63 Sentences With "associative arrays" | Random Sentence Generator

The following is a comparison of associative arrays (also "mapping", "hash", and "dictionary") in various programming languages.

Built-in functions and libraries allow lists to also be used as associative arrays and ordered and unordered sets.

These systems can store and retrieve associative arrays in a native fashion, which can greatly improve performance in common web-related workflows.

For this reason, they are widely used in many kinds of computer software, particularly for associative arrays, database indexing, caches, and sets.

This Comparison of programming languages (associative arrays) compares the features of associative array data structures or array-lookup processing for over 40 computer programming languages.

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 Rexx, Perl, Tcl, JavaScript, Maple, Python, Ruby, Wolfram Language, Go, and Lua, support associative arrays as a primary container type. In many more languages, they are available as library functions without special syntax.

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 using array-like subscripting. Built-in syntactic support for associative arrays was introduced in 1969 by SNOBOL4, under the name "table". TMG offered tables with string keys and integer values.

There is no standard implementation of associative arrays in C, but a 3rd-party library, C Hash Table, with BSD license, is available.here, archived here, with the source code 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 serves as the key.

Perl 5 has built-in, language-level support for associative arrays. Modern Perl refers to associative arrays as hashes; the term associative array is found in older documentation but is considered somewhat archaic. Perl 5 hashes are flat: keys are strings and values are scalars. However, values may be references to arrays or other hashes, and the standard Perl 5 module Tie::RefHash enables hashes to be used with reference keys.

In Lua, they are used as the primitive building block for all data structures. In Visual FoxPro, they are called Collections. The D language also has support for associative arrays.

In PHP, associative arrays and stdClass objects can be considered PDS. Other structured data representations such as XML or JSON can also be used as a PDS if no significant semantic restrictions are used.

While Java arrays can only be indexed by 0-based integers, OptimJ arrays can be indexed by values of any type. Such arrays are typically called associative arrays or maps. In this example, the array `age` contains the age of persons, identified by their name: int[String] age; The type `int[String]` denoting an array of `int` indexed by `String`. Accessing OptimJ arrays using the standard Java syntax: age["Stephan"] = 37; x = age["Lynda"]; Traditionally, associative arrays are heavily used in the expression of optimization problems.

PL/SQL refers to arrays as "collections". The language offers three types of collections: # Associative arrays (Index-by tables) # Nested tables # Varrays (variable-size arrays) Programmers must specify an upper limit for varrays, but need not for index-by tables or for nested tables. The language includes several collection methods used to manipulate collection elements: for example FIRST, LAST, NEXT, PRIOR, EXTEND, TRIM, DELETE, etc. Index-by tables can be used to simulate associative arrays, as in this example of a memo function for Ackermann's function in PL/SQL.

Modern JavaScript handles associative arrays, using Map and WeakMap classes. A map does not contain any keys by default. It only contains what is explicitly put into it. The keys and values can be any type (including functions, objects, or any primitive).

Finally, the GLib library also supports associative arrays, along with many other advanced data types and is the recommended implementation of the GNU Project. Similar to GLib, Apple's cross-platform Core Foundation framework provides several basic data types. In particular, there are reference-counted CFDictionary and CFMutableDictionary.

Basic types include: `boolean`, `string`, `integer`, `float`, `date`, `binary`, `list`, `hash` (associative arrays), and `object`, as well as code `code` for code used as a data type. Complex types are also supported such as `hash`, `list`, `reference>` as well as type-safe hashes.

In February 2009, Bash 4.0 introduced support for associative arrays. Associative array indices are strings, in a manner similar to AWK or Tcl. They can be used to emulate multidimensional arrays. Bash 4 also switches its license to GPLv3; some users suspect this licensing change is why MacOS continues to use older versions.

Tuples are ubiquitous in computing, but absent from most mainstream languages including Java. OptimJ provides a notion of tuple at the language level that can be very useful as indexes in combination with associative arrays. (: int, String :) myTuple = new (: 3, "Three" :); String s = myTuple#1; Tuple types and tuple values are both written between `(:` and `:)`.

