Spark:如何使用嵌套数组转置和分解列 [英] Spark: How to transpose and explode columns with nested arrays

问题描述

我应用了以下问题中的算法(在 NOTE 中)来转置和分解嵌套的 spark 数据框.

I applied an algorithm from the question below(in NOTE) to transpose and explode nested spark dataframe.

当我定义 cols = ['a', 'b'] 时，我得到空的数据框，但是当我定义 cols = ['a'] 时，我得到了转换a 列的数据框.有关更多详细信息，请参阅下面的当前代码部分.任何帮助将不胜感激.

When I define cols = ['a', 'b'] I get empty dataframe, but when I define cols = ['a'] I get transformed dataframe for the a column. See section Current code below for more details. Any help would be appreciated.

我正在寻找所需的输出 2(转置和爆炸)，但即使是所需输出 1(转置)的示例也会非常有用.

I'm looking for required output 2 (Transpose and Explode ) but even example of required output 1 (Transpose) will be very useful.

注意:这是突出问题的最小示例，实际上数据帧架构和数组长度与示例中的一样Pyspark:如何通过在 spark 中合并值来展平嵌套数组

NOTE: This is minimum example to highlight the problem, in reality dataframe schema and arrays length vary as in the example Pyspark: How to flatten nested arrays by merging values in spark

输入df:

+---+------------------+--------+
| id|                 a|       b|
+---+------------------+--------+
|  1|[{1, 1}, {11, 11}]|    null|
|  2|              null|[{2, 2}]|
+---+------------------+--------+


root
 |-- id: long (nullable = true)
 |-- a: array (nullable = true)
 |    |-- element: struct (containsNull = true)
 |    |    |-- date: long (nullable = true)
 |    |    |-- val: long (nullable = true)
 |-- b: array (nullable = true)
 |    |-- element: struct (containsNull = true)
 |    |    |-- date: long (nullable = true)
 |    |    |-- val: long (nullable = true

所需的输出 1 (transpose_df):

Required output 1 (transpose_df):

+---+------+-------------------+
| id| cols |       arrays      |
+---+------+-------------------+
|  1|  a   | [{1, 1}, {11, 11}]|
|  2|  b   | [{2, 2}]          |
+---+------+-------------------+

所需的输出 2 (explode_df):

Required output 2 (explode_df):

+---+----+----+---+
| id|cols|date|val|
+---+----+----+---+
|  1|   a|   1|  1|
|  1|   a|  11| 11|
|  2|   b|   2|  2|
+---+----+----+---+

当前代码:

import pyspark.sql.functions as f

df = spark.read.json(sc.parallelize([
  """{"id":1,"a":[{"date":1,"val":1},{"date":11,"val":11}]}""",
  """{"id":2,"b":[{"date":2,"val":2}]}}"""]))

cols = ['a', 'b']

expressions = [f.expr('TRANSFORM({col}, el -> STRUCT("{col}" AS cols, el.date, el.val))'.format(col=col)) for col in cols ]

transpose_df = df.withColumn('arrays', f.flatten(f.array(*expressions)))
             
explode_df = transpose_df.selectExpr('id', 'inline(arrays)')

explode_df.show()

目前的成果

+---+----+----+---+
| id|cols|date|val|
+---+----+----+---+
+---+----+----+---+

推荐答案

stack 可能比 transpose 更好.

expr = f"stack({len(cols)}," + \
    ",".join([f"'{c}',{c}" for c in cols]) + \
    ")"
#expr = stack(2,'a',a,'b',b)

transpose_df = df.selectExpr("id", expr) \
    .withColumnRenamed("col0", "cols") \
    .withColumnRenamed("col1", "arrays") \
    .filter("not arrays is null")

explode_df = transpose_df.selectExpr('id', 'cols', 'inline(arrays)')

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