Information about Associative Array

An associative array (also map, mapping, hash, dictionary, finite map, lookup table, and in query-processing an index or index file) is an abstract data type composed of a collection of keys and a collection of values, where each key is associated with one value. The operation of finding the value associated with a key is called a lookup or indexing, and this is the most important operation supported by an associative array. The relationship between a key and its value is sometimes called a mapping or binding. For example, if the value associated with the key "bob" is 7, we say that our array maps "bob" to 7. Associative arrays are very closely related to the mathematical concept of a function with a finite domain. As a consequence, a common and important use of associative arrays is in memoization.

From the perspective of a programmer using an associative array, it can be viewed as a generalization of an array: While a regular array maps integers to arbitrarily typed objects (integers, strings, pointers, and, in an OO sense, objects), an associative array maps arbitrarily typed objects to arbitrarily typed objects. (Implementations of the two data structures, though, may be considerably different.)

The operations that are usually defined for an associative array are:
  • Add: Bind a new key to a new value
  • Reassign: Bind an old key to a new value
  • Remove: Unbind a key from a value and remove the key from the key set
  • Lookup: Find the value (if any) that is bound to a key

Examples

One can think of a telephone book as an example of an associative array, where names are the keys and phone numbers are the values. Using the usual array-like notation, we might write
>
telephone['ada']='01-1234-56'
telephone['bert']='02-4321-56'


and so on. These entries can be thought of as two records in a database table:

name telephone
ada01-1234-56
bert02-4321-56


Another example would be a dictionary where words are the keys and definitions are the values.
>
dictionary['toad']='four legged amphibian'
dictionary['cow']='female four-legged domesticated mammalian ruminant'


Since a database equivalent is that of a table containing precisely two fields - key and value, we can use an associative array to store any information which can be held in this form.
>
capital['england']='london'
capital['france']='paris'
bossof['subeditor']='editor'
bossof['reporter']='subeditor'
salary['editor']=50000
salary['reporter']=30000

Data structures for associative arrays

Associative arrays are usually used when lookup is the most frequent operation. For this reason, implementations are usually designed to allow speedy lookup, at the expense of slower insertion and a larger storage footprint than other data structures (such as association lists).

Efficient representations

There are two main efficient data structures used to represent associative arrays, the hash table and the self-balancing binary search tree. Skip lists are also an alternative, though relatively new and not as widely used. Relative advantages and disadvantages include:
  • Hash tables have faster average lookup and insertion time (O(1)), while some kinds of binary search tree have faster worst-case lookup and insertion time (O(log n) instead of O(n)). Hash tables have seen extensive use in realtime systems, but trees can be useful in high-security realtime systems where untrusted users may deliberately supply information that triggers worst-case performance in a hash table, although careful design can remove that issue. Hash tables shine in very large arrays, where O(1) performance is important. Skip lists have worst-case operation time of O(n), but average-case of O(log n), with much less insertion and deletion overhead than balanced binary trees.
  • Hash tables can have more compact storage for small value types, especially when the values are bits.
  • There are simple persistent versions of balanced binary trees, which are especially prominent in functional languages.
  • Building a hash table requires a reasonable hash function for the key type, which can be difficult to write well, while balanced binary trees and skip lists only require a total ordering on the keys. On the other hand, with hash tables the data may be cyclically or partially ordered without any problems.
  • Balanced binary trees and skip lists preserve ordering — allowing one to efficiently iterate over the keys in order or to efficiently locate an association whose key is nearest to a given value. Hash tables do not preserve ordering and therefore cannot perform these operations as efficiently.
  • Balanced binary trees can be easily adapted to efficiently assign a single value to a large ordered range of keys, or to count the number of keys in an ordered range.

Association lists

A simple but generally inefficient type of associative array is an association list, often called an alist for short, which simply stores a linked list of key-value pairs. Each lookup does a linear search through the list looking for a key match.

Strong advantages of association lists include:
  • No knowledge is needed about the keys, such as an order or a hash function.
  • For small associative arrays, common in some applications, association lists can take less time and space than other data structures.
  • Insertions are done in constant time by cons'ing the new association to the head of the list.

Specialized representations

If the keys have a specific type, one can often use specialized data structures to gain performance. For example, integer-keyed maps can be implemented using Patricia trees or Judy arrays, and are useful space-saving replacements for sparse arrays. Because this type of data structure can perform longest-prefix matching, they're particularly useful in applications where a single value is assigned to most of a large range of keys with a common prefix except for a few exceptions, such as in routing tables.

String-keyed maps can avoid extra comparisons during lookups by using tries.

Multimap

A variation of the map (associative array) is the multimap, which is the same as map data structures, but allows a key to be mapped to more than one value. Formally, a multimap can be thought of as a regular associative array that maps unique keys to nonempty multisets of values, although actual implementation may vary. C++'s Standard Template Library provides the "multimap" class for the sorted multimap, SGI's STL provides the "hash_multimap" class, which implements a multimap using a hash table, and some varieties of LPC have built-in multimap support.

Language support

Associative arrays can be implemented in any programming language as a package and many language systems provide them as part of their standard library. In some languages, they are not only built into the standard system, but have special syntax, often array-like subscripting.

