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[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.
This commit is contained in:
Sunitha Kambhampati 2017-02-13 22:49:29 -08:00 committed by Xiao Li
parent 1ab97310e8
commit 9b5e460a91
1 changed files with 2 additions and 2 deletions
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docs/sql-programming-guide.md
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@ -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.