【Spark】HashMap转RDD

1、读取本地文件，转换成map

val path = "文件路径"
val source = Source.fromFile(path).getLines().toList.mkString("").replaceAll(" ","")

val key = JSON.parseObject(source).get("key").toString
val columns = JSON.parseObject(source).get("value").toString

val map = new util.HashMap[String, String]()
map.put("RK", getValue(key))

JSON.parseObject(columns.toString).keySet().asScala.foreach(elem => {
    val valueJson = JSON.parseObject(columns.toString).get(elem).toString
    map.put(elem, getValue(valueJson))
})



def getValue(str: String): String = {

    val value = str.toString.replace("[","").replace("]","")
    JSON.parseObject(value).get("value").toString
}

2、将map转变成rdd

val schema = StructType(map.asScala.toSeq.map {case(k,v) =>
    StruchField(k, StringType, nullable = true)
})

val row = Row.fromSeq(map.values().asScala.toSeq)

val rowRDD = spark.sparkContext.parallelize(Seq(row))

val df = spark.createDataFrame(rowRDD, schema)

备注：数据格式

{
    "key":[
        {
            "name":"RK",
            "type":"String",
            "value":"1234567890"
        }
    ],
    "columns":{
        "column_name1":[
            "name":"column_name1",
            "type":"String",
            "value":"111"   
        ],
        "column_name2":[
            "name":"column_name2",
            "type":"String",
            "value":"222"   
        ],
        "column_name3":[
            "name":"column_name3",
            "type":"String",
            "value":"333"   
        ]
    }
}