[improvement](spark-load) support parquet and orc file (apache#13438)

Add support for parquet/orc in SparkDpp.java
Fixed sparkDpp checkstyle issue

fe/spark-dpp/src/main/java/org/apache/doris/load/loadv2/dpp/SparkDpp.java

@@ -22,6 +22,7 @@
 
 import com.google.common.base.Strings;
 import com.google.gson.Gson;
+import org.apache.commons.collections.CollectionUtils;
 import org.apache.commons.lang3.StringUtils;
 import org.apache.commons.lang3.tuple.Pair;
 import org.apache.hadoop.conf.Configuration;
@@ -75,7 +76,6 @@
 import java.util.Map;
 import java.util.Queue;
 import java.util.Set;
-
 // This class is a Spark-based data preprocessing program,
 // which will make use of the distributed compute framework of spark to
 // do ETL job/sort/preaggregate jobs in spark job
@@ -87,6 +87,7 @@
 // 2. repartition data by using doris data model(partition and bucket)
 // 3. process aggregation if needed
 // 4. write data to parquet file
+
 public final class SparkDpp implements java.io.Serializable {
     private static final Logger LOG = LoggerFactory.getLogger(SparkDpp.class);
 
@@ -212,7 +213,6 @@ private void writeRepartitionAndSortedRDDToParquet(JavaPairRDD<List<Object>, Obj
                             continue;
                         }
 
-
                         String curBucketKey = keyColumns.get(0).toString();
                         List<Object> columnObjects = new ArrayList<>();
                         for (int i = 1; i < keyColumns.size(); ++i) {
@@ -620,6 +620,30 @@ private Dataset<Row> loadDataFromPath(SparkSession spark,
         if (fileGroup.columnsFromPath != null) {
             srcColumnsWithColumnsFromPath.addAll(fileGroup.columnsFromPath);
         }
+
+        if (fileGroup.fileFormat.equalsIgnoreCase("parquet")) {
+            // parquet had its own schema, just use it; perhaps we could add some validation in future.
+            Dataset<Row> dataFrame = spark.read().parquet(fileUrl);
+            if (!CollectionUtils.isEmpty(columnValueFromPath)) {
+                for (int k = 0; k < columnValueFromPath.size(); k++) {
+                    dataFrame = dataFrame.withColumn(
+                        fileGroup.columnsFromPath.get(k), functions.lit(columnValueFromPath.get(k)));
+                }
+            }
+            return dataFrame;
+        }
+
+        if (fileGroup.fileFormat.equalsIgnoreCase("orc")) {
+            Dataset<Row> dataFrame = spark.read().orc(fileUrl);
+            if (!CollectionUtils.isEmpty(columnValueFromPath)) {
+                for (int k = 0; k < columnValueFromPath.size(); k++) {
+                    dataFrame = dataFrame.withColumn(
+                        fileGroup.columnsFromPath.get(k), functions.lit(columnValueFromPath.get(k)));
+                }
+            }
+            return dataFrame;
+        }
+
         StructType srcSchema = createScrSchema(srcColumnsWithColumnsFromPath);
         JavaRDD<String> sourceDataRdd = spark.read().textFile(fileUrl).toJavaRDD();
         int columnSize = dataSrcColumns.size();