I'm building a little 2d game engine. Now I need to store the prototypes of the game objects (all type of informations). A container that will have at most I guess few thousand elements all with unique key and no elements will be deleted or added after a first load. The key value is a string.
Various threads will run, and I need to send to everyone a key(or index) and with that access other information(like a texture for the render process or sound for the mixer process) available only to those threads.
Normally I use vectors because they are way faster to accessing a known element. But I see that unordered map also usually have a constant speed if I use the ::at element access. It would make the code much cleaner and also easier to maintain because I will deal with much more understandable man made strings.
So the question is, the difference in speed between a access to a vector[n] compared to a unorderedmap.at("string") is negligible compared to his benefits?
From what I understand accessing various maps in different part of the program, with different threads running just with a "name" for me is a big deal and the speed difference isn't that great. But I'm too inexperienced to be sure of this. Although I found informations about it seem I can't really understand if I'm right or wrong.
Thank you for your time.
As an alternative, you could consider using an ordered vector because the vector itself will not be modified. You can easily write an implementation yourself with STL lower_bound etc, or use an implementation from libraries ( boost::flat_map).
There is a blog post from Scott Meyers about container performance in this case. He did some benchmarks and the conclusion would be that an unordered_mapis probably a very good choice with high chances that it will be the fastest option. If you have a restricted set of keys, you can also compute a minimal optimal hash function, e.g. with gperf
However, for these kind of problems the first rule is to measure yourself.
My problem was to find a record on a container by a given std::string type as Key access. Considering Keys that only EXISTS(not finding them was not a option) and the elements of this container are generated only at the beginning of the program and never touched thereafter.
I had huge fears unordered map was not fast enough. So I tested it, and I want to share the results hoping I've not mistaken everything.
I just hope that can help others like me and to get some feedback because in the end I'm beginner.
So, given a struct of record filled randomly like this:
struct The_Mess
{
std::string A_string;
long double A_ldouble;
char C[10];
int* intPointer;
std::vector<unsigned int> A_vector;
std::string Another_String;
}
I made a undordered map, give that A_string contain the key of the record:
std::unordered_map<std::string, The_Mess> The_UnOrdMap;
and a vector I sort by the A_string value(which contain the key):
std::vector<The_Mess> The_Vector;
with also a index vector sorted, and used to access as 3thrd way:
std::vector<std::string> index;
The key will be a random string of 0-20 characters in lenght(I wanted the worst possible scenario) containing letter both capital and normal and numbers or spaces.
So, in short our contendents are:
Unordered map I measure the time the program get to execute:
record = The_UnOrdMap.at( key ); record is just a The_Mess struct.
Sorted Vector measured statements:
low = std::lower_bound (The_Vector.begin(), The_Vector.end(), key, compare);
record = *low;
Sorted Index vector:
low2 = std::lower_bound( index.begin(), index.end(), key);
indice = low2 - index.begin();
record = The_Vector[indice];
The time is in nanoseconds and is a arithmetic average of 200 iterations. I have a vector that I shuffle at every iteration containing all the keys, and at every iteration I cycle through it and look for the key I have here in the three ways.
So this are my results:
I think the initials spikes are a fault of my testing logic(the table I iterate contains only the keys generated so far, so it only has 1-n elements). So 200 iterations of 1 key search for the first time. 200 iterations of 2 keys search the second time etc...
Anyway, it seem that in the end the best option is the unordered map, considering that is a lot less code, it's easier to implement and will make the whole program way easier to read and probably maintain/modify.
You have to think about caching as well. In case of std::vector you'll have very good cache performance when accessing the elements - when accessing one element in RAM, CPU will cache nearby memory values and this will include nearby portions of your std::vector.
When you use std::map (or std::unordered_map) this is no longer true. Maps are usually implemented as self balancing binary-search trees, and in this case values can be scattered around the RAM. This imposes great hit on cache performance, especially as maps get bigger and bigger as CPU just cannot cache the memory that you're about to access.
You'll have to run some tests and measure performance, but cache misses can greatly hurt the performance of your program.
You are most likely to get the same performance (the difference will not be measurable).
Contrary to what some people seem to believe, unordered_map is not a binary tree. The underlying data structure is a vector. As a result, cache locality does not matter here - it is the same as for vector. Granted, you are going to suffer if you have collissions due to your hashing function being bad. But if your key is a simple integer, this is not going to happen. As a result, access to to element in hash map will be exactly the same as access to the element in the vector with time spent on getting hash value for integer, which is really non-measurable.
