将映射函数应用于 Spark 数据框中列的所有元素

Question

我正在尝试将函数应用于 Scala 中 Spark 数据帧中列的所有元素。输入是一个看起来像“{count:10}”的字符串，我想只返回 Int 部分——在这个例子中是 10。我可以在一个玩具例子上这样做：

val x = List("{\"count\": 107}", "{\"count\": 9}", "{\"count\": 456}")     
val _list = x.map(x => x.substring(10,x.length-1).toInt)

但是当我尝试将 udf 应用到我的数据框时，我得到一个错误：

val getCounts: String => Int = _.substring(10,x.length-1).toInt
import org.apache.spark.sql.functions.udf
val myUDF = udf(getCounts)

df.withColumn("post_shares_int", myUDF('post_shares)).show

错误输出：

    org.apache.spark.SparkException: Task not serializable

at org.apache.spark.util.ClosureCleaner$.ensureSerializable(ClosureCleaner.scala:304)
    at org.apache.spark.util.ClosureCleaner$.org$apache$spark$util$ClosureCleaner$$clean(ClosureCleaner.scala:294)
    at org.apache.spark.util.ClosureCleaner$.clean(ClosureCleaner.scala:122)
    at org.apache.spark.SparkContext.clean(SparkContext.scala:2060)
    at org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1.apply(RDD.scala:707)
    at org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1.apply(RDD.scala:706)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:111)
    at org.apache.spark.rdd.RDD.withScope(RDD.scala:316)
    at org.apache.spark.rdd.RDD.mapPartitions(RDD.scala:706)
    at org.apache.spark.sql.execution.ConvertToSafe.doExecute(rowFormatConverters.scala:56)
    at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:132)
    at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:130)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
    at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:130)
    at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:187)
    at org.apache.spark.sql.execution.Limit.executeCollect(basicOperators.scala:165)
    at org.apache.spark.sql.execution.SparkPlan.executeCollectPublic(SparkPlan.scala:174)
    at org.apache.spark.sql.DataFrame$$anonfun$org$apache$spark$sql$DataFrame$$execute$1$1.apply(DataFrame.scala:1499)
    at org.apache.spark.sql.DataFrame$$anonfun$org$apache$spark$sql$DataFrame$$execute$1$1.apply(DataFrame.scala:1499)
    at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:56)
....

任何有关如何做到这一点的帮助将不胜感激。

Answer 1

忘记自定义 UDF，已经有一个函数可用于该任务，即regexp_extract，记录在here

df.withColumn(
  "post_shares_int", 
  regexp_extract(df("post_shares"), '^{\\w+:(\\d+)}$', 1)
).show

根据下面的评论，最好使用 get_json_object 解析 json 字符串

df.withColumn(
  "post_shares_int", 
  get_json_object(df("post_shares"), '$.count')
).show