hashset underlaying data structure is hashtable .how it will identify duplicates and why it is good for if our frequent operation is search operation ?
It uses hash code of the object which is quickly computed integer. This hash code tries to be as even distributed over all potential object values as possible.
As a result it can distribute the inserted values into a array (hashtable) with very low probability of conflict. Then the search operation is quite quick - get the hash code, access the array, compare and get the value - usually constant time. The same actually happens for finding duplicates.
The conflicts of hash code are resolved as well - there can be potentially more values for the same entry within the hash table - there comes the equal into play. But they are rather rare so they don't affect average performance significantly.
As I referred to the documentation of LinkedHashMap, It says, a double linked list(DLL) is maintained internally
I was trying to understand why a DLL was chosen over S(ingle)LL
The biggest advantage I get with a DLL would be traversing backwards, but I dont see any use case for LinkedHashMap() exploiting this advantage, since there is no previous() sort of operation like next() in the Iterable interface..
Can anyone explain why was a DLL, and not a SLL?
It's because with an additional hash map, you can implement deletes in O(1). If you are using a singly linked list, delete would take O(n).
Consider a hash map for storing a key value pair, and another internal hash map with keys pointing to nodes in the linked list. When deleting, if it's a doubly linked list, I can easily get to the previous element and make it point to the following element. This is not possible with a singly linked list.
http://www.quora.com/Java-programming-language/Why-is-a-Java-LinkedHashMap-or-LinkedHashSet-backed-by-a-doubly-linked-list
What property makes Hash table, Hash list and Hash tree different from each other? Which one is used when? When is table superior than tree.
Hashtable: it's a data structure in which you can insert pairs of (key, value) in which the key is used to compute an hashcode that is needed to decide where to store the value associated with its key. This kind of structure is useful because calculating a hashcode is O(1), so you can find or place an item in constant time. (Mind that there are caveats and different implementations that change this performance slightly)
Hashlist: it is just a list of hashcodes calculated on various chunks of data. Eg: you split a file in many parts and you calculate a hashcode for each part, then you store all of them in a list. Then you can use that list to verify integrity of the data.
Hashtree: it is similar to a hashlist but instead of having a list of hashes you have got a tree, so every node in the tree is a hashcode that is calculated on its children. Of course leaves will be the data from which you start calculating the hashcodes.
Hashtable is often useful (they are also called hashmaps) while hashlists and hashtrees are somewhat more specific and useful for exact purposes..
I know a map is a data structure that maps keys to values. Isn't a dictionary the same? What is the difference between a map and a dictionary1?
1. I am not asking for how they are defined in language X or Y (which seems to be what generally people are asking here on SO), I want to know what is their difference in theory.
Two terms for the same thing:
"Map" is used by Java, C++
"Dictionary" is used by .Net, Python
"Associative array" is used by PHP
"Map" is the correct mathematical term, but it is avoided because it has a separate meaning in functional programming.
Some languages use still other terms ("Object" in Javascript, "Hash" in Ruby, "Table" in Lua), but those all have separate meanings in programming too, so I'd avoid them.
See here for more info.
One is an older term for the other. Typically the term "dictionary" was used before the mathematical term "map" took hold. Also, dictionaries tend to have a key type of string, but that's not 100% true everywhere.
Summary of Computer Science terminology:
a dictionary is a data structure representing a set of elements, with insertion, deletion, and tests for membership; the elements may be, but are not necessarily, composed of distinct key and value parts
a map is an associative data structure able to store a set of keys, each associated with one (or sometimes more than one - e.g. C++ multimap) value, with the ability to access and erase existing entries given only the key.
Discussion
Answering this question is complicated by programmers having seen the terms given more specific meanings in particular languages or systems they've used, but the question asks for a language agnostic comparison "in theory", which I'm taking to mean in Computing Science terms.
The terminology explained
The Oxford University Dictionary of Computer Science lists:
dictionary any data structure representing a set of elements that can support the insertion and deletion of elements as well as test for membership
For example, we have a set of elements { A, B, C, D... } that we've been able to insert and could start deleting, and we're able to query "is C present?".
The Computing Science notion of map though is based on the mathematical linguistic term mapping, which the Oxford Dictionary defines as:
mapping An operation that associates each element of a given set (the domain) with one or more elements of a second set (the range).
As such, a map data structure provides a way to go from elements of a given set - known as "keys" in the map, to one or more elements in the second set - known as the associated "value(s)".
The "...or more elements in the second set" aspect can be supported by an implementation is two distinct way:
Many map implementations enforce uniqueness of the keys and only allow each key to be associated with one value, but that value might be able to be a data structure itself containing many values of a simpler data type, e.g. { {1,{"one", "ichi"}, {2, {"two", "ni"}} } illustrates values consisting of pairs/sets of strings.
