As we know, although Kubernetes (K8s) can be used to build and deploy various applications, including databases, there are still some challenges and limitations related to databases that cannot be fully addressed. Here are some of these issues:
1. Data Consistency: Kubernetes does not directly provide guarantees for data consistency in databases, while databases typically require integrity and consistency of data. This may rely on mechanisms such as database replication, sharding, and transactions to ensure data consistency, rather than solely relying on Kubernetes.
2. Data Persistence and Performance: While Kubernetes provides solutions for persistent storage, such as Persistent Volumes (PVs), it may still not fully meet the requirements of high-performance databases, such as large-scale data processing or high-concurrency workloads in relational databases.
3. Distributed Deployment and Maintenance of Databases: Distributed databases often require data synchronization and load balancing among different nodes. Kubernetes can provide partial support, such as StatefulSets for managing stateful applications, but the distributed nature and complexity of databases may require additional configuration and management to ensure data consistency and high availability.
4. Database Scaling and Fault Recovery: In large-scale applications, databases may need horizontal scaling and fault recovery capabilities. While Kubernetes can dynamically scale and manage containers, scaling and fault recovery at the database level may require more complex configuration and strategies, depending on the specific database engine and architecture.
5. Database Backup and Recovery: Backup and recovery are critical tasks in database management. Although Kubernetes provides snapshot and backup functionality, the backup and recovery process for databases may require additional considerations, such as handling transaction logs and ensuring data consistency.
While Kubernetes provides some convenience and abstraction layers for building databases, the above issues indicate that specialized database architectures and solutions are still required for specific database requirements and scenarios to meet the needs of data consistency, persistence, performance, and scalability.
I have been downloaded kubeblocks and running it on local k8s, AWS EKS, GCP GKE, it can create many kind of databases very quickly. Also, kubeblocks includes day2 operations keep me free from tons of DBA jobs.
I will keep trying kubeblocks. When the day it meet the needs of data consistency, persistence, performance, and scalability, go production and take a good sleep.