spark sql非join情况的谓词下推优化器PushPredicateThroughNonJoin

select a,b,id from (
  select  A,B,monotonically_increasing_id() as id from 
  testdata2  where a>2 
)tmp  where  b<1
如果下推，就相当于：

select a,b,id from (
  select  A,B,monotonically_increasing_id() as id from 
  testdata2  where a>2 and  b<1
)tmp

select a,b from (
  select  A,B,count(1) as c from testdata2 
  where a>2  group by a,b
)tmp where c=1 and b<5

select a,b,rn from (
select  A,B,row_number() over(partition by a order by b desc )
 as rn from testdata2
)tmp where a>1 and b<5

select tmpc from (
select  A as tmpc  from testdata2
union all
select  b as tmpc  from testdata2
) tmp where tmpc>1 and rand()>0.1

def canPushThrough(p: UnaryNode): Boolean = p match {
    // Note that some operators (e.g. project, aggregate, union) are being handled separately
    // (earlier in this rule).
    case _: AppendColumns => true
    case _: Distinct => true
    case _: Generate => true
    case _: Pivot => true
    case _: RepartitionByExpression => true
    case _: Repartition => true
    case _: ScriptTransformation => true
    case _: Sort => true
    case _: BatchEvalPython => true
    case _: ArrowEvalPython => true
    case _ => false
  }