Other map implementations allow duplicate keys each mapping to the same or different values - which functionally satisfies the "associates...each [key] element...with...more [than one] [value] elements" case. For example, { {1, "one"}, {1, "ichi"}, {2, "two"}, {2, "ni"} }.
Dictionary and map contrasted
So, using the strict Comp Sci terminology above, a dictionary is only a map if the interface happens to support additional operations not required of every dictionary:
the ability to store elements with distinct key and value components
the ability to retrieve and erase the value(s) given only the key
A trivial twist:
a map interface might not directly support a test of whether a {key,value} pair is in the container, which is pedantically a requirement of a dictionary where the elements happen to be {key,value} pairs; a map might not even have a function to test for a key, but at worst you can see if an attempted value-retrieval-by-key succeeds or fails, then if you care you can check if you retrieved an expected value.
Communicate unambiguously to your audience
⚠ Despite all the above, if you use dictionary in the strict Computing Science meaning explained above, don't expect your audience to follow you initially, or be impressed when you share and defend the terminology. The other answers to this question (and their upvotes) show how likely it is that "dictionary" will be synonymous with "map" in the experience of most programmers. Try to pick terminology that will be more widely and unambiguously understood: e.g.
associative container: any container storing key/value pairs with value-retrieval and erasure by key
hash map: a hash table implementation of an associative container
hash set enforcing unique keys: a hash table implementation of a dictionary storing element/values without treating them as containing distinct key/value components, wherein duplicates of the elements can not be inserted
balance binary tree map supporting duplicate keys: ...
Crossreferencing Comp Sci terminology with specific implementations
C++ Standard Library
maps: map, multimap, unordered_map, unordered_multimap
other dictionaries: set, multiset, unordered_set, unordered_multiset
note: with iterators or std::find you can erase an element and test for membership in array, vector, list, deque etc, but the container interfaces don't directly support that because finding an element is spectacularly inefficient at O(N), in some cases insert/erase is inefficient, and supporting those operations undermines the deliberately limited API the container implies - e.g. deques should only support erase/pop at the front and back and not in terms of some key. Having to do more work in code to orchestrate the search gently encourages the programmer to switch to a container data structure with more efficient searching.
...may add other languages later / feel free to edit in...
My 2 cents.
Dictionary is an abstract class in Java whereas Map is an interface. Since, Java does not support multiple inheritances, if a class extends Dictionary, it cannot extend any other class.
Therefore, the Map interface was introduced.
Dictionary class is obsolete and use of Map is preferred.
Typically I assume that a map is backed by a hash table; it connotes an unordered store.
Dictionaries connote an ordered store.
There is a tree-based dictionary called a Trie.
In Lisp, it might look like this:
(a (n (d t)) n d )
Which encapsulates the words:
a
and
ant
an
ad
The traversal from the top to the leaf yields a word.
Not really the same thing. Maps are a subset of dictionary. Dictionary is defined here as having the insert, delete, and find functions. Map as used by Java (according to this) is a dictionary with the requirement that keys mapping to values are strictly mapped as a one-to-one function. A dictionary might have more than one key map to one value, or one key map to several values (like chaining in a hasthtable), eg Twitter hashtag searches.
As a more "real world" example, looking up a word in a dictionary can give us a number of definitions for the same word, and when we find an entry that points us to another entry (see other word), a number of words for the same list of definitions. In the real world, maps are much broader, allowing us to have locations for names or names for coordinates, but also we can find a nearest neighbor or other attributes (populations, etc), so IMHO there could be argument for a greater expansion of the map type to possibly have graph based implementations, but it would be best to always assume just the key-value pair, especially since nearest neighbor and other attributes to the value could all just be data members of the value.
java maps, despite the one-to-one requirement, can implement something more like a generalized dictionary if the value is generalized as a collection itself, or if the values are merely references to collections stored elsewhere.
Remember that Java maintainers are not the maintainers of ADT definitions, and that Java decisions are specifically for Java.
Other terms for this concept that are fairly common: associative array and hash.
Yes, they are the same, you may add "Associative Array" to the mix.
using Hashtable or a Hash ofter refers to the implementation.
These are two different terms for the same concept.
Hashtable and HashMap also refer to the same concept.
so on a purely theoretical level.
A Dictionary is a value that can be used to locate a Linked Value.
A Map is a Value that provides instructions on how to locate another values
all collections that allow non linear access (ie only get first or get last) are a Map, as even a simple Array has an index that maps to the correct value. So while a Dictionary is a Type of map, maps are a much broader range of possible function.
