将转换从配置单元sql查询移动到Spark

Question

val temp = sqlContext.sql(s"SELECT A, B, C, (CASE WHEN (D) in (1,2,3) THEN ((E)+0.000)/60 ELSE 0 END) AS Z from TEST.TEST_TABLE")
val temp1 = temp.map({ temp => ((temp.getShort(0), temp.getString(1)), (USAGE_TEMP.getDouble(2), USAGE_TEMP.getDouble(3)))})
.reduceByKey((x, y) => ((x._1+y._1),(x._2+y._2)))

我希望在scala中完成转换，而不是上面在hive层上进行计算(案例评估)的代码。我该怎么做呢？

在Map中填充数据时，是否可以执行相同的操作？

Answer 1

val temp = sqlContext.sql(s"SELECT A, B, C, D, E from TEST.TEST_TABLE")

val tempTransform = temp.map(row => {
  val z = List[Double](1, 2, 3).contains(row.getDouble(3)) match {
    case true => row.getDouble(4) / 60
    case _ => 0
  }
  Row(row.getShort(0), Row.getString(1), Row.getDouble(2), z)
})

val temp1 = tempTransform.map({ temp => ((temp.getShort(0), temp.getString(1)), (USAGE_TEMP.getDouble(2), USAGE_TEMP.getDouble(3)))})
  .reduceByKey((x, y) => ((x._1+y._1),(x._2+y._2)))

Answer 2

您也可以使用此语法

new_df = old_df.withColumn('target_column', udf(df.name))

由本example引用

val sqlContext = new SQLContext(sc)
import sqlContext.implicits._ // for `toDF` and $""
import org.apache.spark.sql.functions._ // for `when`

val df = sc.parallelize(Seq((4, "blah", 2), (2, "", 3), (56, "foo", 3), (100, null, 5)))
    .toDF("A", "B", "C")

val newDf = df.withColumn("D", when($"B".isNull or $"B" === "", 0).otherwise(1))

在您的示例中，执行数据帧形式的sql，如下面的val temp = sqlContext.sql(s"SELECT A, B, C, D, E from TEST.TEST_TABLE")

并将withColumn与case或when otherwise一起应用，或者在需要时应用spark udf

，调用scala函数逻辑而不是hiveudf