Github user liancheng commented on a diff in the pull request:
https://github.com/apache/spark/pull/3759#discussion_r22204548
--- Diff: docs/sql-programming-guide.md ---
@@ -835,8 +835,7 @@ Spark SQL can cache tables using an in-memory columnar
format by calling `sqlCon
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.
+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.
--- End diff --
Maybe a bit rewording:
> Note that starting from Spark 1.2, both `schemaRDD.cache()` and
`sqlContext.cacheTable(...)` leverage in-memory columnar format.
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