Ensuring Your App's Robustness
May 5, 2024
We've entered an era where software can be immensely critical, often forming the backbone of vast business processes. Nowadays, many individuals rely on apps for their livelihoods. Developing a robust app capable of handling a substantial volume of requests, seamlessly integrating new features, and maintaining stability for continuous 24/7 use has become more crucial than ever before. Fortunately, numerous tools are available to help address this challenge. However, before introducing further complexity to your code base, it's essential to consider a few tips to maximize your app's utility in meeting the demands of modern digital business requirements.
Performance Matters
In server-side applications, performance is one of the most important factors to consider when aiming to handle high-traffic apps. Many people agree with the sentiment that “premature optimization is the root of all evil“, but ignoring poor performance in your app implementation can lead to undetectable problems over time. Sacrificing your app's runtime execution time in favor of developer velocity is a reasonable choice in the early stages of development when you need to deliver your app as soon as possible. However, if your user base grows rapidly and your app becomes very slow or unable to handle requests, you may lose your loyal customers and miss out on opportunities to profit from your business. Nowadays, many people don't want to wait longer than 2 seconds and are quick to develop trust issues. Improving your app's performance may not be as challenging as you think, especially if you're already familiar with the patterns. Spending a few minutes to find the best practices for enhancing performance can save you a lot of time and money in the future, allowing you to focus on adding new features rather than fixing old code that consumes too many system resources. Creating the Right Data Structures A data structure is a specialized format for organizing, processing, retrieving and storing data. There are several basic and advanced types of data structures, all designed to arrange data to suit a specific purpose. Data structures make it easy for users to access and work with the data they need in appropriate ways. Most importantly, data structures frame the organization of information so that machines and humans can better understand it [1].
Creating the Right Data Structures
Choosing the optimal data structure in your code should be among the first considerations to ensure your app is scalable and performs well. Most apps with a large user base also handle vast amounts of data, with the interaction between the backend and database often being the most resource-intensive aspect of applications with complex business logic. An optimized data structure can simplify queries and require less post-processing, leading to improved performance and reduced hosting costs. Additionally, it enhances the developer experience by minimizing the need for transforming requests or responses to meet application service requirements or API response standards. However, there's no one-size-fits-all solution for every application. Different requirements demand different solutions, and as the developer, you must thoroughly analyze every potentially complex query required for building the app and determine the best solution to reduce complexity while meeting all business requirements.
Query Optimization
Query optimization plays a crucial role in improving the performance of database systems, enabling efficient data processing and analytics. By selecting the optimal execution plan, query optimization can significantly reduce the time required to retrieve and analyze data, resulting in faster response times and improved overall system performance [2]. Optimizing your query execution should be the first step when encountering poor performance in any endpoint of your app. Overload data transfer between the database and backend is a common issue in intensive data systems. Paying attention to using dynamic query selection or retrieving only specific fields and relations required can enhance your query performance. For common use cases like scanning, sorting, or joining data, utilizing indexing methods to store a subset of columns in your table in a sorted order can expedite data retrieval compared to scanning the entire table. Moreover, for tables that have grown too large, employing table partitioning can be the best solution. Table partitioning is a database feature that allows a table's data to be split into smaller physical tables, acting as a single large table. This feature improves performance by reducing the data that needs to be scanned by the database system and enhancing the capability of bulk data mutation.
Wisely Using Reusable Functions
Reusable functions are pieces of code that can be utilized across different applications or contexts without significant modification. They help save time, enhance quality, and promote consistency in software projects. However, as software developers, we sometimes tend to overuse this clean code pattern in an attempt to make our code more readable and reduce the number of lines.This habit can backfire if we're not cautious about the increased complexity that may arise when writing overly generic code with numerous state checkers or complicated logic determinations. Utilizing high-level language features like generics and reflection in reusable functions can potentially increase app compile and run-time performance, though the difference might not always be noticeable.Excessive reliance on reusable functions and abstractions can result in code that is easy to read but challenging to maintain and debug. Unusual abstractions also raise the barrier to entry for new team members joining our project. Sometimes, experienced developers who solely maintain their code base tend to create unique patterns or unnecessary automations that can hinder the development process if they break or become incompatible with new features that need to be added. Therefore, opting for standard and conventional solutions most of the time is often the best approach to getting the job done efficiently.
Minimizing Package Dependencies
Package dependency refers to a package installed on the development system and used within the project. In today's modern era of software development, it's relatively easy to find open-source code to fulfill our basic and common requirements for building software. However, this abundance of choices can often lead to confusion, as many packages offer similar solutions with comparable implementations and APIs. While using package dependencies can seem convenient, it can incur unexpected costs if tightly coupled with your codebase and difficult to refactor or replace. For instance, if you rely on a package that is rarely maintained and your programming language introduces new features you wish to implement, conflicts may arise if the package relies on an outdated version of the language. Moreover, when a new version of the package is released, it might break your codebase due to unconventional implementations that not addressed in the package's migration changelog. Addressing such issues can consume days or even weeks as you navigate GitHub issues seeking solutions, especially if you lack a deep understanding of the package's inner workings. While implementing solutions from scratch may initially require more time, it can ultimately save time in the long run if executed effectively. However, for packages that are widely used, proven to be robust, and easy to extend, reinventing the wheel may not be necessary. Prioritizing core business needs and client requirements remains the primary strategy for project survival.
Reordering Code Execution Sequence
When it comes to solving problems, especially in software development, we often write the logical implementation sequence as we think of it. While this may make the code easier to write and understand, it's not necessarily the best approach for creating robust software. Instead, it's essential to rethink how we order our code execution to achieve better results. The first consideration should always be to utilize all guard clauses and validators upfront, if possible. If your code mutates persistent data and a validator cancels the process later on, it can be challenging to rollback the data and ensure that no other processes relying on it are affected. Personally, I tend to order my validators from the most error-prone to stop the process early in execution and then return it to authorized processes. This approach can save resources and execution time. To facilitate easy reordering of code, implementing the Single Responsibility Principle (SRP) in your codebase is crucial. SRP states that a module should be responsible for one, and only one, purpose. This principle allows you to encapsulate logic that is easier to understand and manage. Lastly, ensuring that every state is immutable is essential. This is a fundamental principle of functional programming that is highly useful in the real world. Implementing immutable state makes it easier to reorder code execution without worrying about the impact on your data.
Conclusion
In this modern era, creating a stable, reliable, and robust application is more crucial than ever before. Many individuals and businesses cannot afford to risk their operations on software with a poor reputation. This article is written to provide insights on minimizing the fragility of your application based on my experience. Hopefully, it also raises awareness of potential pitfalls that can make your code less robust, unscalable, and difficult to maintain.
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