scala - 使用 Datastax 的 Spark Cassandra Connector 在 TableDef 上设置 Cassandra 聚类顺序

Question

每次我尝试使用新的 TableDef 在 cassandra 中创建一个新表时，我都会以升序的聚类顺序结束，并尝试降序。

我使用的是 Cassandra 2.1.10、Spark 1.5.1 和 Datastax Spark Cassandra Connector 1.5.0-M2。

我正在创建一个新的 TableDef

val table = TableDef("so", "example", 
  Seq(ColumnDef("parkey", PartitionKeyColumn, TextType)),
  Seq(ColumnDef("ts", ClusteringColumn(0), TimestampType)),
  Seq(ColumnDef("name", RegularColumn, TextType)))

rdd.saveAsCassandraTableEx(table, SomeColumns("key", "time", "name"))

我期望在 Cassandra 中看到的是

CREATE TABLE so.example (
    parkey text,
    ts timestamp,
    name text,
    PRIMARY KEY ((parkey), ts)
) WITH CLUSTERING ORDER BY (ts DESC);

我最终得到的是

CREATE TABLE so.example (
    parkey text,
    ts timestamp,
    name text,
    PRIMARY KEY ((parkey), ts)
) WITH CLUSTERING ORDER BY (ts ASC);

如何强制将聚类顺序设置为降序？

Answer 1

我无法找到执行此操作的直接方法。此外，您可能还需要指定许多其他选项。我最终扩展了 ColumnDef 和 TableDef 并覆盖了 TableDef 中的 cql 方法。下面是我提出的解决方案示例。如果有人有更好的方法或者这成为 native 支持，我很乐意更改答案。

// Scala Enum
object ClusteringOrder {
  abstract sealed class Order(val ordinal: Int) extends Ordered[Order]
    with Serializable {
    def compare(that: Order) = that.ordinal compare this.ordinal

    def toInt: Int = this.ordinal
  }

  case object Ascending extends Order(0)
  case object Descending extends Order(1)

  def fromInt(i: Int): Order = values.find(_.ordinal == i).get

  val values = Set(Ascending, Descending)
}

// extend the ColumnDef case class to add enum support
class ColumnDefEx(columnName: String, columnRole: ColumnRole, columnType: ColumnType[_],
  indexed: Boolean = false, val clusteringOrder: ClusteringOrder.Order = ClusteringOrder.Ascending)
  extends ColumnDef(columnName, columnRole, columnType, indexed)

// Mimic the ColumnDef object
object ColumnDefEx {
  def apply(columnName: String, columnRole: ColumnRole, columnType: ColumnType[_],
    indexed: Boolean, clusteringOrder: ClusteringOrder.Order): ColumnDef = {
    new ColumnDefEx(columnName, columnRole, columnType, indexed, clusteringOrder)
  }

  def apply(columnName: String, columnRole: ColumnRole, columnType: ColumnType[_],
    clusteringOrder: ClusteringOrder.Order = ClusteringOrder.Ascending): ColumnDef = {
    new ColumnDefEx(columnName, columnRole, columnType, false, clusteringOrder)
  }

  // copied from ColumnDef object
  def apply(column: ColumnMetadata, columnRole: ColumnRole): ColumnDef = {
    val columnType = ColumnType.fromDriverType(column.getType)
    new ColumnDefEx(column.getName, columnRole, columnType, column.getIndex != null)
  }
}

// extend the TableDef case class to override the cql method
class TableDefEx(keyspaceName: String, tableName: String, partitionKey: Seq[ColumnDef],
  clusteringColumns: Seq[ColumnDef], regularColumns: Seq[ColumnDef], options: String)
  extends TableDef(keyspaceName, tableName, partitionKey, clusteringColumns, regularColumns) {

  override def cql = {
    val stmt = super.cql
    val ordered = if (clusteringColumns.size > 0)
      s"$stmt\r\nWITH CLUSTERING ORDER BY (${clusteringColumnOrder(clusteringColumns)})"
    else stmt
    appendOptions(ordered, options)
  }

  private[this] def clusteringColumnOrder(clusteringColumns: Seq[ColumnDef]): String =
    clusteringColumns.map { col =>
      col match {
        case c: ColumnDefEx => if (c.clusteringOrder == ClusteringOrder.Descending)
          s"${c.columnName} DESC" else s"${c.columnName} ASC"
        case c: ColumnDef => s"${c.columnName} ASC"
      }
    }.toList.mkString(", ")

  private[this] def appendOptions(stmt: String, opts: String) =
    if (stmt.contains("WITH") && opts.startsWith("WITH")) s"$stmt\r\nAND ${opts.substring(4)}"
    else if (!stmt.contains("WITH") && opts.startsWith("AND")) s"WITH ${opts.substring(3)}"
    else s"$stmt\r\n$opts"
}

// Mimic the TableDef object but return new TableDefEx
object TableDefEx {
  def apply(keyspaceName: String, tableName: String, partitionKey: Seq[ColumnDef],
    clusteringColumns: Seq[ColumnDef], regularColumns: Seq[ColumnDef], options: String = "") =
    new TableDefEx(keyspaceName, tableName, partitionKey, clusteringColumns, regularColumns,
      options)

  def fromType[T: ColumnMapper](keyspaceName: String, tableName: String): TableDef =
    implicitly[ColumnMapper[T]].newTable(keyspaceName, tableName)
}

这允许我以这种方式创建新表:

val table = TableDefEx("so", "example", 
  Seq(ColumnDef("parkey", PartitionKeyColumn, TextType)),
  Seq(ColumnDefEx("ts", ClusteringColumn(0), TimestampType, ClusteringOrder.Descending)),
  Seq(ColumnDef("name", RegularColumn, TextType)))

rdd.saveAsCassandraTableEx(table, SomeColumns("key", "time", "name"))