CSC/ECE 517 Fall 2009/wiki3 4 ashi4: Difference between revisions

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== Why DRY? ==
== Why DRY? ==
The question should be <b>Why not DRY</b>. When it comes to building or extending a software system, it feels extremely easy to just put in the logic in terms of code and make sure that the code works and the overall system functions properly. However, it is most convenient to not bother whether or not the code follows the best practices or is it easy to maintain. But it is cosidered to be a really good technique to apply the DRY principle here for various reasons;
1. Duplicating the code for convenience causes a lot of overhead in terms of time and space.
2. The system might function really well but it becomes extremely difficult to maintain it since the source code has turned clumsy.
3. It is said that the best way to check whether the code really follows the best practices is to realise whether or not the code needs extensive documentation.
4. It becomes very difficult to debug the code in case of anomalies.
5. It is always a good practice to have one reference.


== DRY on Data ==
== DRY on Data ==

Revision as of 00:12, 19 November 2009

DRY Principle for Data


Introduction

Donot Repeat Yourself (DRY) also known as Duplication Is Evil (DIE) is one of the most fundamental principles of programming, or rather we can say that it is the most important principle of good programming. This principle was formulated by Andrew Hunt and David Thomas in their book The Pragmatic Programmer. The principle states that "Every piece of knowledge must have a single, unambiguous, authoritative representation within a system.". This principle has been broadly accepted and acknowledged by the developers across the globe. It is considered to be one of the best coding practices to build an efficient and flexible code which is not only easy to understand but easy to maintain too. A parallel concept to DRY is Orthogonality which is making the components of the system as functionally independent as possible. It is well understood and an accepted fact that the DRY and the Orthogonality principle together lead to a very well structured code as discussed here. But if we observe the DRY principle more carefully, it is easy to notice that DRY is not only applicable to code but it is applicable to any piece of knowledge that can be represented in some way; one such form of knowledge representation is data. In fact, data and knowledge are used more or less interchangeably. The purpose of this document is to emphasize on the fact that the DRY principle is not only applicable to the code but to the data as well.

Some Basic Terminologies

Why DRY?

The question should be Why not DRY. When it comes to building or extending a software system, it feels extremely easy to just put in the logic in terms of code and make sure that the code works and the overall system functions properly. However, it is most convenient to not bother whether or not the code follows the best practices or is it easy to maintain. But it is cosidered to be a really good technique to apply the DRY principle here for various reasons;

1. Duplicating the code for convenience causes a lot of overhead in terms of time and space.


2. The system might function really well but it becomes extremely difficult to maintain it since the source code has turned clumsy. 3. It is said that the best way to check whether the code really follows the best practices is to realise whether or not the code needs extensive documentation. 4. It becomes very difficult to debug the code in case of anomalies. 5. It is always a good practice to have one reference.

DRY on Data

When Data Duplication wins over DRY...

Guidelines on whether or not to use DRY for Data

Conclusion

References

1. Andrew Hunt and David Thomas, "Pragmatic Programmer : From Journeyman to Master", Addison-Weasley Publications, October-99.