Arrays 如何在Spark中将数组项分隔为单独的列？

如果其中的值在数组中，如何将一列分隔为4

预期产出：

+---------------------------+
|address                    |
+---------------------------+
|[San Jone, 19422, CA, 126]|
|[Queens, 11372, NY, 5543]  |
+---------------------------+

编辑：

这是我的.json文件，一旦我创建了一个数据帧，我需要将地址分成4列。

尝试下面的代码

 [
    {
        "firstName": "Rack",
        "lastName": "Jackon",
        "gender": "man",
        "age": 24,
        "address": {
            "streetAddress": "126",
            "city": "San Jone",
            "state": "CA",
            "postalCode": "394221"
        }
    },
   


{
    "firstName": "Apache",
    "lastName": "Spark",
    "gender": "Woman",
    "age": 24,
    "address": {
        "streetAddress": "5543",
        "city": "Queens",
        "state": "NY",
        "postalCode": "11372"
    }
}

]

---

我是usnig databricks，我想它是spark 3.0，您提到的地址是数组，但看起来像是结构类型。您可以使用

地址。*

它将根据您的要求创建新列。它给出了以下错误：AnalysisException:字段名应为字符串文字，但为0；你能发布你的代码吗？val columns=Seq（“city”、“zip”、“state”、“street”）.zipWithIndex test.select（columns.map（c=>col（s“address”）（c.\u 2.as（c.\u 1））：\u*）。show我只需复制粘贴你的代码。如果问题更清楚，我已经编辑好了。如果可以，请回答。非常感谢。

 [
    {
        "firstName": "Rack",
        "lastName": "Jackon",
        "gender": "man",
        "age": 24,
        "address": {
            "streetAddress": "126",
            "city": "San Jone",
            "state": "CA",
            "postalCode": "394221"
        }
    },
   


{
    "firstName": "Apache",
    "lastName": "Spark",
    "gender": "Woman",
    "age": 24,
    "address": {
        "streetAddress": "5543",
        "city": "Queens",
        "state": "NY",
        "postalCode": "11372"
    }
}

]

scala> df.show(false)
+--------------------------+
|address                   |
+--------------------------+
|[San Jone, 19422, CA, 126]|
|[Queens, 11372, NY, 5543] |
+--------------------------+

scala> val columns = Seq("city","zip","state","street").zipWithIndex

scala> df.select(columns.map(c => col(s"address")(c._2).as(c._1)):_*).show(false)
+--------+-----+-----+------+
|city    |zip  |state|street|
+--------+-----+-----+------+
|San Jone|19422|CA   |126   |
|Queens  |11372|NY   |5543  |
+--------+-----+-----+------+