Find maximum row per group in Spark DataFrame

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-上瘾入骨i
-上瘾入骨i 2020-11-22 03:47

I\'m trying to use Spark dataframes instead of RDDs since they appear to be more high-level than RDDs and tend to produce more readable code.

In a 14-nodes Google Da

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  • 2020-11-22 04:03

    Using join (it will result in more than one row in group in case of ties):

    import pyspark.sql.functions as F
    from pyspark.sql.functions import count, col 
    
    cnts = df.groupBy("id_sa", "id_sb").agg(count("*").alias("cnt")).alias("cnts")
    maxs = cnts.groupBy("id_sa").agg(F.max("cnt").alias("mx")).alias("maxs")
    
    cnts.join(maxs, 
      (col("cnt") == col("mx")) & (col("cnts.id_sa") == col("maxs.id_sa"))
    ).select(col("cnts.id_sa"), col("cnts.id_sb"))
    

    Using window functions (will drop ties):

    from pyspark.sql.functions import row_number
    from pyspark.sql.window import Window
    
    w = Window().partitionBy("id_sa").orderBy(col("cnt").desc())
    
    (cnts
      .withColumn("rn", row_number().over(w))
      .where(col("rn") == 1)
      .select("id_sa", "id_sb"))
    

    Using struct ordering:

    from pyspark.sql.functions import struct
    
    (cnts
      .groupBy("id_sa")
      .agg(F.max(struct(col("cnt"), col("id_sb"))).alias("max"))
      .select(col("id_sa"), col("max.id_sb")))
    

    See also How to select the first row of each group?

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  • 2020-11-22 04:28

    I think what you might be looking for are window functions: http://spark.apache.org/docs/latest/api/python/pyspark.sql.html?highlight=window#pyspark.sql.Window

    https://databricks.com/blog/2015/07/15/introducing-window-functions-in-spark-sql.html

    Here is an example in Scala (I don't have a Spark Shell with Hive available right now, so I was not able to test the code, but I think it should work):

    case class MyRow(name: String, id_sa: String, id_sb: String)
    
    val myDF = sc.parallelize(Array(
        MyRow("n1", "a1", "b1"),
        MyRow("n2", "a1", "b2"),
        MyRow("n3", "a1", "b2"),
        MyRow("n1", "a2", "b2")
    )).toDF("name", "id_sa", "id_sb")
    
    import org.apache.spark.sql.expressions.Window
    
    val windowSpec = Window.partitionBy(myDF("id_sa")).orderBy(myDF("id_sb").desc)
    
    myDF.withColumn("max_id_b", first(myDF("id_sb")).over(windowSpec).as("max_id_sb")).filter("id_sb = max_id_sb")
    

    There are probably more efficient ways to achieve the same results with Window functions, but I hope this points you in the right direction.

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