diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md
index bfb008edb05af..1b5fde991e405 100644
--- a/docs/sql-programming-guide.md
+++ b/docs/sql-programming-guide.md
@@ -831,12 +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 you call `schemaRDD.cache()` alike `sqlContext.cacheTable(...)` in 1.2 release of Spark, tables will be cached using the in-memory columnar format.
-
 Configuration of in-memory caching can be done using the `setConf` method on SQLContext or by running
 `SET key=value` commands using SQL.