如何为结构数组的Spark SQL创建架构

Question

如何为以下 json 创建架构以读取架构。我正在使用hiveContext.read.schema((.json("input.json"(，我想忽略前两个"ErrorMessage"和"IsError"只读报告。以下是 JSON：

 {  
  "ErrorMessage": null,
  "IsError": false,
   "Report":{  
      "tl":[  
         {  
            "TlID":"F6",
            "CID":"mo"
         },
         {  
            "TlID":"Fk",
            "CID":"mo"
         }
      ]
   }
}

我创建了以下架构：

val schema = StructType(
            Array(
                   StructField("Report", StructType(
                     Array(
                                                     StructField
                                                     ("tl",ArrayType(StructType(Array(
                                                                    StructField("TlID", StringType),
                                                                    StructField("CID", IntegerType)
                                                                  )))))))))
Below is my json.printSchema() :
root
 |-- Report: struct (nullable = true)
 |    |-- tl: array (nullable = true)
 |    |    |-- element: struct (containsNull = true)
 |    |    |    |-- TlID: string (nullable = true)
 |    |    |    |-- CID: integer (nullable = true)

Answer 1

架构不正确。 数据中的CID显然不是String("mo"(。用

val schema = StructType(Array(
  StructField("Report", StructType(
    Array(
        StructField
        ("tl",ArrayType(StructType(Array(
                       StructField("CID", StringType),
                       StructField("TlID", StringType)
                          )))))))))

和：

val df = Seq("""{  
  "ErrorMessage": null,
  "IsError": false,
   "Report":{  
      "tl":[  
         {  
            "TlID":"F6",
            "CID":"mo"
         },
         {  
            "TlID":"Fk",
            "CID":"mo"
         }
      ]
   }
}""").toDS
spark.read.schema(schema).json(df).show(false)
+--------------------------------+
|Report                          |
+--------------------------------+
|[WrappedArray([mo,F6], [mo,Fk])]| 
+--------------------------------+ 

Answer 2

Datatype: array<struct<metrics_name:string,metrics_value:string>>

  import org.apache.spark.sql.types.{ArrayType}
  StructField("usage_metrics", ArrayType(StructType(
    Array(
      StructField("metric_name", StringType, true),
      StructField("metric_value", StringType, true)
    )
  ))))