How to Address Code Smells (part1)

5 Simple Code Optimization Techniques Every Developer Should Know: As developers, writing clean and efficient code is crucial not just for performance but also for maintainability. Here are five simple optimization techniques that can make a big difference: Efficient Data Structures: Choosing the right data structure can drastically impact the speed and memory usage of your application. Always consider the trade-offs of time complexity versus space complexity. Lazy Loading: Delay the loading of resources until they’re actually needed. This can significantly reduce initial load times and save memory. Minimize I/O Operations: Input and output operations can be slow. Batch your I/O operations or reduce their frequency to enhance performance. Loop Unrolling: By reducing the overhead of loop control, you can speed up your loops. While it may increase code size, it's often worth it for performance-critical sections. Avoiding Unnecessary Computations: Reuse previously computed results instead of recalculating them. Caching is your friend here! #tips #CodeOptimization #ProgrammingTips #EfficientCoding #SoftwareDevelopment #CodingBestPractices #DeveloperLife #TechTips #CodePerformance #CleanCode #CodingCommunity

Don't Let DRY Make Your Code Too Thirsty The DRY (Don't Repeat Yourself) principle means that every piece of knowledge should only exist once in your codebase. Sounds great, right? But when does DRY become TOO DRY? Sometimes, in an effort to eliminate all repetition, we can end up over-abstracting our code. This can lead to code that is hard to understand, maintain, or extend. For example, if you find yourself creating overly generic methods or classes that try to handle too many scenarios, you might be taking DRY too far. This makes the code confusing for others (or even your future self) and increases the chance of introducing bugs when changes are needed. DRY isn't always the best choice. In cases like DTOs or database schemas, repetition can be more readable and clear. Reusing too much can make your design rigid and harder to change when requirements evolve. Pros of DRY: • Reduces repetition, making your code easier to maintain. • Less copy-pasting means fewer chances for mistakes and errors. • Changes in logic require fewer edits, which reduces the risk of bugs. Cons of DRY: • Too much abstraction can make your code hard to understand. • Reusing too much logic across different parts can make changes risky and cause unexpected problems. Where have you found DRY to be more trouble than it's worth? How do you balance avoiding repetition without over-complicating your code?

When it comes to clean code, simplicity is key. But what makes a code base "simple"? In my experience, it's all about keeping things modular and reusable, and focusing on the architecture and principles like SOLID and TDD. This allows us to write better code faster, maintain complex systems with ease, and make changes without breaking things. Tools like Prefect and Dagster are becoming increasingly popular for this very reason. They offer a level of simplicity that makes it easy to integrate with the rest of your codebase and make changes without introducing new problems. Of course, the deployment process might be a little trickier with these tools. But in the end, the simplicity of the code base will be worth it. 💻🧰 #dataengineer #dataengineering #programmerhumour #codinghumor #justunderstandingdata

Don't Let DRY Make Your Code Too Thirsty The DRY (Don't Repeat Yourself) principle means that every piece of knowledge should only exist once in your codebase. Sounds great, right? But when does DRY become TOO DRY? Sometimes, in an effort to eliminate all repetition, we can end up over-abstracting our code. This can lead to code that is hard to understand, maintain, or extend. For example, if you find yourself creating overly generic methods or classes that try to handle too many scenarios, you might be taking DRY too far. This makes the code confusing for others (or even your future self) and increases the chance of introducing bugs when changes are needed. DRY isn't always the best choice. In cases like DTOs or database schemas, repetition can be more readable and clear. Reusing too much can make your design rigid and harder to change when requirements evolve. Pros of DRY: • Reduces repetition, making your code easier to maintain. • Less copy-pasting means fewer chances for mistakes and errors. • Changes in logic require fewer edits, which reduces the risk of bugs. Cons of DRY: • Too much abstraction can make your code hard to understand. • Reusing too much logic across different parts can make changes risky and cause unexpected problems. Where have you found DRY to be more trouble than it's worth? How do you balance avoiding repetition without over-complicating your code?

Here is the latest on our blog series about SOLID principles! Today, we're diving deep into the Single Responsibility Principle (SRP) and its crucial role in creating scalable and maintainable code. Learn how to apply SRP effectively in your projects through practical examples and step-by-step guides. Check out the full article for detailed insights and best practices: [Read more about SRP on Kumar Vinay's Blog](https://lnkd.in/gCZFUTC2) #SoftwareEngineering #CodeQuality #SRP #SOLIDPrinciples #ProgrammingTips

Bytes of Best Practices: Commit Clean Code, Every Time Welcome to the second post in my Bytes of Best Practices series! In early days of my career, One time I spent an entire day fixing a production issue caused by messy SQL code and ineffective code review that resulted incorrect statement and caused huge escalation!!. A task that should’ve taken minutes turned into a nightmare, all because of poorly formatted, unreadable code. That experience taught me a valuable lesson: clean code isn’t optional, it’s essential. Here’s how you can establish the habit of committing clean code: 1. Follow Code Conventions: Use your organization’s standards as a foundation for consistent style and structure. 2. Leverage Plugins: Tools like Prettier, ESLint, and Poor Man’s T-SQL Formatters ensure your code meets conventions automatically. 3. Automate Code Reviews: Integrate these tools into your commit process using CI/CD pipelines to catch issues before they hit production. Why it matters: Reduces time spent debugging messy code. Improves team collaboration with readable, maintainable code. Enhances work-life balance by minimizing weekend production fire drills. Here's an example where on left you can see horizontal spacing is used and uneven letter casing making it hard to read even though there is nothing complex here, imagine there is a bug in here and you have to triage it on production priority calls. Right?? Imagine having tools that automatically align your code to best practices every time you commit, saving hours and ensuring peace of mind. How do you ensure your commits are clean and error-free? Let’s discuss in the comments!

