如何将多列附加到一列？答案

【问题标题】：How to append multiple columns to one column?如何将多列附加到一列？
【发布时间】：2019-09-25 09:00:29
【问题描述】：

我正在对地址进行分组，其中有多种类型的地址，但我需要对它们进行分组并应用计数和排序。

输入

Address1  Address2   Address3
    a1       b1        c1 
    b2       a2        e2

输出要求

Address4
a1
b2
b1
a2
c1
e2

【问题讨论】：

标签： java apache-spark apache-spark-sql apache-spark-dataset

【解决方案1】：

与@Blokje5 给出的实现相同，但使用更高级别的数据集api。


        Dataset<Row> Ad1 = df.select(functions.col("Address1").as("Address4"));

        Dataset<Row> Ad2 = df.select("Address2");
        Dataset<Row> Ad3 = df.select("Address3");


        Dataset<Row> Union_DS = Ad1.union(Ad2).union(Ad3);

        Union_DS.show();


        Dataset<Row> Union_Sorted = Union_DS
        .groupBy("Address4")
        .agg(functions.count(functions.col("Address4")).as("Count"))

        .sort(functions.desc("Count"))

        ;
        Union_Sorted.show();

【讨论】：

【解决方案2】：

您应该能够在 Spark SQL 中使用 UNION 解决这个问题：

spark.sql(
  """
    |SELECT Address4
    |FROM (
    | SELECT Address1 FROM table
    | UNION
    | SELECT Address2 FROM table
    | UNION
    | SELECT Address3 FROM table
    | )
  """.stripMargin).show()

【讨论】：

【解决方案3】：

#You can be able to do it with the below approach
val input_rdd = spark.sparkContext.parallelize(List(("a1", "b1", "c1"), ("a1", "b2", "c1"), ("a1", "b1", "c2"), ("a2", "b2", "c2")))
    val input_df = input_rdd.toDF("Address1", "Address2", "Address3")
    input_df.show()
+--------+--------+--------+
|Address1|Address2|Address3|
+--------+--------+--------+
|      a1|      b1|      c1|
|      a1|      b2|      c1|
|      a1|      b1|      c2|
|      a2|      b2|      c2|
+--------+--------+--------+
    val out_address1_df = input_df.groupBy("Address1").agg(count(input_df("Address1")).as("count_address1")).
      select(input_df("Address1").as("ADDRESS"), col("count_address1").as("COUNT"))
    //out_address1_df.show()
    val out_address2_df = input_df.groupBy("Address2").agg(count(input_df("Address2")).as("count_address2")).
      select(input_df("Address2").as("ADDRESS"), col("count_address2").as("COUNT"))
    //out_address2_df.show()
    val out_address3_df = input_df.groupBy("Address3").agg(count(input_df("Address3")).as("count_address3")).
      select(input_df("Address3").as("ADDRESS"), col("count_address3").as("COUNT"))

    val output_df = out_address1_df.unionAll(out_address2_df).unionAll(out_address3_df)
    output_df.show()
+-------+-----+
|ADDRESS|COUNT|
+-------+-----+
|     a2|    1|
|     a1|    3|
|     b2|    2|
|     b1|    2|
|     c1|    2|
|     c2|    2|
+-------+-----+

【讨论】：