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sqlscalaapache-sparkjoinapache-spark-sql
Powers
Powers

Reputation: 19308

Including null values in an Apache Spark Join

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

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"))

Here is the output of joinedDf.show():

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

This is the output I would like:

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

Upvotes: 67

Views: 76608

Answers (8)

michael_s
michael_s

Reputation: 2575

Since I struggled a little with this to get a generic function I present my solution based on the answers and comments here. This also drops the superfluous columns of the right DataFrame.

import org.apache.spark.sql.DataFrame

def failSafeEqJoin(
  left: DataFrame,
  right: DataFrame,
  columns: Seq[String],
  joinType: String = "inner"
): DataFrame = {
  val failSafeEq = columns.map(n => left(n) <=> right(n)).reduceLeft(_ && _)
  val joined     = left.join(right, failSafeEq, joinType)
  def dropRight(df: DataFrame): DataFrame = {
    columns.foldLeft(df) { case (acc, c) =>
      acc.drop(right(c))
    }
  }
  dropRight(joined)
}

see also: How to avoid duplicate columns after join

Upvotes: 0

BlueBike
BlueBike

Reputation: 19

This is a late answer but there is an elegant way to create eqNullSafe joins in PySpark:

from pyspark.sql.dataframe import DataFrame

def null_safe_join(self, other:DataFrame, cols:list, mode:str):
    """
    Function for null safe joins. In normal joins, null values will be disregarded.
    In a null safe join, null values will be treated as equals.
    """
    join_cond = [self[col].eqNullSafe(other[col]) for col in cols]
    return (
        self.join(other, join_cond, mode)
        .drop(*[other[col] for col in cols])
    )

DataFrame.null_safe_join = null_safe_join

This will add the null_safe_join method to the DataFrame class, allowing it to be used as a method on any DataFrame object like so:

joinedDf = numbersDf.null_safe_join(lettersDf, ["numbers"], "inner")

This performs an inner eqNullSafe join between numbersDf and lettersDf on the column numbers.

Upvotes: 1

gavriil
gavriil

Reputation: 1

Based on timothyzhang's idea one can further improve it by removing duplicate columns:

def dropDuplicateColumns(df: DataFrame, rightDf: DataFrame, cols: Seq[String]): DataFrame 
= cols.foldLeft(df)((df, c) => df.drop(rightDf(c)))

def joinTablesWithSafeNulls(rightDF: DataFrame, leftDF: DataFrame, columns: Seq[String], joinType: String): DataFrame = 
{

val colExpr: Column = leftDF(columns.head) <=> rightDF(columns.head)

val fullExpr = columns.tail.foldLeft(colExpr) {
  (colExpr, p) => colExpr && leftDF(p) <=> rightDF(p)
}

val finalDF = leftDF.join(rightDF, fullExpr, joinType)

val filteredDF = dropDuplicateColumns(finalDF, rightDF, columns)

filteredDF

}

Upvotes: -1

Marcos Pindado
Marcos Pindado

Reputation: 341

Complementing the other answers, for PYSPARK < 2.3.0 you would not have Column.eqNullSafe neither IS NOT DISTINCT FROM.

You still can build the <=> operator with an sql expression to include it in the join, as long as you define alias for the join queries:

from pyspark.sql.types import StringType
import pyspark.sql.functions as F

numbers_df = spark.createDataFrame (["123","456",None,""], StringType()).toDF("numbers")
letters_df = spark.createDataFrame ([("123", "abc"),("456", "def"),(None, "zzz"),("", "hhh") ]).\
    toDF("numbers", "letters")

joined_df = numbers_df.alias("numbers").join(letters_df.alias("letters"),
                                             F.expr('numbers.numbers <=> letters.numbers')).\
    select('letters.*')
joined_df.show()


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

Upvotes: 4

timothyzhang
timothyzhang

Reputation: 800

Based on K L's idea, you could use foldLeft to generate join column expression:

def nullSafeJoin(rightDF: DataFrame, columns: Seq[String], joinType: String)(leftDF: DataFrame): DataFrame = 
{

  val colExpr: Column = leftDF(columns.head) <=> rightDF(columns.head)
  val fullExpr = columns.tail.foldLeft(colExpr) { 
    (colExpr, p) => colExpr && leftDF(p) <=> rightDF(p) 
  }

  leftDF.join(rightDF, fullExpr, joinType)
}

then, you could call this function just like:

aDF.transform(nullSafejoin(bDF, columns, joinType))

Upvotes: 7

K L
K L

Reputation: 1

Try the following method to include the null rows to the result of JOIN operator:

def nullSafeJoin(leftDF: DataFrame, rightDF: DataFrame, columns: Seq[String], joinType: String): DataFrame = {

    var columnsExpr: Column = leftDF(columns.head) <=> rightDF(columns.head)

    columns.drop(1).foreach(column => {
        columnsExpr = columnsExpr && (leftDF(column) <=> rightDF(column))
    })

    var joinedDF: DataFrame = leftDF.join(rightDF, columnsExpr, joinType)

    columns.foreach(column => {
        joinedDF = joinedDF.drop(leftDF(column))
    })

    joinedDF
}

Upvotes: -2

zero323
zero323

Reputation: 330063

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

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).

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

and %<=>% in SparkR:

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

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

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")

Upvotes: 118

jasonS
jasonS

Reputation: 309

val numbers2 = numbersDf.withColumnRenamed("numbers","num1") //rename columns so that we can disambiguate them in the join
val letters2 = lettersDf.withColumnRenamed("numbers","num2")
val joinedDf = numbers2.join(letters2, $"num1" === $"num2" || ($"num1".isNull &&  $"num2".isNull) ,"outer")
joinedDf.select("num1","letters").withColumnRenamed("num1","numbers").show  //rename the columns back to the original names

Upvotes: 12

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