SnappyData:一个用于事务、分析和流媒体的混合系统:演示

Jags Ramnarayan, Sudhir Menon, S. Wale, Hemant Bhanawat
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引用次数: 7

摘要

越来越多的应用程序依赖于工作流,这些工作流涉及(1)连续流处理,(2)事务性和重写工作负载,以及(3)交互式SQL分析。这些应用程序需要使用高速流来触发实时警报,将它们摄取到写入优化的存储中,并执行olap风格的分析,以快速获得深入的见解。因此,对混合工作负载的需求导致了几种复合数据体系结构,例如“lambda”体系结构,需要将多个系统拼接在一起——这是一项困难、耗时且昂贵的工作。相反,我们的系统SnappyData通过以下方式实现了这一承诺:(i)在一个统一的系统中实现流,交易和交互式分析-而不是拼接不同的解决方案-以及(ii)通过利用大量概要和完整数据集的最先进的近似查询引擎提供真正的交互速度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
SnappyData: a hybrid system for transactions, analytics, and streaming: demo
An increasing number of applications rely on workflows that involve (1) continuous stream processing, (2) transactional and write-heavy workloads, and (3) interactive SQL analytics. These applications need to consume high-velocity streams to trigger real-time alerts, ingest them into a write-optimized store, and perform OLAP-style analytics to derive deep insight quickly. Consequently, the demand for mixed workloads has resulted in several composite data architectures, exemplified in the "lambda" architecture, requiring multiple systems to be stitched together---an exercise that can be hard, time consuming and expensive. Instead, our system, SnappyData, fulfills this promise by (i) enabling streaming, transactions and interactive analytics in a single unifying system---rather than stitching different solutions---and (ii) delivering true interactive speeds via a state-of-the-art approximate query engine that leverages a multitude of synopses as well as the full dataset.
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