加入，汇总然后选择Apache Spark中的特定列

Question

产品，并且相应的销售是正确从CSV文件加载的，例如So

    Dataset<Row> dfProducts = sparkSession.read()
            .option("mode", "DROPMALFORMED")
            .option("header", "true")
            .option("inferSchema", "true")
            .option("charset", "UTF-8")
            .csv(new ClassPathResource("products.csv").getURL().getPath());
    Dataset<Row> dfSaledetails = sparkSession.read()
            .option("mode", "DROPMALFORMED")
            .option("header", "true")
            .option("inferSchema", "true")
            .option("charset", "UTF-8")
            .csv(new ClassPathResource("saledetails.csv").getURL().getPath());

产品具有列（product_id，product_name，...）。销售有列（product_id，金额，...）

我需要实现的是基于COM (product_id)的两个数据集，product_id组，Sum列amount，然后仅选择/仅显示特定列（Product_name和Sumpation的结果）

以下是我的尝试

    Dataset<Row> dfSalesTotals = dfSaledetails
            .join(dfProducts, dfSaledetails.col("product_id").equalTo(dfProducts.col("product_id")))
            .groupBy(dfSaledetails.col("product_id"))
            .agg(sum(dfSaledetails.col("amount")).alias("total_amount"))
            .select(dfProducts.col("product_name"), col("total_amount"));
    dfSalesTotals.show();

抛出以下错误

Caused by: org.apache.spark.sql.AnalysisException: Resolved attribute(s) product_name#215 missing from product_id#272,total_amount#499 in operator 
!Project [product_name#215, total_amount#499].;;
!Project [product_name#215, total_amount#499]
+- Aggregate [product_id#272], [product_id#272, sum(amount#277) AS total_amount#499]
   +- Join Inner, (product_id#272 = product_id#212)
      :- Relation[sale_detail_auto_id#266,sale_auto_id#267,sale_id#268,agent_id#269,sale_detail_id#270,inventory_id#271,product_id#272,unit_cost#273,unit_price#274,vat#275,quantity#276,amount#277,promotion_id#278,discount#279] csv
  +- Relation[product_id#212,user_group_id_super_owner#213,product_category#214,product_name#215,product_type#216,product_code#217,distributor_code#218,product_units#219,product_unitCost#220,product_manufacturer#221,product_distributor#222,create_date#223,update_date#224,vat#225,product_weight#226,carton_size#227,product_listStatus#228,active_status#229,distributor_type#230,bundle_type#231,barcode_type#232,product_family_id#233] csv

Answer 1

如果要保留product_name，则应在groupBy

中

.groupBy(
  dfSaledetails.col("product_id"),
  col("product_name")))

或agg

.agg(
  sum(dfSaledetails.col("amount")).alias("total_amount"), 
  first(col("product_name")).alias("product_name"))