Built-in syntactic support for associative arrays was introduced by Snobol4, under the name "table". MUMPS made multi-dimensional associative arrays, optionally persistent, its key data structure. SETL supported them as one possible implementation of sets and maps. Most modern scripting languages, starting with awk and including Perl, tcl, Javascript, Python, and Ruby, support associative arrays as their primary array type.

In many more languages, they are available as library functions without special syntax.

Associative arrays have a variety of names. In Smalltalk, Objective-C and Python they are called dictionaries; in Perl and Ruby they are called hashes; in C++ and Java they are called maps (see Map) and in Common Lisp and Windows PowerShell they are called hashtables (since both typically use this implementation). In PHP all arrays can be associative, except that the keys are limited to integers and strings and can only be a single level of subscripts.

In the scripting language Lua, associative arrays, called tables, are used as the primitive building block for all data structures, even arrays. Likewise, in JavaScript, all objects are associative arrays. In MUMPS, the associative arrays are typically stored as B-trees.

Awk

Awk has built-in, language-level support for associative arrays.

For example:

>
phonebook["Sally Smart"] = "555-9999"
phonebook["John Doe"] = "555-1212"
phonebook["J. Random Hacker"] = "555-1337"


You can also loop through an associated array as follows:

>
for (name in phonebook) {
	print name, " ", phonebook[name]
}


You can also check if an element is in the associative array, and delete elements from an associative array.

Multi-dimensional associative arrays can be implemented in standard Awk using concatenation and e.g. SUBSEP:

>
{ # for every input line
	multi[$1 SUBSEP $2]++;
}
#
END {
	for (x in multi) {
		split(x, arr, SUBSEP);
		print arr[1], arr[2], multi[x];
	}
}


Thompson AWK [1] provides built-in multi-dimensional associative arrays:

>
{ # for every input line
	multi[$1][$2]++;
}
#
END {
	for (x in multi)
		for (y in multi[x])
		print x, y, multi[x][y];
}

C

There is no standard implementation of an associative array in C, but a 3rd party library with BSD license is available here. POSIX 1003.1-2001 describes the functions hcreate(), hdestroy() and hsearch().

Another 3rd party library, uthash, also creates associative arrays from C structures. A structure represents a value, and one of the structure fields acts as the key.

Finally, the Glib library also supports associative arrays, along with many other advanced data types and is the recommended implementation of the GNU Project.

ColdFusion

You can use a ColdFusion structure to perform as an associative array. Here is a sample in ColdFusion:

>

	phoneBook = StructNew();
	phoneBook["Sally Smart"] = "555-9999";
	phoneBook["John Doe"] = "555-1212";
	phoneBook["J. Random Hacker"] = "555-1337";
	sName = "A Person";
	phoneBook[sName] = "555-4321";
	phoneBook.UnknownComic = "???";


	#phoneBook["UnknownComic"]#

C#

>
Hashtable ht = new Hashtable();
ht.Add("testKey", "AssociatedData");
MessageBox.Show((string) ht["testKey"]);


>
Dictionary dic = new Dictionary();
dic.Add(7, "Bond");
MessageBox.Show(dic[7]);


In the above sample the Hashtable class is only capable of associating a String key with a value of Object type. Because in .NET all types (save pointers) ultimately derive from Object anything can be put into a Hashtable, even data of different types. This could lead to errors if consuming code expects data to be of a singular type. In the above code casting is required to convert the Object variables back to their original type. Additionally, casting value-types (structures such as integers) to Object to put into the Hashtable and casting them back requires boxing/unboxing which incurs both a slight performance penalty and pollutes the heap with garbage. This changes in C# 2.0 with generic hashtables called dictionaries. There are significant performance and reliability gains to these strongly typed collections because they do not require boxing/unboxing or explicit type casts and introduce compile-time type checks.

C++

C++ also has a form of associative array called std::map (see Standard Template Library#Containers). One could create a map with the same information as above using C++ with the following code:

>
#include 
#include 
int main() {
	std::map phone_book;
	phone_book["Sally Smart"] = "555-9999";
	phone_book["John Doe"] = "555-1212";
	phone_book["J. Random Hacker"] = "553-1337";
	return 0;
}


You can iterate through the list with the following code:

>
std::map::iterator curr,end;
for( curr = phone_book.begin(), end = phone_book.end();  curr != end;  curr++ )
	cout <<  curr->first + " = " + curr->second << endl;


In C++, the std::map class is templated which allows the data types of keys and values to be different for different map instances. For a given instance of the map class the keys must be of the same base type. The same must be true for all of the values. Although std::map is typically implemented using a self-balancing binary search tree, C++'s Technical Report 1 (TR1) defines a second map called std::tr1::unordered_map with the algorithmic characteristics of a hash table. This is a common vendor extension to the STL as well, usually called hash_map, being available from such implementations as SGI and STLPort.

Cocoa/GNUstep (Objective-C)

Cocoa (API) and GNUstep handle associative arrays using NSMutableDictionary (a mutable version of NSDictionary) class cluster. This class allows assignments between any two objects to be made. A copy of the key object is made before it is inserted into NSMutableDictionary, therefore the keys must conform to the NSCopying protocol. When being inserted to a dictionary, the value object receives a retain message to increase its reference count. The value object will receive the release message when it will be deleted from the dictionary (both explicitly or by adding to the dictionary a different object with the same key).

