scala - Set Cassandra Clustering Order on TableDef with Datastax's Spark Cassandra Connector -

every time try create new table in cassandra new tabledef end clustering order of ascending , i'm trying descending.

i'm using cassandra 2.1.10, spark 1.5.1, , datastax spark cassandra connector 1.5.0-m2.

i'm creating new 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")) 

what i'm expecting see in cassandra is

create table so.example (     parkey text,     ts timestamp,     name text,     primary key ((parkey), ts) ) clustering order (ts desc); 

what end

create table so.example (     parkey text,     ts timestamp,     name text,     primary key ((parkey), ts) ) clustering order (ts asc); 

how can force set clustering order descending?

i not able find direct way of doing this. additionally there lot of other options may want specify. ended extending columndef , tabledef , overriding cql method in tabledef. example of solution came below. if has better way or becomes natively supported i'd happy change answer.

// scala enum object clusteringorder {   abstract sealed class order(val ordinal: int) extends ordered[order]     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 columndef case class 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 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 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 tabledef case class override 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"$stmt\r\nwith clustering order (${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 tabledef object 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) } 

this allowed me create new tables in manner:

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"))