【Spark】Spark Dataframe实现列转行

转换之前先看下数据结构
多行存在空值需要过滤，不同的状态（yes、maybe、invited、no）存在多个值，需要转换成（events userid status）的状态

首先考虑单独两行映射

val df = spark.read.format("csv").option("header","true").load("file:///opt/data/event_attendees.csv")

scala> df.printSchema
root
 |-- event: string (nullable = true)
 |-- yes: string (nullable = true)
 |-- maybe: string (nullable = true)
 |-- invited: string (nullable = true)
 |-- no: string (nullable = true

df.filter(col("yes").isNotNull).select(col("event"),col("yes")).withColumn("userid",explode(split(col("yes")," "))).drop($"yes").withColumn("status",lit("yes")).show(3)
+----------+----------+------+
|     event|    userid|status|
+----------+----------+------+
|1159822043|1975964455|   yes|
|1159822043| 252302513|   yes|
|1159822043|4226086795|   yes|
+----------+----------+------+
only showing top 3 rows

同理 将其余隔行依次映射

scala> val no = df.filter(col("no").isNotNull).select(col("event"),col("no")).withColumn("userid",explode(split(col("no")," "))).drop($"no").withColumn("status",lit("no"))
+----------+----------+------+
|     event|    userid|status|
+----------+----------+------+
|1159822043|3575574655|    no|
|1159822043|1077296663|    no|
|1186208412|1728988561|    no|
+----------+----------+------+
only showing top 3 rows

no: Unit = ()

scala> val invited = df.filter(col("invited").isNotNull).select(col("event"),col("invited")).withColumn("userid",explode(split(col("invited")," "))).drop($"invited").withColumn("status",lit("invited")).show(3)
+----------+----------+-------+
|     event|    userid| status|
+----------+----------+-------+
|1159822043|1723091036|invited|
|1159822043|3795873583|invited|
|1159822043|4109144917|invited|
+----------+----------+-------+
only showing top 3 rows

invited: Unit = ()

scala> val maybe = df.filter(col("maybe").isNotNull).select(col("event"),col("maybe")).withColumn("userid",explode(split(col("maybe")," "))).drop($"maybe").withColumn("status",lit("maybe")).show(3)
+----------+----------+------+
|     event|    userid|status|
+----------+----------+------+
|1159822043|2733420590| maybe|
|1159822043| 517546982| maybe|
|1159822043|1350834692| maybe|
+----------+----------+------+
only showing top 3 rows

maybe: Unit = ()

进行拼接

scala> yes.union(no).union(maybe).union(invited).show()
+----------+----------+------+
|     event|    userid|status|
+----------+----------+------+
|1159822043|1975964455|   yes|
|1159822043| 252302513|   yes|
|1159822043|4226086795|   yes|
|1159822043|3805886383|   yes|
|1159822043|1420484491|   yes|
|1159822043|3831921392|   yes|
|1159822043|3973364512|   yes|
| 686467261|2394228942|   yes|
| 686467261|2686116898|   yes|
| 686467261|1056558062|   yes|
| 686467261|3792942231|   yes|
| 686467261|4143738190|   yes|
| 686467261|3422491949|   yes|
| 686467261|  96269957|   yes|
| 686467261| 643111206|   yes|
| 686467261|1267277110|   yes|
| 686467261|1602677715|   yes|
| 686467261| 175167653|   yes|
| 855842686|2406118796|   yes|
| 855842686|3550897984|   yes|
+----------+----------+------+
only showing top 20 rows

scala> yes.union(no).union(maybe).union(invited).count()
res15: Long = 11245010 

补充 ：超强合集 一遍打完收工

val dfResult = Seq("yes","maybe","invited","no").map(s =>dfAttendees.select($"event".as("event_id"),col(s)).withColumn("user_id",explode(split(col(s)," "))).withColumn("attend_type",lit(s)).drop(s)).reduce((df1,df2)=>df1.union(df2))