This topic describes the features, architecture, benefits, and scenarios of serverless ApsaraDB RDS for MySQL instances. This topic also describes how to use the instances.
Introduction
Serverless RDS instances allow you to scale CPU and memory resources in real time, which is new to RDS instances with cloud disks. Serverless RDS instances also allow you to isolate network resources, namespaces, and storage resources. Serverless RDS instances support the pay-as-you-go billing method for computing resources and provide the following benefits: low resource usage, ease of use, flexibility, and low price. The instances can help you quickly and independently scale computing resources to adapt to fluctuating workloads, reduce costs, and improve efficiency.
The following figure shows the changes in resources that are used by a regular RDS instance and a serverless RDS instance if your workloads greatly fluctuate.
The preceding figure provides the following information:
Regular RDS instance: A large number of resources are wasted during off-peak hours. As a result, resources are insufficient during peak hours, which affects the performance of the regular RDS instance.
Serverless RDS instance:
The serverless RDS instance scales resources based on your business requirements, and a small number of resources are wasted. This improves the resource usage.
The serverless RDS instances can provide sufficient resources during peak hours, which ensures the performance and service stability.
You are charged based on the on-demand resources that are used to run your workloads. This significantly reduces costs.
No manual specification changes are required. This improves O&M efficiency and reduces costs for O&M administrator and developers.
The automatic start and stop feature is supported for serverless RDS instances. If no connections are established, the serverless RDS instance is automatically suspended to release computing resources. If a connection is established, the serverless RDS instance is automatically started. The process is imperceptible to users. For more information, see Configure the automatic start and stop feature for a serverless ApsaraDB RDS for MySQL instance.
The serverless RDS instance optimizes high-throughput write operations and high-performance batch processing operations and support auto scaling. This is suitable for scenarios in which large amounts of data and large traffic fluctuations are involved.
Architectures
Benefits
Low cost: Serverless RDS instances do not rely on other infrastructure and services and can provide stable and efficient data storage and access services. This deployment model is ideal for startups and scenarios in which you want to run workloads immediately after resources are created. You are charged only for the resources that you use based on the pay-as-you-go billing method.
Large storage capacity: You can purchase a storage capacity of up to 32 TB for a serverless RDS instance. The system automatically expands the storage capacity based on the data volume of the RDS instance. This effectively prevents your services from being adversely affected by insufficient storage.
Auto scaling of computing resources: The computing resources that are required for read and write operations can be automatically scaled without human intervention. This greatly reduces O&M costs and risks of human errors.
Fully managed and maintenance-free services: Serverless RDS instances are fully managed by Alibaba Cloud, allowing you to focus on developing your applications instead of O&M operations, such as version upgrade, system deployment, scaling, and alert handling. The O&M operations are performed in the background and are transparent at the service layer.
Billing rules
For more information, see Pricing of serverless RDS instances.
Scenarios
Scenarios that require infrequent access to underlying databases, such as databases in development and testing environments
Software as a service (SaaS) scenarios, such as website building of small and medium-sized enterprises
Individual developers
Educational scenarios, such as teaching and student experiments
Scenarios that handle inconsistent and unpredictable workloads, such as IoT and edge computing
Scenarios which require fully managed or maintenance-free services
Scenarios in which services are changing or unpredictable
Scenarios in which intermittent scheduled tasks are involved