Evolving Databases for New-Gen Big Data Applications
Evolving Databases for New-Gen Big Data Applications
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发表时间：
2017
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通讯作者：
Ronald Barber;Christian Garcia-Arellano;Ronen Grosman;René Müller;Vijayshankar Raman;Richard Sidle;Matt Spilchen;Adam J. Storm;Yuanyuan Tian;Pınar Tözün;D. Zilio;Matthew Huras;G. Lohman;C. Mohan;Fatma Özcan;H. Pirahesh
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作者：
Ronald Barber;Christian Garcia-Arellano;Ronen Grosman;René Müller;Vijayshankar Raman;Richard Sidle;Matt Spilchen;Adam J. Storm;Yuanyuan Tian;Pınar Tözün;D. Zilio;Matthew Huras;G. Lohman;C. Mohan;Fatma Özcan;H. Pirahesh
大规模实时分析应用程序（实时库存/定价、为您提供建议的移动的应用程序、欺诈检测、风险分析等）的日益普及强调需要能够同时处理快速事务和分析的分布式数据管理系统。然而，事务和分析请求的有效处理需要系统中不同的优化和架构决策。本文介绍了Wild Fire系统，它的目标是混合传输和分析处理（HTAP）。Wild Fire利用Spark生态系统实现大规模数据处理，处理不同类型的复杂分析请求，并进行列式数据处理，同时实现快速交易和分析。
The rising popularity of large-scale real-time analytics applications (real-time inventory/pricing, mobile apps that give you suggestions, fraud detection, risk analysis, etc.) emphasize the need for distributed data management systems that can handle fast transactions and analytics concurrently. Ef-ficient processing of transactional and analytical requests, however, require different optimizations and architectural decisions in a system. This paper presents the Wildfire system, which targets Hybrid Transactional and Analytical Processing (HTAP). Wildfire leverages the Spark ecosystem to enable large-scale data processing with different types of complex analytical requests, and columnar data processing to enable fast transactions and analytics concurrently.