From 85e7c52bac6ae2f58a9340fde1dc94506666049d Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Wed, 17 Sep 2014 12:41:49 -0700
Subject: [PATCH] [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
(cherry picked from commit cbf983bb4a550ff26756ed7308fb03db42cffcff)
Signed-off-by: Michael Armbrust <michael@databricks.com>
---
docs/sql-programming-guide.md | 8 ++++----
1 file changed, 4 insertions(+), 4 deletions(-)
diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md
index 2c226411b0..1d12394a19 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.
--
GitLab