Trill

TLDR

The paper introduces Trill, a new query processor for analytics, and outlines its design and architecture. Trill combines a tempo‑relational model, a high‑level language library integrated with existing fabrics, and a streaming batched‑columnar representation with dynamic compilation to deliver high throughput from real‑time to offline analytics. Experimental results show Trill achieves 2–4 orders of magnitude higher throughput than comparable streaming engines, comparable throughput to modern commercial columnar DBMS for offline queries, and has been adopted across many Microsoft usage scenarios.

Abstract

This paper introduces Trill -- a new query processor for analytics. Trill fulfills a combination of three requirements for a query processor to serve the diverse big data analytics space: (1) Query Model : Trill is based on a tempo-relational model that enables it to handle streaming and relational queries with early results, across the latency spectrum from real-time to offline; (2) Fabric and Language Integration : Trill is architected as a high-level language library that supports rich data-types and user libraries, and integrates well with existing distribution fabrics and applications; and (3) Performance : Trill's throughput is high across the latency spectrum. For streaming data, Trill's throughput is 2-4 orders of magnitude higher than comparable streaming engines. For offline relational queries, Trill's throughput is comparable to a major modern commercial columnar DBMS. Trill uses a streaming batched-columnar data representation with a new dynamic compilation-based system architecture that addresses all these requirements. In this paper, we describe Trill's new design and architecture, and report experimental results that demonstrate Trill's high performance across diverse analytics scenarios. We also describe how Trill's ability to support diverse analytics has resulted in its adoption across many usage scenarios at Microsoft.