[SQL] minor - internal doc improvement for InsertIntoTable.

## What changes were proposed in this pull request?
I was reading this part of the code and was really confused by the "partition" parameter. This patch adds some documentation for it to reduce confusion in the future.

I also looked around other logical plans but most of them are either already documented, or pretty self-evident to people that know Spark SQL.

## How was this patch tested?
N/A - doc change only.

Author: Reynold Xin <rxin@databricks.com>

Closes #15749 from rxin/doc-improvement.

sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicLogicalOperators.scala

@@ -360,6 +360,22 @@ case class OverwriteOptions(
   }
 }
 
+/**
+ * Insert some data into a table.
+ *
+ * @param table the logical plan representing the table. In the future this should be a
+ *              [[org.apache.spark.sql.catalyst.catalog.CatalogTable]] once we converge Hive tables
+ *              and data source tables.
+ * @param partition a map from the partition key to the partition value (optional). If the partition
+ *                  value is optional, dynamic partition insert will be performed.
+ *                  As an example, `INSERT INTO tbl PARTITION (a=1, b=2) AS ...` would have
+ *                  Map('a' -> Some('1'), 'b' -> Some('2')),
+ *                  and `INSERT INTO tbl PARTITION (a=1, b) AS ...`
+ *                  would have Map('a' -> Some('1'), 'b' -> None).
+ * @param child the logical plan representing data to write to.
+ * @param overwrite overwrite existing table or partitions.
+ * @param ifNotExists If true, only write if the table or partition does not exist.
+ */
 case class InsertIntoTable(
     table: LogicalPlan,
     partition: Map[String, Option[String]],

sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala

@@ -35,13 +35,35 @@ import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.Attribute
 import org.apache.spark.sql.catalyst.plans.physical.Partitioning
 import org.apache.spark.sql.execution.{SparkPlan, UnaryExecNode}
-import org.apache.spark.sql.execution.command.{AlterTableAddPartitionCommand, AlterTableDropPartitionCommand}
 import org.apache.spark.sql.hive._
 import org.apache.spark.sql.hive.HiveShim.{ShimFileSinkDesc => FileSinkDesc}
 import org.apache.spark.SparkException
 import org.apache.spark.util.SerializableJobConf
 
 
+/**
+ * Command for writing data out to a Hive table.
+ *
+ * This class is mostly a mess, for legacy reasons (since it evolved in organic ways and had to
+ * follow Hive's internal implementations closely, which itself was a mess too). Please don't
+ * blame Reynold for this! He was just moving code around!
+ *
+ * In the future we should converge the write path for Hive with the normal data source write path,
+ * as defined in [[org.apache.spark.sql.execution.datasources.FileFormatWriter]].
+ *
+ * @param table the logical plan representing the table. In the future this should be a
+ *              [[org.apache.spark.sql.catalyst.catalog.CatalogTable]] once we converge Hive tables
+ *              and data source tables.
+ * @param partition a map from the partition key to the partition value (optional). If the partition
+ *                  value is optional, dynamic partition insert will be performed.
+ *                  As an example, `INSERT INTO tbl PARTITION (a=1, b=2) AS ...` would have
+ *                  Map('a' -> Some('1'), 'b' -> Some('2')),
+ *                  and `INSERT INTO tbl PARTITION (a=1, b) AS ...`
+ *                  would have Map('a' -> Some('1'), 'b' -> None).
+ * @param child the logical plan representing data to write to.
+ * @param overwrite overwrite existing table or partitions.
+ * @param ifNotExists If true, only write if the table or partition does not exist.
+ */
 case class InsertIntoHiveTable(
     table: MetastoreRelation,
     partition: Map[String, Option[String]],
@@ -81,8 +103,7 @@ case class InsertIntoHiveTable(
         throw new IllegalStateException("Cannot create staging directory  '" + dir.toString + "'")
       }
       fs.deleteOnExit(dir)
-    }
-    catch {
+    } catch {
       case e: IOException =>
         throw new RuntimeException(
           "Cannot create staging directory '" + dir.toString + "': " + e.getMessage, e)
@@ -123,7 +144,7 @@ case class InsertIntoHiveTable(
 
     FileOutputFormat.setOutputPath(
       conf.value,
-      SparkHiveWriterContainer.createPathFromString(fileSinkConf.getDirName, conf.value))
+      SparkHiveWriterContainer.createPathFromString(fileSinkConf.getDirName(), conf.value))
     log.debug("Saving as hadoop file of type " + valueClass.getSimpleName)
     writerContainer.driverSideSetup()
     sqlContext.sparkContext.runJob(rdd, writerContainer.writeToFile _)
@@ -263,7 +284,7 @@ case class InsertIntoHiveTable(
           // version and we may not want to catch up new Hive version every time. We delete the
           // Hive partition first and then load data file into the Hive partition.
           if (oldPart.nonEmpty && overwrite) {
-            oldPart.get.storage.locationUri.map { uri =>
+            oldPart.get.storage.locationUri.foreach { uri =>
               val partitionPath = new Path(uri)
               val fs = partitionPath.getFileSystem(hadoopConf)
               if (fs.exists(partitionPath)) {