[英]Add new column with literal value to a struct column in Dataframe in Spark Scala
[英]Add new column of Map Datatype to Spark Dataframe in scala
我能夠創建一個具有 Map 數據類型的列的新數據框。
val inputDF2 = Seq(
(1, "Visa", 1, Map[String, Int]()),
(2, "MC", 2, Map[String, Int]())).toDF("id", "card_type", "number_of_cards", "card_type_details")
scala> inputDF2.show(false)
+---+---------+---------------+-----------------+
|id |card_type|number_of_cards|card_type_details|
+---+---------+---------------+-----------------+
|1 |Visa |1 |[] |
|2 |MC |2 |[] |
+---+---------+---------------+-----------------+
現在我想創建一個與 card_type_details 類型相同的新列。 我正在嘗試使用 spark withColumn 方法來添加這個新列。
inputDF2.withColumn("tmp", lit(null) cast "map<String, Int>").show(false)
+---------+---------+---------------+---------------------+-----+
|person_id|card_type|number_of_cards|card_type_details |tmp |
+---------+---------+---------------+---------------------+-----+
|1 |Visa |1 |[] |null |
|2 |MC |2 |[] |null |
+---------+---------+---------------+---------------------+-----+
當我檢查兩列的架構時,它是相同的,但值不同。
scala> inputDF2.withColumn("tmp", lit(null) cast "map<String, Int>").printSchema
root
|-- id: integer (nullable = false)
|-- card_type: string (nullable = true)
|-- number_of_cards: integer (nullable = false)
|-- card_type_details: map (nullable = true)
| |-- key: string
| |-- value: integer (valueContainsNull = false)
|-- tmp: map (nullable = true)
| |-- key: string
| |-- value: integer (valueContainsNull = true)
我不確定在添加新列時我是否做得正確。 當我在 tmp 列上應用 .isEmpty 方法時,問題就來了。 我收到空指針異常。
scala> def checkValue = udf((card_type_details: Map[String, Int]) => {
| var output_map = Map[String, Int]()
| if (card_type_details.isEmpty) { output_map += 0.toString -> 1 }
| else {output_map = card_type_details }
| output_map
| })
checkValue: org.apache.spark.sql.expressions.UserDefinedFunction
scala> inputDF2.withColumn("value", checkValue(col("card_type_details"))).show(false)
+---+---------+---------------+-----------------+--------+
|id |card_type|number_of_cards|card_type_details|value |
+---+---------+---------------+-----------------+--------+
|1 |Visa |1 |[] |[0 -> 1]|
|2 |MC |2 |[] |[0 -> 1]|
+---+---------+---------------+-----------------+--------+
scala> inputDF2.withColumn("tmp", lit(null) cast "map<String, Int>")
.withColumn("value", checkValue(col("tmp"))).show(false)
org.apache.spark.SparkException: Failed to execute user defined function($anonfun$checkValue$1: (map<string,int>) => map<string,int>)
Caused by: java.lang.NullPointerException
at $anonfun$checkValue$1.apply(<console>:28)
at $anonfun$checkValue$1.apply(<console>:26)
at org.apache.spark.sql.catalyst.expressions.ScalaUDF$$anonfun$2.apply(ScalaUDF.scala:108)
at org.apache.spark.sql.catalyst.expressions.ScalaUDF$$anonfun$2.apply(ScalaUDF.scala:107)
at org.apache.spark.sql.catalyst.expressions.ScalaUDF.eval(ScalaUDF.scala:1063)
如何添加與 card_type_details 列具有相同值的新列。
要添加與card_type_details具有相同值的tmp
列,您只需執行以下操作:
inputDF2.withColumn("tmp", col("cart_type_details"))
如果您打算添加帶有空映射的列並避免NullPointerException
,則解決方案是:
inputDF2.withColumn("tmp", typedLit(Map.empty[Int, String]))
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