OptimJ associative arrays are very handy when associated to their specific initialization syntax. Initial values can be given in intensional definition, as in: int[String] age = { "Stephan" -> 37, "Lynda" -> 29 }; or can be given in extensional definition, as in: int[String] length[String name : names] = name.length(); Here each of the entries `length[i]` is initialized with `names[i].length()`.

Some languages do not offer a list data structure, but offer the use of associative arrays or some kind of table to emulate lists. For example, Lua provides tables. Although Lua stores lists that have numerical indices as arrays internally, they still appear as dictionaries. In Lisp, lists are the fundamental data type and can represent both program code and data.

Radix trees are useful for constructing associative arrays with keys that can be expressed as strings. They find particular application in the area of IP routing, where the ability to contain large ranges of values with a few exceptions is particularly suited to the hierarchical organization of IP addresses.Knizhnik, Konstantin. "Patricia Tries: A Better Index For Prefix Searches", Dr. Dobb's Journal, June, 2008.

IBM has committed to associative arrays on future versions of the Vue language. Because of the long development lead time DTrace has over ProbeVue, DTrace has considerably more probe point specifications.These are called Providers in DTrace. IBM has plans to add a considerable number of new probe points in future technology level releases of AIX 6.1 and in the next major AIX release.

BSON () is a computer data interchange format. The name "BSON" is based on the term JSON and stands for "Binary JSON".BSON Specification It is a binary form for representing simple or complex data structures including associative arrays (also known as name-value pairs), integer indexed arrays, and a suite of fundamental scalar types. BSON originated in 2009 at MongoDB.

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.

Furthermore, (multi)maps (associative arrays) and (multi)sets are provided, all of which export compatible interfaces. Therefore, using templates it is possible to write generic algorithms that work with any container or on any sequence defined by iterators. As in C, the features of the library are accessed by using the #include directive to include a standard header. The C++ Standard Library provides 105 standard headers, of which 27 are deprecated.

In computing, associative containers refer to a group of class templates in the standard library of the C++ programming language that implement ordered associative arrays. Being templates, they can be used to store arbitrary elements, such as integers or custom classes. The following containers are defined in the current revision of the C++ standard: `set`, `map`, `multiset`, `multimap`. Each of these containers differ only on constraints placed on their elements.

That means multiple replicas can have their own copies of the same data, modify it, and then sync those changes at a later time. ; Document Storage : CouchDB stores data as "documents", as one or more field/value pairs expressed as JSON. Field values can be simple things like strings, numbers, or dates; but ordered lists and associative arrays can also be used. Every document in a CouchDB database has a unique id and there is no required document schema.

The program can create a complete text representation of any group of objects by calling these methods, which are almost always already implemented in the base associative array class."Archives and Serializations Programming Guide", Apple Inc., 2012 For programs that use very large data sets, this sort of individual file storage is not appropriate, and a database management system (DB) is required. Some DB systems natively store associative arrays by serializing the data and then storing that serialized data and the key.

Dynamic arrays or growable arrays are similar to arrays but add the ability to insert and delete elements; adding and deleting at the end is particularly efficient. However, they reserve linear (Θ(n)) additional storage, whereas arrays do not reserve additional storage. Associative arrays provide a mechanism for array-like functionality without huge storage overheads when the index values are sparse. For example, an array that contains values only at indexes 1 and 2 billion may benefit from using such a structure.

In programming languages that have associative arrays or comparable data structures, such as Python, Perl, PHP or Objective-C, it is idiomatic to use them to implement conditional assignment. pet = raw_input("Enter the type of pet you want to name: ") known_pets = { "Dog": "Fido", "Cat": "Meowsles", "Bird": "Tweety", } my_name = known_pets[pet] In languages that have anonymous functions or that allow a programmer to assign a named function to a variable reference, conditional flow can be implemented by using a hash as a dispatch table.

