在 Pyspark 中对多列进行累加和的有效方法

Question

我有一个 table 看起来像：

+----+------+-----+-------+
|time|val1  |val2 |  class|
+----+------+-----+-------+
|   1|    3 |    2|      b|
|   2|    3 |    1|      b|
|   1|    2 |    4|      a|
|   2|    2 |    5|      a|
|   3|    1 |    5|      a|
+----+------+-----+-------+

现在我想对 val1 和 val2 列进行累加。所以我创建了一个 window 函数：

windowval = (Window.partitionBy('class').orderBy('time')
             .rangeBetween(Window.unboundedPreceding, 0))


new_df = my_df.withColumn('cum_sum1', F.sum("val1").over(windowval))
              .withColumn('cum_sum2', F.sum("val2").over(windowval))

但我认为Spark会在原来的table上应用两次window函数，这样效率似乎较低。由于问题非常简单，有没有办法简单地应用一次 window 函数，然后对两列一起进行累加？

Answer 1

But I think Spark will apply window function twice on the original table, which seems less efficient.

你的假设不正确。看一下优化后的logical

就够了

== Optimized Logical Plan ==
Window [sum(val1#1L) windowspecdefinition(class#3, time#0L ASC NULLS FIRST, specifiedwindowframe(RangeFrame, unboundedpreceding$(), currentrow$())) AS cum_sum1#9L, sum(val2#2L) windowspecdefinition(class#3, time#0L ASC NULLS FIRST, specifiedwindowframe(RangeFrame, unboundedpreceding$(), currentrow$())) AS cum_sum2#16L], [class#3], [time#0L ASC NULLS FIRST]
+- LogicalRDD [time#0L, val1#1L, val2#2L, class#3], false

或实体计划

== Physical Plan ==
Window [sum(val1#1L) windowspecdefinition(class#3, time#0L ASC NULLS FIRST, specifiedwindowframe(RangeFrame, unboundedpreceding$(), currentrow$())) AS cum_sum1#9L, sum(val2#2L) windowspecdefinition(class#3, time#0L ASC NULLS FIRST, specifiedwindowframe(RangeFrame, unboundedpreceding$(), currentrow$())) AS cum_sum2#16L], [class#3], [time#0L ASC NULLS FIRST]
+- *(1) Sort [class#3 ASC NULLS FIRST, time#0L ASC NULLS FIRST], false, 0
   +- Exchange hashpartitioning(class#3, 200)
      +- Scan ExistingRDD[time#0L,val1#1L,val2#2L,class#3]

两者都明确表示Window仅应用一次。

在 Pyspark 中对多列进行累加和的有效方法

efficient way to do cumulate sum on multiple columns in Pyspark

python

window-functions

apache-spark

apache-spark-sql

pyspark