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Jan 20, 2020

当前企业对大数据的研究与应用日趋理性,那么,如何根据业务特点,选择一个适合自身场景的查询引擎呢?百分点在某国家级项目中承担了日增超5000亿级的数据处理与分析任务,集群的总数据量已接近百万亿。本报告结合百分点在项目中的业务场景,对HAWQ、Presto、ClickHouse做了综合评测,供大家参考。 一、测试整体方案 A.数据在不同压缩格式下的压缩能力。 优化器 数据库   数据库 数据压缩 5G   详细的评测数据及图片展现如下文所示。   通过对比测试结果可以发现,在相同的数据量 查询       查询 查询 查询 查询   优化器基类提供了计算梯度loss的方法,并可以将梯度应用于变量。优化器里包含了实现了经典的优化算法,如梯度下降和Adagrad。优化器是提供了一个可以使用各种优化算法的接口,可以让用户直接调用一些经典的优化算法,如梯度下降法等等。优化器(optimizers)类的基类。这个类定义了在训练模型的时候添加一个操作的API。用户基本上不会直接使用这个类,但是你会用到他的子类比如GradientDescentOptimizer, AdagradOptimizer, MomentumOptimizer(tensorflow下的优化器包)等等这些算法。

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