It has to then be copied with `idup` to obtain a string to be used for the associative array values (the `idup` property of arrays returns an immutable duplicate of the array, which is required since the `dstring` type is actually `immutable(dchar)[]`). Built-in associative arrays require immutable keys. # The `~=` operator appends a new dstring to the values of the associate dynamic array. # `toLower`, `join` and `chomp` are string functions that D allows the use of with a method syntax.

For example, will print out four lines, each with one element. Icon includes stack-like functions, and to allow arrays to form the bases of stacks and queues. Icon also includes functionality for sets and associative arrays with tables: symbols := table(0) symbols["there"] := 1 symbols["here"] := 2 This code creates a table that will use zero as the default value of any unknown key. It then adds two items into it, with the keys "there" and "here", and values 1 and 2.

Since the OptimJ compiler knows about the structure of all data used in models, it is able to generate a structured graphical view of this data at compile-time. This is especially relevant in the case of associative arrays where the compiler knows the collections used for indexing the various dimensions. The basic graphical view generated by the compiler is reminiscent of an OLAP cube. It can then be customized in many different ways, from simple coloring up to providing new widgets for displaying data elements.

Many programs using associative arrays will at some point need to store that data in a more permanent form, like in a computer file. A common solution to this problem is a generalized concept known as archiving or serialization, which produces a text or binary representation of the original objects that can be written directly to a file. This is most commonly implemented in the underlying object model, like .Net or Cocoa, which include standard functions that convert the internal data into text form.

Specialized associative arrays with integer keys include Patricia tries, Judy arrays, and van Emde Boas trees. Balanced trees require O(log n) time for indexed access, but also permit inserting or deleting elements in O(log n) time, whereas growable arrays require linear (Θ(n)) time to insert or delete elements at an arbitrary position. Linked lists allow constant time removal and insertion in the middle but take linear time for indexed access. Their memory use is typically worse than arrays, but is still linear.

The two resulting child buckets will themselves split when they become full, so the database grows as keys are added. The dbm library and its derivatives are pre-relational databases they manage associative arrays, implemented as on- disk hash tables. In practice, they can offer a more practical solution for high-speed storage accessed by key, as they do not require the overhead of connecting and preparing queries. This is balanced by the fact that they can generally only be opened for writing by a single process at a time.

String interning speeds up string comparisons, which are sometimes a performance bottleneck in applications (such as compilers and dynamic programming language runtimes) that rely heavily on associative arrays with string keys to look up the attributes and methods of an object. Without interning, comparing two distinct strings may involve examining every character of both.The string comparison can halt at the first character mismatch. For strict equality, the lengths of the strings can also be compared before traversing the string: but finding the length of null-terminated strings does itself require traversing the string.

So for example, to access a list of values in a hash, the sigil for an array ("@") is used, not the sigil for a hash ("%"). Perl also has many built-in functions that provide tools often used in shell programming (although many of these tools are implemented by programs external to the shell) such as sorting, and calling operating system facilities. Perl takes lists from Lisp, hashes ("associative arrays") from AWK, and regular expressions from sed. These simplify and facilitate many parsing, text-handling, and data-management tasks.

The Standard Template Library (STL) is a software library for the C++ programming language that influenced many parts of the C++ Standard Library. It provides four components called algorithms, containers, functions, and iterators. The STL provides a set of common classes for C++, such as containers and associative arrays, that can be used with any built-in type and with any user-defined type that supports some elementary operations (such as copying and assignment). STL algorithms are independent of containers, which significantly reduces the complexity of the library.

Self-balancing binary search trees can be used in a natural way to construct and maintain ordered lists, such as priority queues. They can also be used for associative arrays; key-value pairs are simply inserted with an ordering based on the key alone. In this capacity, self-balancing BSTs have a number of advantages and disadvantages over their main competitor, hash tables. One advantage of self-balancing BSTs is that they allow fast (indeed, asymptotically optimal) enumeration of the items in key order, which hash tables do not provide.

Objects also include one feature unrelated to associative arrays: an object has a prototype, so it contains default keys that could conflict with user-defined keys. So, doing a lookup for a property will point the lookup to the prototype's definition if the object does not define the property. 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" }; To prevent the lookup from using the prototype's properties, you can use Object.

