Python PySpark to_json将丢失数组中结构的列名

我试图从嵌套的pyspark数据帧生成json字符串，但丢失了键值。 我的初始数据集类似于以下内容：

data = [
    {"foo": [1, 2], "bar": [4, 5], "buzz": [7, 8]},
    {"foo": [1], "bar": [4], "buzz": [7]},
    {"foo": [1, 2, 3], "bar": [4, 5, 6], "buzz": [7, 8, 9]},
]
df = spark.read.json(sc.parallelize(data))
df.show()
## +---------+---------+---------+
## |      bar|     buzz|      foo|
## +---------+---------+---------+
## |   [4, 5]|   [7, 8]|   [1, 2]|
## |      [4]|      [7]|      [1]|
## |[4, 5, 6]|[7, 8, 9]|[1, 2, 3]|
## +---------+---------+---------+

然后，我使用数组将每个列压缩在一起

df_zipped = (
    df
    .withColumn(
        "zipped",
        F.arrays_zip(
            F.col("foo"),
            F.col("bar"),
            F.col("buzz"),
        )
    )
)
df_zipped.printSchema()
root
 |-- bar: array (nullable = true)
 |    |-- element: long (containsNull = true)
 |-- buzz: array (nullable = true)
 |    |-- element: long (containsNull = true)
 |-- foo: array (nullable = true)
 |    |-- element: long (containsNull = true)
 |-- zipped: array (nullable = true)
 |    |-- element: struct (containsNull = false)
 |    |    |-- foo: long (nullable = true)
 |    |    |-- bar: long (nullable = true)
 |    |    |-- buzz: long (nullable = true)

问题在于如何在压缩的数组中使用json。它会丢失foo、bar和buzz键值，而是将这些键保存为元素索引

(
    df_zipped
    .withColumn("zipped", F.to_json("zipped"))
    .select("zipped")
    .show(truncate=False)
)
+-------------------------------------------------------------+
|zipped                                                       |
+-------------------------------------------------------------+
|[{"0":1,"1":4,"2":7},{"0":2,"1":5,"2":8}]                    |
|[{"0":1,"1":4,"2":7}]                                        |
|[{"0":1,"1":4,"2":7},{"0":2,"1":5,"2":8},{"0":3,"1":6,"2":9}]|
+-------------------------------------------------------------+

---

如何保持“bar”、“buzz”和“foo”而不是0、1、2？

这不是一个非常漂亮的答案（因为您必须明确地指定键），但比

用于：

---

手动指定架构也有效： 对于foo、bar和buzz字段，元素顶部的数组必须已命名，而不是在实际的数据字段本身

data = [
    {"foo": [1, 2], "bar": [4, 5], "buzz": [7, 8]},
    {"foo": [1], "bar": [4], "buzz": [7]},
    {"foo": [1, 2, 3], "bar": [4, 5, 6], "buzz": [7, 8, 9]},
]
df = spark.read.json(sc.parallelize(data))
df.show()
+---------+---------+---------+
|      bar|     buzz|      foo|
+---------+---------+---------+
|   [4, 5]|   [7, 8]|   [1, 2]|
|      [4]|      [7]|      [1]|
|[4, 5, 6]|[7, 8, 9]|[1, 2, 3]|
+---------+---------+---------+

然后手动定义并强制转换到架构：

schema = StructType([
    StructField("foo", IntegerType()),
    StructField("bar", IntegerType()),
    StructField("buzz", IntegerType()),
])

df_zipped = (
    df_test
    .select(
        F.arrays_zip(
            F.col("foo"), 
            F.col("bar"), 
            F.col("buzz"),
                )
        .alias("zipped")
            )
    .filter(F.col("zipped").isNotNull())
    .select(F.col("zipped").cast(ArrayType(schema)))
)

这将产生所需的解决方案：

(
    df_zipped
    .withColumn("zipped", F.to_json("zipped"))
    .select("zipped")
    .show(truncate=False)
)
+----------------------------------------------------------------------------------+
|zipped                                                                            |
+----------------------------------------------------------------------------------+
|[{"foo":1,"bar":4,"buzz":7},{"foo":2,"bar":5,"buzz":8}]                           |
|[{"foo":1,"bar":4,"buzz":7}]                                                      |
|[{"foo":1,"bar":4,"buzz":7},{"foo":2,"bar":5,"buzz":8},{"foo":3,"bar":6,"buzz":9}]|
+----------------------------------------------------------------------------------+

---

注意：在模式中转换为LongType不起作用，但您可以使用

transform

：类似于

F.expr（“transform（zipped，x->concat（“{foo:”，x['foo']，“'bar:”，x['bar']，“'buzz:”，x['buzz']，“}”）”）的方式手动构建字符串。

是的，谢谢您的回答。这很好用。我希望有一种方法，您不必手动定义键/模式，但这可能是不可能的。您可以通过查询模式以获取键的名称来动态构建表达式。这很复杂，不确定是否值得付出努力。在任何情况下，都可以在

数组中指定键
(
    df_zipped
    .withColumn("zipped", F.to_json("zipped"))
    .select("zipped")
    .show(truncate=False)
)
+----------------------------------------------------------------------------------+
|zipped                                                                            |
+----------------------------------------------------------------------------------+
|[{"foo":1,"bar":4,"buzz":7},{"foo":2,"bar":5,"buzz":8}]                           |
|[{"foo":1,"bar":4,"buzz":7}]                                                      |
|[{"foo":1,"bar":4,"buzz":7},{"foo":2,"bar":5,"buzz":8},{"foo":3,"bar":6,"buzz":9}]|
+----------------------------------------------------------------------------------+