NSMutableDictionary *aDictionary = NSMutableDictionary alloc] init]; [aDictionary setObject:@"555-9999" forKey:@"Sally Smart"]; [aDictionary setObject:@"555-1212" forKey:@"John Doe"]; [aDictionary setObject:@"553-1337" forKey:@"Random Hacker"];

To access assigned objects this command may be used:

id anObject = [aDictionary objectForKey:@"Sally Smart"];

All keys or values can be simply enumerated using NSEnumerator

NSEnumerator *keyEnumerator = aDictionary allKeys] objectEnumerator];id key; while (( key = [keyEnumerator nextObject] )) { // ... process it here ... }

What is even more practical, structured data graphs may be easily created using Cocoa (API), especially NSDictionary (NSMutableDictionary). This can be illustrated with this compact example:

NSDictionary *aDictionary = [NSDictionary dictionaryWithObjectsAndKeys: [NSDictionary dictionaryWithObjectsAndKeys: @"555-9999", @"Sally Smart", @"555-1212", @"John Doe", nil], @"students", [NSDictionary dictionaryWithObjectsAndKeys: @"553-1337", @"Random Hacker", nil], @"hackers", nil];

And relevant fields can be quickly accessed using key paths:

id anObject = [aDictionary valueForKeyPath:@"students.Sally Smart"];

D

D programming language phone_book; phone_book["Sally Smart"] = "555-9999"; phone_book["John Doe"] = "555-1212"; phone_book["J. Random Hacker"] = "553-1337"; return 0; }

Keys and values can be any types, but all the keys in an associative array must be of the same type, and the same for values.

You can also loop through all properties and associated values, i.e. as follows:

>
foreach (key, value; phone_book) {
	writefln("Number for $name: $number\n", key, value);
}


A property can be removed as follows:

>
phone_book.remove("Sally Smart");

Delphi

Delphi does not offer direct support for associative arrays. However, you can simulate associative arrays using TStrings object. Here's an example:

>
procedure TForm1.Button1Click(Sender: TObject);
var
DataField: TStrings;
i: Integer;
begin
DataField := TStringList.Create;
DataField.Add(Format('%s=%s', ['Sally Smart', '555-9999']));
DataField.Add(Format('%s=%s', ['John Doe', '555-1212']));
DataField.Add(Format('%s=%s', ['J. Random Hacker', '553-1337']));
// access an entry and display it in a message box
ShowMessage(DataField.Values['Sally Smart']);
// loop through the associative array
for i := 0 to DataField.Count - 1 do
begin
ShowMessage('Number for ' + DataField.Names[i] + ': ' + DataField.ValueFromIndex[i]);
end;
DataField.Free;
end;

Java

In Java associative arrays are implemented as "maps"; they are part of the Java collections framework. Since J2SE 5.0 and the introduction of generics into Java, collections can have a type specified; for example, an associative array mapping strings to strings might be specified as follows:

>
Map phoneBook = new HashMap();
phoneBook.put("Sally Smart", "555-9999");
phoneBook.put("John Doe", "555-1212");
phoneBook.put("J. Random Hacker", "555-1337");


The get method is used to access a key; for example, the value of the expression phoneBook.get("Sally Smart") is "555-9999".

This code uses a hash map to store the associative array, by calling the constructor of the HashMap class; however, since the code only uses methods common to the interface Map, one could also use a self-balancing binary tree by calling the constructor of the TreeMap class (which implements the subinterface SortedMap), without changing the definition of the phone_book variable or the rest of the code, or use a number of other underlying data structures that implement the Map interface.

The hash function in Java is provided by the method Object.hashCode(). Since every class in Java inherits from Object, every object has a hash function. A class can override the default implementation of hashCode() to provide a custom hash function based on the properties of the object.

The Object class also contains the method equals(Object) that tests the object for equality with another object. Maps in Java rely on objects maintaining the following contract between their hashCode() and equals() methods:

For two objects a and b,
  • >a.equals(b) == b.equals(a)
  • >if a.equals(b), then a.hashCode() == b.hashCode()
In order to maintain this contract, a class that overrides equals() must also override hashCode(), and vice versa, so that hashCode() is based on the same properties (or a subset of the properties) as equals().

A further contract that the map has with the object is that the results of the hashCode() and equals() methods will not change once the object has been inserted into the map. For this reason, it is generally a good practice to base the hash function on immutable properties of the object.

JavaScript

JavaScript (and its standardized version: ECMAScript) is a prototype-based object-oriented language. In JavaScript an object is a mapping from property names to values -- that is, an associative array with one caveat: since property names are strings, only string keys are allowed. Other than that difference, objects also include one feature unrelated to associative arrays: a prototype link to the object they inherit from. Doing a lookup for a property will forward the lookup to the prototype if the object does not define the property itself.

