Microservices architecture is the most commonly used building block when building software applications, breaking programs into smaller modules, each of which focuses on a different function of the application being built. It features loosely connected software components that are designed to be self-contained, self-deploying, and cohesive. Microservices architecture can be easier to manage, although the large number of services within that structure can make troubleshooting and debugging difficult. On the contrary, it is easier to isolate faults.
This is a change from a monolithic architecture, where all applications are single-tier. Monolithic architecture combines multiple applications into one big complex program without those singular modules, with everything integrated within the same code. That structure can be more difficult to manage over time. One failed component in a monolithic design can bring down the entire “stack”.
The impetus for change
Embedded modules are ideal for a specific use case, such as a payment management system example, that involves multiple links. First, customer information is stored and connected to other services within a software program. The microservices architecture model leaves each module with its own unique function, making it less difficult to identify where a software bug might be located and possibly easier to isolate and then fix the bug. Within a monolithic structure, testing a singular problem can be more difficult because it is connected to the rest of the code within that program. This is why many companies are moving away from monolithic architecture (still used for simpler use cases) to employ microservice modules.
A microservice failure can be isolated to just that service, preventing cascading failures that could cause an application to crash, known as the “domino effect.” However, since each module is connected to others under the microservice approach, a failure in one module can affect others in the chain. This means that before a software application is released, it must be repeatedly tested for function and load, seeking to minimize any downtime.
Best practices to avoid software failures
There are a number of reasons software programs fail, and some basic best practices can be employed to minimize the likelihood of that happening. They include the following:
- Implementation of load balancing. As the number of website users increases and they log in to add their personal details, a block may affect other functions, such as access to the bank they expect to withdraw money from when they pay. Think back to “Black Friday” and what happened when websites weren’t equipped to handle buyer traffic. On an e-commerce website, when the number of users increases dramatically to take advantage of an online offer that could cause a crash, it can affect other functions, such as access to the checkout page when checking out. Avoid a single point of failure by load balancing system traffic across multiple server locations.
- Program scaling application. This is the ability of a program’s application nodes to automatically adjust and speed up to handle increased traffic through machine learning as it analyzes metrics in real time. Scheduled scaling can be used during forecast peak times or for special sales events like Amazon Prime Day. At off-peak hours, those nodes can go down. Dynamic scaling involves software changes based on metrics including CPU and memory utilization. Predictive scaling details that understand current and predicted future needs, using machine learning and system monitoring modules.
- Use continuous load and stress testing to ensure code reliability. Build a software program with high availability in mind, accessible every day of the year with minimal downtime. Even one hour offline per year can be expensive. Employ chaos engineering during development and beta testing, presenting worst-case scenarios when it comes to the load on a system. Then write a program to overcome those problems without resorting to downtime.
- Develop a backup plan and redundancy schedule. Being able to replicate and recover data in the event of a crash is crucial. Instill this type of business ethic within the corporate structure.
- Monitoring the performance of a system using metrics and observation. Be aware of any deviations from the norm and take immediate action when necessary. A word of caution: the most common reason for software failure is the introduction of a change to the operating system in production.
One step at a time
The first step in developing a software program is choosing the right type of architecture. Using the wrong type can lead to costly downtime and can deter end users from returning for a second visit if other sites or apps offer the same products and services.
The second step is to build in key features, including the ability to scale as demand peaks in the program (perhaps a popular retail site having a sale), redundancy that allows a backup component to take over in the event of failure and the need for a continuous system. tests.
The final step is to set high availability standards and high expectations where downtime is not an option. Following these steps creates a template for designing better system applications that are reliable in all but the rarest of circumstances.