Spark：如何替换sc.parallelize(List(item1,item2)).collect().foreach(row=>{})为并行？

代码场景：

1）设定的几种数据场景，遍历所有场景：依次统计满足每种场景条件下的数据，并把统计结果存入hive；

2）已有代码如下：

    case class IndoorOTTCalibrateBuildingVecotrLegend(oid: Int, minHeight: Int, maxHeight: Int, minGridIDCount: Int, maxGridIDCount: Int, heightType: Int) extends Serializable

    //  实例化建筑物区间段：按照栅格的个数（面积）、楼的高度（商场等场景）来划分场景
    val buildingHeightLegends = List(
      IndoorOTTCalibrateBuildingVecotrLegend(1, 1, 30, 1, 21, BuildingCalibrateHeightType.HeightType1.toString.toInt),
      IndoorOTTCalibrateBuildingVecotrLegend(2, 1, 30, 21, 45, BuildingCalibrateHeightType.HeightType2.toString.toInt),
      IndoorOTTCalibrateBuildingVecotrLegend(3, 1, 30, 45, 100, BuildingCalibrateHeightType.HeightType3.toString.toInt),
      IndoorOTTCalibrateBuildingVecotrLegend(4, 30, 50, 1, 21, BuildingCalibrateHeightType.HeightType4.toString.toInt),
      IndoorOTTCalibrateBuildingVecotrLegend(5, 30, 50, 21, 45, BuildingCalibrateHeightType.HeightType5.toString.toInt),
      IndoorOTTCalibrateBuildingVecotrLegend(6, 30, 50, 45, 100, BuildingCalibrateHeightType.HeightType6.toString.toInt),
      IndoorOTTCalibrateBuildingVecotrLegend(7, 50, 5000, 1, 100, BuildingCalibrateHeightType.HeightType7.toString.toInt)
    )

    spark.sparkContext.parallelize(buildingHeightLegends).collect().foreach(buildingHeightLegend => {
      generateSampleBySenceType(spark, p_city, p_hour_start, p_hour_end, p_fpb_day, p_day_sample, linkLossCalibrateParameter, buildingHeightLegend)
    })

备注：

在generateSampleBySenceType()函数内部包含有:

spark.sql(s"""
|xxx|where t10.heihgt>=${buildingHieghtLegend.MinHeight} and t10.height<${buildingHieghtLegend.MaxHeight}
|and t10.gridcount<=${buildingHieghtLegend.MinGridIDCount} and  t10.gridcount>${buildingHieghtLegend.MaxGridIDCount}
|""".stripMargin)

如果把代码修改：

    val buildingHeightLegends_df = spark.sqlContext.createDataFrame(buildingHeightLegends)
    buildingHeightLegends_df.createOrReplaceTempView("temp_buildingheightlegends")

    sql(s"""|select * from temp_buildingheightlegends""".stripMargin).repartition(buildingHeightLegends.length).foreachPartition(rows => {
for (row <- rows) {
        val buildingHeightLegend = new IndoorOTTCalibrateBuildingVecotrLegend(
          row.getAs[Int]("oid"),
          row.getAs[Int]("minheight"),
          row.getAs[Int]("maxheight"),
          row.getAs[Int]("mingrididcount"),
          row.getAs[Int]("maxgrididcount"),
          row.getAs[Int]("heighttype"))
        generateSampleBySenceType(spark, p_city, p_hour_start, p_hour_end, p_fpb_day, p_day_sample, linkLossCalibrateParameter, buildingHeightLegend)
      }
    })

则会提示：generateSampleBySenceType()内部sql代码位置抛出SparkSession为NULL的异常。

修改方案：

把buildingHeightLegends注册为临时表temp_buildingHeightLegends，去掉外层的foreach，之后在generateSampleBySenceType()内部把temp_buildingHeightLegends与其他结果集合进行cross join：

测试代码如下：

-- 场景表
CREATE TABLE [dbo].[test_senceitems](
[sencetype] [int] NULL,
[minheight] [int] NULL,
[maxheight] [int] NULL,
[mingridcount] [int] NULL,
[maxgridcount] [int] NULL
)
INSERT [dbo].[test_senceitems] ([sencetype], [minheight], [maxheight], [mingridcount], [maxgridcount]) VALUES (1, 1, 30, 1, 21)
INSERT [dbo].[test_senceitems] ([sencetype], [minheight], [maxheight], [mingridcount], [maxgridcount]) VALUES (2, 1, 30, 21, 45)
INSERT [dbo].[test_senceitems] ([sencetype], [minheight], [maxheight], [mingridcount], [maxgridcount]) VALUES (3, 1, 30, 45, 100)
INSERT [dbo].[test_senceitems] ([sencetype], [minheight], [maxheight], [mingridcount], [maxgridcount]) VALUES (4, 30, 50, 1, 21)
INSERT [dbo].[test_senceitems] ([sencetype], [minheight], [maxheight], [mingridcount], [maxgridcount]) VALUES (5, 30, 50, 21, 45)
INSERT [dbo].[test_senceitems] ([sencetype], [minheight], [maxheight], [mingridcount], [maxgridcount]) VALUES (6, 30, 50, 45, 100)
INSERT [dbo].[test_senceitems] ([sencetype], [minheight], [maxheight], [mingridcount], [maxgridcount]) VALUES (7, 50, 5000, 1, 100)

-- 业务过滤统计表
CREATE TABLE [dbo].[test_grid](
[gridid] [nvarchar](50) NULL,
[height] [int] NULL,
[gridcount] [int] NULL
) 

INSERT [dbo].[test_grid] ([gridid], [height], [gridcount]) VALUES (N'g1', 8, 23)
INSERT [dbo].[test_grid] ([gridid], [height], [gridcount]) VALUES (N'g2', 3, 87)
INSERT [dbo].[test_grid] ([gridid], [height], [gridcount]) VALUES (N'g3', 4, 34)
INSERT [dbo].[test_grid] ([gridid], [height], [gridcount]) VALUES (N'g4', 30, 54)
INSERT [dbo].[test_grid] ([gridid], [height], [gridcount]) VALUES (N'g5', 32, 32)
INSERT [dbo].[test_grid] ([gridid], [height], [gridcount]) VALUES (N'g6', 32, 20)
INSERT [dbo].[test_grid] ([gridid], [height], [gridcount]) VALUES (N'g7', 120, 34)
INSERT [dbo].[test_grid] ([gridid], [height], [gridcount]) VALUES (N'g8', 89, 54)
INSERT [dbo].[test_grid] ([gridid], [height], [gridcount]) VALUES (N'g9', 9, 16)

替换generateSampleBySenceType()内部sql(s"""|""".stripMargin)代码类似如下：

select t10.*,t11.* 
from test_grid t10 
cross join test_senceitems t11
where t10.height>=t11.minheight and t10.height<t11.maxheight
and t10.gridcount>=t11.mingridcount and t10.gridcount<t11.maxgridcount