如何在转换为 RDD 的情况下在 spark 数据集中保存嵌套或 JSON 对象?

How to save nested or JSON object in spark Dataset with converting to RDD?

我正在处理 spark 代码,我必须将多个列值保存为对象格式并将结果保存到 mongodb

给定数据集


|---|-----|------|----------|
|A  |A_SRC|Past_A|Past_A_SRC|
|---|-----|------|----------|
|a1 | s1  | a2   | s2       |

我尝试过的

val ds1 = Seq(("1", "2", "3","4")).toDF("a", "src", "p_a","p_src")
val recordCol = functions.to_json(Seq($"a", $"src", $"p_a",$"p_src"),struct("a", "src", "p_a","p_src")) as "A"
ds1.select(recordCol).show(truncate = false)

给我这样的结果

+-----------------------------------------+
|A                                        |
+-----------------------------------------+
|{"a":"1","src":"2","p_a":"3","p_src":"4"}|
+-----------------------------------------+

我期待

+-----------------------------------------+
|A                                        |
+-----------------------------------------+
|{"source":"1","value":"2","p_source":"4","p_value":"3"}|
+-----------------------------------------+

如何更改对象类型中除列名以外的键。在 java ?

中使用地图

您可以在 struct 列中传递 as ,这样它将被保存为您传递的名称。

 Dataset<Row> tstDS = spark.read().format("csv").option("header", "true").load("/home/exa9/Documents/SparkLogs/y.csv");

              tstDS.show();

/****
+---+-----+------+----------+
|  A|A_SRC|Past_A|Past_A_SRC|
+---+-----+------+----------+
| a1|   s1|    a2|        s2|
+---+-----+------+----------+

****/
              tstDS.withColumn("A", 


                      functions.to_json( 
                              functions.struct(

                                      functions.col("A").as("source"),
                                      functions.col("A_SRC").as("value"),
                                      functions.col("Past_A").as("p_source"),
                                      functions.col("Past_A_SRC").as("p_value")

                                      ))
                      )
              .select("A")
              .show(false);

/****

+-----------------------------------------------------------+
|A                                                          |
+-----------------------------------------------------------+
|{"source":"a1","value":"s1","p_source":"a2","p_value":"s2"}|
+-----------------------------------------------------------+

****/