it's almost sorted, use stable_sort on it to finish the ordering. Then use lower_bound to locate the items in the sorted vector.
std::find is a linear scan(it has to be since it works on unsorted data). If you can sort(std::sort guaranties n log(n) behavior) the data then you can use std::binary_search to get log(n) searches. But as pointed out by others it may be copy time is the problem.
If keys are solid and short, perhaps try std::hash_map instead. From MSDN's page on hash_map Class:
The main advantage of hashing over sorting is greater efficiency; a
successful hashing performs insertions, deletions, and finds in
constant average time as compared with a time proportional to the
logarithm of the number of elements in the container for sorting
techniques.
Map creation can be a performance bottleneck (in the sense that it takes a measurable amount of time) if you're creating a large map and you're copying large chunks of data into it. You're also using the obvious (but suboptimal) way of inserting elements into a std::map - if you use something like:
myMap.insert(std::make_pair(theType.key, theType));
this should improve the insertion speed, but it will result in a slight change in behaviour if you encounter duplicate keys - using insert will result in values for duplicate keys being dropped, whereas using your method, the last element with the duplicate key will be inserted into the map.
I would also look into avoiding a making a copy of the data (for example by storing a pointer to it instead) if your profiling results determine that it's the copying of the element that is expensive. But for that you'll have to profile the code, IME guesstimates tend to be wrong...
Also, as a side note, you might want to look into storing the data in a std::set using custom comparator as your contains the key already. That however will not really result in a big speed up as constructing a set in this case is likely to be as expensive as inserting it into a map.
I'm not a C++ expert, but it seems that your problem stems from copying the Type instances, instead of a reference/pointer to the Type instances.
std::map<Type> myMap; // <-- this is wrong, since std::map requires two template parameters, not one
If you add elements to the map and they're not pointers, then I believe the copy constructor is invoked and that will certainly cause delays with a large data structure. Use the pointer instead:
std::map<KeyType, ObjectType*> myMap;
Furthermore, your example is a little confusing since you "insert" a value of type int in the map when you're expecting a value of type Type. I think you should assign the reference to the item, not the index.
myMap[theType.key] = &myVector[i];
Update:
The more I look at your example, the more confused I get. If you're using the std::map, then it should take two template types:
map<T1,T2> aMap;
So what are you REALLY mapping? map<Type, int> or something else?
It seems that you're using the Type.key member field as a key to the map (it's a valid idea), but unless key is of the same type as Type, then you can't use it as the key to the map. So is key an instance of Type??
Furthermore, you're mapping the current vector index to the key in the map, which indicates that you're just want the index to the vector so you can later access that index location fast. Is that what you want to do?
Update 2.0:
After reading your answer it seems that you're using std::map<long,int> and in that case there is no copying of the structure involved. Furthermore, you don't need to make a local reference to the object in the vector. If you just need to access the key, then access it by calling myVector[i].key.
Your building a copy of the table from the broken example you give, and not just a reference.
Why Can't I store references in an STL map in C++?
Whatever you store in the map it relies on you not changing the vector.
Try a lookup map only.
typedef vector<Type> Stuff;
Stuff myVector;
typedef std::map<long, *Type> LookupMap;
LookupMap myMap;
LookupMap::iterator hint = myMap.begin();
for (Stuff::iterator it = myVector.begin(); myVector.end() != it; ++it)
{
hint = myMap.insert(hint, std::make_pair(it->key, &*it));
}
Or perhaps drop the vector and just store it in the map??
Since your vector is already partially ordered, you may want to instead create an auxiliary array referencing (indices of) the elements in your original vector. Then you can sort the auxiliary array using Timsort which has good performance for partially sorted data (such as yours).
I think you've got some other problem. Creating a vector of 1500 <long, int> pairs, and sorting it based on the longs should take considerably less than 0.8 milliseconds (at least assuming we're talking about a reasonably modern, desktop/server type processor).