An object literal is written as { property1 : value1, property2 : value2, ... }. For example:

>
var myObject = {
	"Sally Smart"      : "555-9999",
	"John Doe"         : "555-1212",
	"J. Random Hacker" : "553-1337"
};


If the property name is a valid identifier, the quotes can be omitted, e.g.:

>
var myOtherObject = { foo : 42, bar : false }


Lookup is written using property access notation, either square brackets, which always works, or dot notation, which only works for identifier keys:

>
myObject["John Doe"]
myOtherObject.foo


You can also loop through all properties and associated values as follows:

>
for(var property in myObject) {
	var value = myObject[property];
	alert("myObject[" + property + "] = " + value);
}


A property can be removed as follows:

>
delete myObject["Sally Smart"];


As mentioned before, properties are strings. However, since every native object and primitive can be implicitly converted to a string, you can do:

>
myObject[1]                                             // key is "1"; note that myObject[1] === myObject['1']
myObject'a','b'                                     // key is "a,b"
myObject[{toString:function(){return 'hello world';}}]  // key is "hello world"


Any object, including built-in objects such as Array, can be dynamically extended with new properties. For example:

>
Array.prototype.removeAllObjects = function () {
	/* ... */
}


In modern JavaScript it's considered bad form to use the Array type as an associative array. Consensus is that the Object type is best for this purpose. The reasoning behind this is that if Array is extended via prototype and Object is kept pristine, 'for(in)' loops will work as expected on associative 'arrays'. This issue has been drawn into focus by the popularity of JavaScript frameworks that make heavy and sometimes indiscriminate use of prototype to extend JavaScript's inbuilt types.

See JavaScript Array And Object Prototype Awareness Day for more information on the issue.

KornShell 93 (and compliant shells: ksh93, zsh, ...)

Note: The latest version of bash, 3.2, doesn't support associative arrays properly yet.


Definition:

typeset -A phonebook; phonebook=(["Sally Smart"]="555-9999" ["John Doe"]="555-1212" ["J. Random Hacker"]="555-1337");

Dereference:

${phonebook["John Doe"]};

Lisp

Lisp was originally conceived as a "LISt Processing" language, and one of its most important data types is the linked list, which can be treated as an association list ("alist").

>
'(
	("Sally Smart" . "555-9999")
	("John Doe" . "555-1212")
	("J. Random Hacker" . "553-1337")
)


The syntax (x . y) is used to indicate a consed pair. Keys and values need not be the same type within an alist. Lisp and Scheme provide operators such as assoc to manipulate alists in ways similar to associative arrays.

Because of their linear nature, alists are used for relatively small sets of data. Common Lisp also supports a hash table data type, and for Scheme they are implemented in SRFI 69. Hash tables have greater overhead than alists, but provide much faster access when there are many elements.

It is easy to construct composite abstract data types in Lisp, using structures and/or the object-oriented programming features, in conjunction with lists, arrays, and hash tables.

LPC

LPC implements associative arrays as a fundamental type known as either map or mapping, depending on the driver. The keys and values can be of any type. A mapping literal is written as ([ key_1 : value_1, key_2 : value_2 ]). Procedural use looks like:

>
mapping phone_book = ([]);
phone_book["Sally Smart"] = "555-9999";
phone_book["John Doe"] = "555-1212";
phone_book["J. Random Hacker"] = "555-1337";


Mappings are accessed for reading using the indexing operator in the same way as they are for writing, as shown above. So phone_book["Sally Smart"] would return the string "555-9999", and phone_book["John Smith"] would return 0. Testing for presence is done using the function member(), e.g. if(member(phone_book, "John Smith")) write("John Smith is listed.\n");

Deletion is accomplished using a function called either m_delete() or map_delete(), depending on the driver, used like: m_delete(phone_book, "Sally Smart");

LPC drivers of the "Amylaar" family, which use the 'mapping' type and m_delete(), implement multivalued mappings using a secondary, numeric index. (Drivers of the MudOS family, which uses the 'map' type and map_delete(), do not support multivalued mappings.) Example syntax:

>
mapping phone_book = ([:2]);
phone_book["Sally Smart", 0] = "555-9999";
phone_book["Sally Smart", 1] = "99 Sharp Way";
phone_book["John Doe", 0] = "555-1212";
phone_book["John Doe", 1] = "3 Nigma Drive";
phone_book["J. Random Hacker", 0] = "555-1337";
phone_book["J. Random Hacker", 1] = "77 Massachusetts Avenue";


LPC drivers modern enough to support a foreach() construct allow iteration over their mapping types using it.

Lua

In Lua, table is a fundamental type that can be used either as array (numerical index, fast) or as associative array. The keys and values can be of any type, except nil. The following with focus on non-numerical indexes.

A table literal is written as { value, key = value, [index] = value, ["non id string"] = value }. For example:

>
phone_book = {
	["Sally Smart"]      = "555-9999",
	["John Doe"]         = "555-1212",
	["J. Random Hacker"] = "553-1337", -- Trailing comma is OK
}
aTable = {
	-- Table as value
	subTable = { 5, 7.5, k = true }, -- key is "subTable"
	-- Function as value
	['John Doe'] = function (age) if age < 18 then return "Young" else return "Old!" end end,
	-- Table and function (and other types) can also be used as keys
}


If the key is a valid identifier (not a keyword), the quotes can be omitted. They are case sensitive.