The arrays are heterogeneous: a single array can have keys of different types. PHP's associative arrays can be used to represent trees, lists, stacks, queues, and other common data structures not built into PHP. An associative array can be declared using the following syntax: $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', ); // or, as of PHP 5.4 $phonebook = [ 'Sally Smart' => '555-9999', 'John Doe' => '555-1212', 'J.

D gained some features before C++, such as closures, anonymous functions, compile-time function execution, ranges, built-in container iteration concepts and type inference. D adds to the functionality of C++ by also implementing design by contract, unit testing, true modules, garbage collection, first class arrays, associative arrays, dynamic arrays, array slicing, nested functions, lazy evaluation, scoped (deferred) code execution, and a re-engineered template syntax. D retains C++'s ability to perform low- level programming and to add inline assembler. C++ multiple inheritance was replaced by Java-style single inheritance with interfaces and mixins.

For implementing associative arrays, hash tables, a data structure that maps keys to records using a hash function, are generally faster than binary search on a sorted array of records. Most hash table implementations require only amortized constant time on average. However, hashing is not useful for approximate matches, such as computing the next-smallest, next-largest, and nearest key, as the only information given on a failed search is that the target is not present in any record. Binary search is ideal for such matches, performing them in logarithmic time.

Hash tables are commonly used to implement many types of in-memory tables. They are used to implement associative arrays (arrays whose indices are arbitrary strings or other complicated objects), especially in interpreted programming languages like Ruby, Python, and PHP. When storing a new item into a multimap and a hash collision occurs, the multimap unconditionally stores both items. When storing a new item into a typical associative array and a hash collision occurs, but the actual keys themselves are different, the associative array likewise stores both items.

The Vue language lacks aggregations and instead uses a list data type that offers similar yet limited functionality. The equivalent product from Solaris called DTrace offers an aggregation data type and a powerful set of aggregating functions to represent statistical data. The list data type offers only basic aggregating functions on a single item (as opposed to an associative arrays of aggregations that DTrace offers). The list data type offers a slight simplification over keeping the stats manually but does not allow the list to be reset (say, over an interval) that can easily be done when using manual summaries and basic types.

Custom data types are allowed, but YAML natively encodes scalars (such as strings, integers, and floats), lists, and associative arrays (also known as maps, dictionaries or hashes). These data types are based on the Perl programming language, though all commonly used high-level programming languages share very similar concepts. The colon-centered syntax, used for expressing key-value pairs, is inspired by electronic mail headers as defined in RFC 0822, and the document separator is borrowed from MIME (RFC 2046). Escape sequences are reused from C, and whitespace wrapping for multi-line strings is inspired by HTML.

Array data types are most often implemented as array structures: with the indices restricted to integer (or totally ordered) values, index ranges fixed at array creation time, and multilinear element addressing. This was the case in most "third generation" languages, and is still the case of most systems programming languages such as Ada, C, and C++. In some languages, however, array data types have the semantics of associative arrays, with indices of arbitrary type and dynamic element creation. This is the case in some scripting languages such as Awk and Lua, and of some array types provided by standard C++ libraries.

The "fat comma" forces the word to its left to be interpreted as a string.perldoc.perl.org – perlop – Comma Operator Thus, where this would produce a run-time error under strict (barewords are not allowed): %bad_example = ( bad_bareword, "not so cool" ); the following use of the fat comma would be legal and idiomatic: %good_example = ( converted_to_string => "very monkish" ); This is because the token `converted_to_string` would be converted to the string literal `"converted_to_string"` which is a legal argument in a hash key assignment. The result is easier-to-read code, with a stronger emphasis on the name-value pairing of associative arrays.

