input element is processed independently. Variables allow state, which allow operations that depend on more than one input element, such as aggregation (summing inputs) or throttling (allow at most 5 mails per hour from each sender, or limiting repeated log messages). Data-driven languages frequently have a default action: if no condition matches, line-oriented languages may print the line (as in sed), or deliver a message (as in sieve). In some applications, such as filtering, matching is may be done exclusively (so only first matching statement), while in other cases all matching statements are applied. In either case, failure to match any pattern may be "default behavior" or can be seen as an error, to be caught by a catch-all statement at the end. Benefits and issues While the benefits and issues may vary between implementation, there are a few big potential benefits of and problems with this paradigm. Functionality simply requires that it knows the abstract data type of the variables it is working with. Functions and interfaces can be used on all objects with the same data fields, for instance the object's "position". Data can be grouped into objects or "entities" according to preference with little to no consequence. While data-driven design does prevent coupling of data and functionality, in some cases, data-driven programming has been argued to lead to bad object-oriented design, especially when dealing with more abstract data. This is because a purely data-driven object or entity is defined by the way it is represented. Any attempt to change the structure of the object would immediately break the functions that rely on it. As an example, one might represent driving directions as a series of intersections (two intersecting streets) where the driver must turn right or left. If an intersection (in the United States) is represented in data by the zip code (5-digit number) and two street names (strings of text), bugs may appear when a city where streets intersect multiple times is encountered. While this example may be oversimplified, restructuring of data is a fairly common problem in software engineering, either to eliminate bugs, increase efficiency, or support new features. Languages AWK Oz Perl – data-driven programming as in AWK and sed is one paradigm supported by Perl sed Lua Clojure Tab (language) fdm maildrop procmail Sieve BASIC See also Data-directed programming Backus–Naur form References External links "The important part is moving program logic away from