[SQL][DOCS] Improve table caching section

Author: Michael Armbrust <michael@databricks.com>

Closes #2434 from marmbrus/patch-1 and squashes the following commits:

67215be [Michael Armbrust] [SQL][DOCS] Improve table caching section

1 files changed, 4 insertions, 4 deletions

diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md
index c498b41c43..5212e19c41 100644
--- a/docs/sql-programming-guide.md
+++ b/docs/sql-programming-guide.md
@@ -801,12 +801,12 @@ turning on some experimental options.
 
 ## Caching Data In Memory
 
-Spark SQL can cache tables using an in-memory columnar format by calling `cacheTable("tableName")`.
+Spark SQL can cache tables using an in-memory columnar format by calling `sqlContext.cacheTable("tableName")`.
 Then Spark SQL will scan only required columns and will automatically tune compression to minimize
-memory usage and GC pressure. You can call `uncacheTable("tableName")` to remove the table from memory.
+memory usage and GC pressure. You can call `sqlContext.uncacheTable("tableName")` to remove the table from memory.
 
-Note that if you call `cache` rather than `cacheTable`, tables will _not_ be cached using
-the in-memory columnar format, and therefore `cacheTable` is strongly recommended for this use case.
+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.