A scalable application is an application that can be easily adapted to meet the changing needs of a business. By scaling up or down, these applications can provide the same level of service even as demand for the system grows or wanes. This is often done by designing the application so that it can be run on multiple servers or by adding new servers as needed.
Scalable applications are important because they allow businesses to grow without having to redesign or rebuild their entire application. This can save a lot of time and money. By allowing businesses to easily add or remove features as needed without having to rewrite the entire application, you save development time.
Scalability can also save businesses on hosting and development costs. For example, let’s say you are running an AWS instance. If you preemptively design your product with high levels of demand in mind, you are actively overspending on computing power. But what if your app automatically adapts and the instance increases as more processing power is needed?
Scalability is a must for any business in the current era. With an ever-evolving market, we have to adapt and shift quickly to survive and thrive. A prime example is dynamic markets. A dynamic market is a market where prices are constantly changing in response to the forces of supply and demand, like the stock market, for example.
For this article, we’ll define the volatility of a market as the susceptibility of its variance to contextual influence. In other words, it’s when even small changes have huge effects on both prices and availability. Once again, the stock market is a good example; all it takes is a bad tweet to lose millions of dollars in minutes.
As you can already guess, scalability and dynamic markets are a match made in heaven. A system that can adapt to sudden changes is extremely important in contexts where uncertainty is high. So, what do you have to keep in mind when you design for scalability in this kind of environment?
What Is a Scalable Web Application?
A scalable web application is designed to handle a large amount of traffic and data. It is important for your web application to be scalable because if your website becomes popular, you will need to be able to handle the influx of traffic and data. There are many factors to consider when designing a scalable web application.
For example, you will need to use a database that can handle a large amount of data. You will also need to design your website in such a way that it can be easily scaled up or down depending on the needs of your users.
Applications that are not scalable often have difficulty handling increased traffic and may crash when faced with a sudden influx of users. This can lead to lost data and frustrated customers. Building a scalable web application from the ground up requires careful planning and forethought. Every element of the app must be designed with scalability in mind, from the backend database to the frontend interface.
There are many examples of scalable web applications, but some of the most popular ones include: social media platforms like Facebook and Twitter, e-commerce sites like Amazon and eBay, video streaming services like Netflix and YouTube, and cloud-based productivity tools like Google Drive and Dropbox
The benefits of a scalable design are manifold: A well-designed system can accommodate growth without breaking, it can be easily adapted to changing circumstances, and it is generally more efficient than a rigid one.
A scalable design is not a panacea, however; if poorly implemented, it can introduce new problems and complexities. A team needs to know how scalability works and have the skill set to actually build the architecture. Otherwise you will end up creating more problems than solutions.
Scalability Design Principles
Design decisions need to be made with a focus on how the system will be used and what types of workloads it needs to support. Some key design principles to keep in mind when aiming for scalability are:
- Decouple components – When designing a system, try to decouple its components as much as possible. This makes it easier to scale individual parts of the system independently. For example, using a message queue between a web frontend and backend database can help decouple those two components, allowing them to scale separately if needed.
- Parallelize work – Whenever possible, break up work into smaller pieces that can be processed in parallel. This can help improve performance and allow the system to better utilize multiple cores or processors. For example, MapReduce is a technique often used for parallel data processing tasks such as large-scale log analysis or image processing.
- Scale out rather than scale up – It is usually more scalable (and cost effective) to add additional commodity servers (“scale out”) rather than upgrading an existing server (“scale up”). Horizontal scaling through adding more servers can provide near-linearly increased capacity while still keeping costs low. Conversely, vertical scaling by upgrading an existing server generally reaches a point of diminishing returns, beyond which it becomes very expensive per unit of added capacity.
- Cache aggressively – Caching can be a powerful tool for improving performance and reducing the load on system resources. Whenever possible, try to cache data in memory so that it can be quickly accessed when needed. Additionally, use caching strategies such as memoization or write-through caching to avoid having to recalculate expensive computations unnecessarily.
- Keep it simple – The simpler the design of a system, the easier it usually is to scale. Avoid unnecessary complexity that can make it more difficult to understand how the system works as well as make it harder (and more expensive) to scale.
But what does this mean in pragmatic terms? Let’s take a look at a few of the common techniques used when designing with scalability in mind.
Caching allows you to store frequently accessed data in memory so that it can be quickly retrieved when needed. Many caching techniques can be used to improve the performance of a system. Some common examples are:
- Page caching: This technique is used to cache entire pages or page fragments so that they can be quickly served up to users without having to be generated from scratch each time.
- Object caching: This technique is used to cache objects such as database records or images so that they can be quickly retrieved without having to query the data source each time.
- Database query caching: This technique is used to cache the results of database queries so that subsequent requests for the same data can be served up much faster.
- Memory caching: This technique is used to cache data in memory so that it can be accessed more quickly than if it were stored on a disk.
- Application-level caching: This technique is used to cache data at the application level, rather than at the server level, so that it can be shared across multiple servers and improve performance overall.
Load balancing is the process of distributing workloads evenly across a network so that no single device is overwhelmed. By distributing workloads, load balancing can improve overall performance and prevent outages. There are many different ways to perform load balancing, but some common methods include using a load balancer appliance, round-robin DNS, and software-based load balancing.
Load balancer appliances are hardware devices that are specifically designed to distribute traffic across a network. Round-robin DNS is a method of load balancing that works by assigning each server a different IP address. Software-based load balancing can be performed using a software program or by configuring a web server such as Apache to perform it.
