TiLT: A Time-Centric Approach for Stream Query Optimization and Parallelization

TL;DR

TiLT introduces a new temporal query language and compiler backend for stream processing engines, significantly improving throughput and efficiency in real-world streaming analytics applications.

Contribution

The paper presents TiLT, a novel intermediate representation and compiler for stream query optimization and parallelization, addressing limitations of current SPEs.

Findings

Achieves up to 326x higher throughput than state-of-the-art SPEs

Demonstrates effectiveness across eight real-world applications

Provides a highly expressive temporal query language

Abstract

Stream processing engines (SPEs) are widely used for large scale streaming analytics over unbounded time-ordered data streams. Modern day streaming analytics applications exhibit diverse compute characteristics and demand strict latency and throughput requirements. Over the years, there has been significant attention in building hardware-efficient stream processing engines (SPEs) that support several query optimization, parallelization, and execution strategies to meet the performance requirements of large scale streaming analytics applications. However, in this work, we observe that these strategies often fail to generalize well on many real-world streaming analytics applications due to several inherent design limitations of current SPEs. We further argue that these limitations stem from the shortcomings of the fundamental design choices and the query representation model followed in modern SPEs. To address these challenges, we first propose TiLT, a novel intermediate representation (IR) that offers a highly expressive temporal query language amenable to effective query optimization and parallelization strategies. We subsequently build a compiler backend for TiLT that applies such optimizations on streaming queries and generates hardware-efficient code to achieve high performance on multi-core stream query executions. We demonstrate that TiLT achieves up to 326x (20.49x on average) higher throughput compared to state-of-the-art SPEs (e.g., Trill) across eight real-world streaming analytics applications. TiLT source code is available at https://github.com/ampersand-projects/tilt.git.