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In the era of big data, people are eager to realize high-performance data analysis centered on machine learning algorithms on large-scale clusters composed of ordinary machines, provide services and guidance for actual business, and then realize the final realization of data. Different from the traditional online analytical processing OLAP, the in-depth analysis of big data is mainly based on large-scale machine learning technology. Generally speaking, the training process of machine learning model can be attributed to the optimization of the objective function defined on the large-scale training data. And it is implemented through a loop iterative algorithm. Therefore, compared with traditional OLAP, big data analysis based on machine learning has its own unique characteristics.
(1) Iterative : Because there is usually no closed-form solution for optimization problems, the determination of model parameters cannot be completed at one time, and it is necessary to loop and iterate several times to gradually approach the optimal value point.
(2) Fault tolerance : The algorithm design and model evaluation of machine learning tolerate the existence of non-optimal value points, and the characteristics of multiple iterations also allow some errors to occur in the process of looping, and the final convergence of the model is not affected.
(3) Non-uniformity of parameter convergence : some parameters in the model do not change after a few iterations, while some parameters take a long time to converge.
These characteristics determine that the design of an ideal big data analysis system is very different from the design of other computing systems. When a traditional distributed computing system is directly applied to big data analysis, a large proportion of resources are wasted in communication, waiting, and coordination. and other non-efficient calculations.