From 9b5e460a9168ab78607034434ca45ab6cb51e5a6 Mon Sep 17 00:00:00 2001
From: Sunitha Kambhampati <skambha@us.ibm.com>
Date: Mon, 13 Feb 2017 22:49:29 -0800
Subject: [PATCH] [SPARK-19585][DOC][SQL] Fix the cacheTable and uncacheTable
 api call in the doc
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## What changes were proposed in this pull request?

https://spark.apache.org/docs/latest/sql-programming-guide.html#caching-data-in-memory
In the doc, the call spark.cacheTable(“tableName”) and spark.uncacheTable(“tableName”) actually needs to be spark.catalog.cacheTable and spark.catalog.uncacheTable

## How was this patch tested?
Built the docs and verified the change shows up fine.

Author: Sunitha Kambhampati <skambha@us.ibm.com>

Closes #16919 from skambha/docChange.
---
 docs/sql-programming-guide.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md
index 9cf480caba..235f5ecc40 100644
--- a/docs/sql-programming-guide.md
+++ b/docs/sql-programming-guide.md
@@ -1272,9 +1272,9 @@ turning on some experimental options.
 
 ## Caching Data In Memory
 
-Spark SQL can cache tables using an in-memory columnar format by calling `spark.cacheTable("tableName")` or `dataFrame.cache()`.
+Spark SQL can cache tables using an in-memory columnar format by calling `spark.catalog.cacheTable("tableName")` or `dataFrame.cache()`.
 Then Spark SQL will scan only required columns and will automatically tune compression to minimize
-memory usage and GC pressure. You can call `spark.uncacheTable("tableName")` to remove the table from memory.
+memory usage and GC pressure. You can call `spark.catalog.uncacheTable("tableName")` to remove the table from memory.
 
 Configuration of in-memory caching can be done using the `setConf` method on `SparkSession` or by running
 `SET key=value` commands using SQL.
-- 
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