Lookup is written using either square brackets, which always works, or dot notation, which only works for identifier keys:

>
print(aTable["John Doe"](45))
x = aTable.subTable.k


You can also loop through all keys and associated values with iterators or for loops:

>
simple = { [true] = 1, [false] = 0, [3.14] = math.pi, x = 'x', ["!"] = 42 }
function FormatElement(key, value)
	return "[" .. tostring(key) .. "] = " .. value .. ", "
end
-- Iterate on all keys
table.foreach(simple, function (k, v) io.write(FormatElement(k, v)) end)
print""
for k, v in pairs(simple) do io.write(FormatElement(k, v)) end
print""
k= nil
repeat
	k, v = next(simple, k)
	if k ~= nil then io.write(FormatElement(k, v)) end
until k == nil
print""


An entry can be removed by setting it to nil:

>
simple.x = nil


Likewise, you can overwrite values or add them:

>
simple['%'] = "percent"
simple['!'] = 111

MUMPS

In MUMPS every array is an associative array. The built-in, language-level, direct support for associative arrays applies to private, process-specific arrays stored in memory called "locals" as well as to the permanent, shared arrays stored on disk which are available concurrently by multiple jobs. The name for globals is preceded by the circumflex "^" to distinquish it from local variable names.

SET ^phonebook("Sally Smart")="555-9999" ;; storing permanent data SET phonebook("John Doe")="555-1212" ;; storing temporary data SET phonebook("J. Random Hacker")="553-1337" ;;storing temporary data MERGE ^phonebook=phonebook ;;copying temporary data into permanent data

To access the value of an element, simply requires using the name with the subscript:

WRITE "Phone Number :",^phonebook("Sally Smart"),!

You can also loop through an associated array as follows:

SET NAME="" FOR S NAME=$ORDER(^phonebook(NAME)) QUIT:NAME="" WRITE NAME," Phone Number :",^phonebook(NAME),!

OCaml

The OCaml programming language provides three different associative containers. The simplest is a list of pairs:

>
# let m = [ "Sally Smart", "555-9999"; "John Doe", "555-1212"; "J. Random Hacker", "553-1337"];;
val m : (string * string) list = [ ("Sally Smart", "555-9999"); ("John Doe", "555-1212"); ("J. Random Hacker", "553-1337") ]
# List.assoc "John Doe" m;;
- : string = "555-1212"


The second is a polymorphic hash table:

>
# let m = Hashtbl.create 3;;
val m : ('_a, '_b) Hashtbl.t = 
# Hashtbl.add m "Sally Smart" "555-9999";
Hashtbl.add m "John Doe" "555-1212";
Hashtbl.add m "J. Random Hacker" "553-1337";;
- : unit = ()
# Hashtbl.find m "John Doe";;
- : string = "555-1212"


Finally, functional maps (represented as immutable balanced binary trees):

>
# include (Map.Make(String));;
...
# let m = add "Sally Smart" "555-9999" empty
let m = add "John Doe" "555-1212" m
let m = add "J. Random Hacker" "553-1337" m;;
val m : string t = 
# find "John Doe" m;;
- : string = "555-1212"


Lists of pairs and functional maps both provide a purely functional interface. In contrast, hash tables provide an imperative interface. For many operations, hash tables are significantly faster than lists of pairs and functional maps.

Perl

Perl has built-in, language-level support for associative arrays. Modern Perl vernacular refers to associative arrays as hashes; the term associative array is found in older documentation, but is considered somewhat archaic. Perl hashes are flat: keys are strings and values are scalars. However, values may be references to arrays or other hashes, and the standard Perl module Tie::RefHash enables hashes to be used with reference keys.

A hash variable is marked by a % sigil, to distinguish it from scalar, array and other data types. A hash literal is a key-value list, with the preferred form using Perl's => token, which is mostly semantically identical to the comma and makes the key-value association clearer:

>
%phone_book = (
	'Sally Smart'      => '555-9999',
	'John Doe'         => '555-1212',
	'J. Random Hacker' => '553-1337',
);


Accessing a hash element uses the syntax $hash_name{$key} — the key is surrounded by curly braces and the hash name is prefixed by a $, indicating that the hash element itself is a scalar value, even though it is part of a hash. The value of $phone_book{'John Doe'} is '555-1212'. The % sigil is only used when referring to the hash as a whole, such as when asking for keys %phone_book.

The list of keys and values can be extracted using the built-in functions keys and values, respectively. So, for example, to print all the keys of a hash:

>
foreach $name (keys %phone_book) {
	print $name, "\n";
}


One can iterate through (key, value) pairs using the each function:

>
while (($name, $number) = each %phone_book) {
	print 'Number for ', $name, ': ', $number, "\n";
}


A hash reference, which is a scalar value that points to a hash, is specified in literal form using curly braces as delimiters, with syntax otherwise similar to specifying a hash literal:

>
$phone_book = {
	'Sally Smart'      => '555-9999',
	'John Doe'         => '555-1212',
	'J. Random Hacker' => '553-1337',
};


Values in a hash reference are accessed using the dereferencing operator:

>
print $phone_book->{'Sally Smart'};


When the hash contained in the hash reference needs to be referred to as a whole, as with the keys function, the syntax is as follows:

>
foreach $name (keys %{$phone_book}) {
	print 'Number for ', $name, ': ', $phone_book->{$name}, "\n";
}

PHP

PHP's built-in array type is in reality an associative array. Even when using numerical indexes, PHP internally stores it as an associative array.[1] This is why one in PHP can have non-consecutive numerically indexed arrays.

