Achieving hyperscale is a work of technical art for any business. As companies grow and customers begin to rely more heavily on your service, ensuring reliability during peak business times is mission-critical. An enormous amount of work goes on behind the scenes to ensure scalability, and oftentimes it becomes necessary to redesign or rethink processes and systems to improve them as they face increased pressure.
When customer demand reaches its apex, it’s the ultimate test for any organization’s systems and processes; therefore, it’s important to prepare a comprehensive strategy that focuses on scalability, reliability and hypergrowth in a targeted, calculated and data driven methodology.
The first step on your path to hyperscale is forecasting and gathering data analytics around business and growth patterns for a full year to make smarter forecasts. Set aside time each day throughout the year to go over the allocation of your data and analytics, measuring them for accuracy to see how they stack up to your current predictions for that specific day. This may seem taxing and cumbersome when engineers have other priorities to work on, but devoting time to this in the early months of the year will help you be that much more accurate when it comes to predicting the needs of your customers and how you should go about scaling to meet those needs.
Especially during the holiday season, it’s important to have a minute-by-minute projection of how much traffic you expect and need. That’s where forecasting and an advanced data science model come into play. A common mistake made when it comes to scalability is not getting granular enough about the real pressure points for your systems. Having accurate projections and forecasts in place that feed into your data science model means you can better test your systems and production limits while gaining greater insight into meeting your scaling points. If you find fragilities ahead of time through this, you can assign your engineering team to fix them well in advance.
Another way to truly bolster your scalability efforts is by introducing an operability development model. This entails constantly deploying some controlled chaos into your systems to see how they really stack up and function under pressure, with the goal of up-leveling the capacity of both your operations and system growth. The operability development model allows you to really test the grit of your systems by squeezing the most out of them, prepares you to respond quickly and methodically should the worst case scenario occur and sets you down the path toward continued performance growth and scale.
From here, you’ll want to institute a preventative maintenance model. Think of it like getting an oil change for your car. Instead of waiting until the very moment you need your software to perform exceptionally, start taking a look at the little things that could or are causing problems ahead of time and fix them on a monthly or quarterly basis. A preventative maintenance model helps you inspect, adapt and solve these little nuisances before they become serious problems, granting you the cleanest operating environment possible.
Scalability is king, but availability and reliability are arguably just as critical. Improving availability means reducing latency and getting as close as you can to where your customers utilize your services. This can be achieved by deploying locations around the world to make connections much faster and services more independent. To maintain reliability, you should have processes in place that can quickly respond and offer support to customers 24/7 to maximize consistency and performance.
Another strategy to consider—if it makes sense for your business or if you’re evaluating cloud providers as a growth option—is to double down on your efforts by re-architecting your platform and systems as cloud native from the ground up to support greater levels of scalability and cost efficiency. By going cloud-native, you can achieve independently scalable services so that you don’t have to scale all of your services together. This means your engineers and developers don’t have to spend way too much time figuring out where issues and fragilities occur.
A platform architected as cloud-native allows you to leverage microservices and containerization, creating an environment with more reliable, self-healing, high-tenancy and auto-scaling features that take the most advantage of your hardware and services. An added benefit of a cloud native platform is that you can use your data to find the most opportune times to both scale in advance and descale based on performance and customer expectations. The efficiencies that come from re-architecting as cloud native enable engineers to spend more time on automating software and the delivery pipeline, which is a huge boon for scalability.
At the end of the day, a well-thought-out preparation strategy for mass scalability makes the whole process much more exciting and obtainable. It also greatly reduces lead-up anxiety by getting a head start on planning so that you have more time to sit back and enjoy the fruits of your labor. Lastly, a yearlong scalability strategy enables you to experiment and try new things without the fear of failure. Testing new approaches ahead of time to see what does or doesn’t work will only help you learn and make your scalability efforts that much more effective for the future as your company continues down the path of hypergrowth and hyperscale.