This week, the blog that caught my attention was “How to Make Your Development Environment More Reliable” by Shlomi Ratsabbi and David Gang from the Lightricks Tech Blog. This work highlights common challenges encountered in software development and provides actionable solutions — specifically, how to optimize development environments. The insights offered in this blog align perfectly with our coursework, as we have continuously learned about and worked within our own development environments. This post emphasizes the importance of ensuring consistency and reliability when creating these environments, offering practical advice to achieve this goal.

The writers begin by outlining the necessity of development environments, describing the challenges that arise during software releases. These challenges include discrepancies between local and production configurations, mismatched data, permission conflicts, and system interaction issues. While creating a shared development environment may seem like the obvious solution, the authors point out that this approach introduces its own set of problems, such as debugging difficulties due to parallel testing, interruptions caused by developer collisions, and divergence between shared and production environments.

To address these challenges, the authors advocate for the implementation of branch environments. Branch environments are independent, isolated setups for developing and testing specific features or issues. These environments, when paired with tools like Terraform, Argo CD, and Helm, enable integration with Infrastructure as Code (IaC) and GitOps, automate infrastructure management, and ensure automatic cleanup of unused resources. This approach promotes consistent documentation of dependencies and application details within version control systems like GitHub. The blog includes a clear diagram and code snippets that effectively demonstrate how to set up branch environments using these tools, making it accessible and actionable for readers.

Branch environments offer several key advantages. By isolating changes, they ensure that all updates are properly tracked, simplifying debugging and maintaining consistency across development efforts. This isolation also eliminates conflicts inherent in shared environments, reducing the risk of outdated configurations or data interfering with new testing and development efforts. Tools like Terraform and Argo CD further enhance this process by automating repetitive tasks such as infrastructure provisioning and application deployment, saving developers time and reducing the likelihood of human error.

Additionally, branch environments improve resource efficiency. Since these environments are ephemeral, they are cleaned up automatically when no longer needed, freeing up valuable system resources and lowering costs. The inclusion of tools like Helm simplifies configuration management, even for complex architectures, ensuring a streamlined, manageable workflow.

Overall, this blog provides a thorough and practical framework for tackling one of the most common challenges in software development: creating reliable and consistent environments. The adoption of branch environments combined with IaC and GitOps principles enhances scalability, collaboration, and efficiency. As I continue to develop my own projects, I plan to incorporate these strategies and tools to build environments that are both robust and resource-efficient.

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

This week, I am focusing on the blog “The Software Architect: Demystifying 18 Software Architecture Patterns.” by Amit Verma. Verma is an architect who focuses on creating Java applications among other work. This blog aligns with our course work as we focus on software architecture and we have recently learned the details related to some of the design patterns mentioned here. This post has introduced me to new architecture design patterns to study and provided a vast array of knowledge like when to apply them and how to properly use them in my future projects.

Verma begins by specifying what software architecture is and how it differs from software design. I appreciate how this was explained because it makes the concepts simple to understand. Software architecture focuses on the high-level structure like the blueprint of the project, software design focuses on translating this blueprint into real project specifications which developers can implement. A concept in this post that was new to me was the architecture documentation approach, the C4 model. This model ensures that there is a structure that is able to be implemented by addressing four main levels: context, containers, components, and code. The C4 model encourages architects to create diagrams and written documents to ensure comprehensive project documentation.

The beauty of architectural patterns lies in their repeatability; they provide reusable solutions that address common goals and challenges across projects, enabling architects to achieve a specific, predetermined outcome efficiently. This post provides information related to 18 different architecture patterns that are commonly used and necessary for software developers to be familiar with. Some of the patterns were entirely new to me like the Layered Pattern, Pipe-filter Pattern, and the Microkernel Pattern. Beginning with the Layered Pattern, this architecture separates different functionalities into different layers. One common representation of this uses three main layers, the presentation layer which responsible for the UI and other user-focused components, the business logic layer which focuses on the business rules and the actual code to manage data and make calculations, and the data access layer which is handles database/external data source interactions. Next, the Pipe-filter Pattern uses independent components to process data using separate processing tasks. Beginning with the data source, moving into the pipe which connects components, then using filters to transform the data based on a given function, and finally the data sink or endpoint which receives the processed data output. Lastly, the Microkernel Pattern a.k.a. Pluggable Architecture allows for modular, flexible systems to be built. In this architecture, the system’s core services are handled in the microkernel and all other components are separated known as a plug-and-play concept.