An associative array can be formed in one of two ways:

>
$phonebook = array ();
$phonebook['Sally Smart']      = '555-9999';
$phonebook['John Doe']         = '555-1212';
$phonebook['J. Random Hacker'] = '555-1337';
// or
$phonebook = array (
	'Sally Smart'      => '555-9999',
	'John Doe'         => '555-1212',
	'J. Random Hacker' => '555-1337',
);


You can also loop through an associative array as follows:

>
foreach ($phonebook as $name => $number) {
	echo "Number for $name: $number\n";
}


PHP has an extensive set of functions to operate on arrays.

Pike

Pike has built-in support for Associative Arrays, which are referred to as mappings. Mappings are created as follows:

>
mapping(string:string) phonebook = ([ "Sally Smart":"555-9999", "John Doe":"555-1212", "J. Random Hacker":"555-1337" ]);


Accessing and testing for presence in mappings is done using the indexing operator. So phonebook["Sally Smart"] would return the string "555-9999", and phonebook["John Smith"] would return 0.

Iterating through a mapping can be done using either foreach:

>
foreach(phonebook; string key; string value) {
	write("%s:%s\n", key, value);
}


Or using an iterator object:

>
Mapping.Iterator i = get_iterator(phonebook);
while (i->index()) {
	write("%s:%s\n", i->index(), i->value());
	i->next();
}


Elements of a mapping can be removed using m_delete, which returns the value of the removed index:

>
string sallys_number = m_delete(phonebook, "Sally Smart");

PostScript

In PostScript, associative arrays are called dictionaries. In Level 1 PostScript they must be created explicitly, but Level 2 introduced direct declaration using the double-brace syntax:

% Level 1 declaration 3 dict dup begin /red (rouge) def /green (vert) def /blue (bleu) def end

% Level 2 declaration << /red (rot) /green (gruen) /blue (blau) >>

% Both methods leave the dictionary on the operand stack


Dictionaries can be accessed directly using get or implicitly by placing the dictionary on the dictionary stack using begin:

% With the previous two dictionaries still on the operand stack /red get print % outputs 'rot'

begin green print % outputs 'vert' end


Dictionary contents can be iterated through using forall, though not in any particular order:

% Level 2 example << /This 1 /That 2 /Other 3 >> {exch =print ( is ) print ==} forall May well output: That is 2 This is 1 Other is 3

Dictionaries can be augmented (up to their defined size only in Level 1) or altered using put, and entries can be removed using undef: % define a dictionary for easy reuse: /MyDict << /rouge (red) /vert (gruen) >> def

% add to it MyDict /bleu (blue) put

% change it MyDict /vert (green) put

% remove something MyDict /rouge undef

Python

In Python, associative arrays are called dictionaries. Dictionary literals are marked with curly braces:

>
phonebook = {
	'Sally Smart' : '555-9999',
	'John Doe' : '555-1212',
	'J. Random Hacker' : '553-1337'
}


To access an entry in Python simply use the array indexing operator. For example, the expression phonebook['Sally Smart'] would return '555-9999'.

An example loop iterating through all the keys of the dictionary:

>
for key in phonebook:
	print key, phonebook[key]


Iterating through (key, value) tuples:

>
for key, value in phonebook.iteritems():
	print key, value


Dictionaries can also be constructed with the dict builtin, which is most commonly found inside list comprehensions and generator expressions, and it takes a key-value list:

>
dict((key, value) for key, value in phonebook.items() if 'J' in key)


Dictionary keys can be individually deleted using the del statement. The corresponding value can be returned before the key-value pair are deleted using the pop method of dict types;

>
del phonebook['John Doe']
val = phonebook.pop('Sally Smart')
assert phonebook.keys() == ['J. Random Hacker'] # Only one key left

REXX

In REXX, associative arrays are called Stem variables or Compound variables.

>
KEY = 'Sally Smart'
PHONEBOOK.KEY = '555-9999'
KEY = 'John Doe'
PHONEBOOK.KEY = '555-1212'
KEY = 'J. Ramdon Hacker'
PHONEBOOK.KEY = '553-1337'


Stem variables with numeric keys typically start at 1 and go up from there. The 0 key stem variable is used (by convention) as the count of items in the whole stem.

>
NAME.1 = 'Sally Smart'
NAME.2 = 'John Doe'
NAME.3 = 'J. Random Hacker'
NAME.0 = 3


REXX has no easy way of automatically accessing the keys for a stem variable and typically the keys are stored in a separate associative array with numeric keys.

