raymond-byczko/php-codfish

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dev-master 2019-08-18 01:21 UTC

Requires (Dev)

• phpunit/phpunit: ^8.2

Unknown License

README

Purpose

This repository supports a library for handling large datasets. It is an experimental library, just exploring different ways of dealing with this problem.

Of primary consideration was to explore PHP references in a manner to produce 'linked lists', which are more common in c/c++.

Inspiration

The inspiration for this is a programming problem I encountered for an interview. It is known as the 'longest film' title problem.

In this problem, a file composed of 1 or more records, where each record is found on one line of the file. Each record corresponds to a movie.

One or more fields in each record has a movie title. The problem is to produce the largest concatenation of a number of movie titles to find which is the largest that can be produced. Trivial initial and ending words are discarded when forming the longest title.

Why Codfish

I enjoy catching and eat Codfish. In prior centuries, the sheer multitude of this fish in the north Atlantic, made me think of large datasets.

The Approach

The approach of this library is to assume the given dataset is not sufficiently 'narrow' to be useful for development. Basically, someone said, 'use this test data', or 'use this as your data', but what was given was very large. Further, the data was given, but the expected outcome was not.

From a development standpoint, this is not the best. Its useful to have a number of well defined datasets, with their expected outcome, upon being fed to a certain algorithm.

If one wants to get the input/output relationship right for a very large dataset, it is important to get it right for the smaller datasets.

Start Small

I think it better to start off with something smaller. Lets say 10 records or 20. However, beyond that, it is useful to be able to automate the generation of this test data.

Automated Test Cases

php-codfish is attempting to support, from the beginning, automated test data, with prescribed attributes, that would be easy to use when applied to a large dataset algorithm.

Status

This code is in draft format but its coming along. As it continues I will attempt to keep this updated, and try to present candid opinion of its status. Since its represented on github and packagist, all are welcome to check it out! A documentation folder will be added so anyone wanted to use this can get good guidance without reverse engineering.