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.

The past decade has witnessed the rapid development of cloud computing and data-centric applications. While these innovations offer numerous attractive features for data processing, they also bring in new issues about the loss of data ownership. Though some encrypted databases have emerged recently, they can not fully address these concerns for the data owner. In this paper, we propose an ownership-preserving database (OPDB), a new paradigm that characterizes different roles' responsibilities from nowadays applications and preserves data ownership throughout the entire application. We build Operon to follow the OPDB paradigm, which utilizes the trusted execution environment (TEE) and introduces a behavior control list (BCL). Different from access controls that merely handle accessibility permissions, BCL further makes data operation behaviors under control. Besides, we make Operon practical for real-world applications, by extending database capabilities towards flexibility, functionality and ease of use. Operon is the first database framework with which the data owner exclusively controls its data across different roles' subsystems. We have successfully integrated Operon with different TEEs, i.e. , Intel SGX and an FPGA-based implementation, and various database services on Alibaba Cloud, i.e. , PolarDB and RDS PostgreSQL. The evaluation shows that Operon achieves 71% - 97% of the performance of plaintext databases under the TPC-C benchmark while preserving the data ownership.

\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

The recent byte-addressable and large-capacity commercialized persistent memory (PM) is promising to drive database as a service (DBaaS) into unchartered territories. This paper investigates how to leverage PMs to revisit the conventional LSM-tree based OLTP storage engines designed for DRAM-SSD hierarchy for DBaaS instances. Specifically we (1) propose a light-weight PM allocator named Hal-loc customized for LSM-tree, (2) build a high-performance Semi-persistent Memtable utilizing the persistent in-memory writes of PM, (3) design a concurrent commit algorithm named Reorder Ring to aschieve log-free transaction processing for OLTP workloads and (4) present a Global Index as the new globally sorted persistent level with non-blocking in-memory compaction. The design of Reorder Ring and Semi-persistent Memtable achieves fast writes without synchronized logging overheads and achieves near instant recovery time. Moreover, the design of Semi-persistent Memtable and Global Index with in-memory compaction enables the byte-addressable persistent levels in PM, which significantly reduces the read and write amplification as well as the background compaction overheads. The overall evaluation shows that the performance of our proposal over PM-SSD hierarchy outperforms the baseline by up to 3.8x in YCSB benchmark and by 2x in TPC-C benchmark.

Die-stacked High Bandwidth Memory (HBM) is an emerging memory architecture that achieves much higher memory bandwidth with similar or lower memory access latency and smaller capacity, compared with main memories. Memory-intensive database algorithms may potentially benefit from these new features. Due to the small capacity of such die-stacked HBM, a hybrid memory architecture comprising both main memories and HBMs is promising for main-memory databases. As a starting point, we study a key data structure, hash tables, in such a hybrid memory architecture. In a large hash table distributed among multiple NUMA (non-uniform memory accesses) nodes and accessed by multiple CPU sockets, the data placement and memory access scheduling for workload balance are challenging due to the random memory accesses involved that are difficult to predict. In this work, we propose a deployment algorithm that first estimates the memory access cost and then places data in a way that exploits the hybrid memory architecture in a balanced manner. Evaluation results show that the proposed deployment is able to achieve up to three times performance improvement over the state-of-the-art NUMA-aware scheduling algorithms for hash joins in relational databases on present and simulated future hybrid memory architectures.

Alibaba runs the largest e-commerce platform in the world serving more than 600 million customers, with a GMV (gross merchandise value) exceeding USD 768 billion in FY2018. Online e-commerce transactions have three notable characteristics: (1) drastic increase of transactions per second with the kickoff of major sales and promotion events, (2) a large number of hot records that can easily overwhelm system buffers, and (3) quick shift of the "temperature'' (hot v.s. warm v.s. cold) of different records due to the availability of promotions on different categories over different short time periods. For example, Alibaba's OLTP database clusters experienced a 122 times increase of transactions on the start of the Singles' Day Global Shopping Festival in 2018, processing up to 491,000 sales transactions per second which translate to more than 70 million database transactions per second. To address these challenges, we introduce X-Engine, a write-optimized storage engine of POLARDB built at Alibaba, which utilizes a tiered storage architecture with the LSM-tree (log-structured merge tree) to leverage hardware acceleration such as FPGA-accelerated compactions, and a suite of optimizations including asynchronous writes in transactions, multi-staged pipelines and incremental cache replacement during compactions. Evaluation results show that X-Engine has outperformed other storage engines under such transactional workloads.