Ruby

In Ruby a Hash is used as follows:

>
phonebook = {
	'Sally Smart' => '555-9999',
	'John Doe' => '555-1212',
	'J. Random Hacker' => '553-1337'
}


phonebook['John Doe'] produces '555-1212'

To iterate over the hash, use something like the following:

>
phonebook.each {|key, value| puts key + " => " + value}


Additionally, each key may be shown individually:

>
phonebook.each_key {|key| puts key}


Each value may, of course, also be shown:

>
phonebook.each_value {|value| puts value}

Smalltalk

In Smalltalk a dictionary is used:

>
phonebook := Dictionary new.
phonebook at: 'Sally Smart'      put:  '555-9999'.
phonebook at: 'John Doe'         put:  '555-1212'.
phonebook at: 'J. Random Hacker' put:  '553-1337'.


To access an entry the message #at: is sent to the dictionary object.

>
phonebook at: 'Sally Smart'


gives

>
'555-9999'

SNOBOL

SNOBOL is one of the first (if not the first) programming languages to use associative arrays. Associative arrays in SNOBOL are called Tables.

>
PHONEBOOK = TABLE()
PHONEBOOK['Sally Smart']      = '555-9999'
PHONEBOOK['John Doe']         = '555-1212'
PHONEBOOK['J. Random Hacker'] = '553-1337'

TCL

In Tcl every array is an associative array.

>
set "phonebook(Sally Smart)"       555-9999
set "phonebook(John Doe)"          555-1212
set "phonebook(J. Random Hacker)"  553-1337


The first argument of the set command has to be enclosed by double quotes, as it contains a space. In Tcl, space is used to separate arguments. Alternatively, array setting can be grouped into a single command (keys braced because they contain whitespace):

>
array set phonebook {
	{Sally Smart}      555-9999
	{John Doe}         555-1212
	{J. Random Hacker} 553-1337
}


To access an entry and put it on standard output

>
puts "$phonebook(Sally Smart)"


The result is here

>
555-9999

Visual Basic

There is no standard implementation common to all dialects. Visual Basic can use the Dictionary class from the Microsoft Scripting Runtime (which is shipped with Visual Basic 6):

>
' Requires a reference to SCRRUN.DLL in Project Properties
Dim phoneBook As New Dictionary
phoneBook.Add "Sally Smart", "555-9999"
phoneBook.Item("John Doe") = "555-1212"
phoneBook("J. Random Hacker") = "553-1337"
For Each name In phoneBook
	MsgBox name & " = " & phoneBook(name)
Next


Visual Basic .NET relies on the collection classes provided by .NET Framework:

>
Dim phoneBook As New System.Collections.Generic.Dictionary(Of String, String)
phoneBook("Sally Smart") = "555-9999"
phoneBook("John Doe") = "555-1212"
phoneBook("J. Random Hacker") = "553-1337"
For Each entry As KeyValuePair(Of String, String) In phoneBook
	MsgBox(entry.Key & " = " & entry.Value)
Next

Windows PowerShell

Unlike many other command line interpreters, PowerShell has built-in, language-level support for defining associative arrays.

For example:

>
$phonebook = @{
	'Sally Smart'      = '555-9999';
	'John Doe'         = '555-1212';
	'J. Random Hacker' = '553-1337'
}


Like in JavaScript, if the property name is a valid identifier, the quotes can be omitted, e.g.:

>
$myOtherObject = @{ foo = 42; bar = $false }


It is also possible to create an empty associative array and add single entries or even other associative arrays to it later on.

>
$phonebook = @{}
$phonebook += @{ 'Sally Smart' = '555-9999' }
$phonebook += @{ 'John Doe'    = '555-1212'; 'J. Random Hacker' = '553-1337' }


New entries can also be added by using the array index operator, the property operator or the Add() method of the underlying .NET object:

>
$phonebook = @{}
$phonebook['Sally Smart'] = '555-9999'
$phonebook.'John Doe'     = '555-1212'
$phonebook.Add('J. Random Hacker', '553-1337')


To dereference assigned objects the array index operator, the property operator or the parameterized property Item() of the .NET object can be used:

>
$phonebook['Sally Smart']
$phonebook.'John Doe'
$phonebook.Item('J. Random Hacker')


You can loop through an associative array as follows:

>
$phonebook.Keys | foreach { "Number for {0}: {1}" -f $_,$phonebook.$_ }


An entry can be removed using the Remove() method of the the underlying .NET object:

>
$phonebook.Remove('Sally Smart')

External links

References

1. ^ About the implementation of Arrays in PHP
In computer science, a lookup table is a data structure, usually an array or associative array, used to replace a runtime computation with a simpler lookup operation. The speed gain can be significant, since retrieving a value from memory is often faster than undergoing an
..... Click the link for more information.
In computing, an abstract data type (ADT) is a specification of a set of data and the set of operations that can be performed on the data. Such a data type is abstract in the sense that it is independent of various concrete implementations.
..... Click the link for more information.
Mapping may refer to:
  • Cartography, mapmaking
  • Surveying, accurately determining the position of points in 3-D space
  • Photogrammetry, inferring 3-D information from stereo photographs