💡 New Technology at edenceHealth: Cookiecutter 🤯 Inconsistency is a big headache when it comes to streamlining an ETL workflow. Despite having similar objectives, each team member may have their own style, which can result in a mishmash of project structures, coding solutions, test approaches and documentation formats. The lack of standardization leads to confusion, wasted time deciphering new project structures, and increased chances of errors creeping into the codebase. 🚀 To tackle this challenge, we are currently looking to standardize and unify our codebase. A potential tool to help with this is Cookiecutter. Cookiecutter provides a fantastic template-based approach for scaffolding ETL projects. With just a few commands, we can quickly generate a project structure that includes directories for data extraction, transformation, loading, tests, and documentation. Furthermore, it also sets up configuration files for database connections, logging, and environment variables, giving us a head start in our development process for any new project. However, Cookiecutter remains flexible, which is a MUST for us. Functionalities, pre- and post-processing steps and target data models can all be customized on a project-by-project basis depending on if it’s a Postgres, MySQL or Pandas based ETL. 🙌 By adopting Cookiecutter, every team member can start with the same foundation, making it easy to understand, maintain, and scale our ETL codebase. The advantages are numerous. It empowers our team to write cleaner code, accelerates project kick-off and delivery time, and enables smooth integration of future updates and new features. Most importantly, it gives our development team more time to focus on exciting things as this will free them from mundane tasks! #standardization #scaffolding #framework #ETL #development #newTechnology #dataEngineer #innovation #healthcare #efficiency #workflowOptimization #codeGeneration #automation #developmentTools #openSource #Python #Templates #Cookiecutter #codeQuality #productivity

Think twice before adding a new setter method in your code. 💡In most cases you can choose a more meaningful name for the method. Classes in code are not just reflections of database tables (e.g. 🤢 anemic models), they can represent underlying 🔀 business processes in the first place. For example, setting a new status. The obvious solution is to add setStatus(). In this case, most likely, the logic if status transition is possible is 🤔 scattered in different places outside of the class. Now let’s create a separate method for each status transition: start() … complete() Oh boy, how self-explanatory it becomes, huh? Moreover, usually setters of the same object are called in clusters, like:   obj.setStatus(status.COMPLETED);   obj.setCompletedAt(now);   obj.setCompletedBy(actor); If you have a dedicated method for completion, you can have everything inside as they are part of the same process. ✅ Profit? Looking at a single object you can trace its life cycle. No need to collect this information from the entire code base. I know, it is easier and faster to start with just an anemic model, but if you are going to support it for more than one year it is worth investing some time to understand the business model. You make it easier for yourself and your colleagues in a year to read the code.  I’m sure it will pay off. #coding

#2/365 challenge "Understanding the Complexity of Code Hey fellow tech enthusiasts! As programmers, we strive to write efficient code that scales well. But have you ever wondered how to measure the efficiency of your algorithms? Enter Time Complexity and Space Complexity! Time Complexity: - Measures how long an algorithm takes to complete - Expressed in Big O notation (e.g., O(n), O(n^2), O(log n)) - Affects performance and scalability -The amount of time that an algorithm requires for its execution is known as time complexity. Space Complexity: - Measures how much memory an algorithm uses - Also expressed in Big O notation - Impacts memory usage and caching -The amount of space that an algorithm requires for its execution is known as space complexity. ->There are mainly 3 types of time complexities are available 1.Best case time complexity 2.Worst case time complexity 3.Average case time complexity ->We can calculate the time complexity using 2 approaches 1.Frequency count or step count 2.Asymptotic Notations 1.Frequency Count ->It specifies the no of times a statement is to be executed or how many times the corresponding statement is to be exected that is nothing but frequency count. 2.Asymptotic Notations ->Asymptotic notations are mainly useful in order to calculate the time complexity of an algorithm. ->There are 5 types of notations are available 1.Big OH notation(O) 2.Big omega notation ( Ω) 3.Theta notation (Θ) 4.Little OH notation (0) 5.Little omega notation (ω) Understanding time and space complexity helps you: - Optimize code for better performance - Predict how your algorithm will behave with large inputs - Make informed design choices Share your favorite resources or tips for understanding complexity in the comments! Let's write efficient code that makes a difference! #TimeComplexity #SpaceComplexity #AlgorithmEfficiency #CodeOptimization"