In the past decade, cloud databases have experienced rapid development and growth. PolarDB, Alibaba's cloud-native OLTP database, has evolved significantly to meet the increasing demand for cloud-native architectures and now serves hundreds of thousands of customers across various industries. This paper presents PolarDB's evolution over the past eight years, with a focus on scalability, performance, and cost-efficiency. Initially, PolarDB adopted a primary-replica architecture based on disaggregated storage, with an emphasis on enhancing single-node performance for scale-up in modern many-core systems. To achieve this, we co-designed PolarDB with cutting-edge hardware, including RDMA, to improve performance. Meanwhile, we refined the internal architecture, including improvements to B+ tree concurrency control and transaction management, ensuring high scalability in scale-up scenarios. More recently, our focus has shifted to scaling out PolarDB to meet the performance and scalability needs of ultra-large-scale applications. By leveraging RDMA, we optimized distributed transaction processing, transforming PolarDB into a high-performance, high-scalability and cost-effective distributed database. In the TPC-C benchmark, PolarDB scaled out to 2340 nodes and achieved over 2 billion tpmC, with a jitter rate of no more than 0.16% during the 8-hour stress test. Compared to the second- and third-highest-performing databases in public TPC-C results, PolarDB's tpmC is 2.52× and 2.91× higher, respectively. In terms of cost-effectiveness, PolarDB's per-tpmC cost is 37% and 79.5% lower than that of the other two systems, respectively.

There has been an increasing interest in building cloud-native databases that decouple computation and storage for elasticity. A cloud-native database often adopts a cloud storage underneath its storage engine, leveraging another layer of virtualization and providing a high-performance and elastic storage service without exposing complex storage details. It helps reduce the maintenance cost and expedite development cycles for the database kernels. We have observed that there are significant differences between the local and the cloud storage that invalid many designs inside existing databases when they are ported to the cloud storage. In this paper, we analyze the challenges and opportunities of both B-tree and LSM-tree-based storage engines when they are deployed on a cloud storage. We propose an optimization framework that guides database developers to transform on-premise databases into their cloud-native counterparts. We use a B+-tree-based InnoDB as a demonstration vehicle where we have implemented a suite of optimizations using the proposed framework and extend such efforts to the LSM-tree-based RocksDB. On both engines, our evaluations show significant performance improvements on the cloud storage.

\beginabstract The trend in the DBMS market is to migrate to the cloud for elasticity, high availability, and lower costs. The traditional, monolithic database architecture is difficult to meet these requirements. With the development of high-speed network and new memory technologies, disaggregated data center has become a reality: it decouples various components from monolithic servers into separated resource pools (e.g., compute, memory, and storage) and connects them through a high-speed network. The next generation cloud native databases should be designed for disaggregated data centers. In this paper, we describe the novel architecture of \name, which follows thedisaggregation design paradigm: the CPU resource on compute nodes is decoupled from remote memory pool and storage pool. Each resource pool grows or shrinks independently, providing \revon-demand provisoning at multiple dimensions while improving reliability. We also design our system to mitigate the inherent penalty brought by resource disaggregation, and introduce optimizations such as optimistic locking and index awared prefetching. Compared to the architecture that uses local resources, \name achieves better dynamic resource provisioning capabilities and 5.3 times faster failure recovery speed, while achieving comparable performance. \endabstract

Presents an efficient method for imaging multitargets with inverse synthetic aperture radar (ISAR) based on the time-frequency distribution. We first estimate the Doppler frequency of each target by evaluating the time-frequency distribution of the targets' echo signals, then perform the Doppler compensation and image motion compensation separately for each target. The exponential distribution is utilized to detect the Doppler frequency of each target. The proposed approach is proved to be reliable by experiments and computer simulations.>