Repartition

ProphecySparkBasicsPython0.0.1+ProphecySparkBasicsScala0.0.1+Databricks UC Single Cluster14.3+Databricks UC Shared14.3+Livy3.0.1+

This will repartition or coalesce the input DataFrame based on the specified configuration. There are four different repartitioning options:

Hash Repartitoning

Repartitions the data evenly across various partitions based on the hash value of the specified key.

Parameters

Parameter	Description	Required
DataFrame	Input DataFrame	True
Overwrite default partitions	Flag to overwrite default partitions	False
Number of partitions	Integer value specifying number of partitions	False
Repartition expression(s)	List of expressions to repartition by	True

Generated Code

Python

def hashRepartition(spark: SparkSession, in0: DataFrame) -> DataFrame:
    return in0.repartition(5, col("customer_id"))

Scala

object hashRepartition {

  def apply(spark: SparkSession, in: DataFrame): DataFrame =
    in.repartition(5, col("customer_id"))

}

Random Repartitioning

Repartitions without data distribution defined.

Parameters

Parameter	Description	Required
DataFrame	Input DataFrame	True
Number of partitions	Integer value specifying number of partitions	True

Generated Code

Python

def randomRepartition(spark: SparkSession, in0: DataFrame) -> DataFrame:
    return in0.repartition(5)

Scala

object randomRepartition {

  def apply(spark: SparkSession, in: DataFrame): DataFrame =
    in.repartition(5)

}

Range Repartitoning

Repartitions the data with tuples having keys within the same range on the same worker.

Parameters

Parameter	Description	Required
DataFrame	Input DataFrame	True
Overwrite default partitions	Flag to overwrite default partitions	False
Number of partitions	Integer value specifying number of partitions	False
Repartition expression(s) with sorting	List of expressions to repartition by with corresponding sorting order	True

Generated Code

Python

def RepartitionByRange(spark: SparkSession, in0: DataFrame) -> DataFrame:
    return in0.repartitionByRange(5, col("customer_id").asc())

Scala

object RepartitionByRange {

  def apply(spark: SparkSession, in: DataFrame): DataFrame =
    in.repartitionByRange(5, col("customer_id").asc())

}

Coalesce

Reduces the number of partitions without shuffling the dataset.

Parameters

Parameter	Description	Required
DataFrame	Input DataFrame	True
Number of partitions	Integer value specifying number of partitions	True

Generated Code

Python

def Coalesce(spark: SparkSession, in0: DataFrame) -> DataFrame:
    return in0.coalesce(5)

Scala

object Coalesce {

  def apply(spark: SparkSession, in: DataFrame): DataFrame =
    in.coalesce(5)

}

Video demo