While setting up cloud applications, it is essential to make sure that the system remains available all the time, along with being highly performant, fault-tolerant, and, most importantly, scalable. These also need to have the ability to remain unaffected or quickly recover from any disaster. Here, in this article, we discuss the strategies and methods to follow while designing highly scalable database architecture.
Looking at the traditional way of on-premise web application development, many constraints are associated with the following:
- Provisioning of infrastructure
- Restricted access to the development teams
- Limited support from the operations team
- Scaling the resources based on traffic spikes
- Continuous infrastructure maintenance and so on.
With all these needs, development remains mostly dependent on infrastructure procurement as well as available technology. Usually, the delivery of the needed business features gets delayed, and the functionalities become outdated by the time they get actualized.
Generally, database systems are categorized into two as:
- RDBMS (relational database management systems), and
- Non-relational database management systems (NoSQL)
Now there is a new category also getting added to this as NewSQL. Based on various uses cases, enterprises can adopt any of these for their database system architecture. The NoSQL DBs may build databases as key-value stores or document stores which follow a flexible schema that will change over time compared to the RDBMS systems, which have more rigid schemas. The NoSQL data stores lately gained a lot of acceptance globally as they can very effectively scale horizontally to meet the high-performance needs of big data stores and more.
Let us further explore some design principles to build a highly available and scalable architecture. We will also discuss how the cloud infrastructure will play a vital role in supporting modern-day enterprises’ fluctuating workloads.
In any on-premises database environment, scaling is a big challenge as always. The right estimation of incoming traffic at various times is merely impossible, and configuring the hardware resources to meet the spikes is very tough. In most cases, you may also have to get through various layers of approval to make hardware purchases and support it with proper documentation and solid reasoning to support any infrastructure purchase. But, in the case of adopting a cloud environment, you may avoid this tedious process. You can instantly benefit from it while architecting a data-intensive and scalable application. As RemoteDBA.com suggests, Planning for cloud-native enterprise applications will let you dynamically scale the workload based on the fluctuating traffic and every changing performance needs.
Horizontal vs. Vertical Database Scaling
The biggest challenge while designing any application which is data-intensive is its ability to scale up by meeting all SLAs (service level agreements) in the high-load data scenarios. The database calls are usually costly, and the number of trips to databases to cater to the user requests also plays a crucial in terms of the application performance. The database’s ability to scale up or down dynamically based on the given workload is the major strength of any cloud architecture. It will also make sure the optimum usage of the available resources can help control the expenditure better.
Vertical scaling of your database is done by allocating the additional resources, which help you boost the performance and process more transactions in less time. When you tend to bump into different scenarios in which the database cannot handle the spike in user requests, you may vertically scale the database for larger instance handling and ensure superior performance.
It is very easy to implement vertical scaling. In the cloud-based environment, this is a straightforward approach to changing the database instance size based on the requirement as you host the infrastructure. From the application point of view, this will require only minimal code alterations and can be implemented quickly.
It is also recommended to run some performance tests also for the applications to find out optimal instance sizes for the database, which many matches with the performance SLAs as defined by the business. You may also keep in mind that each time when you decide to increase the size of your database instances, there may be add-on resource costs.
Suppose your objective is to handle an increased number of user requests and thereby process an increased beyond the capabilities of a single DB instance. In that case, you may benefit from scaling out the database instances with horizontal scaling. This can be done in several different possible ways as:
- Add the read replicas if your workload is more read-heavy.
- Implement reading from the cache before exploring the DB to reduce load.
- Sharding the DB on multiple servers to speed up both read and write.
You will be more flexible on the cloud to scale out the database based on the given load. You may not have the overhead of any large database instance if the load decreases. You can also scale in and out anytime based on load. There could be a minimal cost associated with this auto-scaling approach, but this may remain directly proportional to the workload and traffic to give you the best ROI.
While you are trying for rightsizing database instances, you may run some load tests to assess the performance and conclude the optimal matrices in desired read-write performance as defined by SLAs. A horizontal scaling approach will also help you to scale dynamically based on the load spikes and help you be a powerful custodian to control it from the cost perspective. If the workload is increasing, scaling vertically indeed makes sense. So, overall, if your workload is fluctuating and spiky, then horizontal scaling may be the right approach.
By concluding this, we can say that scalability is a crucial trait when designing cloud-native enterprise applications. Given all the technological advancements, the business systems need to handle the workload fluctuations without compromising the performance. No matter how good your applications are with functional features, there is no point if your database is not scalable. Ensure your databases’ dynamic scaling to process the load within the SLAs as defined by the business with an appropriate scaling approach.