This post is rich with vital information related to software architectures and provides knowledge far beyond what I have included here. I know I will be returning to this blog repeatedly as a resource to learn from and to use a basis of how these patterns can be implemented.

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

A blog that recently caught my attention was GitHub Engineer Sarah Vessels, “How to review code effectively: A GitHub staff engineer’s philosophy”. Vessels focuses on code review every single day which has brought her to create her own strategies for successful code review to ensure we are building good software. Though code review can be done in different ways I selected this blog because it directly involves code review via pull request reviews on GitHub which aligns perfectly with our classwork over the past couple of weeks learning to manage Git properly.  

One large part of Vessels job as a code reviewer is having an open discussion with the author of the code posing questions that may have not yet been considered. The phrase two sets of eyes are better than one comes to mind here as we can very frequently catch other’s coding mishaps, but we might miss our own. The writer stresses that acting as a reviewer for another teammate benefits both parties as the reviewer is constantly seeing someone else’s logic and new code, while the author of the code is gaining a new perspective – this exchange of knowledge is extremely valuable. 

This blog also provides tips and tricks of how to manage a queue of pull requests properly providing simple Slack queries to organize new requests by team or outstanding requests that require attention. Another tip from the writer is ensuring the reviewer team stays small – this benefits the development team overall as there is clear accountability for who is to review the changes. Vessels also commented on the benefit of specifying code review requirements/frameworks to ensure a seamless, consistent review process amongst teams.  

The writer also provides samples of good code review feedback and poor code review feedback to highlight the main differences between them. Good feedback should include specific details, references to specific issues/lines, provides a possible solution, and provides reasoning. This blog post also offers vital information related to how to give a good code review. Some of the tips seem like common sense like offering affirmations and asking questions, but an important tip Vessels shares is to be aware of biases and assumptions. The writer highlights that even the most-senior programmers can make mistakes so only you (as the reviewer) have the opportunity to validate the work and catch any issues before deployment.  

GitHub Engineer Sarah Vessels shares her invaluable experience with code review through this blog post which discusses fine-tuning the review process, good vs bad reviews, how to give good reviews, and how to get the most out of a review. As a student, it is often my own code that I must turn back to and review to enhance, but after this reading I am feeling encouraged to seek opportunities to study others code with a focus on the exchange of knowledge and getting experience on my own for how to review code for a development team in the future. 

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

This week I found a great blog titled, “Mastering Design Patterns in Java”, that delves deep into design patterns specifically in Java. This piece of work aligns with our course topics and focuses on a programming language that many of us are most comfortable with. The writer, Dharshi Balasubramaniyam, discusses six notable design patterns in software engineering: 

1. Singleton 

2. Factory 

3. Builder 

4. Adaptor 

5. Decorator 

6. Observer 

The focus of the discussion is how to implement these patterns using detailed examples and how they can be used to deal with common coding scenarios like creating objects, managing inter-class relationships, and optimizing object behavior. 

Our work in our course has focused on some of the design patterns that are discussed in this blog, but the rich examples provided here are incredibly valuable when trying to gain a complete understanding of the patterns and learning when to use them. A great example of this is the mention of the Singleton pattern – I am already familiar with this one, but the example being used made the concept easy to remember and understand. The example references the simple idea of the clipboard. If we had more than one instance of the clipboard being accessed by the user of a device, it would be very likely to have conflicting data saved – to avoid this issue we can apply the Singleton pattern to ensure that there is only ever going to be one clipboard instance at any given time. The writer provides the code which enables this example and shows the value of using this design pattern. 

One new pattern I learned about was the Builder pattern which focuses on simplifying object construction with required and optional properties. The pattern works to manage the parameters by using a constructor with the required properties and different setter methods with optional properties by using an object class and an objectBuilder class. This pattern provides flexibility for the object being created – the given example creates a user with required properties of name and email and optional properties of phone and city. In the case of the example, we can note that the properties will have their own functions for setter methods which return an objectBuilder object – if the function does not get called to set a new value all optional parameters will contain the string “unknown”. This technique makes the code easy to understand and ensures we are not getting errors due to missing parameters as they will always contain some string.  

Using this blog to practice and learn from new examples is extremely helpful and will contribute to the enhancement of my skills as I continue to learn and get more comfortable with writing good, clean code the first time. By implementing the examples shown in the article, I can start noticing opportunities to apply these design patterns in my own work avoiding hours of refactoring code later.  

