Github user OopsOutOfMemory commented on a diff in the pull request:
https://github.com/apache/spark/pull/3935#discussion_r22637899
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala ---
@@ -326,4 +327,17 @@ private[sql] abstract class SparkStrategies extends
QueryPlanner[SparkPlan] {
case _ => Nil
}
}
+
+ case class DDLStrategy(context: SQLContext) extends Strategy {
+ def apply(plan: LogicalPlan): Seq[SparkPlan] = plan match {
+ case describe: logical.DescribeCommand =>
+ val resolvedTable = context.executePlan(describe.table).analyzed
+ resolvedTable match {
+ case LogicalRelation(relation) =>
+ Seq(ExecutedCommand(DDLDescribeCommand(relation,
describe.output, describe.isExtended)))
+ case o: logical.DescribeCommand =>
+ ExecutedCommand(DescribeCommand(planLater(o),
describe.output)) :: Nil
--- End diff --
@scwf Thanks for the suggestion : )
I think in the implementation level, both are ok.
And In my point of view, the `RunnableCommand` is a command with a simple
`action` that gernarlly `without side-effect`, like `add jar`, `add file`,
`drop table`. You can refer `hive.execution.commands.scala`.
Why here I add a strategy is to handle the side-effect is Because the sql
behavior of external datasource is not like common sql. Maybe it will cause
side-effect. And use two level abstractions is more extendable I think.
You can look deep into `CommandStrategy`, the `cache command`, `explain
command` is also two level abstraction.
What do you think? @chenghao-intel
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