With the increasing focus on optimised software architecture and design it is important that software architects think about optimisations in object creation, code structure, and interaction between objects at the architecture or design level. This makes sure that the cost of software maintenance is low and code can be easily reused or is adaptable to change. The key to this is reusability and low maintenance in design patterns. Building on the success of the previous edition, Learning Python Design Patterns, Second Edition will help you implement real-world scenarios with Python's latest release, Python v3.5. We start by introducing design patterns from the Python perspective.
Get an introduction to the visual design of GraphQL data and concepts, including GraphQL structures, semantics, and schemas in this compact, pragmatic book. In it, you will see simple guidelines based on lessons learned from real-life data discovery and unification, as well as using visualization techniques. These, in turn, help you improve the quality of your API designs and give you the skills to produce convincing visual communications about the structure of your API designs. Finally, Visual Design of GraphQL Data shows you how to handle GraphQL with legacy data as well as with Neo4j graph databases. Spending time on schema quality means that you will work from sharper definitions, which in turn leads to greater productivity and well-structured applications.
Curious about how to design applications for the cloud? The Cloud Design Patterns infographic from Microsoft provides a nice reference of cloud architecture design patterns. This infographic depicts the most common problems in designing cloud-hosted applications, and it provides some design patterns to offer guidance to help you design better cloud-hosted applications within Microsoft Azure!
Master JavaEE Design Pattern implementation to improve your coding efficiency Professional JavaEE Design Patterns is the ultimate guide to working more efficiently with JavaEE, and the only resource that covers both the theory and application of design patterns in solving real-world problems. The authors guide readers through both the fundamentals and little-known secrets of JavaEE6/7, presenting patterns throughout, and demonstrating how they are used in day-to-day programming. As the standard computing platform in community-driven enterprise software, JavaEE provides an API and runtime environment above and beyond JavaSE. Written for the experienced JavaEE developer hoping to improve code quality and efficiency, the book contains time saving patterns, including: Implementation and problem-solving with design patterns Connection between existing non-JavaEE design patterns and new JavaEE concepts Unique organization that prioritizes the most relevant patterns before expanding into advanced techniques Individually-based focus that fully explores each pattern Unlike most JavaEE books that simply offer descriptions or recipes, this book drives home the actual implementation and application to keep square pegs away from round holes. For the programmer looking for a comprehensive guide that is actually useful in the everyday workflow, Professional JavaEE Design Patterns is the definitive resource on the market.
Vital Aelion 1, Jonathan Cagan 2, and 1 Gary J. Powers Carnegie Mellon University, Pittsburgh, PA 15213 Abstract A knowledge representation based on first principles and a library of techniques for expanding the space of design alternatives are reviewed. They have been developed for the algorithmic innovation of engineering designs. The re, suiting l stpRINCE design methodology uses optimization information to decide which expansion technique may produce improved designs and induction to examine limiting behavior. Starting from an initial design, lstpRINCE has algorithmically innovated interesting engineering concepts, such as the electric bus, the tapered and hollow beam, the wheel, and the plug flow reactor. 1 Introduction Design may be described as a search for a good solution in a space of possible configurations that satisfy the user's goals. In this paper we review a knowledge representation which is based on first principles and a library of design space expansion techniques for innovating designs. Optimization is used as a search technique but won't be discussed in any detail here. The first principle knowledge of this paper is represented as an algebraic objective, a set of algebraic engineering equations, a design topology, and a set of specifications of constraint and variable types.