In this talk we will focus on how Tachyon can help improve big data analytics (ad-hoc query) efficiency within Baidu.
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During the past several years, Spark has significantly changed the landscape of big data computing. It improves performance of various applications dramatically. However, in certain Spark use cases, the bottleneck is in the I/O stack. In this talk, we will introduce Tachyon, a distributed memory-centric storage system. In addition, we will talk about several production use cases where Tachyon further improves Spark applications’ performance by orders of magnitude.
In the presentation, we will explore several potential industry use cases enabled by the new features. One-click cluster deployment enables users to experiment and prototype with Tachyon on AWS, launching not only Tachyon but also the computation framework and storage system of their choice. Mounting of multiple under storage systems and transparent naming enables more exciting use cases for Tachyon users.
we introduce Tachyon, a memory centric fault-tolerant distributed file system, which enables reliable file sharing at memory-speed across cluster frameworks, such as Spark and MapReduce.