After spending some time exploring algorithms and blogs, I came across a post on a consistent hashing algorithm. I have had an interest in hashing and have found growing use for hashing algorithms as my classes and programming complexity progresses. This article is found on highscalability.com, and it is focused on providing a tutorial to understand how to use a consistent hashing algorithm for partitioning and load-balancing. The article’s title is Consistent Hashing Algorithm by NK

The blog identifies consistent hashing as an algorithm used to distribute data across multiple nodes in a distributed system, minimizing data movement when nodes are added or removed. It works by placing both nodes and data keys on a virtual hash ring. A virtual hash ring is a technique where each node is assigned a position on the ring based on the hash value of its identifier. The blog explains further that data keys are also hashed to determine their position on the rin as well. When a key needs to be stored or retrieved, the system traverses the ring in a clockwise direction until it finds the first node that is closest to the key’s hash position. This method ensures that only a small portion of the data needs to be redistributed when nodes are added or removed, making the system more easily scalable. The blog identifies these strategies and techniques to be best used in web development to handle caches. Through several figures, NK identifies as a way to improve load balancing and avoid hotspots, consistent hashing can use virtual versions of these nodes, where each physical node is mapped to multiple positions on the ring virtually. This technique can be essential for scaling distributed systems, like caching systems and distributed databases, that need to handle dynamic loads while maintaining high availability.

This is a topic that interested me greatly as I have–in conversations with friends in Computer Science–been consistently recommended to learn more about hashing. Through the CS-343 course, I have been looking for more ideas for algorithms and techniques to learn to improve my understanding as my senior year progresses. I will be prepared to answer questions regarding consistent hashing thanks to this article. This is of great value to me as I will continue to look for more on this topic, as I explore software development. While I did not cover every example or style found in the article, I found it all extremely interesting and comprehensive. This blog has many examples and well-made figures to explain things in a simple manner, and I will continue to use highscalability to learn more.

Source:https://highscalability.com/consistent-hashing-algorithm/

From the blog CS@Worcester – WSU CS Blog: Ben Gelineau by Ben Gelineau and used with permission of the author. All other rights reserved by the author.

Ever work with code that feels like it’s about to fall apart? That’s often due to design smells, which are signs of poor design that make code harder to work with. Here’s a quick rundown of six big ones: Rigidity, Fragility, Immobility, Viscosity, Needless Complexity, Needless Repetition, and Opacity

1. Rigidity

Rigidity is when code is hard to change. You try to update a small feature, and suddenly you’re changing ten other things just to make it work. This makes the code stiff, leading to slow updates and frustration.

2. Fragility

Fragility means that the code breaks easily. A small change in one part of the codebase suddenly causes errors all over. Fragile code is typically too tightly coupled, meaning different parts rely too much on each other, making even minor updates risky.

3. Immobility

Immobility happens when you can’t easily reuse code in other projects. Maybe you’ve written a method that could be helpful elsewhere, but it relies on so many project-specific details that you can’t transfer it without dragging a ton of dependencies. Immobile code wastes potential and limits flexibility.

4. Viscosity

Viscosity means the code is easier to “quick-fix” than fix the right way. The messy design makes doing things properly so difficult that developers often take shortcuts, creating even more technical debt. Viscous code tempts us to compromise, resulting in even messier code over time.

5. Needless Complexity

Needless complexity is adding extra, unneeded elements to the code. This could be because someone thought, “We might need this someday,” or because they’re solving non-existent problems. Extra complexity doesn’t just add confusion; it makes the code harder to read, test, and debug. Simple solutions are usually best.

6. Needless Repetition

Needless repetition is when you see similar code blocks everywhere instead of consolidating them. When you repeat code instead of centralizing it, it violates the DRY (Don’t Repeat Yourself) principle. This makes code harder to maintain, as changes need to be made in multiple places instead of just one.

7. Opacity

Opacity is when code is confusing and hard to read. Maybe variable names don’t make sense, logic is overly complex, or comments are missing. Opaque code is like a puzzle, requiring extra time to understand, and slowing down productivity.

Why It Matters

Design smells don’t stop your code from working, but they make it harder to understand, change, and maintain. Ignoring them means the code gets more frustrating to deal with over time. By refactoring to remove these smells, you make the code cleaner, easier to work with, and more enjoyable for everyone on your team. Next time you’re reviewing code, look out for these design smells, your future self will thank you!

From the blog CS@Worcester – Anairdo's WSU Computer Science Blog by anairdoduri and used with permission of the author. All other rights reserved by the author.

A good programmer and software developer will create code and software that is easy to maintain and modify. When problems arise, issues could be taken care of fairly easily. But sometimes, the easiest solutions are not the best ones, it could make the software more difficult to maintain or change. Using design patterns can help identify these issues and show some solutions to those issues. 

In class, we worked with a few design patterns, specifically the strategy design pattern, the singleton, and the simple factory pattern. Each one does something a little different from each other, but they help make things organized in a sense. We used them all together, implementing each one by one, but each could be used separately.

