[GitHub] [hudi] XuQianJin-Stars commented on a change in pull request #4291: [HUDI-2990] Sync to HMS when deleting partitions

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XuQianJin-Stars commented on a change in pull request #4291:
URL: https://github.com/apache/hudi/pull/4291#discussion_r836131643



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File path: hudi-spark-datasource/hudi-spark/src/main/scala/org/apache/spark/sql/hudi/command/AlterHoodieTableDropPartitionCommand.scala
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@@ -82,16 +84,26 @@ extends RunnableCommand {
       }.mkString("/")
     }.mkString(",")
 
+    val enableHive = isEnableHive(sparkSession)
     withSparkConf(sparkSession, Map.empty) {
       Map(
         "path" -> hoodieCatalogTable.tableLocation,
         TBL_NAME.key -> hoodieCatalogTable.tableName,
         TABLE_TYPE.key -> hoodieCatalogTable.tableTypeName,
         OPERATION.key -> DataSourceWriteOptions.DELETE_PARTITION_OPERATION_OPT_VAL,
-        PARTITIONS_TO_DELETE.key -> partitionsToDelete,
+        PARTITIONS_TO_DELETE.key -> partitionsToDrop,
         RECORDKEY_FIELD.key -> hoodieCatalogTable.primaryKeys.mkString(","),
         PRECOMBINE_FIELD.key -> hoodieCatalogTable.preCombineKey.getOrElse(""),
-        PARTITIONPATH_FIELD.key -> hoodieCatalogTable.partitionFields.mkString(",")
+        PARTITIONPATH_FIELD.key -> partitionFields,
+        HIVE_SYNC_ENABLED.key -> enableHive.toString,
+        META_SYNC_ENABLED.key -> enableHive.toString,
+        HIVE_SYNC_MODE.key -> HiveSyncMode.HMS.name(),
+        HIVE_USE_JDBC.key -> "false",
+        HIVE_DATABASE.key -> hoodieCatalogTable.table.identifier.database.getOrElse("default"),
+        HIVE_TABLE.key -> hoodieCatalogTable.table.identifier.table,
+        HIVE_SUPPORT_TIMESTAMP_TYPE.key -> "true",
+        HIVE_PARTITION_FIELDS.key -> partitionFields,
+        HIVE_PARTITION_EXTRACTOR_CLASS.key -> classOf[MultiPartKeysValueExtractor].getCanonicalName

Review comment:
       > is this hardcoding of configs okay? is the user still able to override these with properties supplied at the SQL statement itself?
   
   Regarding this configuration, I need a pr to handle it uniformly. As you said, if spark sql has hive support enabled, you can replace this information from the sql configuration without hard configuration.




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