[SPARK-4916][SQL][DOCS]Update SQL programming guide about cache section

`SchemeRDD.cache()` now uses in-memory columnar storage.

Author: luogankun <[email protected]>

Closes #3759 from luogankun/SPARK-4916 and squashes the following commits:

7b39864 [luogankun] [SPARK-4916]Update SQL programming guide
6018122 [luogankun] Merge branch 'master' of https://github.com/apache/spark into SPARK-4916
0b93785 [luogankun] [SPARK-4916]Update SQL programming guide
99b2336 [luogankun] [SPARK-4916]Update SQL programming guide

Author: luogankun

docs/sql-programming-guide.md

@@ -831,13 +831,10 @@ turning on some experimental options.
 
 ## Caching Data In Memory
 
-Spark SQL can cache tables using an in-memory columnar format by calling `sqlContext.cacheTable("tableName")`.
+Spark SQL can cache tables using an in-memory columnar format by calling `sqlContext.cacheTable("tableName")` or `schemaRDD.cache()`.
 Then Spark SQL will scan only required columns and will automatically tune compression to minimize
 memory usage and GC pressure. You can call `sqlContext.uncacheTable("tableName")` to remove the table from memory.
 
-Note that if you call `schemaRDD.cache()` rather than `sqlContext.cacheTable(...)`, tables will _not_ be cached using
-the in-memory columnar format, and therefore `sqlContext.cacheTable(...)` is strongly recommended for this use case.
-
 Configuration of in-memory caching can be done using the `setConf` method on SQLContext or by running
 `SET key=value` commands using SQL.