Acyclic conjunctive queries form the backbone of most analytical workloads, and have been extensively studied in the literature from both theoretical and practical angles. However, there is still a large divide between theory and practice. While the 40-year-old Yannakakis algorithm has strong theoretical running time guarantees, it has not been adopted in real systems due to its high hidden constant factor. In this paper, we strive to close this gap by proposing Yannakakis+, an improved version of the Yannakakis algorithm, which is more practically efficient while preserving its theoretical guarantees. Our experiments demonstrate that Yannakakis+ consistently outperforms the original Yannakakis algorithm by 2x to 5x across a wide range of queries and datasets. Another nice feature of our new algorithm is that it generates a traditional DAG query plan consisting of standard relational operators, allowing Yannakakis+ to be easily plugged into any standard SQL engine. Our system prototype currently supports four different SQL engines (DuckDB, PostgreSQL, SparkSQL, and AnalyticDB from Alibaba Cloud), and our experiments show that Yannakakis+ is able to deliver better performance than their native query plans on 160 out of the 162 queries tested, with an average speedup of 2.41x and a maximum speedup of 47,059x.

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.

With data explosion in scale and variety, OLAP databases play an increasingly important role in serving real-time analysis with low latency (e.g., hundreds of milliseconds), especially when incoming queries are complex and ad hoc in nature. Moreover, these systems are expected to provide high query concurrency and write throughput, and support queries over structured and complex data types (e.g., JSON, vector and texts). In this paper, we introduce AnalyticDB, a real-time OLAP database system developed at Alibaba. AnalyticDB maintains all-column indexes in an asynchronous manner with acceptable overhead, which provides low latency for complex ad-hoc queries. Its storage engine extends hybrid row-column layout for fast retrieval of both structured data and data of complex types. To handle large-scale data with high query concurrency and write throughput, AnalyticDB decouples read and write access paths. To further reduce query latency, novel storage-aware SQL optimizer and execution engine are developed to fully utilize the advantages of the underlying storage and indexes. AnalyticDB has been successfully deployed on Alibaba Cloud to serve numerous customers (both large and small). It is capable of holding 100 trillion rows of records, i.e., 10PB+ in size. At the same time, it is able to serve 10m+ writes and 100k+ queries per second, while completing complex queries within hundreds of milliseconds.