Existing disaggregated databases separate execution and storage layers, enabling independent and elastic scaling of resources. In most cases, this design makes transaction concurrency control (CC) a critical bottleneck, which demands significant computing resources for concurrent conflict management and struggles to scale due to the coordination overhead for concurrent conflict resolution. Coupling CC with execution or storage limits performance and elasticity, as CC's resource needs do not align with the free scaling of the transaction execution layer or the storage-bound data layer. This paper proposes Concurrency Control as a Service (CCaaS), which decouples CC from databases, building an execution-CC-storage three-layer decoupled database, allowing independent scaling and upgrades for improved elasticity, resource utilization, and development agility. However, adding a new layer increases latency due to the shift in communication from hardware to network. To address this, we propose a Sharded Multi-Write OCC (SM-OCC) algorithm with an asynchronous log push-down mechanism to minimize network communications overhead and transaction latency. Additionally, we implement a multi-write architecture with a deterministic conflict resolution method to reduce coordination overhead in the CC layer, thereby improving scalability. CCaaS is designed to be connected by a variety of execution and storage engines. Existing disaggregated databases can be revolutionized with CCaaS to achieve high elasticity, scalability, and high performance. Results show that CCaaS achieves 1.02–3.11X higher throughput and 1.11–2.75X lower latency than SoTA disaggregated databases.

Cloud-native databases have been widely deployed due to high elasticity, high availability and low cost. However, most existing cloud-native databases do not support multiple writers and thus have limitations on write throughput and scalability. To alleviate this limitation, there is a need for multi-primary databases which provide high write throughput and high scalability. In this paper, we present a cloud-native multi-primary database, GaussDB, which adopts a three layer (compute-memory-storage) disaggregation framework, where the compute layer is in charge of transaction processing, the memory layer is responsible for global buffer management and global lock management, and the storage layer is used for page and log persistence. To provide multi-primary capabilities, GaussDB logically partitions the pages to different compute nodes and then assigns the ownership of each page to a compute node. For each transaction posed to a compute node, if the compute node owns all relevant pages of this query, the compute node can process the query locally; otherwise, GaussDB transfers the ownership of relevant pages to this node. To capture data affinity and reduce page transmission costs, GaussDB designs a novel page placement and query routing method. To improve recovery performance, GaussDB employs a two-tier (memory-storage) checkpoint recovery method which uses memory checkpoints combined with on-demand page recovery to significantly improve recovery performance. We have implemented and deployed GaussDB internally at Huawei and with customers, and the results show that GaussDB achieves higher throughput, lower latency, and faster recovery than state-of-the-art baselines.

Multinational enterprises conduct global business that has a demand for geo-distributed transactional databases. Existing state-of-the-art databases adopt a sharded master-follower replication architecture. However, the single-master serving mode incurs massive cross-region writes from clients, and the sharded architecture requires multiple round-trip acknowledgments (e.g., 2PC) to ensure atomicity for cross-shard transactions. These limitations drive us to seek yet another design choice. In this paper, we propose a strongly consistent OLTP database GeoGauss with full replica multi-master architecture. To efficiently merge the updates from different master nodes, we propose a multi-master OCC that unifies data replication and concurrent transaction processing. By leveraging an epoch-based delta state merge rule and the optimistic asynchronous execution, GeoGauss ensures strong consistency with light-coordinated protocol and allows more concurrency with weak isolation, which are sufficient to meet our needs. Our geo-distributed experimental results show that GeoGauss achieves 7.06X higher throughput and 17.41X lower latency than the state-of-the-art geo-distributed database CockroachDB on the TPC-C benchmark.

Huawei is a leading global provider of information and communication technologies (ICT) infrastructure and smart devices. With integrated solutions across four key domains: telecommunication networks, IT, smart devices, and cloud services, Huawei is committed to bringing digital transformation to every person, home and organization for a fully connected and intelligent world. Founded in 1987, Huawei currently has more than 180,000 employees, and operates in more than 170 countries and regions with revenue over 100 billion USD in 2018. Data management plays a key role in all of the four key domains above. We have developed innovative products and solutions to support rapid business growth driven by customer requirements. While many data management problems are common, each domain also has its own special requirements and challenges. In this paper, we will go through recent advancements in Huawei data management technologies including a petabyte scale enterprise analytics platform (FusionInsight MPPDB) and a highly available in-memory database for telecommunication networks (GMDB). In addition, we discuss data management challenges that we are facing in the areas of autonomous databases and device-edge-cloud collaboration data platforms.