# PODNAME: Dallycot::Manual::Intro
# ABSTRACT: Introduction to and overview of Dallycot

=encoding utf8


This document aims to answer the questions:

=over 4

=item *

What needs does Dallycot fill?

=item *

Why would I use Dallycot?

=item *

How does everything broadly fit together?


Dallycot is designed to work with the asynchronous nature of the web. When
running a program in Dallycot, the web is your memory, providing data and code

I<N.B.>: Almost everything about Dallycot is subject to change. For now, Dallycot
provides read-only access to information.

=head2 Writing Dallycot

Dallycot provides a custom functional language designed to think the way linked data
thinks. See L<the W3C Linked Data Platform specification|http://www.w3.org/TR/ldp/>
for an example of the kind of data services Dallycot will target. Dallycot
prefers JSON-LD when communicating with services.

Dallycot is not a query language. Projects like L<Marmotta|http://marmotta.apache.org/>
provide linked data query languages.

These examples probably don't work yet, but indicate what we're working towards.

=head3 Example: Name things that share a subject with I<The Lord of the Rings>

The following snippet describes walking the DBPedia graph from the resource
representing I<The Lord of the Rings> to anything that shares a common subject
with that book, and extracting the label (imagine how the directional edges
point from node to node in the data graph).

     -> :dcterms:subject
     <- :dcterms:subject
     -> :rdf:label

=head3 Example: Euclid's algorithm for GCD

This uses simple recursion to calculate the greatest common divisor.

   gcd := (
     gcd_f(self, a, b) :> (
       (a = 0) : b
       (b = 0) : a
       (a > b) : self(self, a mod b, b)
       (     ) : self(self, a, b mod a)
     gcd_f(gcd_f, ___)

For now, a symbol such as C<gcd_f> isn't defined until after the expression
has finished running, so recusion requires a form of the Y-combinator, such
as provided by L<Dallycot::Library::Core::Functions>.

=head2 When Not to Use Dallycot

Because anything might result in retrieving information from the web, Dallycot
makes extensive use of promises. Promises are great for managing asynchronous
execution, but introduce overhead that makes programs slower if all of the
information is local to the processor.

Dallycot is not designed to be good at:

=over 4

=item * Immediate gratification

Some programs take a while. When coupled with ad hoc information
retrieval over the web, programs can seem to slow to a crawl. This is
the nature of linked data in general, not Dallycot. If you already know
exactly which data you will need, and how the data fits together, then
consider processing it locally with a SPARQL service or a general purpose
programming language designed to work exclusively with local data.

=item * Scientific computing

Use programs such as Matlab, Mathematica, or R. Consider the scientific
computing libraries available to Perl, Python, or Ruby.