TQEx: Tensor-based Query Engine Enhanced by Bridging the Gap

With the development of AI and the growing demand for computational power, hardware is becoming increasingly specialized and heterogeneous. The emergence of diverse specialized hardware architectures, each with distinct characteristics and programming abstractions, poses significant portability and sustainability challenges for existing data processing systems. Tensor Computation Runtimes (TCRs) abstract away the low-level hardware complexities by providing users with a hardware-independent tensor-based interface, enabling data scientists to effectively leverage the powerful capabilities of new hardware accelerators (collectively referred to as XPU). Built on TCRs, the existing relational query engine TQP demonstrates portability across a wide range of target hardware and sustainability along with the ongoing evolution of TCRs and hardware. However, it neglects the big gap between irregular SQL workloads and uniform tensor operations when mapping SQL operators to tensor programs, which causes significant storage and computation overhead. In this paper, for the first time, we analyze the underlying gap between SQL and tensors, and provide guidelines to bridge it. Following these guidelines, we build a new Tensor-based Query Engine Enhanced (TQEx) by bridging the gap from multiple aspects: develop efficient storage and computation strategies for variable-length data, and design efficient SQL operators such as join and aggregate based on tensors. We also extend TQEx to multi-XPUs for large-scale data processing. Extensive experimental studies show that our query engine, TQEx, achieves a 9.6× speedup (with a peak of 41.9×) over TQP on TPC-H, and it is also 27.9× faster than leading GPU databases such as HeavyDB. On TPC-H at scale factor 100, TQEx outperforms DuckDB by 12.2× and HeavyDB by 22.7× on supported queries.