Merge Multiple Spark Rows to One
I Have a Dataframe Which Looks Like One Given Below. All the Values for a Corresponding Id Is the Same Except for the Mappingcol Field...
I have a dataframe which looks like one given below. All the values for a corresponding id is the same except for the mappingcol field.
+--------------------+----------------+--------------------+-------+
|misc |fruit |mappingcol |id |
+--------------------+----------------+--------------------+-------+
|ddd |apple |Map("name"->"Sameer"| 1 |
|ref |banana |Map("name"->"Riyazi"| 2 |
|ref |banana |Map("lname"->"Nikki"| 2 |
|ddd |apple |Map("lname"->"tenka"| 1 |
+--------------------+----------------+--------------------+-------+
I want to merge the rows with same row in such a way that I get exactly one row for one id and the value of mappingcol needs to be merged. The output should look like :
+--------------------+----------------+--------------------+-------+
|misc |fruit |mappingcol |id |
+--------------------+----------------+--------------------+-------+
|ddd |apple |Map("name"->"Sameer"| 1 |
|ref |banana |Map("name"->"Riyazi"| 2 |
+--------------------+----------------+--------------------+-------+
the value for mappingcol for id = 1 would be :
Map(
"name" -> "Sameer",
"lname" -> "tenka"
)
I know that maps can be merged using ++ operator, so thats not what im worried about. I just cant understand how to merge the rows, because if I use a groupBy, I have nothing to aggregate the rows on.
2 Answers
You can use by groupBy and then managing a little the map
df.groupBy("id", "fruit", "misc").agg(collect_list("mappingcol"))
.as[(Int, String, String, Seq[Map[String, String]])]
.map { case (id, fruit, misc, list) => (id, fruit, misc, list.reduce(_ ++ _)) }
.toDF("id", "fruit", "misc", "mappingColumn")
- With the first line, tou group by your desired columns and aggregate the map pairs in the same element (an array)
- With the second line (as), you convert your structure to a Dataset of a Tuple4 with the last element being a sequence of maps
- With the third line (map), you merge all the elements to a single map
- With the last line (toDF) to give the columns the original names
OUTPUT
+---+------+----+--------------------------------+
|id |fruit |misc|mappingColumn |
+---+------+----+--------------------------------+
|1 |apple |ddd |[name -> Sameer, lname -> tenka]|
|2 |banana|ref |[name -> Riyazi, lname -> Nikki]|
+---+------+----+--------------------------------+
You can definitely do the above with a Window function!
This is in PySpark not Scala but there's almost no difference when only using native Spark functions.
The below code only works on a map column that 1 one key, value pair per row, as it how your example data is, but it can be made to work with map columns with multiple entries.
from pyspark.sql import Window
map_col = 'mappingColumn'
group_cols = ['id', 'fruit', 'misc']
# or, a lazier way if you have a lot of columns to group on
cols = df.columns # save as list
group_cols_2 = cols.remove('mappingCol') # remove what you're not grouping by
w = Window.partitionBy(group_cols)
# unpack map value and key into a pair struct column
df1 = df.withColumn(map_col , F.struct(F.map_keys(map_col)[0], F.map_values(map_col)[0]))
# Collect all key values into an array of structs, here each row
# contains the map entries for all rows in the group/window
df1 = df1.withColumn(map_col , F.collect_list(map_col).over(w))
# drop duplicate values, as you only want one row per group
df1 = df1.dropDuplicates(group_cols)
# return the values for map type
df1 = df1.withColumn(map_col , F.map_from_entries(map_col))
You can save the output of each step to a new column to see how each step works, as I have done below.
from pyspark.sql import Window
map_col = 'mappingColumn'
group_cols = list('id', 'fruit', 'misc')
w = Window.partitionBy(group_cols)
df1 = df.withColumn('test', F.struct(F.map_keys(map_col)[0], F.map_values(map_col)[0]))
df1 = df1.withColumn('test1', F.collect_list('test').over(w))
df1 = df1.withColumn('test2', F.map_from_entries('test1'))
df1.show(truncate=False)
df1.printSchema()
df1 = df1.dropDuplicates(group_cols)
Sarah Jenkins is a veteran tech journalist with over 12 years of experience covering artificial intelligence, mobile innovations, and digital ethics. Her insights have appeared in leading technology publications worldwide.
