SPARK-3259 - User data should be given to the master

Author: Allan Douglas R. de Oliveira <allan@chaordicsystems.com>

Closes #2162 from douglaz/user_data_master and squashes the following commits:

10d15f6 [Allan Douglas R. de Oliveira] Give user data also to the master
[SPARK-3118][SQL]add "SHOW TBLPROPERTIES tblname;" and "SHOW COLUMNS (FROM|IN) table_name [(FROM|IN) db_name]" support

JIRA issue: [SPARK-3118] https://issues.apache.org/jira/browse/SPARK-3118

eg:
> SHOW TBLPROPERTIES test;
SHOW TBLPROPERTIES test;
numPartitions	0
numFiles	1
transient_lastDdlTime	1407923642
numRows	0
totalSize	82
rawDataSize	0

eg:
> SHOW COLUMNS  in test;
SHOW COLUMNS  in test;
OK
Time taken: 0.304 seconds
id
stid
bo

Author: u0jing <u9jing@gmail.com>

Closes #2034 from u0jing/spark-3118 and squashes the following commits:

b231d87 [u0jing] add golden answer files
35f4885 [u0jing] add 'show columns' and 'show tblproperties' support
[SPARK-3197] [SQL] Reduce the Expression tree object creations for aggregation function (min/max)

Aggregation function min/max in catalyst will create expression tree for each single row, however, the expression tree creation is quite expensive in a multithreading env currently. Hence we got a very bad performance for the min/max.
Here is the benchmark that I've done in my local.

Master | Previous Result (ms) | Current Result (ms)
------------ | ------------- | -------------
local | 3645 | 3416
local[6] | 3602 | 1002

The Benchmark source code.
```
case class Record(key: Int, value: Int)

object TestHive2 extends HiveContext(new SparkContext("local[6]", "TestSQLContext", new SparkConf()))

object DataPrepare extends App {
  import TestHive2._

  val rdd = sparkContext.parallelize((1 to 10000000).map(i => Record(i % 3000, i)), 12)

  runSqlHive("SHOW TABLES")
  runSqlHive("DROP TABLE if exists a")
  runSqlHive("DROP TABLE if exists result")
  rdd.registerAsTable("records")

  runSqlHive("""CREATE TABLE a (key INT, value INT)
                 | ROW FORMAT SERDE
                 | 'org.apache.hadoop.hive.serde2.columnar.LazyBinaryColumnarSerDe'
                 | STORED AS RCFILE
               """.stripMargin)
  runSqlHive("""CREATE TABLE result (key INT, value INT)
                 | 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)
}

object PerformanceTest extends App {
  import TestHive2._

  hql("SHOW TABLES")
  hql("set spark.sql.shuffle.partitions=12")

  val cmd = "select min(value), max(value) from a group by key"

  val results = ("Result1", benchmark(cmd)) ::
                ("Result2", benchmark(cmd)) ::
                ("Result3", benchmark(cmd)) :: Nil
  results.foreach { case (prompt, result) => {
      println(s"$prompt: took ${result._1} ms (${result._2} records)")
    }
  }

  def benchmark(cmd: String) = {
    val begin = System.currentTimeMillis()
    val count = hql(cmd).count
    val end = System.currentTimeMillis()
    ((end - begin), count)
  }
}
```

Author: Cheng Hao <hao.cheng@intel.com>

Closes #2113 from chenghao-intel/aggregation_expression_optimization and squashes the following commits:

db40395 [Cheng Hao] remove the transient and add val for the expression property
d56167d [Cheng Hao] Reduce the Expressions creation
[SPARK-3256] Added support for :cp <jar> that was broken in Scala 2.10.x for REPL

As seen with [SI-6502](https://issues.scala-lang.org/browse/SI-6502) of Scala, the _:cp_ command was broken in Scala 2.10.x. As the Spark shell is a friendly wrapper on top of the Scala REPL, it is also affected by this problem.

My solution was to alter the internal classpath and invalidate any new entries. I also had to add the ability to add new entries to the parent classloader of the interpreter (SparkIMain's global).

The advantage of this versus wiping the interpreter and replaying all of the commands is that you don't have to worry about rerunning heavy Spark-related commands (going to the cluster) or potentially reloading data that might have changed. Instead, you get to work from where you left off.

Until this is fixed upstream for 2.10.x, I had to use reflection to alter the internal compiler classpath.

The solution now looks like this:
![screen shot 2014-08-13 at 3 46 02 pm](https://cloud.githubusercontent.com/assets/2481802/3912625/f02b1440-232c-11e4-9bf6-bafb3e352d14.png)

Author: Chip Senkbeil <rcsenkbe@us.ibm.com>

Closes #1929 from rcsenkbeil/FixReplClasspathSupport and squashes the following commits:

f420cbf [Chip Senkbeil] Added SparkContext.addJar calls to support executing code on remote clusters
a826795 [Chip Senkbeil] Updated AddUrlsToClasspath to use 'new Run' suggestion over hackish compiler error
2ff1d86 [Chip Senkbeil] Added compilation failure on symbols hack to get Scala classes to load correctly
a220639 [Chip Senkbeil] Added support for :cp <jar> that was broken in Scala 2.10.x for REPL