Related
I have a simple requirement, i need a map of type . however i need fastest theoretically possible retrieval time.
i used both map and the new proposed unordered_map from tr1
i found that at least while parsing a file and creating the map, by inserting an element at at time.
map took only 2 minutes while unordered_map took 5 mins.
As i it is going to be part of a code to be executed on Hadoop cluster and will contain ~100 million entries, i need smallest possible retrieval time.
Also another helpful information:
currently the data (keys) which is being inserted is range of integers from 1,2,... to ~10 million.
I can also impose user to specify max value and to use order as above, will that significantly effect my implementation? (i heard map is based on rb trees and inserting in increasing order leads to better performance (or worst?) )
here is the code
map<int,int> Label // this is being changed to unordered_map
fstream LabelFile("Labels.txt");
// Creating the map from the Label.txt
if (LabelFile.is_open())
{
while (! LabelFile.eof() )
{
getline (LabelFile,inputLine);
try
{
curnode=inputLine.substr(0,inputLine.find_first_of("\t"));
nodelabel=inputLine.substr(inputLine.find_first_of("\t")+1,inputLine.size()-1);
Label[atoi(curnode.c_str())]=atoi(nodelabel.c_str());
}
catch(char* strerr)
{
failed=true;
break;
}
}
LabelFile.close();
}
Tentative Solution: After review of comments and answers, i believe a Dynamic C++ array would be the best option, since the implementation will use dense keys. Thanks
Insertion for unordered_map should be O(1) and retrieval should be roughly O(1), (its essentially a hash-table).
Your timings as a result are way OFF, or there is something WRONG with your implementation or usage of unordered_map.
You need to provide some more information, and possibly how you are using the container.
As per section 6.3 of n1836 the complexities for insertion/retreival are given:
http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2005/n1836.pdf
One issue you should consider is that your implementation may need to continually be rehashing the structure, as you say you have 100mil+ items. In that case when instantiating the container, if you have a rough idea about how many "unique" elements will be inserted into the container, you can pass that in as a parameter to the constructor and the container will be instantiated accordingly with a bucket-table of appropriate size.
The extra time loading the unordered_map is due to dynamic array resizing. The resizing schedule is to double the number of cells each when the table exceeds it's load factor. So from an empty table, expect O(lg n) copies of the entire data table. You can eliminate these extra copies by sizing the hash table upfront. Specifically
Label.reserve(expected_number_of_entries / Label.max_load_factor());
Dividing by the max_load_factor is to account for the empty cells that are necessary for the hash table to operate.
unordered_map (at least in most implementations) gives fast retrieval, but relatively poor insertion speed compared to map. A tree is generally at its best when the data is randomly ordered, and at its worst when the data is ordered (you constantly insert at one end of the tree, increasing the frequency of re-balancing).
Given that it's ~10 million total entries, you could just allocate a large enough array, and get really fast lookups -- assuming enough physical memory that it didn't cause thrashing, but that's not a huge amount of memory by modern standards.
Edit: yes, a vector is basically a dynamic array.
Edit2: The code you've added some some problems. Your while (! LabelFile.eof() ) is broken. You normally want to do something like while (LabelFile >> inputdata) instead. You're also reading the data somewhat inefficiently -- what you apparently expecting is two numbers separated by a tab. That being the case, I'd write the loop something like:
while (LabelFile >> node >> label)
Label[node] = label;
I am using a red black tree implementation in C++ (std::map), but currently, I see that my unsigned long long int indices get bigger and bigger, for larger experiment. I am going for 700,000,000 indices, and each index stores a std::set that contains a few more int elements (about 1-10). We got 128 GB RAM, but I see that we start to run short of it; in fact, if possible, I wanna go down even to 1,000,000,000 indices, if possible, in my experiment.
I gave this some thought, and was thinking about a forest of several maps put together. Basically, after a map hits a certain size threshold (or perhaps when bad_alloc starts to be thrown), save it to disk, clear it off the memory and then create another map and keep on doing until I got all indices. However, during the loading part, this will be very inefficient, as we can only hold one map in the RAM at a time. Worse, we need to check all maps for consistency.
So in this case, what are some of the data structure should I be looking for?
