记spark2.3.x的一个bug

背景

在做spark重构代码的时候，账号的二期的有些计算，使用了sparkSQL，这个时候，发现要进行多次的left join，考虑效率问题，提出重分区：

 val price =
      s"""
         |select
         |identify_id,weibo_type,price_info
         |from dm_account.hogwarts_account
         |where identify_id is not null AND weibo_type is not null AND price_info is not null
         |""".stripMargin
    val priceTable = "industry_" + seqNum
    val priceDF = sparkSession.sql(price)
      .withColumn("platform_type", getIdOrPlatform(col("weibo_type"), lit("platform")))
      .withColumn("price", getPriceFromPriceInfo(col("price_info")).cast(DoubleType))
      .filter(s"price is not null AND price >0 AND platform_type is not null AND ${platformFilter}")
      .select("identify_id", "platform_type", "price").toDF()
      .repartition(20) //2.3.x有bug
      .createTempView(priceTable)

注意使用的repartition函数，看重载，应该有如下方式：
def repartition(numPartitions: Int, partitionExprs: Column): Dataset[T]*

但是死活运行不了，报错：At least one partition-by expression must be specified
看源码：

2.3.x

/**
   * Returns a new Dataset partitioned by the given partitioning expressions into
   * `numPartitions`. The resulting Dataset is range partitioned.
   *
   * At least one partition-by expression must be specified.
   * When no explicit sort order is specified, "ascending nulls first" is assumed.
   * Note, the rows are not sorted in each partition of the resulting Dataset.
   *
   * @group typedrel
   * @since 2.3.0
   */
  @scala.annotation.varargs
  def repartitionByRange(numPartitions: Int, partitionExprs: Column*): Dataset[T] = {
    require(partitionExprs.nonEmpty, "At least one partition-by expression must be specified.")
    val sortOrder: Seq[SortOrder] = partitionExprs.map(_.expr match {
      case expr: SortOrder => expr
      case expr: Expression => SortOrder(expr, Ascending)
    })
    withTypedPlan {
      RepartitionByExpression(sortOrder, planWithBarrier, numPartitions)
    }
  }

原来是2.3.x引入了新的底层实现，把pom改成2。2.2解决问题。