Optimizing code for performance
There are many ways to optimize code for performance. Some common techniques include using faster algorithms, reducing memory usage, and minimizing the number of computations. One way to optimize code is to use faster algorithms. A classic example is using a sorting algorithm like quicksort instead of bubble sort. Quicksort is typically much faster than bubble sort, so it can lead to significant performance gains. Another example is using a more efficient data structure like a hash table instead of a linked list. Hash tables can often provide better performance because they have lower lookup times.
Another way to optimize code is to reduce memory usage. This can be done by using smaller data types when possible, avoiding unnecessary copies of data, and reclaiming memory when it is no longer needed. By reducing the amount of memory used by an application, we can often see improved speed because there will be less pressure on the garbage collector and CPU caches will be more effective.
You can also optimize code by minimizing the number of computations that need to be performed. This can involve caching intermediate results, avoiding redundant calculations, and parallelizing computations (if possible). By doing fewer computations overall, we can again see significant improvements in our applications due to reduced pressure on the CPU and caches.
10 Steps to Build a Scalable Application
Now, let’s take our principles and turn them into a roadmap. This isn’t the only path to building a scalable app, but in broad terms, these steps cover all of the aspects you are going to have to deal with sooner or later in an incremental manner.
- Understand your application’s requirements.
- Identify the main components of your application.
- Design a component layout that can be scaled horizontally and vertically.
- Decide on the communication protocols between components.
- Implement load balancing between components.
- Use caching to improve performance.
- Implement failover mechanisms for high availability.
- Monitor your application’s performance and resource utilization regularly.
- Test to ensure that your application can be easily deployed in multiple environments.
- Test your application thoroughly before going live.
Choose the Right Hosting Solution
Before going live, you have to choose the right hosting solution. A cloud-based solution can be a good option as it can be easily scaled up or down as needed. AWS, Azure, and Google Cloud are all excellent solutions with some very impressive tools to facilitate scaling.
Remember that most cloud services have an everything as a service (XaaS) model. So the more optimized your system is, the less you’ll pay for it in the long run. Do keep in mind that not all instances are designed for scalability, so make sure you understand the nature and services offered by your host provider. In the best-case scenario, you can always get help from a cloud engineer.
One of the differentiating features of cloud solutions is that most of them offer autoscaling. Instead of having to physically upgrade a system, you can spin up an instance or increase its memory/processing power with the click of a button. Use autoscaling to scale up and down as the need arises.
With the right forecast tools like a machine learning algorithm, you could even predict potential fluctuations and preemptively change the scale of your product. Think of it like the way stockbrokers use economic models to pull out of a position before it collapses. This way you only pay for the resources you need when you need them.
It’s also important to consider using a content delivery network (CDN) to help distribute content and reduce the load on the server. Some popular examples of CDNs include Cloudflare, Akamai Technologies, and Amazon CloudFront.
Finally, it’s essential to monitor the performance of the application and make sure it’s able to handle the increasing load. This includes monitoring things like CPU usage, memory usage, and response times. If any issues are detected, then steps can be taken to resolve them before they cause problems for users.
Remember that most cloud solutions offer very user-friendly budget trackers and have options to set soft and hard ceilings on your expenses. Make good use of these tools to manage your budget.
How to Scale in Volatile Markets
The technical aspect is only one side of the equation, but we also have to consider the business side when talking about volatile markets and decision-making. There are some strategies you can use to help you scale up in volatile markets.
For example, one way to reduce risk in volatile markets is to diversify your customer base. This means having customers in different industries or geographical areas. This will help to protect your business if one industry or region is affected by a downturn.
Another aspect is building a strong brand. A strong brand can help you weather tough times. Customers are more likely to stick with a brand they trust during periods of economic uncertainty. Investing in branding and marketing will pay off when customers are looking for stability during turbulent times.
It’s also important to keep a close eye on your cash flow in volatile markets. Make sure you have enough cash on hand to cover expenses and unexpected costs that may arise. You may need to tighten up your spending to preserve cash reserves.
Your business model may need to be adapted to survive in volatile markets. Review your costs and revenue streams to see where you can make changes. You may need to find new ways to generate revenue or reduce costs to stay afloat during tough times.
And always have a contingency plan for unexpected events. This is especially important in volatile markets. Make sure you have a plan for how you will respond if there is a sudden change in the market conditions. Having a plan B will help you react quickly and minimize the impact of market volatility on your business. It will also be a safety net to reduce the risks from strategic shifts.
The Scalability Paradox
The crux of the issue, then, is if we face an economic downturn, how can we invest in scalability? That’s an excellent question, and the answer is that you don’t reinforce your ship once you set sail. You have to do so at the harbor, before starting your journey. Don’t wait until a dynamic market becomes volatile before adopting a scalable solution.
The key is to be proactive and not reactive when it comes to scalability. That means you need to have a plan in place before marker trends change so that you can be prepared for anything that comes your way. By being proactive, you can avoid the common mistakes that businesses make during a crisis like an economic downturn, such as cutting costs without first looking at ways to improve efficiency.
If you are just starting, design your project or product with scalability in mind, and save yourself the trouble of having to transition down the line. On the other hand, if you already have a system in place, it’s never too late to make a thorough diagnostic on how you can transition to a looser and more flexible model.
Keep in mind, though, that it’s not always possible to design a scalable application. The trick is to find which aspects of the system can be turned into modules and refactored to make them scalable. Perhaps you won’t be able to upgrade the whole system, but that at least is a first step.
In a world of digital acceleration, scalability is a powerful strategy to keep our businesses lean and adaptable, able to accommodate sudden changes for better, or for worse. Remember, it’s not the strongest animal that survives, but the one who adapts better.