The surge in data analytics has fostered burgeoning demand for AnalyticDB on Alibaba Cloud, which has well served thousands of customers from various business sectors. The most notable feature is the diversity of the workloads it handles, including batch processing, real-time data analytics, and unstructured data analytics. To improve the overall performance for such diverse workloads, one of the major challenges is to optimize long-running complex queries without sacrificing the processing efficiency of short-running interactive queries. While existing methods attempt to utilize runtime dynamic statistics for adaptive query processing, they often focus on specific scenarios instead of providing a holistic solution. To address this challenge, we propose a new framework called Anser , which enhances the design of traditional distributed data warehouses by embedding a new information sharing mechanism. This allows for the efficient management of the production and consumption of various dynamic information across the system. Building on top of Anser , we introduce a novel scheduling policy that optimizes both data and information exchanges within the physical plan, enabling the acceleration of complex analytical queries without sacrificing the performance of short-running interactive queries. We conduct comprehensive experiments over public and in-house workloads to demonstrate the effectiveness and efficiency of our proposed information sharing framework.

The explosion of time series advances the development of time series databases. To reduce storage overhead in these systems, data compression is widely adopted. Most existing compression algorithms utilize the overall characteristics of the entire time series to achieve high compression ratio, but ignore local contexts around individual points. In this way, they are effective for certain data patterns, and may suffer inherent pattern changes in real-world time series. It is therefore strongly desired to have a compression method that can always achieve high compression ratio in the existence of pattern diversity. In this paper, we propose a two-level compression model that selects a proper compression scheme for each individual point, so that diverse patterns can be captured at a fine granularity. Based on this model, we design and implement AMMMO framework, where a set of control parameters is defined to distill and categorize data patterns. At the top level, we evaluate each sub-sequence to fill in these parameters, generating a set of compression scheme candidates (i.e., major mode selection). At the bottom level, we choose the best scheme from these candidates for each data point respectively (i.e., sub-mode selection). To effectively handle diverse data patterns, we introduce a reinforcement learning based approach to learn parameter values automatically. Our experimental evaluation shows that our approach improves compression ratio by up to 120% (with an average of 50%), compared to other time-series compression methods.

Summary: AC-DIAMOND (v1) is a DNA-protein alignment tool designed to tackle the efficiency challenge of aligning large amount of reads or contigs to protein databases. When compared with the previously most efficient method DIAMOND, AC-DIAMOND gains a 6- to 7-fold speed-up, while retaining a similar degree of sensitivity. The improvement is rooted at two aspects: first, using a compressed index of seeds with adaptive-length to speed-up the matching between query and reference sequences; second, adopting a compact form of dynamic programing to fully utilize the parallelism of the SIMD capability. Availability and implementation: Software source codes and binaries available at https://github.com/Maihj/AC-DIAMOND/. Supplementary information: Supplementary data are available at Bioinformatics online.