To try to get an idea of what we should see here, I did a quick bit of test code:
#include <vector>
#include <algorithm>
#include <time.h>
#include <iostream>
int main() {
const int size = 1500;
const int reps = 100;
std::vector<std::pair<long, int> > init;
std::vector<std::pair<long, int> > data;
long total = 0;
// Generate "original" array
for (int i=0; i<size; i++)
init.push_back(std::make_pair(rand(), i));
clock_t start = clock();
for (int i=0; i<reps; i++) {
// copy the original array
std::vector<std::pair<long, int> > data(init.begin(), init.end());
// sort the copy
std::sort(data.begin(), data.end());
// use data that depends on sort to prevent it being optimized away
total += data[10].first;
total += data[size-10].first;
}
clock_t stop = clock();
std::cout << "Ignore: " << total << "\n";
clock_t ticks = stop - start;
double seconds = ticks / (double)CLOCKS_PER_SEC;
double ms = seconds * 1000.0;
double ms_p_iter = ms / reps;
std::cout << ms_p_iter << " ms/iteration.";
return 0;
}
Running this on my somewhat "trailing edge" (~5 year-old) machine, I'm getting times around 0.1 ms/iteration. I'd expect searching in this (using std::lower_bound or std::upper_bound) to be somewhat faster than searching in an std::map as well (since the data in the vector is allocated contiguously, we can expect better locality of reference, leading to better cache usage).
Thanks everyone for the responses. I ended up creating a vector of pairs (std::vector of std::pair(long, int)). Then I sorted the vector by the long value. I created a custom comparator that only looked at the first part of the pair. Then I used lower_bound to search for the pair. Here's how I did it all:
typedef std::pair<long,int> Key2VectorIndexPairT;
typedef std::vector<Key2VectorIndexPairT> Key2VectorIndexPairVectorT;
bool Key2VectorIndexPairComparator(const Key2VectorIndexPairT& pair1, const Key2VectorIndexPairT& pair2)
{
return pair1.first < pair2.first;
}
...
Key2VectorIndexPairVectorT sortedVector;
sortedVector.reserve(originalVector.capacity());
// Assume "original" vector contains unsorted elements.
for (int i = 0; i < originalVector.size(); ++i)
{
const TheStruct& theStruct = originalVector[i];
sortedVector.insert(Key2VectorIndexPairT(theStruct.key, i));
}
std::sort(sortedVector.begin(), sortedVector.end(), Key2VectorIndexPairComparator);
...
const long keyToSearchFor = 20;
const Key2VectorIndexPairVectorT::const_iterator cItorKey2VectorIndexPairVector = std::lower_bound(sortedVector.begin(), sortedVector.end(), Key2VectorIndexPairT(keyToSearchFor, 0 /* Provide dummy index value for search */), Key2VectorIndexPairComparator);
if (cItorKey2VectorIndexPairVector->first == keyToSearchFor)
{
const int vectorIndex = cItorKey2VectorIndexPairVector->second;
const TheStruct& theStruct = originalVector[vectorIndex];
// Now do whatever you want...
}
else
{
// Could not find element...
}
This yielded a modest performance gain for me. Before the total time for my calculations were 3.75 milliseconds and now it is down to 2.5 milliseconds.
There are two ways of map insertion:
m[key] = val;
Or
m.insert(make_pair(key, val));
My question is, which operation is faster?
People usually say the first one is slower, because the STL Standard at first 'insert' a default element if 'key' is not existing in map and then assign 'val' to the default element.
But I don't see the second way is better because of 'make_pair'. make_pair actually is a convenient way to make 'pair' compared to pair<T1, T2>(key, val). Anyway, both of them do two assignments, one is assigning 'key' to 'pair.first' and two is assigning 'val' to 'pair.second'. After pair is made, map inserts the element initialized by 'pair.second'.