In this blog post, Giridhar Talla writes about design patterns, and describes them as an “existing solution.” They define design patterns as “solutions to commonly occurring design problems in developing flexible software using object-oriented programming.” Design patterns can vary, and can be grouped into 3 groups, creational patterns, creating objects to increase code flexibility and reuse, structural patterns, turning relations between objects and classes into complex structures, and behavioral patterns, defining how objects communicate with another. The specific design patterns Talla goes over are the singleton design pattern, the decorator design pattern, the strategy design pattern, the state design pattern, and the command design pattern. Each pattern is unique in its own way, flexible and simple. After reading, I found the state design principle very interesting. Instead of a lot of conditional statements, you could create multiple states. The user can change how the application works at runtime, which allows you to design finite state machines. I find this cool and unique, as it is something I have not personally done, but the concept seems really interesting.

A good programmer should be able to create reusable and extendable code, it is just good practice. Design patterns can help you make them flexible and maintainable. I chose this blog post because I thought design patterns were really useful and interesting, and I wanted to learn more about other kinds of patterns. Since design patterns are somewhat recognizable due to their reuse, it is not hard to explain unseen software to people who have not looked at it before. Even if it is done wrong, it will help you or a team understand what is wrong with the overall thing, and then from there, you can apply the correct one. I am fairly certain that I will be using these in the future.

From the blog CS@Worcester – Cao's Thoughts by antcao and used with permission of the author. All other rights reserved by the author.

In the world of software development, design patterns have long been touted as the foundation for writing reusable, scalable, and maintainable code. However, in his blog post “Rethinking Design Patterns,” Jeff Atwood, the founder of Stack Overflow, offers a criticizing look at the overuse and potential drawbacks of design patterns. Atwood argues that while design patterns can be incredibly helpful, they are often overused or misapplied, leading to unnecessary complexity and rigid software designs, highlighting that the key to good software design is not slavishly applying patterns but ensuring that the solution is simple, clear, and flexible

I chose this blog post because it directly relates to our course’s exploration of design patterns and software design principles. Throughout the course, we have discussed the value of design patterns as reusable solutions to common problems, but Atwood’s post reminded me that patterns should not be applied indiscriminately. The post offered a necessary counterpoint to the overwhelming focus on patterns, encouraging me to prioritize simplicity and clarity in code design. The Duck Simulator assignment in our course is a perfect example of how design patterns can either simplify or complicate a project, depending on how they’re applied. Initially, in the Duck Simulator, we began with a simple inheritance model where different types of ducks, such as MallardDuck and RubberDuck, inherited from a common Duck class. While inheritance is a useful mechanism for shared behaviors, it quickly became clear that adding new duck types with unique behaviors (like the RubberDuck that doesn’t fly) led to a rigid structure that was difficult to modify.

This is something I’ve experienced in my own coding assignments: sometimes, I’ve reached for a design pattern without fully understanding the problem or whether the pattern was the best fit. For example, I have often used the Strategy Pattern when refactoring code, but I now realize that I may have overcomplicated simple problems that could have been solved with more straightforward solutions. Going forward, I plan to apply Atwood’s advice by being more discerning in my use of design patterns. Instead of immediately opting for a design pattern, I will first evaluate whether a simpler solution exists.

You can read the full post here: Rethinking Design Patterns.

From the blog SoftwareDiary by Oanh Nguyen and used with permission of the author. All other rights reserved by the author.

Here we are again looking at another Design Pattern for software. Again from our friend Christopher Okhravi getting into the nitty gritty of the “Factor Method Pattern” and how it works using logic and examples all from the book “Head First Design Patterns”. I picked this because the last one of these I watched I thought really worked for explaining the Strategy Pattern for me so I wanted to give another one a watch for some more complicated patterns. First Mr.Okhravi walks us through how exactly a Factory Pattern works where we have a series of classes that all implement the same superclass and we then create these products through separate classes that instantiate them called a “Factory”. But when we instantiate one of these classes we don’t actually know which class we want to instantiate, we just need some kind of logic to produce them inside the factory that decides how the classes are instantiated. So this Design Pattern was certainly more of a challenge to wrap my head around but I think with this resource it’s helped me to understand it somewhat better. This pattern at least for me when working on it in the design pattern homework was definitely a little too convoluted for me to fully parse but it did still make some sense when I put it into practice. This video though has helped me to at least better understand the underlying logic of it all and really grasp the way it can work for other things and how you can even have multiple factories to produce objects in other ways. This resource definitely gave me a better appreciation for what the factory pattern is capable of and the different ways it can used. Not to mention that it seems infinitely customizable in that it’s scalability is huge. For the future I think I can expect to be using this at least for large scale projects that will have many different objects that need to added or used. This also makes me want to see what other design patterns there are other than the ones we have explored in class. So I think next I’ll be looking into other patterns and seeing what ones interest me. Other options I’ve been considering are patterns like the Decorator pattern or the Observer pattern both which look pretty interesting and I can’t wait to try them out!