Many programming languages provide hash table functionality, either as built-in associative arrays or as standard library modules. In C++11, for example, the `unordered_map` class provides hash tables for keys and values of arbitrary type. The Java programming language (including the variant which is used on Android) includes the `HashSet`, `HashMap`, `LinkedHashSet`, and `LinkedHashMap` generic collections. In PHP 5 and 7, the Zend 2 engine and the Zend 3 engine (respectively) use one of the hash functions from Daniel J. Bernstein to generate the hash values used in managing the mappings of data pointers stored in a hash table.

Groovy features not available in Java include both static and dynamic typing (with the keyword `def`), operator overloading, native syntax for lists and associative arrays (maps), native support for regular expressions, polymorphic iteration, string interpolation, added helper methods, and the safe navigation operator `?.` to check automatically for null pointers (for example, `variable?.method()`, or `variable?.field`). Since version 2 Groovy also supports modularity (being able to ship only the needed jars according to the project needs, thus reducing the size of Groovy's library), type checking, static compiling, Project Coin syntax enhancements, multicatch blocks and ongoing performance enhancements using the `invokedynamic` instruction introduced in Java 7.

In Smalltalk, Objective-C, .NET, Python, REALbasic, Swift, VBA and Delphi they are called dictionaries; in Perl, Ruby and Seed7 they are called hashes; in C++, Java, Go, Clojure, Scala, OCaml, Haskell they are called maps (see map (C++), unordered_map (C++), and ); in Common Lisp and Windows PowerShell, they are called hash tables (since both typically use this implementation); in Maple and Lua, they are called tables. In PHP, all arrays can be associative, except that the keys are limited to integers and strings. In JavaScript (see also JSON), all objects behave as associative arrays with string-valued keys, while the Map and WeakMap types take arbitrary objects as keys.

AWK (awk) is a domain-specific language designed for text processing and typically used as a data extraction and reporting tool. Like sed and grep, it's a filter, and is a standard feature of most Unix-like operating systems. The AWK language is a data-driven scripting language consisting of a set of actions to be taken against streams of textual data – either run directly on files or used as part of a pipeline – for purposes of extracting or transforming text, such as producing formatted reports. The language extensively uses the string datatype, associative arrays (that is, arrays indexed by key strings), and regular expressions.

He was co-director (with Edmond Schonberg) of the team at NYU that produced Ada/Ed, an interpreter for Ada 83 written in SETL and the first Ada implementation to pass the strenuous ACVC validation suite, mandated for being allowed to use the trademarked name Ada. Dewar and Schonberg went on to produce GNAT, a free-software compiler for Ada that forms part of the GNU Compiler Collection. Dewar also participated in the SETL project at NYU, and co-authored the handbook Programming With Sets: An Introduction to SETL. He influenced the design of the ABC programming language, in particular its SETL- style high-level data types, such as associative arrays.

SNOBOL is distinctive in format and programming style, which are radically different from contemporary procedural languages such as Fortran and Algol. SNOBOL4 supports a number of built-in data types, such as integers and limited precision real numbers, strings, patterns, arrays, and tables (associative arrays), and also allows the programmer to define additional data types and new functions. SNOBOL4's programmer-defined data type facility was advanced at the time--it is similar to the records of the earlier COBOL and the later Pascal programming languages. All SNOBOL command lines are of the form :label subject pattern = object : transfer Each of the five elements is optional.

Perl 6, renamed as "Raku", also has built-in, language-level support for associative arrays, which are referred to as hashes or as objects performing the "associative" role. As in Perl 5, Perl 6 default hashes are flat: keys are strings and values are scalars. One can define a hash to not coerce all keys to strings automatically: these are referred to as "object hashes", because the keys of such hashes remain the original object rather than a stringification thereof. A hash variable is typically marked by a `%` sigil, to visually distinguish it from scalar, array, and other data types, and to define its behaviour towards iteration.

Random Hacker' => '555-1337', ]; // or $phonebook['contacts']['Sally Smart']['number'] = '555-9999'; $phonebook['contacts']['John Doe']['number'] = '555-1212'; $phonebook['contacts']['J. Random Hacker']['number'] = '555-1337'; PHP can loop through an associative array as follows: foreach ($phonebook as $name => $number) { echo 'Number for ', $name, ': ', $number, " "; } // For the last array example it is used like this foreach ($phonebook['contacts'] as $name => $num) { echo 'Name: ', $name, ', number: ', $num['number'], " "; } PHP has an extensive set of functions to operate on arrays. Associative arrays that can use objects as keys, instead of strings and integers, can be implemented with the `SplObjectStorage` class from the Standard PHP Library (SPL).

