使用scala：

如果将模式定义为

val schema: StructType = new StructType()
    .add("id", StringType)
    .add("time_series", ArrayType(new StructType()
      .add("time", StringType)
      .add("value", DoubleType)
    ))

然后可以使用sparksql内置函数 from_json 以及 explode ```
import org.apache.spark.sql.functions._
import spark.implicits._

val df1 = df
.selectExpr("CAST(value as STRING) as json")
.select(from_json('json, schema).as("data"))
.select(col("data.id").as("id"), explode(col("data.time_series")).as("time_series"))
.select(col("id"), col("time_series.time").as("time"), col("time_series.value").as("value"))

您的输出将是

+-----------+-----------------+---------+
|id |time |value |
+-----------+-----------------+---------+
|34cx34fs987|20200903 00:00:00|342342.12|
|34cx34fs987|20200903 00:00:05|342421.88|
|34cx34fs987|20200903 00:00:10|351232.92|
+-----------+-----------------+---------+

pyspark中的示例代码

df2 = df.select("id", f.explode("time_series").alias("col"))
df2.select("id", "col.time", "col.value").show()