I am using Coldfusion 10, though I would be interested in answers for any version.
It is known that structs in Coldfusion are not ordered (e.g. Stop ColdFusion from sorting my structs/arrays), and that you should not rely on any particular order of iteration. However, the keys are obviously iterated in some order, even though it's not sort order or insertion order. Is the order stable and reliable? Is it the order of some underlying Java type?
I should emphasize that I do not intend to rely on this, but I would like to know anyway.
Hashmaps like ColdFusion's struct work with a hash of the key. The hashes are managed in buckets and do not guarantee order over time as the memory address mapping is not linear.
If you need ordered structs in ColdFusion, you can use any Java class that implements the Map interface and fulfills whatever your requirement is. LinkedHashMap is an example for insertion order. These will work with ColdFusion, but be aware of case sensitivity and a general performance hit.
Regarding best practise: Avoid using ordered structs by using arrays with the ordered keys (remember there is also structSort() in ColdFusion). The only "good" reason to use LinkedHashMap is serializeJSON() when it comes to providing a RESTful service, as order and case sensitivity matter.
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I'm trying to decide on whether to use the QList or QMap class in some of my future Qt projects. In order to determine the best choice for me, I'd like to determine some of their similarities and some of their differences in order to understand what works best in certain instances. Is my understanding of these similarities and differences correct?
Similarities:
Both are containers
Both contain unordered data
Differences:
QMap has key value pairs whilst QList only has values
QMap uses a hash function to place values in the appropriate index
whilst QList simply appends the entries
Are there any more items of similarities and differences?
I could look at the generic computer science definitions but I read somewhere there could be nuanced differences in the Qt framework.
QList and QMap differ in the way the data is being organized. This results in different performance and slightly different memory consumption (for most use cases the latter usually doesn't matter). You can find the computational complexity in the Qt documentation. If you are storing a lot of elements this might make a big difference. Think about how frequently you want to access the data when selecting a container (searching vs. inserting vs. deleting).
[Keep in mind, though, that algorithmic complexity is a theoretical property that is only useful for large n. In practice a linear search through an array with a small number of elements (<1,000) often outperforms lists/trees due to locality of reference. If you care about performance don't guess, always measure.]
Both contain unordered data
That's actually not true for QMap. QMap is implemented as a self-balancing binary search tree which is a sorted data structure.
BTW: You can often implement your code in a generic way that makes it easy to switch to another container type later (e.g. if the access pattern changes or your assumptions turn out to be wrong). Using auto can help making this painless.
I think you made some mistakes about QMap.
The QMap keeps its content always sorted by key. See the Documentation here. So it is not unordered as you mentioned.
Then a QMap does not use a hash function. It stores elements by comparing them with operator<().
In fact, you a confusing QMap and QHash. The QHash is indeed arbitrarily ordered and its elements needs to provide an operator==() for the comparison and a qHash(key) function.
I think it can help you to better understand what you need to use.
I'm wondering if an unordered_map would be a good choice as container for my specific problem. What I've read about maps does not really cover my are, which is:
The container will store between 100 and 500 objects (not
int/double...)
The size will never change.
The order is not important as the objects themselves contain some kind of "index".
Very often (!) I need to filter all elements in the container that have some
property (e.g. have color==blue)
Currently I use vectors, which works. However if e.g. an unordered_map would improve performance (in regard to "filtering") I could image to change that.
std::unordered_map wouldn't really help you if you have multiple search criteria (sometimes color == blue, sometimes flavour == up), because maps only offer fast query on a single, pre-determined key.
I'd say std::vector is just fine for you, ideally wrapped in your own structure which will provide the lookup interface. If profiling later tells you this is not fast enough, you could build your own indexes above such data. You wouldn't even have to do that manually, boost::multi_index is a generic container designed for multiple-criterion lookup.
I would use vector or simply array for storing actual data. And have a few maps that maps key with pointer to actual data.
This would give higher memory usage, but in case searching by different indexes is often needed you may sacrifice a bit of memory.
A hash table (which std::unordered_map is) provides constant-time lookup for one key (key-value pair). However, its constant factors are always higher (i. e. the lookup is slower) than a simple array (which provides constant-time lookup for integer indices).
If you need to filter a collection of elements based on some criteria, then you need to inspect each individual element. In this case, a hash table would be strictly worse than an array/vector performance-wise, since its computational complexity is the same as that of array indexing, but with worse constant factors.
So no, there's no reason why you would want to use an unordered_map in this case.
This is very strange for me, i expected it to be a hash table.
I saw 3 reasons in the following answer (which maybe correct but i don't think that they are the real reason).
Hash tables v self-balancing search trees
Although hash might be not a trivial operation. I think that for most of the types it is pretty simple.
when you use map you expect something that will give you amortized O(1) insert, delete, find , not log(n).
i agree that trees have better worst case performance.
I think that there is a bigger reason for that, but i can't figure it out.
