[SQL][SPARK-2212]Hash Outer Join

This patch is to support the hash based outer join. Currently, outer join for big relations are resort to `BoradcastNestedLoopJoin`, which is super slow. This PR will create 2 hash tables for both relations in the same partition, which greatly reduce the table scans.

Here is the testing code that I used:
```
package org.apache.spark.sql.hive

import org.apache.spark.SparkContext
import org.apache.spark.SparkConf
import org.apache.spark.sql._

case class Record(key: String, value: String)

object JoinTablePrepare extends App {
  import TestHive2._

  val rdd = sparkContext.parallelize((1 to 3000000).map(i => Record(s"${i % 828193}", s"val_$i")))

  runSqlHive("SHOW TABLES")
  runSqlHive("DROP TABLE if exists a")
  runSqlHive("DROP TABLE if exists b")
  runSqlHive("DROP TABLE if exists result")
  rdd.registerAsTable("records")

  runSqlHive("""CREATE TABLE a (key STRING, value STRING)
                 | ROW FORMAT SERDE 
                 | 'org.apache.hadoop.hive.serde2.columnar.LazyBinaryColumnarSerDe' 
                 | STORED AS RCFILE
               """.stripMargin)
  runSqlHive("""CREATE TABLE b (key STRING, value STRING)
                 | ROW FORMAT SERDE 
                 | 'org.apache.hadoop.hive.serde2.columnar.LazyBinaryColumnarSerDe' 
                 | STORED AS RCFILE
               """.stripMargin)
  runSqlHive("""CREATE TABLE result (key STRING, value STRING)
                 | ROW FORMAT SERDE 
                 | 'org.apache.hadoop.hive.serde2.columnar.LazyBinaryColumnarSerDe' 
                 | STORED AS RCFILE
               """.stripMargin)

  hql(s"""from records 
             | insert into table a
             | select key, value
           """.stripMargin)
  hql(s"""from records 
             | insert into table b select key + 100000, value
           """.stripMargin)
}

object JoinTablePerformanceTest extends App {
  import TestHive2._

  hql("SHOW TABLES")
  hql("set spark.sql.shuffle.partitions=20")
  
  val leftOuterJoin = "insert overwrite table result select a.key, b.value from a left outer join b on a.key=b.key"
  val rightOuterJoin = "insert overwrite table result select a.key, b.value from a right outer join b on a.key=b.key"
  val fullOuterJoin = "insert overwrite table result select a.key, b.value from a full outer join b on a.key=b.key"

  val results = ("LeftOuterJoin", benchmark(leftOuterJoin)) :: ("LeftOuterJoin", benchmark(leftOuterJoin)) :: 
                ("RightOuterJoin", benchmark(rightOuterJoin)) :: ("RightOuterJoin", benchmark(rightOuterJoin)) :: 
                ("FullOuterJoin", benchmark(fullOuterJoin)) :: ("FullOuterJoin", benchmark(fullOuterJoin)) :: Nil
  val explains = hql(s"explain $leftOuterJoin").collect ++ hql(s"explain $rightOuterJoin").collect ++ hql(s"explain $fullOuterJoin").collect
  println(explains.mkString(",\n"))
  results.foreach { case (prompt, result) => {
      println(s"$prompt: took ${result._1} ms (${result._2} records)")
    }
  }

  def benchmark(cmd: String) = {
    val begin = System.currentTimeMillis()
    val result = hql(cmd)
    val end = System.currentTimeMillis()
    val count = hql("select count(1) from result").collect.mkString("")
    ((end - begin), count)
  }
}
```
And the result as shown below:
```
[Physical execution plan:],
[InsertIntoHiveTable (MetastoreRelation default, result, None), Map(), true],
[ Project [key#95,value#98]],
[  HashOuterJoin [key#95], [key#97], LeftOuter, None],
[   Exchange (HashPartitioning [key#95], 20)],
[    HiveTableScan [key#95], (MetastoreRelation default, a, None), None],
[   Exchange (HashPartitioning [key#97], 20)],
[    HiveTableScan [key#97,value#98], (MetastoreRelation default, b, None), None],
[Physical execution plan:],
[InsertIntoHiveTable (MetastoreRelation default, result, None), Map(), true],
[ Project [key#102,value#105]],
[  HashOuterJoin [key#102], [key#104], RightOuter, None],
[   Exchange (HashPartitioning [key#102], 20)],
[    HiveTableScan [key#102], (MetastoreRelation default, a, None), None],
[   Exchange (HashPartitioning [key#104], 20)],
[    HiveTableScan [key#104,value#105], (MetastoreRelation default, b, None), None],
[Physical execution plan:],
[InsertIntoHiveTable (MetastoreRelation default, result, None), Map(), true],
[ Project [key#109,value#112]],
[  HashOuterJoin [key#109], [key#111], FullOuter, None],
[   Exchange (HashPartitioning [key#109], 20)],
[    HiveTableScan [key#109], (MetastoreRelation default, a, None), None],
[   Exchange (HashPartitioning [key#111], 20)],
[    HiveTableScan [key#111,value#112], (MetastoreRelation default, b, None), None]
LeftOuterJoin: took 16072 ms ([3000000] records)
LeftOuterJoin: took 14394 ms ([3000000] records)
RightOuterJoin: took 14802 ms ([3000000] records)
RightOuterJoin: took 14747 ms ([3000000] records)
FullOuterJoin: took 17715 ms ([6000000] records)
FullOuterJoin: took 17629 ms ([6000000] records)
```

Without this PR, the benchmark will run seems never end.
Also I think you should move this into the closure too.  When codegen is turned on the code needs to be jit-ed on each machine when the task is run.  The code generation logic internally does caching to prevent recompiling the same logic multiple times on the same machine.