From 5e94c7e724dc7f96b165fc4bb78058179d305f2c Mon Sep 17 00:00:00 2001
From: Nicholas Chammas <nicholas.chammas@gmail.com>
Date: Sun, 10 Dec 2023 23:52:34 -0500
Subject: [PATCH 1/3] setConf -> conf.set

---
 docs/sql-data-sources-avro.md    | 2 +-
 docs/sql-data-sources-parquet.md | 2 +-
 docs/sql-performance-tuning.md   | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/docs/sql-data-sources-avro.md b/docs/sql-data-sources-avro.md
index 82f876eae2c56..fcd5b532d3cca 100644
--- a/docs/sql-data-sources-avro.md
+++ b/docs/sql-data-sources-avro.md
@@ -330,7 +330,7 @@ Data source options of Avro can be set via:
   </tr></table>
 
 ## Configuration
-Configuration of Avro can be done using the `setConf` method on SparkSession or by running `SET key=value` commands using SQL.
+Configuration of Avro can be done via `SparkSession.conf.set` or by running `SET key=value` commands using SQL.
 <table>
   <thead><tr><th><b>Property Name</b></th><th><b>Default</b></th><th><b>Meaning</b></th><th><b>Since Version</b></th></tr></thead>
   <tr>
diff --git a/docs/sql-data-sources-parquet.md b/docs/sql-data-sources-parquet.md
index 20f6d556cdf76..7088b359a5140 100644
--- a/docs/sql-data-sources-parquet.md
+++ b/docs/sql-data-sources-parquet.md
@@ -431,7 +431,7 @@ Other generic options can be found in <a href="https://spark.apache.org/docs/lat
 
 ### Configuration
 
-Configuration of Parquet can be done using the `setConf` method on `SparkSession` or by running
+Configuration of Parquet can be done via `SparkSession.conf.set` or by running
 `SET key=value` commands using SQL.
 
 <table>
diff --git a/docs/sql-performance-tuning.md b/docs/sql-performance-tuning.md
index 2dec65cc553ed..ab8a1e3fa57f0 100644
--- a/docs/sql-performance-tuning.md
+++ b/docs/sql-performance-tuning.md
@@ -31,7 +31,7 @@ Spark SQL can cache tables using an in-memory columnar format by calling `spark.
 Then Spark SQL will scan only required columns and will automatically tune compression to minimize
 memory usage and GC pressure. You can call `spark.catalog.uncacheTable("tableName")` or `dataFrame.unpersist()` to remove the table from memory.
 
-Configuration of in-memory caching can be done using the `setConf` method on `SparkSession` or by running
+Configuration of in-memory caching can be done via `SparkSession.conf.set` or by running
 `SET key=value` commands using SQL.
 
 <table>

From 09ccff992e8f276088dae9ad217682c0bc00cae5 Mon Sep 17 00:00:00 2001
From: Nicholas Chammas <nicholas.chammas@gmail.com>
Date: Sun, 10 Dec 2023 23:52:57 -0500
Subject: [PATCH 2/3] typo

---
 docs/sql-performance-tuning.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/sql-performance-tuning.md b/docs/sql-performance-tuning.md
index ab8a1e3fa57f0..69540ecffd121 100644
--- a/docs/sql-performance-tuning.md
+++ b/docs/sql-performance-tuning.md
@@ -297,7 +297,7 @@ This feature coalesces the post shuffle partitions based on the map output stati
    </tr>
  </table>
 
-### Spliting skewed shuffle partitions
+### Splitting skewed shuffle partitions
  <table>
    <thead><tr><th>Property Name</th><th>Default</th><th>Meaning</th><th>Since Version</th></tr></thead>
    <tr>

From 2c8c37e9e448364805427d4857d0d09ea5699bd4 Mon Sep 17 00:00:00 2001
From: Nicholas Chammas <nicholas.chammas@gmail.com>
Date: Tue, 12 Dec 2023 10:20:18 -0500
Subject: [PATCH 3/3] SparkSession -> spark

---
 docs/sql-data-sources-avro.md    | 2 +-
 docs/sql-data-sources-parquet.md | 2 +-
 docs/sql-performance-tuning.md   | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/docs/sql-data-sources-avro.md b/docs/sql-data-sources-avro.md
index fcd5b532d3cca..898afe9de87ff 100644
--- a/docs/sql-data-sources-avro.md
+++ b/docs/sql-data-sources-avro.md
@@ -330,7 +330,7 @@ Data source options of Avro can be set via:
   </tr></table>
 
 ## Configuration
-Configuration of Avro can be done via `SparkSession.conf.set` or by running `SET key=value` commands using SQL.
+Configuration of Avro can be done via `spark.conf.set` or by running `SET key=value` commands using SQL.
 <table>
   <thead><tr><th><b>Property Name</b></th><th><b>Default</b></th><th><b>Meaning</b></th><th><b>Since Version</b></th></tr></thead>
   <tr>
diff --git a/docs/sql-data-sources-parquet.md b/docs/sql-data-sources-parquet.md
index 7088b359a5140..7d80343214815 100644
--- a/docs/sql-data-sources-parquet.md
+++ b/docs/sql-data-sources-parquet.md
@@ -431,7 +431,7 @@ Other generic options can be found in <a href="https://spark.apache.org/docs/lat
 
 ### Configuration
 
-Configuration of Parquet can be done via `SparkSession.conf.set` or by running
+Configuration of Parquet can be done via `spark.conf.set` or by running
 `SET key=value` commands using SQL.
 
 <table>
diff --git a/docs/sql-performance-tuning.md b/docs/sql-performance-tuning.md
index 69540ecffd121..4ede18d1938bf 100644
--- a/docs/sql-performance-tuning.md
+++ b/docs/sql-performance-tuning.md
@@ -31,7 +31,7 @@ Spark SQL can cache tables using an in-memory columnar format by calling `spark.
 Then Spark SQL will scan only required columns and will automatically tune compression to minimize
 memory usage and GC pressure. You can call `spark.catalog.uncacheTable("tableName")` or `dataFrame.unpersist()` to remove the table from memory.
 
-Configuration of in-memory caching can be done via `SparkSession.conf.set` or by running
+Configuration of in-memory caching can be done via `spark.conf.set` or by running
 `SET key=value` commands using SQL.
 
 <table>