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.

The serverless database has recently attracted increasing attention both in industry and academia due to its high elasticity and the “pay-as-you-go” model. This paper delivers a thorough review of current shared-storage-based commercial serverless databases, pinpointing two major challenges: (1) they either experience difficulties with instance migration during scaling up or restrict the resource usage within a single physical host to avoid potential migration. (2) they lack the ability to scale out secondary nodes due to the absence of strong consistency support in secondary nodes. Based on our experience in building serverless databases, this paper proposes two fundamental requirements to address these two issues: seamless and instant migration and read scale-out. The former allows for instance migration when there are insufficient resources on the resident host during scaling up without application disruption, whereas the latter necessitates strong consistency on secondary nodes to process read requests. To fulfill these fundamental requirements, we propose PolarDB Serverless, a shared-storage-based serverless database achieving seamless scale-up and read scale-out. It supports read scale-out by inheriting the strong consistency feature from PolarDB, making it possible to process strongly consistent reads on secondary nodes. In the pursuit of achieving seamless migration, PolarDB Serverless introduces a transaction migration policy. It ensures there is no interruption to the application during migrations, allowing transactions to continue on the new instance without any disruptions. It also minimizes the overhead of migration, achieving a fast migration. In our evaluation, especially in the context of database migration scenarios, it's noteworthy that the migration of a database instance takes just half a second without causing any exceptions for applications. PolarDB Serverless is the first shared-storage-based serverless database supporting both seamless scale-up and read scale-out and is already commercially available at Alibaba Cloud.

Transaction performance in geo-replicated databases heavily relies on the request location: when not issued by the primary region, transactions are forced to involve costly wide-area communication. While existing systems distribute primary roles across regions, such assignment typically occurs at the shard level, making it difficult to align with geographically dispersed access to individual records. This paper introduces PolyBase, a pioneering architecture to address such misalignment, leveraging the widely adopted Paxos-based log replication mechanisms. It enables flexible row-level consensus group affiliation , which runs on an unchanged Paxos protocol , but dynamically re-assigns database rows between Paxos log replication groups, whose leaders become the primary region, enjoying faster writes and up-to-date versions for reads. With carefully designed data structures and protocols, PolyBase significantly reduces wide-area RTTs without compromising transaction or log replication consistency or reliability guarantees. We implemented PolyBase with optimized re-assignment policies and integrated it into two popular databases (RocksDB and MySQL). Our evaluation on AWS, using a production e-commerce workload and microbench-marks confirms that PolyBase offers significantly higher transaction throughput and lower average/tail latency compared to baselines.

A classic design of cloud-native databases adopts an architecture that consists of one read/write (RW) node and one or more read-only (RO) nodes. In such a design, the propagation of write-ahead logs (WALs) from the RW node to the RO node(s) is typically performed asynchronously. Consequently, system designers either have to accept a loose consistency guarantee, where a read from the RO node may return stale data, or tolerate significant performance degradation in terms of read latency, as it then needs to wait for the log to be propagated and applied. Most commercial cloud-native databases, such as Amazon Aurora, choose performance over strong consistency. As a result, it makes RO nodes useless for many applications requiring read-after-write consistency (a form of strong consistency), and the support for serverless databases (i.e., allowing the RO nodes to be scaled out automatically) is impossible as they require a single endpoint. This paper proposes PolarDB-SCC (PolarDB-Strongly Consistent Cluster), a cloud-native database architecture that guarantees strongly consistent reads with very low latency. The core idea is to eliminate unnecessary waits and reduce the necessary wait time on RO nodes while still supporting strong consistency. To achieve this, it tracks the RW node's modification timestamp at three progressively finer-grained levels. We further design a Linear Lamport timestamp to reduce the RO node's timestamp fetching operations and leverage the RDMA network for all the data transferring ( e.g. , timestamp fetching and log shipment) to minimize network overhead and extra CPU usage. Our evaluation shows that PolarDB-SCC does not incur any noticeable overhead for ensuring strongly consistent reads compared with the eventually consistent (stale) read policy. To the best of our knowledge, PolarDB-SCC is the first "read-write splitting" cloud-native database that supports strongly consistent read with negligible overhead. Compared with a straightforward read-wait design, PolarDB-SCC improves throughput by up to 4.51× and reduces median latency by up to 3.66× in SysBench's read-write workload. PolarDB-SCC is already commercially available at Alibaba Cloud.

Traffic simulators are important tools for tasks such as urban planning and transportation management. Microscopic simulators allow per-vehicle movement simulation, but require longer simulation time. The simulation overhead is exacerbated when there is traffic congestion and most vehicles move slowly. This in particular hurts the productivity of emerging urban computing studies based on reinforcement learning, where traffic simulations are heavily and repeatedly used for designing policies to optimize traffic related tasks.