如何通过转换为RDD在Spark Dataset中保存嵌套或JSON对象? [英] How to save nested or JSON object in spark Dataset with converting to RDD?

问题描述

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

I am working on the spark code where I have to save multiple column values as a object format and save the result to mongodb

给出数据集

|---|-----|------|----------|
|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)

给我类似的结果

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

我期待类似

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

如何更改除列名之外的对象类型的键.在Java中使用地图?

How can I change the keys in the object type other than column names. using maps in java ?

推荐答案

您可以在 struct 列中传递 as ，这样该名称将保存为您的名字通过.

You can pass as in the column struct , so that that will be saved as the name you passed.

 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"}|
+-----------------------------------------------------------+

****/

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