Serverless Data Analytics in the IBM Cloud
Serverless Data Analytics in the IBM Cloud
复制标题
IBM Cloud 中的无服务器数据分析
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
P. López
中科院分区：
文献类型：
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作者：
Josep Sampé;G. Vernik;Marc Sánchez Artigas;P. López
出乎意料的是，无服务器计算的兴起也间接地开始了大规模数据并行的“民主化”。PyWren所预示的这一新趋势旨在使未经培训的用户能够通过AWS Lambda等平台在云中大规模执行单机代码。受这一愿景的启发，这篇行业论文介绍了IBM-PyWren，它继续了PyWren在这一领域的开创性工作。但是，必须注意的是，IBM-PyWren不仅仅是在IBM Cloud Functions上重新实现PyWren的API。相反，它必须被视为PyWren的高级扩展，以运行更广泛的MapReduce作业。我们描述了设计，创新功能（API扩展，数据发现和分区，可组合性等）。和IBM-PyWren的性能，沿着其实施过程中遇到的挑战。
Unexpectedly, the rise of serverless computing has also collaterally started the "democratization" of massive-scale data parallelism. This new trend heralded by PyWren pursues to enable untrained users to execute single-machine code in the cloud at massive scale through platforms like AWS Lambda. Inspired by this vision, this industry paper presents IBM-PyWren, which continues the pioneering work begun by PyWren in this field. It must be noted that IBM-PyWren is not, however, just a mere reimplementation of PyWren's API atop IBM Cloud Functions. Rather, it is must be viewed as an advanced extension of PyWren to run broader MapReduce jobs. We describe the design, innovative features (API extensions, data discovering & partitioning, composability, etc.) and performance of IBM-PyWren, along with the challenges encountered during its implementation.