Here’s the video!

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From the blog CS@Worcester – aRomeoDev by aromeo4f978d012d4 and used with permission of the author. All other rights reserved by the author.

This week, I found a study on adopting design patterns in an IT organization. The study was conducted by the University of Oregon, and a link to the study can be found at the end of the blog. I selected this study due to the fact that it dove a lot into software design patterns and why, if they are implemented correctly, they can be extremely beneficial for a company to use.

The overall purpose of this study was to, as stated in the study, provide a scholarly annotated bibliography that examines software design patterns. It wants to provide IT leaders with proof that the implementation of design patterns can achieve increased operational efficiency or the delivery of strategic benefits.

By the end of this study, you can see some clear benefits of using a good design pattern. Operationally, there is a reduced development time as well as as a reduction of errors. This is due to the development process being streamlined, as well as being reusable with tested solutions. Strategically, patterns can contribute to a system that scales over time without a lot of rework, which is huge for organizations with rapidly evolving needs. Patterns also must create a shared language which inherently allows for better collaboration between different teams working on a project.

The study also goes over how it can be implemented using phased adoption; implementing patterns gradually allowing for people to adjust. On top of this, it mentions periodic review and sharing of documentation/knowledge, two very important ideas in terms of efficiently and affectively using design patterns.

Having read this study, it certainly reinforces a lot of the ideas we mentioned when discussing design patterns. I wouldn’t necessarily say everything I read was new, and the new sounding information may have just been worded in a different way, but the study certainly did a good job as sort of drilling the benefits of why a good design pattern can be highly beneficial.

This study also opened my eyes in the sense that I feel as though in my career I may keep an eye out for how different design patterns could be implemented into any projects that I’m working on. Being able to cut back on time, while still completing different jobs in an effective manner is something that will be very important and possibly put you above other people. If I demonstrate a knowledge of design patterns and can implement them well, or at least suggest them, it may at the very minimum open my companies eyes to different possibilities.

Link to study: https://scholarsbank.uoregon.edu/server/api/core/bitstreams/b5d95a74-77d6-454b-9ed5-1f2af2633f8a/content

From the blog CS@Worcester – RBradleyBlog by Ryan Bradley and used with permission of the author. All other rights reserved by the author.

REST (Representational State Transfer) APIs have become a cornerstone in modern software development, enabling seamless communication between different systems. For those new to the field, understanding REST APIs is essential as it forms the foundation for integrating various services and building scalable applications. The blog post “Rest API Tutorial — A Complete Beginner’s Guide” from Moesif provides an excellent introduction to this topic. Here, I will summarize the content of the blog, explain why I selected this resource, and reflect on what I learned from it.

Summary of the Blog Post

The Moesif blog post starts by explaining what an API (Application Programming Interface) is and introduces REST as an architectural style for designing networked applications. It highlights that RESTful APIs are stateless, meaning each request from a client to a server must contain all the information the server needs to fulfill it. The post further discusses key concepts such as HTTP methods (GET, POST, PUT, DELETE), status codes, and the importance of endpoints in API structure.

The guide provides practical examples to illustrate these concepts, making it easier for beginners to grasp. It also touches on best practices, such as the use of proper status codes to indicate request outcomes and keeping URLs clean and intuitive. The post ends by emphasizing the importance of good documentation and consistent API versioning to ensure ease of use and maintainability.

Why I Selected This Resource

I chose this particular blog post because of its comprehensive yet beginner-friendly approach. As a computer science student, I am currently learning about software architecture and development practices. This guide stood out for its clarity in explaining complex concepts and its practical examples, which help bridge the gap between theory and real-world application. I wanted to understand REST APIs better, not just from a theoretical standpoint but in a way that I could apply in future projects, making this resource ideal.

Reflections on the Content

Reading through the blog post was eye-opening. It clarified the purpose and usage of REST APIs and reinforced my understanding of HTTP methods and status codes. The emphasis on statelessness and how each request must be self-sufficient was particularly insightful, as I previously struggled with this concept. Additionally, I learned the significance of designing intuitive endpoints and properly using status codes to indicate different outcomes, such as 200 for success or 404 for not found.

This resource has given me the confidence to start building simple RESTful APIs. I now appreciate why good API documentation and versioning are critical — they help developers maintain and scale services effectively. Moving forward, I intend to apply this knowledge in my coursework and future software projects, ensuring that the APIs I develop are well-structured, easy to use, and maintainable.

Conclusion

Overall, the “Rest API Tutorial — A Complete Beginner’s Guide” was a valuable resource that provided me with a solid foundation in RESTful API development. I highly recommend it to any beginner looking to understand how APIs work and how to implement them in practical projects. For those interested, you can read the full post here.

From the blog Discoveries in CS world by mgl1990 and used with permission of the author. All other rights reserved by the author.