From cab668e28225ae7484e83a22d359bd5b962d9d31 Mon Sep 17 00:00:00 2001 From: Sunitha Kambhampati Date: Mon, 13 Feb 2017 16:24:00 -0800 Subject: [PATCH] Fix the cacheTable and uncacheTable api call in the doc --- 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 9cf480caba3e..235f5ecc40c9 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.