..... Click the link for more information.
function expresses dependence between two quantities, one of which is given (the independent variable, argument of the function, or its "input") and the other produced (the dependent variable, value of the function, or "output").
..... Click the link for more information.
domain is most often defined as the set of values, D for which a function is defined.[1] A function that has a domain N is said to be a function over N, where N is an arbitrary set.
..... Click the link for more information.
In computing, memoization is an optimization technique used primarily to speed up computer programs by having function calls avoid repeating the calculation of results for previously-processed inputs.
..... Click the link for more information.
array is a data structure consisting of a group of elements that are accessed by indexing. In most programming languages each element has the same data type and the array occupies a contiguous area of storage. Most programming languages have a built-in array data type.
..... Click the link for more information.
Object-oriented programming (OOP) is a programming paradigm that uses "objects" and their interactions to design applications and computer programs. It is based on several techniques, including inheritance, modularity, polymorphism, and encapsulation.
..... Click the link for more information.
In the programming paradigm of object-oriented programming, an object is the individual run-time unit that is used as the basic building block of programs. These objects act on each other, as opposed to a traditional view in which a program may be seen as a collection of functions,
..... Click the link for more information.
data structure is a way of storing data in a computer so that it can be used efficiently. Often a carefully chosen data structure will allow the most efficient algorithm to be used. The choice of the data structure often begins from the choice of an abstract data type.
..... Click the link for more information.
In computer science, a hash table, or a hash map, is a data structure that associates keys with values. The primary operation it supports efficiently is a lookup: given a key (e.g. a person's name), find the corresponding value (e.g. that person's telephone number).
..... Click the link for more information.
In computer science, a self-balancing binary search tree or height-balanced binary search tree is a binary search tree that attempts to keep its height, or the number of levels of nodes beneath the root, as small as possible at all times, automatically.
..... Click the link for more information.
Invented in 1990 by William Pugh, a skip list is a probabilistic data structure, based on parallel linked lists, with efficiency comparable to a binary search tree (order O(log n) average time for most operations).
..... Click the link for more information.
binary search tree (BST) is a binary tree data structure which has the following properties:
  • Each node has a value.
  • A total order is defined on these values.
  • The left subtree of a node contains only values less than the node's value.

..... Click the link for more information.
Overhead may be:
  • Overhead (business), the ongoing operating costs of running a business
  • Engineering overhead, ancillary design features required by a component of a device

..... Click the link for more information.
In computing, a persistent data structure is a data structure which always preserves the previous version of itself when it is modified; such data structures are effectively immutable, as their operations do not (visibly) update the structure in-place, but instead always yield a
..... Click the link for more information.
Functional programming is a programming paradigm that treats computation as the evaluation of mathematical functions and avoids state and mutable data. It emphasizes the application of functions, in contrast with the imperative programming style that emphasizes changes in state.
..... Click the link for more information.
A hash function [1] is a reproducible method of turning some kind of data into a (relatively) small number that may serve as a digital "fingerprint" of the data. The algorithm "chops and mixes" (i.e.
..... Click the link for more information.
In mathematics, a total order, linear order, simple order, or (non-strict) ordering on a set X is any binary relation on X that is antisymmetric, transitive, and total.
..... Click the link for more information.
In combinatorial mathematics, a cyclic order on a set X with n elements is an arrangement of X as on a clock face, for an n-hour clock. That is, rather than an order relation on X, we define on X
..... Click the link for more information.
partially ordered set (or poset) formalizes the intuitive concept of an ordering, sequencing, or arrangement of the elements of a set. A poset consists of a set together with a binary relation that describes, for certain pairs of elements in the set, the requirement that one
..... Click the link for more information.
linked list is one of the fundamental data structures, and can be used to implement other data structures. It consists of a sequence of nodes, each containing arbitrary data fields and one or two references ("links") pointing to the next and/or previous nodes.
..... Click the link for more information.
In computer science, linear search is a search algorithm, also known as sequential search, that is suitable for searching a set of data for a particular value.

It operates by checking every element of a list one at a time in sequence until a match is found.
..... Click the link for more information.
CONS, Connection-Oriented Network Service, is one of the two OSI network layer protocols, the other being CLNS (Connectionless Network Service). It is basically X.25, with a few adjustments.
..... Click the link for more information.
A radix tree, Patricia trie/tree, or crit bit tree is a specialized set data structure based on the trie that is used to store a set of strings. In contrast with a regular trie, the edges of a Patricia trie are labelled with sequences of characters rather than
..... Click the link for more information.
In computer science and software engineering, a Judy array is a complex but very fast associative array data structure for storing and looking up values using integer or string keys. Unlike arrays, Judy arrays may be sparse; that is, they may have large ranges of unassigned indices.
..... Click the link for more information.
In computer networking a routing table, or Routing Information Base (RIB), is an electronic table (file) or database type object that is stored in a router or a networked computer.
..... Click the link for more information.
trie, or prefix tree, is an ordered tree data structure that is used to store an associative array where the keys are usually strings. Unlike a binary search tree, no node in the tree stores the key associated with that node; instead, its position in the tree shows what key
..... Click the link for more information.
multimap is a generalization of a map or associative array abstract data type in which more than one value may be associated with and returned for a given key. Both map and multimap are particular cases of container (data structure) (see for example Standard Template
..... Click the link for more information.
LPC
Paradigm: prototype-based
Designed by: Lars Pensjö
Developer: Lars Pensjö and others
Major implementations: LPC
Dialects: Amylaar, MudOS, LDMud, DGD
Influenced by: C, C++, Lisp, Perl
Influenced: Pike

The LPC programming language
..... Click the link for more information.


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