Designing a low-maintenance system is a desirable goal for any software engineer, as it can save time, money, and headaches in the long run. However, what does it mean to have a low-maintenance system, and how can you achieve it? In this article, we will explore some principles and practices that can help you create a system that is easy to operate, monitor, and update.
The first step to design a low-maintenance system is to define clear and realistic requirements for the system's functionality, performance, scalability, reliability, and security. This will help you avoid unnecessary complexity, over-engineering, and feature creep that can make the system hard to maintain. You should also prioritize the most important and frequently used features, and avoid adding features that are rarely needed or used.
It totally depends upon the level of operational overhead you need. Going Serverless is one option if you totally want to delegate the resource management overload to any cloud provider and if this is not the case you need to make sure that you have identified clear boundaries in your domain and the way data will come in and out of the system. Once done you can then define inter and intra service controls to reduce maintenance overhead.
Designing a low-maintenance system is like building a simple, cozy home. Start with clear needs and skip the extras. Prioritize what you use often and keep it streamlined. Think of it as decluttering your space for easier upkeep.
Design a low maintenance system should come up with a clean approach with limited problems to solve and that can extend. Sometimes over engineering makes systems hard to maintain, it's good to take time for a designing system instead of focusing to push the deliverables, if it takes 10 percent more bandwidth it should think in cleaner way
The next step is to choose the appropriate technologies for your system, such as programming languages, frameworks, libraries, databases, and tools. You should consider the trade-offs between different technologies, such as their compatibility, stability, support, documentation, and learning curve. You should also avoid using too many or too new technologies, as they can increase the technical debt and the risk of bugs and errors. You should aim for a consistent and coherent technology stack that meets your requirements and fits your team's skills and preferences.
The third step is to follow best practices for coding, testing, documenting, and deploying your system. You should write clean, modular, and readable code that follows the standards and conventions of your chosen technologies. You should also write comprehensive and automated tests that cover all the critical and edge cases of your system. You should document your system's architecture, design, assumptions, dependencies, and interfaces, as well as any changes or updates you make. You should also use a version control system and a continuous integration and delivery pipeline to manage and automate your code changes and deployments.
To build a strong and easy-to-maintain system, stick to good habits. - write clear code - test it well - keep helpful documentation - use tools like Git to track changes - set up automated processes for testing and deploying your code
The fourth step is to design your system for scalability, which means that it can handle increasing or varying amounts of load without compromising its performance or availability. You should consider the scalability dimensions of your system, such as data volume, data velocity, data variety, user concurrency, and geographic distribution. You should also apply scalability patterns and techniques, such as load balancing, caching, sharding, replication, microservices, and serverless computing, to optimize your system's resources and throughput.
Oftentimes we always focused on scalability but in the end we achieve scalability by adding more infrastructure. Always think about the application load vs infrastructure required to achieve that load. With the help of clear requirements of system and estimations of Data volumes, variety, velocity, concurrency & cost will help us to design our system. After analysing estimations we can choose right technology and scalability techniques.
The fifth step is to design your system for reliability, which means that it can recover from failures and errors without losing data or functionality. You should consider the reliability aspects of your system, such as fault tolerance, fault isolation, fault detection, fault recovery, and fault prevention. You should also apply reliability principles and practices, such as redundancy, backup, failover, retry, timeout, circuit breaker, logging, and alerting, to minimize the impact and frequency of failures and errors.
The sixth and final step is to design your system for security, which means that it can protect its data and functionality from unauthorized or malicious access or manipulation. You should consider the security threats and risks of your system, such as data breaches, denial of service, injection attacks, cross-site scripting, and man-in-the-middle attacks. You should also apply security measures and mechanisms, such as encryption, authentication, authorization, firewall, HTTPS, and API keys, to secure your system's data and communication channels.
- Try to use technologies that provide backwards compatibility support - Avoid cutting the edge - eliminate dependencies to keep it maintainable - use of automation where applicable - keep it simple stupid - keep reusability at hand
Focus on principles - KISS (Keep It Simple, Stupid) and YAGNI(You aren't gonna need it) to keep low maintenance system. Don't over-engineer the design, and build use cases and improvements iteratively.
Google's search engine is a quintessential example of a low-maintenance system design. To achieve this, Google employs a microservices architecture, allowing for the modular updating and maintenance of individual components without affecting the overall system. This architecture facilitates easy deployment of updates, bug fixes, and new features. Additionally, Google uses extensive automation for testing and deployment, significantly reducing manual intervention and the potential for human error. By leveraging scalable cloud infrastructure, Google ensures that its system can handle varying loads with minimal manual adjustment
Automate maintenance tasks: Use automation tools and scripts to automate routine maintenance tasks such as backups, monitoring, and software updates. This can reduce the amount of manual effort required to maintain your system and minimize the risk of human error. Monitor and analyze performance: Implement monitoring tools to track the performance of your system and identify potential issues. Use this data to optimize your system and proactively address maintenance issues before they become critical.