Histogram可以更直观的反映数据的分布情况,有了Histogram就可以对执行参数和执行计划有着更有针对性的优化。但想要得到准确的Histogram,需要巨大的计算量。如果能近似得到相对准确Histogram,就会变得很有价值。
目前HIVE中实现了针对Numeric的近似的Histogram的计算逻辑。NumericHistogram的实现说明如下:
/**
* A generic, re-usable histogram class that supports partial aggregations.
* The algorithm is a heuristic adapted from the following paper:
* Yael Ben-Haim and Elad Tom-Tov, "A streaming parallel decision tree algorithm",
* J. Machine Learning Research 11 (2010), pp. 849--872. Although there are no approximation
* guarantees, it appears to work well with adequate data and a large (e.g., 20-80) number
* of histogram bins.
*/
感兴趣的可以参考论文,“A streaming parallel decision tree algorithm”。
我简单的测试了下:
package sunwg.test;
public class testHis {
public static void main(String[] args) {
NumericHistogram numericHistogram = new NumericHistogram();
numericHistogram.allocate(10);
for (double i=1.0; i<=50.0; i++) {
numericHistogram.add(i);
}
System.out.println(Math.round(numericHistogram.quantile(0.1)));
System.out.println(Math.round(numericHistogram.quantile(0.2)));
System.out.println(Math.round(numericHistogram.quantile(0.3)));
System.out.println(Math.round(numericHistogram.quantile(0.4)));
System.out.println(Math.round(numericHistogram.quantile(0.5)));
System.out.println(Math.round(numericHistogram.quantile(0.6)));
System.out.println(Math.round(numericHistogram.quantile(0.7)));
System.out.println(Math.round(numericHistogram.quantile(0.8)));
System.out.println(Math.round(numericHistogram.quantile(0.9)));
System.out.println(Math.round(numericHistogram.quantile(1.0)));
}
结果如下:
3
8
12
18
24
29
33
38
42
48
基本上还是挺靠谱的,如果想提高准确率,可以增加num_bins的个数,也就是上面的10。
numericHistogram.allocate(10);
并且,NumericHistogram也支持多个partial Histogram的merge操作。
之所以要看这些内容,主要希望数据集成可以通过对数据的研究,获得数据的特征,选择更合适的splitpk,将任务可以拆分得更加平均,减少长尾task,也把用户从优化中解放出来。