With the increasing migration of applications from on-premise data centers to clouds, cloud databases have become a pivotal technique for cloud vendors. By leveraging modern cloud infrastructures, they provide equivalent or superior performance to traditional databases at a lower cost and higher elasticity.
Consequently, in recent years, major cloud vendors have launched their cloud-native databases, such as Amazon Aurora, Azure Hyperscale, Google Spanner, and Alibaba PolarDB.
Theoretically, a cloud database is a database service built and accessed through a cloud platform. It serves many of the same functions as a traditional database with the added flexibility of cloud computing.
A cloud database management system is a distributed database that allows us delivery of computing as service instead of product and sharing of resources as well as the software between multiple devices over a network and has a tendency to grow significantly in the future.
However, even the state-of-the-art cloud-native databases still embrace the monolithic server architecture where CPU and memory are tightly coupled. This makes it hard to fulfill web-scale applications’ ever-growing and highly elastic resource demands. This post will explore key challenges in implementing cloud databases and their success.
1. Internet Speed
Compared to the internet speed used to access the data center, the data transfer speed is relatively very fast. This hampers the cloud database’s performance. This impacts the cloud database’s performance. The speed of the internet determines how quickly queries are sent to the database and how quickly data is retrieved from the data center. Faster speed cables, which will be very expensive and defeat the purpose of having a cloud database, are the answer to this problem.
2. Query and Transactional Workloads
The query workload and the transaction workload are very dissimilar. On the other hand, we cannot estimate the time needed for the query workload, whereas we can estimate the time needed for the transactional workload. The number of queries affects query workload, and it is unknown how many users will be making queries to the database.
Although a workload and a database may need to be managed, the main question is how to get the most performance out of the available machine. In this regard, the number of machines must be reduced while maintaining efficiency. The system should understand the number of hardware resources needed for each workload. The machines where the workloads are located and the joining mechanism used may be the same.
Making virtual machines for each database and many virtual machines for numerous databases built on the same machine is the best solution. It takes more machines, say two to three, to share the same workload. Eventually, this results in a 6–10 fold decrease in performance and speed. Because each virtual machine has its operating system and database, performance is decreased. Each virtual machine has a separate buffer loop when these two crucial parts are split. Using the same database server across multiple machines is preferable because it will improve performance.
4. Elastic Scalability
A good cloud database as a service can handle any workload since we’re talking cloud databases. The problem in the cloud database is when the workload exceeds the system’s capacity. When the workload increases, the cloud database must be able to scale out on its own. The cloud database’s performance and efficiency are improved by scaling the database.
When it comes to cloud computing, privacy has been the key concern. Cloud computing is more advanced in terms of usability for users and hackers who like to break into the system. To maintain the company’s customer records, privacy in the cloud database is essential. The businesses cannot afford the data kept in their database to leak out. It is very simple to store data securely if it is encrypted in the database.