
        
        
            
                
                
                    df = spark.createDataFrame([("Alex", 20), ("Bob", 30), ("Cathy", 40)], ["name", "age"])
df.show()
                
            
            +-----+---+
| name|age|
+-----+---+
| Alex| 20|
|  Bob| 30|
|Cathy| 40|
+-----+---+

By default, the number of partitions depends on the parallelism level of your PySpark configuration:


        
        
            
                
                
                    df.rdd.getNumPartitions()
                
            
            8

In my case, our PySpark DataFrame is split into 8 partitions by default.

We can see how the rows of our DataFrame are partitioned using the glom() method of the underlying RDD:


        
        
            
                
                
                    df.rdd.glom().collect()
                
            
            [[],
 [],
 [Row(name='Alex', age=20)],
 [],
 [],
 [Row(name='Bob', age=30)],
 [],
 [Row(name='Cathy', age=40)]]

Here, we can see that we have indeed 8 partitions, but only 3 of the partitions have a Row in them.

Now, let's repartition our DataFrame such that the Rows are divided into only 2 partitions:


        
        
            
                
                
                    df_new = df.repartition(2)
df_new.rdd.getNumPartitions()
                
            
            2

The distribution of the rows in our repartitioned DataFrame is now:


        
        
            
                
                
                    df_new.rdd.glom().collect()
                
            
            [[Row(name='Alex', age=20),
  Row(name='Bob', age=30),
  Row(name='Cathy', age=40)],
 []]

As demonstrated here, there is no guarantee that the rows will be evenly distributed in the partitions.

Partitioning a PySpark DataFrame by column values

Consider the following PySpark DataFrame:


        
        
            
                
                
                    df = spark.createDataFrame([("Alex", 20), ("Bob", 30), ("Cathy", 40), ("Alex", 50)], ["name", "age"])
df.show()
                
            
            +-----+---+
| name|age|
+-----+---+
| Alex| 20|
|  Bob| 30|
|Cathy| 40|
| Alex| 50|
+-----+---+

To repartition this PySpark DataFrame by the column name into 2 partitions:


        
        
            
                
                
                    df_new = df.repartition(2, "name")
df_new.rdd.glom().collect()
                
            
            [[Row(name='Alex', age=20),
  Row(name='Cathy', age=40),
  Row(name='Alex', age=50)],
 [Row(name='Bob', age=30)]]

Here, notice how the rows with the same value for name ('Alex' in this case) end up in the same partition.

We can also repartition by multiple column values:


        
        
            
                
                
                    df_new = df.repartition(4, "name", "age")
df_new.rdd.glom().collect()
                
            
            [[Row(name='Alex', age=20)],
 [Row(name='Bob', age=30)],
 [Row(name='Alex', age=50)],
 [Row(name='Cathy', age=40)]]

Here, we are repartitioning by the name and age columns into 4 partitions.

We can also use the default number of partitions by specifying column labels only:


        
        
            
                
                
                    df_new = df.repartition("name")
df_new.rdd.getNumPartitions()
                
            
            1

Published by Isshin Inada

Edited by 0 others

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Official PySpark Documentation

https://spark.apache.org/docs/3.2.0/api/python/reference/api/pyspark.sql.DataFrame.repartition.html

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