Cloud-native databases, with advantages such as out-of-the-box functionality, elastic scalability, and pay-as-you-go, are currently a research hotspot in academia and industry. Currently, cloud-native databases only support “single writer and multiple readers”, that is, read-write transactions are concentrated on a single read-write node, and read-only transactions are distributed to multiple read-only nodes. This limitation restricts the system’s ability to process read-write transactions, making it difficult to meet the demands of write-intensive businesses. To this end, this study proposes the D3C (deterministic concurrency control cloud-native database) architecture. It breaks through the limitation of “single writer and multiple readers” and supports concurrency execution of read-write transactions on multiple read-write nodes by designing a cloud-native database transaction processing mechanism based on deterministic concurrency control. D3C splits transactions into sub-transactions and independently executes them on each node according to a predefined global order, ensuring serializability for transaction execution on multiple read-write nodes. Additionally, this study introduces mechanisms like asynchronous batch data persistence mechanisms based on multi-version to ensure transaction processing performance and proposes a consistency point-based fault recovery mechanism to achieve high availability. Experimental results show that D3C can achieve 5.1 times the performance of the “single writer and multiple readers” architecture in write-intensive scenarios while meeting the key requirements of cloud-native databases.