Distributed Numerical and Machine Learning Computations via Two-Phase Execution of Aggregated Join Trees
Distributed Numerical and Machine Learning Computations via Two-Phase Execution of Aggregated Join Trees
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DOI：
10.14778/3450980.3450991
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发表时间：
2021-03
期刊：
影响因子：
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通讯作者：
Dimitrije Jankov;Binhang Yuan;Shangyu Luo;C. Jermaine
中科院分区：
文献类型：
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作者：
Dimitrije Jankov;Binhang Yuan;Shangyu Luo;C. Jermaine
当数字学习和机器学习（ML）计算在相关上表达时，经典查询执行策略（基于哈希的连接和聚合）可以做出分发计算的工作差。在本文中，我们为数值计算提出了两相执行策略，该策略在相关上表示，以聚合的联接树（即表示为一系列的关系联接，然后是聚集）。在飞行员运行中，收集了谱系信息；该血统用于最佳计划在单个记录级别上的计算。然后，实际执行计算。我们从实验上表明，使用这种两阶段策略的关系系统可能是分布式ML计算的绝佳平台。
When numerical and machine learning (ML) computations are expressed relationally, classical query execution strategies (hash-based joins and aggregations) can do a poor job distributing the computation. In this paper, we propose a two-phase execution strategy for numerical computations that are expressed relationally, as aggregated join trees (that is, expressed as a series of relational joins followed by an aggregation). In a pilot run, lineage information is collected; this lineage is used to optimally plan the computation at the level of individual records. Then, the computation is actually executed. We show experimentally that a relational system making use of this two-phase strategy can be an excellent platform for distributed ML computations.