So the first way is 1. 'default construct of typeof(val)' 2. assignment
the second way is 1. assignment 2. 'copy construct of typeof(val)'
Both accomplish different things.
m[key] = val;
Will insert a new key-value pair if the key doesn't exist already, or it will overwrite the old value mapped to the key if it already exists.
m.insert(make_pair(key, val));
Will only insert the pair if key doesn't exist yet, it will never overwrite the old value. So, choose accordingly to what you want to accomplish.
For the question what is more efficient: profile. :P Probably the first way I'd say though. The assignment (aka copy) is the case for both ways, so the only difference lies in construction. As we all know and should implement, a default construction should basically be a no-op, and thus be very efficient. A copy is exactly that - a copy. So in way one we get a "no-op" and a copy, and in way two we get two copies.
Edit: In the end, trust what your profiling tells you. My analysis was off like #Matthieu mentions in his comment, but that was my guessing. :)
Then, we have C++0x coming, and the double-copy on the second way will be naught, as the pair can simply be moved now. So in the end, I think it falls back on my first point: Use the right way to accomplish the thing you want to do.
On a lightly loaded system with plenty of memory, this code:
#include <map>
#include <iostream>
#include <ctime>
#include <string>
using namespace std;
typedef map <unsigned int,string> MapType;
const unsigned int NINSERTS = 1000000;
int main() {
MapType m1;
string s = "foobar";
clock_t t = clock();
for ( unsigned int i = 0; i < NINSERTS; i++ ) {
m1[i] = s;
}
cout << clock() - t << endl;
MapType m2;
t = clock();
for ( unsigned int i = 0; i < NINSERTS; i++ ) {
m2.insert( make_pair( i, s ) );
}
cout << clock() - t << endl;
}
produces:
1547
1453
or similar values on repeated runs. So insert is (in this case) marginally faster.
Performance wise I think they are mostly the same in general. There may be some exceptions for a map with large objects, in which case you should use [] or perhaps emplace which creates fewer temporary objects than 'insert'. See the discussion here for details.
You can, however, get a performance bump in special cases if you use the 'hint' function on the insert operator. So, looking at option 2 from here:
iterator insert (const_iterator position, const value_type& val);
the 'insert' operation can be reduced to constant time (from log(n)) if you give a good hint (often the case if you know you are adding things at the back of your map).
We have to refine the analysis by mentioning that the relative performance depends on the type(size) of the objects being copied as well.
I did a similar experiment (to nbt) with a map of (int -> set). I know it is a terrible thing to do, but, illustrative for this scenario. The "value", in this case a set of ints, has 20 elements in it.
I execute a million iterations of the []= Vs. insert operations and do RDTSC/iter-count.
[] = set | 10731 cycles
insert(make_pair<>) | 26100 cycles
It shows the magnitude of penalty added due to the copying. Of course, CPP11(move ctor's)
will change the picture.
My take on it:
Worth reminding that maps is a balanced binary tree, most of the modifications and checks take O(logN).
Depends really on the problem you are trying to solve.
1) if you just want to insert the value knowing that it is not there yet,
then [] would do two things:
a) check if the item is there or not
b) if it is not there will create pair and do what insert does (
double work of O( logN ) ), so I would use insert.
2) if you are not sure if it is there or not, then a) if you did check if the item is there by doing something like if( map.find( item ) == mp.end() ) couple of lines above somewhere, then use insert, because of double work [] would perform b) if you didn't check, then it depends, cause insert won't modify the value if it is there, [] will, otherwise they are equal
My answer is not on efficiency but on safety, which is relevant to choosing an insertion algorithm:
The [] and insert() calls would trigger destructors of the elements. This may have dangerous side effects if, say, your destructors have critical behaviors inside.
After such a hazard, I stopped relying on STL's implicit lazy insertion features and always use explicit checks if my objects have behaviors in their ctors/dtors.
See this question:
Destructor called on object when adding it to std::list