[SQL][Hiveconsole] Bring hive console code up to date and update README.md

Add `import org.apache.spark.sql.Dsl._` to make DSL query works.
Since queryExecution is not avaliable in DataFrame, so remove it.

Author: OopsOutOfMemory <victorshengli@126.com>
Author: Sheng, Li <OopsOutOfMemory@users.noreply.github.com>

Closes #4330 from OopsOutOfMemory/hiveconsole and squashes the following commits:

46eb790 [Sheng, Li] Update SparkBuild.scala
d23ee9f [OopsOutOfMemory] minor
d4dd593 [OopsOutOfMemory] refine hive console

Author: OopsOutOfMemory

project/SparkBuild.scala

@@ -245,6 +245,7 @@ object SQL {
         |import org.apache.spark.sql.catalyst.plans.logical._
         |import org.apache.spark.sql.catalyst.rules._
         |import org.apache.spark.sql.catalyst.util._
+        |import org.apache.spark.sql.Dsl._
         |import org.apache.spark.sql.execution
         |import org.apache.spark.sql.test.TestSQLContext._
         |import org.apache.spark.sql.types._

sql/README.md

@@ -22,59 +22,34 @@ export HADOOP_HOME="<path to>/hadoop-1.0.4"
 
 Using the console
 =================
-An interactive scala console can be invoked by running `build/sbt hive/console`.  From here you can execute queries and inspect the various stages of query optimization.
+An interactive scala console can be invoked by running `build/sbt hive/console`.
+From here you can execute queries with HiveQl and manipulate DataFrame by using DSL.
 
 ```scala
 catalyst$ build/sbt hive/console
 
 [info] Starting scala interpreter...
-import org.apache.spark.sql.catalyst.analysis._
-import org.apache.spark.sql.catalyst.dsl._
-import org.apache.spark.sql.catalyst.errors._
-import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.catalyst.plans.logical._
-import org.apache.spark.sql.catalyst.rules._
-import org.apache.spark.sql.catalyst.util._
-import org.apache.spark.sql.execution
+import org.apache.spark.sql.Dsl._
 import org.apache.spark.sql.hive._
-import org.apache.spark.sql.hive.TestHive._
+import org.apache.spark.sql.hive.test.TestHive._
 import org.apache.spark.sql.types._
+import org.apache.spark.sql.parquet.ParquetTestData
 Welcome to Scala version 2.10.4 (Java HotSpot(TM) 64-Bit Server VM, Java 1.7.0_45).
 Type in expressions to have them evaluated.
 Type :help for more information.
 
 scala> val query = sql("SELECT * FROM (SELECT * FROM src) a")
-query: org.apache.spark.sql.DataFrame =
-== Query Plan ==
-== Physical Plan ==
-HiveTableScan [key#10,value#11], (MetastoreRelation default, src, None), None
+query: org.apache.spark.sql.DataFrame = org.apache.spark.sql.DataFrame@74448eed
 ```
 
-Query results are RDDs and can be operated as such.
+Query results are `DataFrames` and can be operated as such.
 ```
 scala> query.collect()
 res2: Array[org.apache.spark.sql.Row] = Array([238,val_238], [86,val_86], [311,val_311], [27,val_27]...
 ```
 
-You can also build further queries on top of these RDDs using the query DSL.
+You can also build further queries on top of these `DataFrames` using the query DSL.
 ```
-scala> query.where('key === 100).collect()
-res3: Array[org.apache.spark.sql.Row] = Array([100,val_100], [100,val_100])
-```
-
-From the console you can even write rules that transform query plans.  For example, the above query has redundant project operators that aren't doing anything.  This redundancy can be eliminated using the `transform` function that is available on all [`TreeNode`](https://github.com/apache/spark/blob/master/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/trees/TreeNode.scala) objects.
-```scala
-scala> query.queryExecution.analyzed
-res4: org.apache.spark.sql.catalyst.plans.logical.LogicalPlan =
-Project [key#10,value#11]
- Project [key#10,value#11]
-  MetastoreRelation default, src, None
-
-
-scala> query.queryExecution.analyzed transform {
-     |   case Project(projectList, child) if projectList == child.output => child
-     | }
-res5: res17: org.apache.spark.sql.catalyst.plans.logical.LogicalPlan =
-Project [key#10,value#11]
- MetastoreRelation default, src, None
+scala> query.where('key > 30).select(avg('key)).collect()
+res3: Array[org.apache.spark.sql.Row] = Array([274.79025423728814])
 ```