spark中合并多个不同行不同列的DataFrame在创建定制化数据结构

// 第一步：先将df设置临时表：
//只要是关于df中的sql执行，都要创建临时表	df.createTempView("time01")

val rdd12: DataFrame = rdd03.map(_.split("    ")) //按照分隔符把一行分为两个子段
  .map(x => (x(0).toString, x(1)))
  .toDF("time", "Logistics_status")
rdd12.show(false)
+----------------+---------------------------+
|time            |Logistics_status                                                                  |
+----------------+---------------------------+
|2020-09-12 12:05|.不重要的字段		    |
|2020-09-12 07:10|.不重要的字段 		    |
|2020-09-12 02:58|,不重要的字段              |
|2020-09-11 22:28|,不重要的字段              |
|2020-09-11 07:31|,不重要的字段              |
|2020-09-11 04:12|,不重要的字段         	    |
|2020-09-10 21:26|,不重要的字段 		    |
|2020-09-10 20:11|,不重要的字段              |
|2020-09-01 06:59|,不重要的字段              |
+----------------+--------------------------+
rdd12.createTempView("time04")
//在说下关于时间格式中求max和min值：
val rdd99 = spark.sql(
      """
select
    |max(to_timestamp(time,'yyyy-MM-dd HH:mm')) as max,
    |min(to_timestamp(time,'yyyy-MM-dd HH:mm')) as min,
     datediff(max(to_date(time)),min(to_date(time))) as day  //最大值和最小值中间隔的天数
from
test05
      """.stripMargin)
      rdd99.show(false)
      rdd99.createTempView("time01")
+-------------------+-------------------+---+
|max                |min                |day|
+-------------------+-------------------+---+
|2020-09-12 12:05:00|2020-09-01 06:59:00|11 |
+-------------------+-------------------+---+
val rdd18: DataFrame = spark.sql(
      """
select
substring(TrackingRequest.trackNo,0,22) as trackNo, //这是切割字符串算子
TrackingRequest.shipClassType as shipClassType,
|TrackingRequest.labelId as labelId,
current_timestamp as create_time
from
test02
  """.stripMargin)
  rdd18.show(false)
  rdd18.createTempView("test03")
+----------------------+-------------+-------+-----------------------+
|trackNo               |shipClassType|labelId|create_time            |
+----------------------+-------------+-------+-----------------------+
|94001aaaaaaaaa63367192|USPS         |0      |2020-09-29 20:14:13.635|
+----------------------+-------------+-------+-----------------------+

//现在将这三个df合并为一条df   这里运用的sql中的full join  
//这里有个bug  在spark中用full join时要先开启这个设置
spark.conf.set("spark.sql.crossJoin.enabled", "true")
//然后在执行join操作：
val rdd24: DataFrame = spark.sql(
      """
 | select
 |c.trackNo as trackNo,
 |c.shipClassType as shipClassType,
 |c.labelId as labelId,
 |d.max as Latest_status_time,
 |d.min as Billing_start_time,
 |d.day as days,
 |c.time as time,
 |c.Logistics_status as Logistics_status
 |from
   (
 |select
 |b.trackNo as trackNo,
 |b.shipClassType as shipClassType,
 |b.labelId as labelId,
 |b.create_time as create_time,
 |a.time as time,
 |a.Logistics_status as Logistics_status
 |from
 |test04 a
 |full join
 |test03 b
) c
 |full join
 |time01 d limit 5
      """
        .stripMargin)
rdd24.show(false)
rdd24.printSchema()
+----------------------+-------------+-------+-------------------+-------------------+----+----------------+----------------------
|trackNo               |shipClassType|labelId|Latest_status_time |Billing_start_time |days|time            |Logistics_status                                                                  
+----------------------+-------------+-------+-------------------+-------------------+----+----------------+----------------------
|9400aaaaaaaaaaa3367192|wwww         |0      |2020-09-12 12:05:00|2020-09-01 06:59:00|11  |2020-09-12 12:05|.不重要的字段
|9400aaaaaaaaaaa3367192|wwww	     |0      |2020-09-12 12:05:00|2020-09-01 06:59:00|11  |2020-09-12 07:10|.不重要的字段
|9400aaaaaaaaaaa3367192|wwww         |0      |2020-09-12 12:05:00|2020-09-01 06:59:00|11  |2020-09-12 02:58|.不重要的字段
|9400aaaaaaaaaaa3367192|wwww         |0      |2020-09-12 12:05:00|2020-09-01 06:59:00|11  |2020-09-11 22:28|.不重要的字段
|9400aaaaaaaaaaa3367192|wwww         |0      |2020-09-12 12:05:00|2020-09-01 06:59:00|11  |2020-09-11 07:31|.不重要的字段
+----------------------+-------------+-------+-------------------+-------------------+----+----------------+----------------------
root
 |-- trackNo: string (nullable = true)
 |-- shipClassType: string (nullable = true)
 |-- labelId: long (nullable = true)
 |-- Latest_status_time: timestamp (nullable = true)
 |-- Billing_start_time: timestamp (nullable = true)
 |-- days: integer (nullable = true)
 |-- time: string (nullable = true)
 |-- Logistics_status: string (nullable = true)

如果需要修改数据结构添加array数组数据结构请点击：
https://blog.youkuaiyun.com/data_curd/article/details/108874932