From 10a80b272e898043e250c2b24a792c9474cf0d10 Mon Sep 17 00:00:00 2001
From: Wenchen Fan <wenchen@databricks.com>
Date: Tue, 26 Dec 2017 12:30:10 +0800
Subject: [PATCH] clean up

---
 .../examples/sql/hive/SparkHiveExample.scala  | 75 +++++++++++--------
 .../apache/spark/sql/internal/SQLConf.scala   |  4 +-
 2 files changed, 46 insertions(+), 33 deletions(-)

diff --git a/examples/src/main/scala/org/apache/spark/examples/sql/hive/SparkHiveExample.scala b/examples/src/main/scala/org/apache/spark/examples/sql/hive/SparkHiveExample.scala
index b193bd595127..70fb5b27366e 100644
--- a/examples/src/main/scala/org/apache/spark/examples/sql/hive/SparkHiveExample.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/sql/hive/SparkHiveExample.scala
@@ -102,40 +102,53 @@ object SparkHiveExample {
     // |  5| val_5|  5| val_5|
     // ...
 
-    // Create Hive managed table with Parquet
-    sql("CREATE TABLE records(key int, value string) STORED AS PARQUET")
-    // Save DataFrame to Hive managed table as Parquet format
-    val hiveTableDF = sql("SELECT * FROM records")
-    hiveTableDF.write.mode(SaveMode.Overwrite).saveAsTable("database_name.records")
-    // Create External Hive table with Parquet
-    sql("CREATE EXTERNAL TABLE records(key int, value string) " +
-      "STORED AS PARQUET LOCATION '/user/hive/warehouse/'")
-    // to make Hive Parquet format compatible with Spark Parquet format
-    spark.sqlContext.setConf("spark.sql.parquet.writeLegacyFormat", "true")
-
-    // Multiple Parquet files could be created accordingly to volume of data under directory given.
-    val hiveExternalTableLocation = "/user/hive/warehouse/database_name.db/records"
-
-    // Save DataFrame to Hive External table as compatible Parquet format
-    hiveTableDF.write.mode(SaveMode.Overwrite).parquet(hiveExternalTableLocation)
-
-    // Turn on flag for Dynamic Partitioning
-    spark.sqlContext.setConf("hive.exec.dynamic.partition", "true")
-    spark.sqlContext.setConf("hive.exec.dynamic.partition.mode", "nonstrict")
-
-    // You can create partitions in Hive table, so downstream queries run much faster.
-    hiveTableDF.write.mode(SaveMode.Overwrite).partitionBy("key")
-      .parquet(hiveExternalTableLocation)
+    // Create a Hive managed Parquet table, with HQL syntax instead of the Spark SQL native syntax
+    // `USING hive`
+    sql("CREATE TABLE hive_records(key int, value string) STORED AS PARQUET")
+    // Save DataFrame to the Hive managed table
+    val df = spark.table("src")
+    df.write.mode(SaveMode.Overwrite).saveAsTable("hive_records")
+    // After insertion, the Hive managed table has data now
+    sql("SELECT * FROM hive_records").show()
+    // +---+-------+
+    // |key|  value|
+    // +---+-------+
+    // |238|val_238|
+    // | 86| val_86|
+    // |311|val_311|
+    // ...
 
-    // Reduce number of files for each partition by repartition
-    hiveTableDF.repartition($"key").write.mode(SaveMode.Overwrite)
-      .partitionBy("key").parquet(hiveExternalTableLocation)
+    // Prepare a Parquet data directory
+    val dataDir = "/tmp/parquet_data"
+    spark.range(10).write.parquet(dataDir)
+    // Create a Hive external Parquet table
+    sql(s"CREATE EXTERNAL TABLE hive_ints(key int) STORED AS PARQUET LOCATION '$dataDir'")
+    // The Hive external table should already have data
+    sql("SELECT * FROM hive_ints").show()
+    // +---+
+    // |key|
+    // +---+
+    // |  0|
+    // |  1|
+    // |  2|
+    // ...
 
-    // Control the number of files in each partition by coalesce
-    hiveTableDF.coalesce(10).write.mode(SaveMode.Overwrite)
-      .partitionBy("key").parquet(hiveExternalTableLocation)
-    // $example off:spark_hive$
+    // Turn on flag for Hive Dynamic Partitioning
+    spark.sqlContext.setConf("hive.exec.dynamic.partition", "true")
+    spark.sqlContext.setConf("hive.exec.dynamic.partition.mode", "nonstrict")
+    // Create a Hive partitioned table using DataFrame API
+    df.write.partitionBy("key").format("hive").saveAsTable("hive_part_tbl")
+    // Partitioned column `key` will be moved to the end of the schema.
+    sql("SELECT * FROM hive_part_tbl").show()
+    // +-------+---+
+    // |  value|key|
+    // +-------+---+
+    // |val_238|238|
+    // | val_86| 86|
+    // |val_311|311|
+    // ...
 
     spark.stop()
+    // $example off:spark_hive$
   }
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala
index 84fe4bb711a4..f16972e5427e 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala
@@ -336,8 +336,8 @@ object SQLConf {
     .createWithDefault(true)
 
   val PARQUET_WRITE_LEGACY_FORMAT = buildConf("spark.sql.parquet.writeLegacyFormat")
-    .doc("Whether to follow Parquet's format specification when converting Parquet schema to " +
-      "Spark SQL schema and vice versa.")
+    .doc("Whether to be compatible with the legacy Parquet format adopted by Spark 1.4 and prior " +
+      "versions, when converting Parquet schema to Spark SQL schema and vice versa.")
     .booleanConf
     .createWithDefault(false)