From your description, I think you have this:
typedef std::map<long long, std::set<int>> MyMap;
where the map is very big, and the individual sets are quite small. There are several sources of overhead here:
the individual entries in the map, each of which is a separate allocation;
the individual entries in the sets, ditto;
the structures which describe each set, independent of their contents.
With standard library components, it's not possible to eliminate all of these overheads; the semantics of associative containers pretty well mandates the individual allocation of each entry, and the use of red-black trees requires the addition of several pointers to each entry (in theory, only two pointers are required, but efficient implementation of iterators is difficult without parent pointers.)
However, you can reduce the overhead without losing functionality by combining the map with the sets, using a datastructure like this:
typedef std::set<std::pair<long long, int>> MyMap;
You can still answer all the same queries, although a few of them are slightly less convenient. Remember that std::pair's default comparator sorts in lexicographical order, so all of the elements with the same first value will be contiguous. So you can, for example, query whether a given index has any ints associated with it by using:
it = theMap.lower_bound(std::make_pair(index, INT_MIN));
if (it != theMap.end() && it->first == index) {
// there is at least one int associated with index
}
The same call to lower_bound will give you a begin iterator for the ints associate with the key, while a call toupper_bound(std::make_pair(key, INT_MAX))` will give you the corresponding end iterator, so you can easily iterate over all the values associated with a given key.
That still might not be enough to store 700 million indices with associated sets of integers in 128GB unless the average set size is really small. The next step would have to be a b-tree of some form, which is not in the standard library. B-trees avoid the individual entry overhead by combining a number of entries into a single cluster; that should be sufficient for your needs.
it looks like it is time to switch to B-trees (may be B+ or B*) -- this structure used in databases to manage indices. take a look here -- this is replacement for std-like associative containers w/ btree inside... but btrees can be used to keep indices in memory and on disk...
For such a large scale dataset, you should really work with a proper database server such as an SQL server. These servers are intended to work with cached large-scale datasets. An SQL server saves the data to a permenant cache such as a HDD, while maintaining good read/write performance by caching frequently accessed pages etc.
I have a std::map for some packet processing program.
I didn't noticed before profiling but unfortunately this map lookup alone consume about 10% CPU time (called too many time).
Usually there only exist at most 10 keys in the input data. So I'm trying to implement a kind of key cache in front of the map.
Key value is 13 bit integer. I know there are only 8192 possible keys and array of 8192 items can give constant time lookup but I feel already ashamed and don't want use such a naive approach :(
Now, I'm just guessing some method of hashing that yield 4 bit code value for 13 bit integer very fast.
Any cool idea?
Thanks in advance.
UPDATE
Beside my shame, I don't have total control over source code and it's almost prohibited to make new array for this purpose.
Project manager said (who ran the profiler) linked list show small performance gain and recommended using std::list instead of std::map.
UPDATE
Value of keys are random (no relationship) and doesn't have good distribution.
Sample:
1) 0x100, 0x101, 0x10, 0x0, 0xffe
2) 0x400, 0x401, 0x402, 0x403, 0x404, 0x405, 0xff
Assuming your hash table either contains some basic type -- it's almost no memory at all. Even on 64-bit systems it's only 64kb of memory. There is no shame in using a lookup table like that, it has some of the best performance you can get.
You may want to go with middle solution and open addressing technique: one array of size 256. Index to an array is some simple hash function like XOR of two bytes. Element of the array is struct {key, value}. Collisions are handled by storing collided element at the next available index. If you need to delete element from array, and if deletion is rare then just recreate array (create a temporary list from remaining elements, and then create array from this list).
If you pick your hash function smartly there would not be any collisions almost ever. For instance, from your two examples one such hash would be to XOR low nibble of high byte with high nibble of low byte (and do what you like with remaining 13-th bit).
Unless you're writing for some sort of embedded system where 8K is really significant, just use the array and move on. If you really insist on doing something else, you might consider a perfect hash generator (e.g., gperf).
If there are really only going to be something like 10 active entries in your table, you might seriously consider using an unsorted vector to hold this mapping. Something like this:
typedef int key_type;
typedef int value_type;
std::vector<std::pair<key_type, value_type> > mapping;
inline void put(key_type key, value_type value) {
for (size_t i=0; i<mapping.size(); ++i) {
if (mapping[i].first==key) {
mapping[i].second=value;
return;
}
}
mapping.push_back(std::make_pair(key, value));
}
inline value_type get(key_type key) {
for (size_t i=0; i<mapping.size(); ++i) {
if (mapping[i].first==key) {
return mapping[i].second;
}
}
// do something reasonable if not found?
return value_type();
}
Now, the asymptotic speed of these algorithms (each O(n)) is much worse than you'd have with either a red-black tree (like std::map at O(log n)) or hash table (O(1)). But you're not talking about dealing with a large number of objects, so asymptotic estimates don't really buy you much.
