Apache spark 使用变量模式从dataFrame列读取JSON

我有一个数据框架，它有一个列，其中包含一些嵌套的JSON，也有变量模式。每行中的JSON具有不同的模式

例如

Key     Value
1       {"foo":"bar"}
2       {"key1":"val1","key2":"val2"}

我需要对此进行解析，并创建一个最终的数据框架，其中包含根据JSON模式组合而成的所有列及其各自的值，如下所示

Key     foo     key1        key2
1       bar     null        null
2       null    val1        val2

输出：

+---+---------------------------------------------+
|num|keyvalue                                     |
+---+---------------------------------------------+
|1  |{"foo":"bar"}                                |
|2  |{"key1":"val1","key2":"val2"}                |
|3  |{"key1":"val1","key3":"val3", "key4": "val4"}|
+---+---------------------------------------------+
 

将keyvalue json对象中的值转换为scala映射对象。我们称之为映射的df

import scala.util.parsing.json._
import org.apache.spark.sql.functions.{col, udf}
import org.apache.spark.sql.Row
import org.apache.spark.sql.types._

import spark.implicits._

val stringToMap = udf((str: String) => JSON.parseFull(str).get.asInstanceOf[Map[String, String]])

val mapped_df =df.withColumn("mapped", stringToMap(col("keyvalue")))

mapped_df.show(false)

输出（映射的_-df）：-

通过收集上面映射列中的所有唯一键来创建新的数据帧架构

var schema = List(StructField("number", IntegerType))
val col_rdd = mapped_df.select(col("mapped")).rdd.map(x => {
    val maps: Map[String, String] = x.getAs[Map[String, String]]("mapped")
    val m = maps.map(x =>  x._1)
    m
})

val schem = col_rdd.flatMap(x => x).collect().sorted.toSet
val new_schema = schem.toList.map(x => StructField(x, StringType, true))
schema = schema ++ new_schema

输出-

schema: List[org.apache.spark.sql.types.StructField] = List(
StructField(number,IntegerType,true), 
StructField(key4,StringType,true), 
StructField(key1,StringType,true), 
StructField(key2,StringType,true), 
StructField(key3,StringType,true), 
StructField(foo,StringType,true))

现在我们创建了模式。将映射的RDF转换为rdd，并对其执行以下操作，以使其与我们的新模式一致：

val df_rdd = mapped_df.rdd.map(row => {
    val num = List(row.getAs[Int]("num"))
    val map_val: Map[String, String] = row.getAs[Map[String, String]]("mapped")
    val new_cols = schem.toList.map(x => map_val.getOrElse(x, null))
    Row.fromSeq(num ++ new_cols)
})

val new_dataframe = spark.createDataFrame(df_rdd, StructType(schema))


new_dataframe.show(false)

新数据帧与给定数据帧

New Dataframe

+------+----+----+----+----+----+
|number|key4|key1|key2|key3| foo|
+------+----+----+----+----+----+
|     1|null|null|null|null| bar|
|     2|null|val1|val2|null|null|
|     3|val4|val1|null|val3|null|
+------+----+----+----+----+----+

Given Dataframe:

+---+---------------------------------------------+
|num|keyvalue                                     |
+---+---------------------------------------------+
|1  |{"foo":"bar"}                                |
|2  |{"key1":"val1","key2":"val2"}                |
|3  |{"key1":"val1","key3":"val3", "key4": "val4"}|
+---+---------------------------------------------+

---

谢谢

如果我们有如下的嵌套结构val data=Seq（（1，“{”foo:“bar”}”），（2，“{”key1:“val1”，“key2:“val2”}”），（3，“{”key1:“{”sub_key1:“sub_val1”}，“key3:“val3”，“key4:“val4”}”）@Harshit在上述情况下，dataframe中key1列下的预期值是多少？您还想创建一个新的列Called sub_键吗？您能为上面的示例提供预期的输出数据帧吗？键的值是整个映射，即{“sub_key1”：“sub_val1”}，是的，我需要一个名为sub_key1的新列。

val df_rdd = mapped_df.rdd.map(row => {
    val num = List(row.getAs[Int]("num"))
    val map_val: Map[String, String] = row.getAs[Map[String, String]]("mapped")
    val new_cols = schem.toList.map(x => map_val.getOrElse(x, null))
    Row.fromSeq(num ++ new_cols)
})

val new_dataframe = spark.createDataFrame(df_rdd, StructType(schema))


new_dataframe.show(false)

New Dataframe

+------+----+----+----+----+----+
|number|key4|key1|key2|key3| foo|
+------+----+----+----+----+----+
|     1|null|null|null|null| bar|
|     2|null|val1|val2|null|null|
|     3|val4|val1|null|val3|null|
+------+----+----+----+----+----+

Given Dataframe:

+---+---------------------------------------------+
|num|keyvalue                                     |
+---+---------------------------------------------+
|1  |{"foo":"bar"}                                |
|2  |{"key1":"val1","key2":"val2"}                |
|3  |{"key1":"val1","key3":"val3", "key4": "val4"}|
+---+---------------------------------------------+