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

After our recent studies relating to Docker managing dependencies and ensuring consistent development environments, I was interested in learning more about how to use it because I thought something like this could have saved me many hours of troubleshooting while completing a recent research project. This article, written by Medium user Dispanshu, highlights the capabilities of Docker and how to efficiently use the service in a cloud environment.  

The article focuses on optimizing Docker images to achieve high-performance, low-cost deployment. The problem some developers run into is having very large images which slow the build processes, waste storage, and reduce application speeds. I learned from this work that large images result from including unnecessary tools/dependencies/files, inefficient layer caching, and including other full images (like Python in this case). Dispanshu focuses on achieving the solution in 5 parts: 

1. Multi-stage builds 

2. Layer optimizations 

3. Minimal base images (including Scratch) 

4. Advanced techniques like distroless images 

5. Security best practices 

Using these techniques, the image receives a size reduction from 1.2GB to 8MB! The most impactful change being multi-stage builds to which the writer accredits over 90% of this size reduction. I have never used these techniques before, but my interest definitely peaked when I saw the massive size reduction that resulted from these changes.  

The multi-stage builds technique references the build stage and the production stage. By using this technique build-time dependencies are separated from the actual runtime environment which avoids the inclusion of any unnecessary files or tools in the resulting image. Another technique recommends minimal base images using the slim or alpine version (for Python) over the full version for the build stage and for production stage it is recommended to use the scratch base image (no OS, no dependencies, no data or apps). Using a scratch image has pros and cons, but when we are considering image sizes and optimization this is an ideal route. 

Another interesting piece of this article is the information relating to advanced techniques like distroless images, using Docker Buildkit, using .dockerignore file, and eliminating any excess files. The way that distroless images are explained by the writer makes the concept and the use case very clear. The high-level differences between the Full Distribution Image, the Scratch Image, and the Distroless Image are described as the different ways we can pack for a trip:  

1. Pack your entire wardrobe (Full Distribution Image) 

2. Pack nothing and buy everything at your destination (Scratch Image) 

3. Pack only what you’ll actually need (Distroless Image) 

The analogy makes understanding the relationship between these three image options seemingly obvious, but I can imagine applying any of these techniques described would require some perseverance. This article describes an architecture that juggles simplicity, performance, cost, and security with very impactful results. The results of this article are proof of the value these techniques can provide, and I will be seeking to apply them in my future work.

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

A blog from Amir Lavasani caught my attention this week as it perfectly aligns with topics we are currently focusing on in our course like working with OpenAPI Specification (OAS) using YAML. Working with REST APIs is relatively new for me and fully comprehending how these requests and interactions work is still in progress.  Lavasani structures this post as a tutorial for building a blog posts API, but there is a surplus of background information and knowledge provided for each step. Please consider reading this blog post and giving the tutorial a try!  

This tutorial starts as we all should with planning out anticipated endpoints and methods, understanding the schema of JSON objects that will be used, and acknowledging project requirements. This step, though simple, is helpful to remind us that it is vital to plan to ensure a clean and concise structure when we have implemented our work. Moving into the OAS architecture, Lavasani simplifies this into The Metadata Section, The Info Object, The Servers Object, and The Paths Object (and all objects that fall within). Authentication is touched on briefly, but the writer provides links to outside sources to learn more. For me, this post reiterates all the OpenAPI work we have completed in class with another example of project application and provides additional resources to learn from and work with.   

Most of the basics that this post touches on we have already reviewed in our course, the writer provides valuable information related to the minor details. A hint that was useful to me was the ability to use Markdown syntax in the description fields in OAS – this can ensure ease of use and understanding of the API. I also learned how the full URL is constructed based on the paths object. We know our paths are based on the endpoints we define and the operations (HTTP methods) they support, but to make sense of it all these pieces of information are concatenated with the URL from the servers to create the full URL. This is somewhat obvious, but seeing it spelled out as Lavasani does is very useful to fully reinforce what we know about these interactions. Another new piece of knowledge relates to the parameter object. I was not initially aware of all of the ways we can set operation parameters. We know how to use parameters via the URL path or the query string, but we can also use headers for this purpose or cookies which is useful to know for future implementations. Lastly, the writer mentions the OpenAPI Generator which can generate client-side and server-side code from an OAS – though I am not familiar with this service I can see it’s practicality and I will likely try to complete this tutorial and follow up with learning about the outside tools mentioned.   

This blog provides a practical example of working with the OpenAPI Specification, reinforcing concepts we’ve studied in class while providing useful insights. 

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