Additionally, std::vector buys you both low overhead and locality of reference, which neither std::map nor std::list can offer. So it's more likely that a small std::vector will stay entirely within the L1 cache. As it's almost certainly the memory bottleneck that's causing your performance issues, using a std::vector with even my poor choice of algorithm will likely be faster than either a tree or linked list. Of course, only a few solid profiles will tell you for sure.
There are certainly algorithms that might be better choices: a sorted vector could potentially give even better performance; a well tuned small hash table might work as well. I suspect that you'll run into Amdahl's law pretty quickly trying to improve on a simple unsorted vector, however. Pretty soon you might find yourself running into function call overhead, or some other such concern, as a large contributor to your profile.
I agree with GWW, you don't use so much memory in the end...
But if you want, you could use an array of 11 or 13 linkedlists, and hash the keys with the % function. If the key number is less than the array size, complexity tents still to be O(1).
When you always just have about ten keys, use a list (or array). Do some benchmarking to find out whether or not using a sorted list (or array) and binary search will improve performance.
You might first want to see if there are any unnecessary calls to the key lookup. You only want to do this once per packet ideally -- each time you call a function there is going to be some overhead, so getting rid of extra calls is good.
Map is generally pretty fast, but if there is any exploitable pattern in the way keys are mapped to items you could use that and potentially do better. Could you provide a bit more information about the keys and the associated 4-bit values? E.g. are they sequential, is there some sort of pattern?
Finally, as others have mentioned, a lookup table is very fast, 8192 values * 4 bits is only 4kb, a tiny amount of memory indeed.
I would use a lookup table. It's tiny unless you are using a micrcontroller or something.
Otherwise I would do this -
Generate a table of say 30 elements.
For each lookup calculate a hash value of (key % 30) and compare it with the stored key in that location in the table. If the key is there then you found your value. if the slot is empty, then add it. If the key is wrong then skip to the next free cell and repeat.
With 30 cells and 10 keys collisions should be rare but if you get one it's fast to skip to the next cell, and normal lookups are simply a modulus and a compare operation so fairly fast
I have a simple requirement, i need a map of type . however i need fastest theoretically possible retrieval time.
i used both map and the new proposed unordered_map from tr1
i found that at least while parsing a file and creating the map, by inserting an element at at time.
map took only 2 minutes while unordered_map took 5 mins.
As i it is going to be part of a code to be executed on Hadoop cluster and will contain ~100 million entries, i need smallest possible retrieval time.
Also another helpful information:
currently the data (keys) which is being inserted is range of integers from 1,2,... to ~10 million.
I can also impose user to specify max value and to use order as above, will that significantly effect my implementation? (i heard map is based on rb trees and inserting in increasing order leads to better performance (or worst?) )
here is the code
map<int,int> Label // this is being changed to unordered_map
fstream LabelFile("Labels.txt");
// Creating the map from the Label.txt
if (LabelFile.is_open())
{
while (! LabelFile.eof() )
{
getline (LabelFile,inputLine);
try
{
curnode=inputLine.substr(0,inputLine.find_first_of("\t"));
nodelabel=inputLine.substr(inputLine.find_first_of("\t")+1,inputLine.size()-1);
Label[atoi(curnode.c_str())]=atoi(nodelabel.c_str());
}
catch(char* strerr)
{
failed=true;
break;
}
}
LabelFile.close();
}
Tentative Solution: After review of comments and answers, i believe a Dynamic C++ array would be the best option, since the implementation will use dense keys. Thanks
Insertion for unordered_map should be O(1) and retrieval should be roughly O(1), (its essentially a hash-table).
Your timings as a result are way OFF, or there is something WRONG with your implementation or usage of unordered_map.
You need to provide some more information, and possibly how you are using the container.
