在 Apache Spark Join 中包含空值 [英] Including null values in an Apache Spark Join

问题描述

我想在 Apache Spark 连接中包含空值.Spark 默认不包含 null 的行.

I would like to include null values in an Apache Spark join. Spark doesn't include rows with null by default.

这是默认的 Spark 行为.

Here is the default Spark behavior.

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|
+-------+-------+

推荐答案

Spark 提供了一个特殊的 NULL 安全相等运算符:

Spark provides a special NULL safe equality operator:

numbersDf
  .join(lettersDf, numbersDf("numbers") <=> lettersDf("numbers"))
  .drop(lettersDf("numbers"))

+-------+-------+
|numbers|letters|
+-------+-------+
|    123|    abc|
|    456|    def|
|   null|    zzz|
|       |    hhh|
+-------+-------+

注意不要将它与 Spark 1.5 或更早版本一起使用.在 Spark 1.6 之前，它需要笛卡尔积 (SPARK-11111 - 快速空安全连接).

Be careful not to use it with Spark 1.5 or earlier. Prior to Spark 1.6 it required a Cartesian product (SPARK-11111 - Fast null-safe join).

在 Spark 2.3.0 或更高版本中，您可以在 PySpark 中使用 Column.eqNullSafe:

In Spark 2.3.0 or later you can use Column.eqNullSafe in PySpark:

numbers_df = sc.parallelize([
    ("123", ), ("456", ), (None, ), ("", )
]).toDF(["numbers"])

letters_df = sc.parallelize([
    ("123", "abc"), ("456", "def"), (None, "zzz"), ("", "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 %<=>% 在 SparkR 中:

numbers_df <- createDataFrame(data.frame(numbers = c("123", "456", NA, "")))
letters_df <- createDataFrame(data.frame(
  numbers = c("123", "456", NA, ""),
  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:

With SQL (Spark 2.2.0+) you can use 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:

This is can be used with DataFrame API as well:

numbersDf.alias("numbers")
  .join(lettersDf.alias("letters"))
  .where("numbers.numbers IS NOT DISTINCT FROM letters.numbers")

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