PySpark DataFrame - 动态加入多列 [英] PySpark DataFrame - Join on multiple columns dynamically

问题描述

假设我在 Spark 上有两个 DataFrame

let's say I have two DataFrames on Spark

firstdf = sqlContext.createDataFrame([{'firstdf-id':1,'firstdf-column1':2,'firstdf-column2':3,'firstdf-column3':4}, \
{'firstdf-id':2,'firstdf-column1':3,'firstdf-column2':4,'firstdf-column3':5}])

seconddf = sqlContext.createDataFrame([{'seconddf-id':1,'seconddf-column1':2,'seconddf-column2':4,'seconddf-column3':5}, \
{'seconddf-id':2,'seconddf-column1':6,'seconddf-column2':7,'seconddf-column3':8}])

现在我想通过多列(任何大于一的数字)加入它们

Now I want to join them by multiple columns (any number bigger than one)

我拥有的是第一个 DataFrame 的列数组和第二个 DataFrame 的列数组，这些数组具有相同的大小，我想通过这些数组中指定的列加入.例如:

What I have is an array of columns of the first DataFrame and an array of columns of the second DataFrame, these arrays have the same size, and I want to join by the columns specified in these arrays. For example:

columnsFirstDf = ['firstdf-id', 'firstdf-column1']
columnsSecondDf = ['seconddf-id', 'seconddf-column1']

由于这些数组的大小可变，我不能使用这种方法:

Since these arrays have variable sizes I can't use this kind of approach:

from pyspark.sql.functions import *

firstdf.join(seconddf, \
    (col(columnsFirstDf[0]) == col(columnsSecondDf[0])) &
    (col(columnsFirstDf[1]) == col(columnsSecondDf[1])), \
    'inner'
)

有什么办法可以动态加入多个列吗?

Is there any way that I can join on multiple columns dynamically?

推荐答案

为什么不使用简单的推导式:

Why not use a simple comprehension:

firstdf.join(
    seconddf, 
   [col(f) == col(s) for (f, s) in zip(columnsFirstDf, columnsSecondDf)], 
   "inner"
)

因为你使用了逻辑，所以提供一个没有 & 操作符的条件列表就足够了.

Since you use logical it is enough to provide a list of conditions without & operator.

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