As per section 6.3 of n1836 the complexities for insertion/retreival are given:
http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2005/n1836.pdf
One issue you should consider is that your implementation may need to continually be rehashing the structure, as you say you have 100mil+ items. In that case when instantiating the container, if you have a rough idea about how many "unique" elements will be inserted into the container, you can pass that in as a parameter to the constructor and the container will be instantiated accordingly with a bucket-table of appropriate size.
The extra time loading the unordered_map is due to dynamic array resizing. The resizing schedule is to double the number of cells each when the table exceeds it's load factor. So from an empty table, expect O(lg n) copies of the entire data table. You can eliminate these extra copies by sizing the hash table upfront. Specifically
Label.reserve(expected_number_of_entries / Label.max_load_factor());
Dividing by the max_load_factor is to account for the empty cells that are necessary for the hash table to operate.
unordered_map (at least in most implementations) gives fast retrieval, but relatively poor insertion speed compared to map. A tree is generally at its best when the data is randomly ordered, and at its worst when the data is ordered (you constantly insert at one end of the tree, increasing the frequency of re-balancing).
Given that it's ~10 million total entries, you could just allocate a large enough array, and get really fast lookups -- assuming enough physical memory that it didn't cause thrashing, but that's not a huge amount of memory by modern standards.
Edit: yes, a vector is basically a dynamic array.
Edit2: The code you've added some some problems. Your while (! LabelFile.eof() ) is broken. You normally want to do something like while (LabelFile >> inputdata) instead. You're also reading the data somewhat inefficiently -- what you apparently expecting is two numbers separated by a tab. That being the case, I'd write the loop something like:
while (LabelFile >> node >> label)
Label[node] = label;
I'm implementing a session store for a web-server. Keys are string
and stored objects are pointers. I tried using map but need something
faster. I will look up an object 5-20 times
as frequent than insert.
I tried using hash-map but failed. I felt like I got more constraints than more free time.
I'm coding c/c++ under Linux.
I don't want to commit to boost, since my web server is going to outlive boost. :)
This is a highly relevant question since the hardware (ssd disk) is
changing rapidly. What was the right solution will not be in 2 years.
I was going to suggest a map, but I see you have already ruled this out.
I tried using map but need something
faster.
These are the std::map performance bounds courtesy of the Wikipedia page:
Searching for an element takes O(log n) time
Inserting a new element takes O(log n) time
Incrementing/decrementing an iterator takes O(log n) time
Iterating through every element of a map takes O(n) time
Removing a single map element takes O(log n) time
Copying an entire map takes O(n log n) time.
How have you measured and determined that a map is not optimised sufficiently for you? It's quite possible that any bottlenecks you are seeing are in other parts of the code, and a map is perfectly adequate.
The above bounds seem like they would fit within all but the most stringent scalability requirements.
The type of data structure that will be used will be determined by the data you want to access. Some questions you should ask:
How many items will be in the session store? 50? 100000? 10000000000?
How large is each item in the store (byte size)?
What kind of string input is used for the key? ASCII-7? UTF-8? UCS2?
...
Hash tables generally perform very well for look ups. You can optimize them heavily for speed by writing them yourself (and yes, you can resize the table). Suggestions to improve performance with hash tables:
Choose a good hash function! this will have preferably even distribution among your hash table and will not be time intensive to compute (this will depend on the format of the key input).
Make sure that if you are using buckets to not exceed a length of 6. If you do exceed 6 buckets then your hash function probably isn't distributing evenly enough. A bucket length of < 3 is preferable.
Watch out for how you allocate your objects. If at all possible, try to allocate them near each other in memory to take advantage of locality of reference. If you need to, write your own sub-allocator/heap manager. Also keep to aligned boundaries for better access speeds (aligned is processor/bus dependent so you'll have to determine if you want to target a particular processor type).
BTrees are also very good and in general perform well. (Someone can insert info about btrees here).
I'd recommend looking at the data you are storing and making sure that the data is as small as possible. use shorts, unsigned char, bit fields as necessary. There are other additional ways to squeeze out improved performance as well such as allocating your string data at the end of your struct at the same time that you allocate the struct. i.e.
struct foo {
int a;
char my_string[0]; // allocate an instance of foo to be
// sizeof(int) + sizeof(your string data) etc
}
You may also find that implementing your own string compare routine can actually boost performance dramatically, however this will depend upon your input data.
It is possible to make your own. But you shouldn't have any problems with boost or std::tr1::unordered_map.
A ternary trie may be faster than a hash map for a smaller number of elements.