Github user cloud-fan commented on a diff in the pull request:
https://github.com/apache/spark/pull/20018#discussion_r158210425
--- Diff:
examples/src/main/scala/org/apache/spark/examples/sql/hive/SparkHiveExample.scala
---
@@ -102,8 +101,63 @@ object SparkHiveExample {
// | 4| val_4| 4| val_4|
// | 5| val_5| 5| val_5|
// ...
- // $example off:spark_hive$
+ /*
+ * Save DataFrame to Hive Managed table as Parquet format
+ * 1. Create Hive Database / Schema with location at HDFS if you want
to mentioned explicitly else default
+ * warehouse location will be used to store Hive table Data.
+ * Ex: CREATE DATABASE IF NOT EXISTS database_name LOCATION hdfs_path;
+ * You don't have to explicitly give location for each table, every
tables under specified schema will be located at
+ * location given while creating schema.
+ * 2. Create Hive Managed table with storage format as 'Parquet'
+ * Ex: CREATE TABLE records(key int, value string) STORED AS PARQUET;
+ */
+ val hiveTableDF = sql("SELECT * FROM records").toDF()
--- End diff --
actually, I think `spark.table("records")` is a better example.
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