如何进行外连接：Spark Scala SQLContext

Question

我正在尝试获取 Total(count of all) 和 Top Elements (count after filters)，以便我可以找到所有 jsons (top/total) 中每个 placeName 的百分位数，评分 > 3：

  // sc : An existing SparkContext.
    val sqlContext = new org.apache.spark.sql.SQLContext(sc)
    val df = sqlContext.jsonFile("temp.txt")
    //df.show()


    val res =  df.withColumn("visited", explode($"visited"))

    val result = res.groupBy($"customerId", $"visited.placeName")

Tried with joins :
val result1 =res.groupBy($"customerId", $"visited.placeName").agg(count("*").alias("total"))

val result2 = res
.filter($"visited.rating" < 4)
  .groupBy($"requestId", $"visited.placeName")  
  .agg(count("*").alias("top"))

result1.show()

result2.show()
percentile = result1.join(result2, List("placeName","customerId"), "outer")
 sqlContext.sql("select top/total as percentile from temp groupBy placeName") 

但给了我错误。

我可以在 udf 中这样做吗：

 val result1 =  result.withColumn("Top", getCount(res , true))
                    .withColumn("Total",getCount(result, false)).show()


    def getCount(df: DataFrame, flag: Boolean): Int {
            if (flag == "true") return df.filter($"visited.rating" < 3).groupBy($"customerId", $"visited.placeName").agg(count("*"))
            else return  df.agg(count("*"))
          }

我的架构：

 {
        "country": "France",
        "customerId": "France001",
        "visited": [
            {
                "placeName": "US",
                "rating": "2",
                "famousRest": "N/A",
                "placeId": "AVBS34"

            },
              {
                "placeName": "US",
                "rating": "3",
                "famousRest": "SeriousPie",
                "placeId": "VBSs34"

            },
              {
                "placeName": "Canada",
                "rating": "3",
                "famousRest": "TimHortons",
                "placeId": "AVBv4d"

            }        
    ]
}

US top = 1 count = 3
Canada top = 1 count = 3


{
        "country": "Canada",
        "customerId": "Canada012",
        "visited": [
            {
                "placeName": "UK",
                "rating": "3",
                "famousRest": "N/A",
                "placeId": "XSdce2"

            },


    ]
}
UK top = 1 count = 1


{
        "country": "France",
        "customerId": "France001",
        "visited": [
            {
                "placeName": "US",
                "rating": "4.3",
                "famousRest": "N/A",
                "placeId": "AVBS34"

            },
              {
                "placeName": "US",
                "rating": "3.3",
                "famousRest": "SeriousPie",
                "placeId": "VBSs34"

            },
              {
                "placeName": "Canada",
                "rating": "4.3",
                "famousRest": "TimHortons",
                "placeId": "AVBv4d"

            }        
    ]
}

US top = 2 count = 3
Canada top = 1 count = 3

所以最后我需要类似的东西：

PlaceName  percentile
US         57.14            (1+1+2)/(3+1+3) *100
Canada     33.33            (1+1)/(3+3) *100
UK         100               1*100

架构：

root
|-- country: string(nullable=true)
|-- customerId:string(nullable=true)
|-- visited: array (nullable = true)
|    |-- element: struct (containsNull = true)
|    |   |-- placeId: string (nullable = true)
|    |   |-- placeName: string (nullable = true) 
|    |   |-- famousRest: string (nullable = true)
|    |   |-- rating: string (nullable = true)

Answer 1

鉴于您提供的代码，尚不清楚源代码的结构以及为什么会出现此特定错误，但通常此代码甚至远程有效。

• getCount 不是 UDF - 不是关键但重要的区别。
• getCount 不是有效函数，因为范围内没有 col 类型。除非您出于某种原因将其用作 o.a.s.sql.DataFrame 的类型别名，否则它甚至无法编译！
• 即使类型匹配，Spark 也不支持嵌套操作/转换，因此您无法使用 UDF 在 Spark DataFrame 上执行查询或聚合。