ComStar: Compression-Aware Stream Query for Heterogeneous Hybrid Architecture

Abstract

The exponential increase of stream data in the Big Data era poses critical challenges for SQL queries on compressed streams. These challenges are exacerbated by diverse computational demands and varying application scenarios in stream processing, which lead to increased hardware requirements. Hybrid computing architectures provide a transformative solution in this context by integrating heterogeneous processing units, such as discrete GPUs, CPU-GPU integrated architectures, and edge computing devices to enhance performance. In this paper, we introduce ComStar, a novel compression-aware stream SQL query system that leverages the capabilities of hybrid computing architectures to execute direct queries on compressed stream data without decompression, greatly improving query performance. ComStar incorporates nine lightweight compression algorithms and features an adaptive compression algorithm selector, which optimally chooses the appropriate algorithm based on data characteristics and network conditions. Additionally, ComStar implements a hierarchical multi-tier execution to select the optimal architecture and specific devices for compressed stream SQL queries, enabling fine-grained and efficient execution across the hybrid architecture. Our experiments demonstrate that ComStar achieves an average throughput improvement of 75.6% under 100 Mbps network conditions, leveraging its unique compression-aware query capabilities to outperform contemporary solutions. At a higher network speed of 1 Gbps, ComStar improves throughput by an average of 47.4%. Additionally, ComStar achieves a 28.6% improvement in the throughput/price ratio compared to traditional methods, and a 71.4% enhancement in the throughput/power ratio. Furthermore, the ComStar’s adaptive compression algorithm selector achieves 95.6% accuracy. These results underscore the effectiveness of our system in addressing the challenges posed by the increasing volume of stream data.