In Practice a its usually the mapping function that defines the name, so a HashMap is a mapped data structure that uses a hashing algorithm to link the key to the value, where as a Dictionary doesn't specify how the keys are linked to a value so could be stored via a linked list, tree or any other algorithm. from the usage end you usually don't care what the algorithm only that they work so you use a generic dictionary and only shift to one of the other structures only when you need to enfore the type of algorithm
The main difference is that a Map, requires that all entries(value & key pair) have a unique key. If collisions occur, i.e. when a new entry has the same key as an entry already in the collection, then collision handling is required.
Usually, we handle collisions using either Separate Chaining. Or Linear Probing.
A Dictionary allows for multiple entries to be linked to the same key.
When a Map has implemented Separate Chaining, then it tends to resemble a Dictionary.
I'm in a data structures class right now and my understanding is the dict() data type that can also be initialized as just dictionary = {} or with keys and values, is basically the same as how the list/array data type is used to implement stacks and queues. So, dict() is the type and maps are a resulting data structure you can choose to implement with the dictionary data type in the same way you can use the list type and choose to implement a stack or queue data structure with it.
I have often heard people talking about hashing and hash maps and hash tables. I wanted to know what they are and where you can best use them for.
First you shoud maybe read this article.
When you use lists and you are looking for a special item you normally have to iterate over the complete list. This is very expensive when you have large lists.
A hashtable can be a lot faster, under best circumstances you will get the item you are looking for with only one access.
How is it working? Like a dictionary ... when you are looking for the word "hashtable" in a dictionary, you are not starting with the first word under 'a'. But rather you go straight forward to the letter 'h'. Then to 'ha', 'has' and so on, until you found your word. You are using an index within your dictionary to speed up your search.
A hashtable does basically the same. Every item gets an unique index (the so called hash). You use this hash for lookups. The hash may be an index in a normal linked list. For instance your hash could be a number like 2130 which means that you should look at position 2130 in your list. A lookup at a known index within a normal list is very easy and fast.
The problem of the whole approach is the so called hash function which assigns this index to each item. When you are looking for an item you should be able to calculate the index in advance. Just like in a real dictionary, where you see that the word 'hashtable' starts with the letter 'h' and therefore you know the approximate position.
A good hash function provides hashcodes that are evenly distrubuted over the space of all possible hashcodes. And of course it tries to avoid collisions. A collision happens when two different items get the same hashcode.
In C# for instance every object has a GetHashcode() method which provides a hash for it (not necessarily unique). This can be used for lookups and sorting with in your dictionary.
When you start using hashtables you should always keep in mind, that you handle collisions correctly. It can happen quite easily in large hashtables that two objects got the same hash (maybe your overload of GetHashcode() is faulty, maybe something else happened).
Basically, a HashMap allows you to store items with identifiers. They are stored in a table format with the identifier being hashed using a hashing algorithm.
Typically they are more efficient to retrieve items than search trees etc.
You may find this helpful: http://www.relisoft.com/book/lang/pointer/8hash.html
Hope it helps,
Chris
Hashing (in the noncryptographic sense) is a blanket term for taking an input and then producing an output to identify it with. A trivial example of a hash is adding the sum of the letters of a string, i.e:
f(abc) = 6
Note that this trivial hash scheme would create a collision between the strings abc, bca, ae, etc. An effective hash scheme would produce different values for each string, naturally.
Hashmaps and hashtables are datastructures (like arrays and lists), that use hashing to store data. In a hashtable, a hash is produced (either from a provided key, or from the object itself) that determines where in the table the object is stored. This means that as long as the user of the hashtable is aware of the key, retrieving the object is extremely fast.
In a list, in comparison, you would need to in some way search through the list in order to find your sought object. This also represents the backside of hashtables, which is that it is very complicated to find an object in it without knowing the key, because where the object is stored in the table has no relevance to its value nor when it was inputed.
Hashmaps are similar to hashtables, but only one example of each object is stored in it (hence no key needs to be provided, the object itself is the key).
This is of course a very simple explanation, so I suggest you read in depth from this point on. I hope I didn't make any silly mistakes. =)
Hashmap is used for storing data in key value pairs. We can use a hashmap for storing objects in a application and use it further in the same application for storing, updating, deleting values. Hashmap key and values are stored in a bucket to a specific entry, this entry location is determined using Hashcode function. This hashcode function determines the hash where the value is stored. The detailed explanantion of how hashmap works is described in this video: https://youtu.be/iqYC1odZSNo
Hash maps saves a lot of time as compared to other search criteria. We have a hash key that corresponds to a hash code which further helps to find its index value. In terms of implementation, hash maps takes a string converts it into an integer and remaps it to convert it into an index of an array which helps to find the required value.
To go in detail we can look for handling collisions in hash maps. Like instead of using array we can go with the linked list.
There is a short video available to understand it.
Available here :
Implementation example --> https://www.youtube.com/watch?v=shs0KM3wKv8
Sample:
int hashCode(String s)
{
logic
}