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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 Dataproc cluster, I have about 6 millions names that are translated to ids by two different systems: sa and sb. Each Row contains name, id_sa and id_sb. My goal is to produce a mapping from id_sa to id_sb such that for each id_sa, the corresponding id_sb is the most frequent id among all names attached to id_sa.

Let's try to clarify with an example. If I have the following rows:

[Row(name='n1', id_sa='a1', id_sb='b1'),
 Row(name='n2', id_sa='a1', id_sb='b2'),
 Row(name='n3', id_sa='a1', id_sb='b2'),
 Row(name='n4', id_sa='a2', id_sb='b2')]


My goal is to produce a mapping from a1 to b2. Indeed, the names associated to a1 are n1, n2 and n3, which map respectively to b1, b2 and b2, so b2 is the most frequent mapping in the names associated to a1. In the same way, a2 will be mapped to b2.

1 Answer

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by (32.3k points)

Simply use join method:

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"))

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