Advanced processor architectures have been driving new designs, implementations and optimizations of main-memory hash join algorithms recently. The newly released Intel Xeon Phi many-core processor of the Knights Landing architecture (KNL) embraces interesting hardware features such as many low-frequency out-of-order cores connected on a 2D mesh, and high-bandwidth multi-channel memory (MCDRAM). In this paper, we experimentally revisit the state-of-the-art main-memory hash join algorithms to study how the new hardware features of KNL affect the algorithmic design and tuning as well as to identify the opportunities for further performance improvement on KNL. Our experiments show that, although many existing optimizations are still valid on KNL with proper tuning, even the state-of-the-art algorithms have severely underutilized the memory bandwidth and other hardware resources.

Recently, Intel Xeon Phi is emerging as a many-core processor with up to 61 x86 cores. In this demonstration, we present PhiDB, an OLAP query processor with simultaneous multi-threading (SMT) capabilities on Xeon Phi as a case study for parallel database performance on future many-core processors. With the trend towards many-core architectures, query operator optimizations, and efficient query scheduling on such many-core architectures remain as challenging issues. This motivates us to redesign and evaluate query processors. In PhiDB, we apply Xeon Phi aware optimizations on query operators to exploit hardware features of Xeon Phi, and design a heuristic algorithm to schedule the concurrent execution of query operators for better performance, to demonstrate the performance impact of Xeon Phi aware optimizations. We have also developed a user interface for users to explore the underlying performance impacts of hardware-conscious optimizations and scheduling plans.

Energy efficiency is a major design and optimization factor for query co-processing of databases in embedded devices. Recently, GPUs of new-generation embedded devices have evolved with the programmability and computational capability for general-purpose applications. Such CPU-GPU architectures offer us opportunities to revisit GPU query co-processing in embedded environments for energy efficiency. In this paper, we experimentally evaluate and analyze the performance and energy consumption of a GPU query co-processor on such hybrid embedded architectures. Specifically, we study four major database operators as micro-benchmarks and evaluate TPC-H queries on CARMA, which has a quad-core ARM Cortex-A9 CPU and a NVIDIA Quadro 1000M GPU. We observe that the CPU delivers both better performance and lower energy consumption than the GPU for simple operators such as selection and aggregation. However, the GPU outperforms the CPU for sort and hash join in terms of both performance and energy consumption. We further show that CPU-GPU query co-processing can be an effective means of energy-efficient query co-processing in embedded systems with proper tuning and optimizations.

Modern processor technologies have driven new designs and implementations in main-memory hash joins. Recently, Intel Many Integrated Core (MIC) co-processors (commonly known as Xeon Phi) embrace emerging x86 single-chip many-core techniques. Compared with contemporary multi-core CPUs, Xeon Phi has quite different architectural features: wider SIMD instructions, many cores and hardware contexts, as well as lower-frequency in-order cores. In this paper, we experimentally revisit the state-of-the-art hash join algorithms on Xeon Phi co-processors. In particular, we study two camps of hash join algorithms: hardware-conscious ones that advocate careful tailoring of the join algorithms to underlying hardware architectures and hardware-oblivious ones that omit such careful tailoring. For each camp, we study the impact of architectural features and software optimizations on Xeon Phi in comparison with results on multi-core CPUs. Our experiments show two major findings on Xeon Phi, which are quantitatively different from those on multi-core CPUs. First, the impact of architectural features and software optimizations has quite different behavior on Xeon Phi in comparison with those on the CPU, which calls for new optimization and tuning on Xeon Phi. Second, hardware oblivious algorithms can outperform hardware conscious algorithms on a wide parameter window. These two findings further shed light on the design and implementation of query processing on new-generation single-chip many-core technologies.