In c# for example Dictionary is a hash table.
It's largely a historical accident. The standard containers (along with iterators and algorithms) were one of the very last additions before the feature set of the standard was frozen. As it happened, they didn't have what they considered an adequate definition of a hash-based map at the time, and there wasn't time to add it before features were frozen, so the original specification included only a tree-based map.
C++ 11 added std::unordered_map (as well as std::unordered_set and multi versions of both), which is based on hashing though.
The reason is that map is explicitly called out as an ordered container. It keeps the elements sorted and allows you to iterate in sorted order in linear time. A hashtable couldn't fulfill those requirements.
In C++11 they added std::unordered_map which is a hashtable implementation.
A hash table requires an additional hash function. The current implementation of map which uses a tree can work without an extra hash function by using operator<. Additionally the map allows sorted access to elements, which may be beneficial for some applications. With C++ we now have the hash versions available in form of unordered_set.
Simple answer: because a hash table cannot satisfy the complexity requirements of iteration over a std::map.
Why does std::map hold these requirements? Unanswerable question. Historical factors contribute but, overall, that's just the way it is.
Hashes are available as std::unordered_map.
It doesn't really matter what the two are called, or what they're called in some other language.
Alright as a preface I have a need to cache a relatively small subset of rarely modified data to avoid querying the database as frequently for performance reasons. This data is heavily used in a read-only sense as it is referenced often by a much larger set of data in other tables.
I've written a class which will have the ability to store basically the entirety of the two tables in question in memory while listening for commit changes in conjunction with a thread safe callback mechanism for updating the cached objects.
My current implementation has two std::vectors one for the elements of each table. The class provides both access to the entirety of each vector as well as convenience methods for searching for a specific element of table data via std::find, std::find_if, etc.
Does anyone know if using std::list, std::set, or std::map over std::vector for searching would be preferable? Most of the time that is what will be requested of these containers after populating once from the database when a new connection is made.
I'm also open to using C++0x features supported by VS2010 or Boost.
For searching a particular value, with std::set and std::map it takes O(log N) time, while with the other two it takes O(N) time; So, std::set or std::map are probably better. Since you have access to C++0x, you could also use std::unordered_set or std::unordered_map which take constant time on average.
For find_if, there's little difference between them, because it takes an arbitrary predicate and containers cannot optimize arbitrarily, of course.
However if you will be calling find_if frequently with a certain predicate, you can optimize yourself: use a std::map or std::set with a custom comparator or special keys and use find instead.
A sorted vector using std::lower_bound can be just as fast as std::set if you're not updating very often; they're both O(log n). It's worth trying both to see which is better for your own situation.
Since from your (extended) requirements you need to search on multiple fields, I would point you to Boost.MultiIndex.
This Boost library lets you build one container (with only one exemplary of each element it contains) and index it over an arbitrary number of indices. It also lets you precise which indices to use.
To determine the kind of index to use, you'll need extensive benchmarks. 500 is a relatively low number of entries, so constant factors won't play nicely. Furthermore, there can be a noticeable difference between single-thread and multi-thread usage (most hash-table implementations can collapse on MT usage because they do not use linear-rehashing, and thus a single thread ends up rehashing the table, blocking all others).
I would recommend a sorted index (skip-list like, if possible) to accomodate range requests (all names beginning by Abc ?) if the performance difference is either unnoticeable or simply does not matter.
If you only want to search for distinct values, one specific column in the table, then std::hash is fastest.
If you want to be able to search using several different predicates, you will need some kind of index structure. It can be implemented by extending your current vector based approach with several hash tables or maps, one for each field to search for, where the value is either an index into the vector, or a direct pointer to the element in the vector.
Going further, if you want to be able to search for ranges, such as all occasions having a date in July you need an ordered data structure, where you can extract a range.
Not an answer per se, but be sure to use a typedef to refer to the container type you do use, something like typedef std::vector< itemtype > data_table_cache; Then use your typedef type everywhere.
I've got a situation where I want to use an associative container, and I chose to use a std::unordered_map, because it's perfectly feasible that this container could be used to hold millions or more of elements. But now I also need to iterate in order. I considered having the value types link to each other in a list, but now I'm going to have issues with memory management.
Should I change container, say to a std::map? Or just iterate once through my unordered_map, insert into a vector, and sort, then iterate? It's pretty unlikely that I will need to iterate in an ordered fashion repeatedly.
Well, you know the O() of the various operations of the two alternatives you've picked. You should pick based on that and do a cost/benefit analysis based on where you need the performance to happen and which container does best for THAT.
Of course, I couldn't possibly know enough to do that analysis for you.
You could use Boost.MultiIndex, specifying the unordered (hashed) index as well as an ordered one, on the same underlying object collection.
Possible issues with this - there is no natural mapping from an existing associative container model, and it might be overkill if you don't need the second index all the time.