如何使用 Apache Spark 和 Scala 创建嵌套 json答案

【问题标题】：How to create nested json using Apache Spark with Scala如何使用 Apache Spark 和 Scala 创建嵌套 json
【发布时间】：2019-09-25 10:33:32
【问题描述】：

我正在尝试从我的 spark 数据框创建一个嵌套 JSON，该数据框具有以下结构的数据。

Vendor_Name,count,Categories,Category_Count,Subcategory,Subcategory_Count
Vendor1,10,Category 1,4,Sub Category 1,1
Vendor1,10,Category 1,4,Sub Category 2,2
Vendor1,10,Category 1,4,Sub Category 3,3
Vendor1,10,Category 1,4,Sub Category 4,4

使用带有 Scala 的 Apache-Spark 以以下格式输出所需的 json。

[{
        "vendor_name": "Vendor 1",
        "count": 10,
        "categories": [{
            "name": "Category 1",
            "count": 4,
            "subCategories": [{
                    "name": "Sub Category 1",
                    "count": 1
                },
                {
                    "name": "Sub Category 2",
                    "count": 1
                },
                {
                    "name": "Sub Category 3",
                    "count": 1
                },
                {
                    "name": "Sub Category 4",
                    "count": 1
                }
            ]
        }]

【问题讨论】：

能否添加数据框创建、架构等的代码？

标签： scala apache-spark azure-databricks

【解决方案1】：

//read file into DataFrame    
scala> val df = spark.read.format("csv").option("header", "true").load(<input CSV path>)
    df: org.apache.spark.sql.DataFrame = [Vendor_Name: string, count: string ... 4 more fields]

    scala> df.show(false)
    +-----------+-----+----------+--------------+--------------+-----------------+
    |Vendor_Name|count|Categories|Category_Count|Subcategory   |Subcategory_Count|
    +-----------+-----+----------+--------------+--------------+-----------------+
    |Vendor1    |10   |Category 1|4             |Sub Category 1|1                |
    |Vendor1    |10   |Category 1|4             |Sub Category 2|2                |
    |Vendor1    |10   |Category 1|4             |Sub Category 3|3                |
    |Vendor1    |10   |Category 1|4             |Sub Category 4|4                |
    +-----------+-----+----------+--------------+--------------+-----------------+

    //convert into desire Json format
    scala> val df1 = df.groupBy("Vendor_Name","count","Categories","Category_Count").agg(collect_list(struct(col("Subcategory").alias("name"),col("Subcategory_Count").alias("count"))).alias("subCategories")).groupBy("Vendor_Name","count").agg(collect_list(struct(col("Categories").alias("name"),col("Category_Count").alias("count"),col("subCategories"))).alias("categories"))
    df1: org.apache.spark.sql.DataFrame = [Vendor_Name: string, count: string ... 1 more field]

    scala> df1.printSchema
    root
     |-- Vendor_Name: string (nullable = true)
     |-- count: string (nullable = true)
     |-- categories: array (nullable = true)
     |    |-- element: struct (containsNull = true)
     |    |    |-- name: string (nullable = true)
     |    |    |-- count: string (nullable = true)
     |    |    |-- subCategories: array (nullable = true)
     |    |    |    |-- element: struct (containsNull = true)
     |    |    |    |    |-- name: string (nullable = true)
     |    |    |    |    |-- count: string (nullable = true)

    //Write df in json format
    scala> df1.write.format("json").mode("append").save(<output Path>)

【讨论】：

有没有文档链接或者教程链接，可以更详细的了解collect_list、struct()、col()、agg()。