将数组类型的列处理为udf时的Spark - java.lang.ClassCastException [数组[Map[String，String]

import org.apache.spark.sql.api.java.UDF1;
import org.apache.spark.sql.types.*;
import static org.apache.spark.sql.functions.*;

public class ArrayColumnUDFExample {
    public static void main(String[] args) {
        // 创建SparkSession
        SparkSession spark = SparkSession.builder()
                .appName("Array Column UDF Example")
                .getOrCreate();

        // 创建示例数据
        List<Row> data = Arrays.asList(
                RowFactory.create(Arrays.asList(
                        ImmutableMap.of("key1", "value1", "key2", "value2"),
                        ImmutableMap.of("key3", "value3", "key4", "value4")
                )),
                RowFactory.create(Arrays.asList(
                        ImmutableMap.of("key5", "value5", "key6", "value6"),
                        ImmutableMap.of("key7", "value7", "key8", "value8")
                ))
        );

        // 定义数据模式
        StructType schema = new StructType(new StructField[]{
                new StructField("array_column", new ArrayType(
                        new MapType(StringType, StringType), true), false, Metadata.empty())
        });

        // 创建DataFrame
        Dataset<Row> df = spark.createDataFrame(data, schema);

        // 注册UDF
        spark.udf().register("process_array_column", new UDF1<WrappedArray<Row>, String>() {
            @Override
            public String call(WrappedArray<Row> array) throws Exception {
                // 处理数组列的逻辑
                StringBuilder result = new StringBuilder();
                for (Row row : array) {
                    Map<String, String> map = JavaConverters.mapAsJavaMapConverter((Map<String, String>) row.get(0)).asJava();
                    for (Map.Entry<String, String> entry : map.entrySet()) {
                        result.append(entry.getKey()).append(":").append(entry.getValue()).append(",");
                    }
                }
                return result.toString();
            }
        }, DataTypes.StringType);

        // 使用UDF处理数组列
        df.withColumn("processed_column", callUDF("process_array_column", col("array_column")))
                .show(false);
    }
}