我想在Apache Spark连接中包含空值。 Spark默认情况下不包含null行。
这是默认的Spark行为。
val numbersDf = Seq(
("123"),("456"),(null),("")
).toDF("numbers")
val lettersDf = Seq(
("123","abc"),("456","def"),(null,"zzz"),("","hhh")
).toDF("numbers","letters")
val joinedDf = numbersDf.join(lettersDf,Seq("numbers"))
这是joinedDf.show()的输出：
+-------+-------+ |numbers|letters| +-------+-------+ | 123| abc| | 456| def| | | hhh| +-------+-------+
这是我想要的输出：
+-------+-------+ |numbers|letters| +-------+-------+ | 123| abc| | 456| def| | | hhh| | null| zzz| +-------+-------+
解决方法
Scala提供了一个特殊的NULL安全等于运算符：
numbersDf
.join(lettersDf,numbersDf("numbers") <=> lettersDf("numbers"))
.drop(lettersDf("numbers"))
+-------+-------+ |numbers|letters| +-------+-------+ | 123| abc| | 456| def| | null| zzz| | | hhh| +-------+-------+
小心不要在Spark 1.5或更早版本中使用它。在Spark 1.6之前，它需要一个笛卡尔积(SPARK-11111 – 快速零安@R_502_432@)。
在Spark 2.3.0或更高版本中，您可以在PySpark中使用Column.eqNullSafe：
numbers_df = sc.parallelize([
("123",),(None,)
]).toDF(["numbers"])
letters_df = sc.parallelize([
("123","hhh")
]).toDF(["numbers","letters"])
numbers_df.join(letters_df,numbers_df.numbers.eqNullSafe(letters_df.numbers))
+-------+-------+-------+ |numbers|numbers|letters| +-------+-------+-------+ | 456| 456| def| | null| null| zzz| | | | hhh| | 123| 123| abc| +-------+-------+-------+
和SparkR中的％< =>％：
numbers_df <- createDataFrame(data.frame(numbers = c("123","456",NA,"")))
letters_df <- createDataFrame(data.frame(
numbers = c("123",""),letters = c("abc","def","zzz","hhh")
))
head(join(numbers_df,letters_df,numbers_df$numbers %<=>% letters_df$numbers))
numbers numbers letters 1 456 456 def 2 <NA> <NA> zzz 3 hhh 4 123 123 abc
使用sql(Spark 2.2.0)，您可以使用IS NOT DISTINCT FROM：
SELECT * FROM numbers JOIN letters ON numbers.numbers IS NOT DISTINCT FROM letters.numbers
这也可以与DataFrame API一起使用：
numbersDf.alias("numbers")
.join(lettersDf.alias("letters"))
.where("numbers.numbers IS NOT DISTINCT FROM letters.numbers")
