将具有模式的 Spark Dataframe 转换为 json 字符串的 DataFrame

Question

我有一个像这样的数据框：

+--+--------+--------+----+-------------+------------------------------+
|id|name    |lastname|age |timestamp    |creditcards                   |
+--+--------+--------+----+-------------+------------------------------+
|1 |michel  |blanc   |35  |1496756626921|[[hr6,3569823], [ee3,1547869]]|
|2 |peter   |barns   |25  |1496756626551|[[ye8,4569872], [qe5,3485762]]|
+--+--------+--------+----+-------------+------------------------------+

我的 df 的架构如下所示：

root
 |-- id: string (nullable = true)
 |-- name: string (nullable = true)
 |-- lastname: string (nullable = true)
 |-- age: string (nullable = true)
 |-- timestamp: string (nullable = true)
 |-- creditcards: array (nullable = true)
 |    |-- element: struct (containsNull = true)
 |    |    |-- id: string (nullable = true)
 |    |    |-- number: string (nullable = true)

我想将每一行转换为知道我的架构的 json 字符串。所以这个数据框将有一个包含 json 的列字符串。第一行应该是这样的：

{
    "id":"1",
    "name":"michel",
    "lastname":"blanc",
    "age":"35",
    "timestamp":"1496756626921",
    "creditcards":[
        {
            "id":"hr6",
            "number":"3569823"
        },
        {
            "id":"ee3",
            "number":"1547869"  
        }
    ]
}

数据框的第二行应该是这样的：

{
    "id":"2",
    "name":"peter",
    "lastname":"barns",
    "age":"25",
    "timestamp":"1496756626551",
    "creditcards":[
        {
            "id":"ye8",
            "number":"4569872"
        },
        {
            "id":"qe5",
            "number":"3485762"  
        }
    ]
}

我的目标不是将数据帧写入 json 文件。我的目标是将 df1 转换为第二个 df2，以便将 df2 的每个 json 行推送到 kafka 主题我有以下代码来创建数据帧：

    val line1 = """{"id":"1","name":"michel","lastname":"blanc","age":"35","timestamp":"1496756626921","creditcards":[{"id":"hr6","number":"3569823"},{"id":"ee3","number":"1547869"}]}"""
    val line2 = """{"id":"2","name":"peter","lastname":"barns","age":"25","timestamp":"1496756626551","creditcards":[{"id":"ye8","number":"4569872"}, {"id":"qe5","number":"3485762"}]}"""

    val rdd = sc.parallelize(Seq(line1, line2))
    val df = sqlContext.read.json(rdd)
    df show false
    df printSchema

你有什么主意吗？

Answer 1

如果您需要的只是一个单列 DataFrame/Dataset，其中每个列值代表 JSON 中原始 DataFrame 的每一行，您可以简单地应用于toJSON您的 DataFrame，如下所示：

df.show
// +---+------------------------------+---+--------+------+-------------+
// |age|creditcards                   |id |lastname|name  |timestamp    |
// +---+------------------------------+---+--------+------+-------------+
// |35 |[[hr6,3569823], [ee3,1547869]]|1  |blanc   |michel|1496756626921|
// |25 |[[ye8,4569872], [qe5,3485762]]|2  |barns   |peter |1496756626551|
// +---+------------------------------+---+--------+------+-------------+

val dsJson = df.toJSON
// dsJson: org.apache.spark.sql.Dataset[String] = [value: string]

dsJson.show
// +--------------------------------------------------------------------------+
// |value                                                                     |
// +--------------------------------------------------------------------------+
// |{"age":"35","creditcards":[{"id":"hr6","number":"3569823"},{"id":"ee3",...|
// |{"age":"25","creditcards":[{"id":"ye8","number":"4569872"},{"id":"qe5",...|
// +--------------------------------------------------------------------------+

[更新]

要添加name为附加列，您可以使用以下命令从 JSON 列中提取它from_json：

val result = dsJson.withColumn("name", from_json($"value", df.schema)("name"))

result.show
// +--------------------+------+
// |               value|  name|
// +--------------------+------+
// |{"age":"35","cred...|michel|
// |{"age":"25","cred...| peter|
// +--------------------+------+