如何从JSON编码的列中提取值？[重复]

Question

我有一个具有以下模式的Spark数据帧。

[{ "map": {
    "completed-stages": 1,
    "total-stages": 1 },
    "rec": "test-plan",
    "status": {
        "state": "SUCCESS"
    }
  },
  { "map": {
    "completed-stages": 1,
    "total-stages": 1 },
    "rec": "test-proc",
    "status": {
        "state": "FAILED"
  }
}]

我想将其转换为另一个具有以下模式的DF[{"rec"："test-plan"，"status"："SUCCESS"}，{"rec"："test-pROC"，"status"："FAILED"}]

我已经编写了以下代码，但它不编译，并抱怨编码错误。

val fdf = DF.map(f => {
        val listCommands = f.get(0).asInstanceOf[WrappedArray[Map[String, Any]]]
        val m = listCommands.map(h => {
            var rec = "none"
            var status = "none"

            if(h.exists("status" == "state" -> _)) {
                status = (h.get("status") match {
                    case Some(x) => x.asInstanceOf[HashMap[String, String]].getOrElse("state", "none")
                    case _ => "none"
                })

                if(h.contains("rec")) {
                    rec = (h.get("rec") match {
                        case Some(x: String) => x
                        case _ => "none"
                    })
                }
            }

          Map("status"->status, "rec"->rec)
        })

      val rm = m.flatten
      rm
    })

请建议正确的方法。

Answer 1

您没有为df提供模式，因此下面的内容可能不适合您。我将json示例保存在test.json文件中，并用val < code > df = spark . read . option(" multiLine "，true)读取它。json("test.json")在这种情况下，您只需< code>df.select($"rec "，$"status.state ")、write.json("test1.json")

Answer 2

这将会很棘手，因为JSONs的顶级元素是不一样的，也就是说，您有< code>map1和< code>map2，因此模式是不一致的。我将与“数据生产者”交谈并请求更改，以便用单独的元素来描述命令的名称。

给定数据帧的模式如下：

scala> commands.printSchema
root
 |-- commands: array (nullable = true)
 |    |-- element: string (containsNull = true)

以及其中元素(行)的数量:

scala> commands.count
res1: Long = 1

您必须首先分解元素的< code>commands数组，然后访问感兴趣的字段。

// 1. Explode the array
val commandsExploded = commands.select(explode($"commands") as "command")
scala> commandsExploded.count
res2: Long = 2

让我们创建JSON编码记录的模式。可以如下所示。

// Note that it accepts map1 and map2 fields
import org.apache.spark.sql.types._
val schema = StructType(
  StructField("map1",
    StructType(
      StructField("completed-stages", LongType, true) ::
      StructField("total-stages", LongType, true) :: Nil), true) ::
  StructField("map2",
    StructType(
      StructField("completed-stages", LongType, true) ::
      StructField("total-stages", LongType, true) :: Nil), true) ::
  StructField("rec", StringType,true) ::
  StructField("status", StructType(
    StructField("state", StringType, true) :: Nil), true
  ) :: Nil)

因此，您应该使用from_json标准函数，该函数接受包含json编码字符串和模式的列。

val commands = commandsExploded.select(from_json($"command", schema) as "command")
scala> commands.show(truncate = false)
+-------------------------------+
|command                        |
+-------------------------------+
|[[1, 1],, test-plan, [SUCCESS]]|
|[, [1, 1], test-proc, [FAILED]]|
+-------------------------------+

让我们看看命令数据集的架构。

scala> commands.printSchema
root
 |-- command: struct (nullable = true)
 |    |-- map1: struct (nullable = true)
 |    |    |-- completed-stages: long (nullable = true)
 |    |    |-- total-stages: long (nullable = true)
 |    |-- map2: struct (nullable = true)
 |    |    |-- completed-stages: long (nullable = true)
 |    |    |-- total-stages: long (nullable = true)
 |    |-- rec: string (nullable = true)
 |    |-- status: struct (nullable = true)
 |    |    |-- state: string (nullable = true)

复杂字段如＜code＞rec

val recs = commands.select(
  $"command.rec" as "rec",
  $"command.status.state" as "status")

scala> recs.show
+---------+-------+
|      rec| status|
+---------+-------+
|test-plan|SUCCESS|
|test-proc| FAILED|
+---------+-------+

将其转换为单记录JSON编码数据集需要Dataset.toJSONcollect_list标准函数。

val result = recs.toJSON.agg(collect_list("value"))
scala> result.show(truncate = false)
+-------------------------------------------------------------------------------+
|collect_list(value)                                                            |
+-------------------------------------------------------------------------------+
|[{"rec":"test-plan","status":"SUCCESS"}, {"rec":"test-proc","status":"FAILED"}]|
+-------------------------------------------------------------------------------+