Common inheritance is not needed for the set of types that can be used, and so it is very similar to duck typing. A program defining a custom data type can use operator overloading to define the meaning of `<` for that type, thus allowing its use with the `max()` function template. While this may seem a minor benefit in this isolated example, in the context of a comprehensive library like the STL it allows the programmer to get extensive functionality for a new data type, just by defining a few operators for it. Merely defining `<` allows a type to be used with the standard `sort()`, `stable_sort()`, and `binary_search()` algorithms or to be put inside data structures such as `set`s, heaps, and associative arrays.

In these applications, the set of all inputs is some sort of metric space, and the hashing function can be interpreted as a partition of that space into a grid of cells. The table is often an array with two or more indices (called a grid file, grid index, bucket grid, and similar names), and the hash function returns an index tuple. This principle is widely used in computer graphics, computational geometry and many other disciplines, to solve many proximity problems in the plane or in three-dimensional space, such as finding closest pairs in a set of points, similar shapes in a list of shapes, similar images in an image database, and so on. Hash tables are also used to implement associative arrays and dynamic sets.

Lua syntax for control structures was mostly borrowed from Modula (`if`, `while`, `repeat`/`until`), but also had taken influence from CLU (multiple assignments and multiple returns from function calls, as a simpler alternative to reference parameters or explicit pointers), C++ ("neat idea of allowing a local variable to be declared only where we need it"), SNOBOL and AWK (associative arrays). In an article published in Dr. Dobb's Journal, Lua's creators also state that LISP and Scheme with their single, ubiquitous data-structure mechanism (the list) were a major influence on their decision to develop the table as the primary data structure of Lua. Lua semantics have been increasingly influenced by Scheme over time, especially with the introduction of anonymous functions and full lexical scoping. Several features were added in new Lua versions.

A tabular data card proposed for Babbage's Analytical Engine showing a key–value pair, in this instance a number and its base-ten logarithm A key–value database, or key–value store, is a data storage paradigm designed for storing, retrieving, and managing associative arrays, and a data structure more commonly known today as a dictionary or hash table. Dictionaries contain a collection of objects, or records, which in turn have many different fields within them, each containing data. These records are stored and retrieved using a key that uniquely identifies the record, and is used to find the data within the database. A table showing different formatted data values associated with different keys Key–value databases work in a very different fashion from the better known relational databases (RDB).

Associative arrays may also be stored in unbalanced binary search trees or in data structures specialized to a particular type of keys such as radix trees, tries, Judy arrays, or van Emde Boas trees, though the ability of these implementation methods within comparison to hash tables varies; for instance, Judy trees remain indicated to perform with a smaller quantity of efficiency than hash tables, while carefully selected hash tables generally perform with increased efficiency in comparison to adaptive radix trees, with potentially greater restrictions on the types of data that they can handle. The advantages of these alternative structures come from their ability to handle operations beyond the basic ones of an associative array, such as finding the mapping whose key is the closest to a queried key, when the query is not itself present in the set of mappings.

This is simple but inefficient, as operations like set membership or element deletion are O(n), as they require scanning the entire list. Sets are often instead implemented using more efficient data structures, particularly various flavors of trees, tries, or hash tables. As sets can be interpreted as a kind of map (by the indicator function), sets are commonly implemented in the same way as (partial) maps (associative arrays) – in this case in which the value of each key-value pair has the unit type or a sentinel value (like 1) – namely, a self-balancing binary search tree for sorted sets (which has O(log n) for most operations), or a hash table for unsorted sets (which has O(1) average-case, but O(n) worst-case, for most operations). A sorted linear hash table may be used to provide deterministically ordered sets.