Question

我试图在文件中获得所有json对象的平均评分。我加载了文件并转换为数据框但在解析avg时遇到错误。样品申请：

{
        "country": "Egypt",
        "customerId": "Egypt009",
        "visited": [
            {
                "placeName": "US",
                "rating": "1.3",
                "famousRest": "McDonald",
                "placeId": "Dedcf3"

            },
              {
                "placeName": "US",
                "rating": "3.3",
                "famousRest": "EagleNest",
                "placeId": "CDfet3"

            },


}

{
        "country": "Canada",
        "customerId": "Canada012",
        "visited": [
            {
                "placeName": "UK",
                "rating": "3.3",
                "famousRest": "N/A",
                "placeId": "XSdce2"

            },


    ]
}

所以对于这个json，美国平均评级为（2.3 + 3.3）/ 2 = 2.8

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)

val sqlContext = new org.apache.spark.sql.SQLContext(sc)
val df = sqlContext.jsonFile("temp.txt")
df.show()

对于我们这个平均值=（3.3 +1.3）/ 2 = 2.3

总而言之，平均评分为：（2.8 + 2.3）/ 2 = 2.55（只有两个请求在他们的访问列表中有＆＃39; US＆＃39;）

我的架构：

val app = df.select("strategies"); app.registerTempTable("app"); app.printSchema(); app.show()
app.foreach({
  t =>  t.select("placeName", "rating").where(t("placeName") == "US")
}).show()

I am getting : 
<console>:31: error: value select is not a member of org.apache.spark.sql.Row t => t.select("placeName", "rating").where(t("placeName") == "US") ^

做的时候：

{{1}}

有人可以告诉我这里做错了吗？

Answer 1

假设app是Dataframe（您的代码示例无法理解......您创建了df变量并查询app变量），那么您应该'请致电foreach以便从中进行选择：

app.select("placeName", "rating").where(t("placeName") == "US")

foreach会在每条记录（类型Row）上调用一个函数。这主要用于调用一些副作用（例如打印到控制台/发送到外部服务等）。大多数情况下，您不会使用它来选择/转换Dataframes。

<强>更新：

关于如何计算仅限美国访问量的平均值的原始问题：

// explode to make a record out of each "visited" Array item, 
// taking only "placeName" and "rating" columns
val exploded: DataFrame = df.explode(df("visited")) {
  case Row(visits: Seq[Row]) => 
    visits.map(r => (r.getAs[String]("placeName"), r.getAs[String]("rating")))
}

// make some order: rename columns named _1, _2 (since we used a tuple),
// and cast ratings to Double:
val ratings: DataFrame = exploded
  .withColumnRenamed("_1", "placeName")
  .withColumn("rating", exploded("_2").cast(DoubleType))
  .select("placeName", "rating")

ratings.printSchema()
ratings.show()
/* prints:
root
 |-- placeName: string (nullable = true)
 |-- rating: double (nullable = true)

+---------+------+
|placeName|rating|
+---------+------+
|       US|   1.3|
|       US|   3.3|
|       UK|   3.3|
+---------+------+
 */

// now filter US only and get average rating:
val avg = ratings
  .filter(ratings("placeName") === "US")
  .select(mean("rating"))

avg.show()
/* prints:
 +-----------+
 |avg(rating)|
 +-----------+
 |        2.3|
 +-----------+
  */

forEach Spark Scala出错：value select不是org.apache.spark.sql.Row的成员

1 个答案: