PySpark / Spark foreachPartition Vs foreach – Check Not Obvious Differences Between These 2 Functions!

PySpark / Spark foreachPartition Vs foreach
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In this post we will dive into topic: Spark foreachPartition Vs foreach – Check The Differences Between These 2 Functions!

Introduction

Apache Spark RDD Partitions

A Resilient Distributed Dataset (RDD) in Apache Spark is a distributed collection of data that can be analysed in parallel. An RDD is divided into divisions, which are smaller, independent bits of data, to permit parallel processing. Spark can do distributed computing by processing each partition on a distinct node in a cluster.

An RDD can be partitioned in a variety of ways, including by the value of a single key in the data or by applying a hash function to each element. An RDD is typically partitioned into a specific number of partitions when it is first built. The number of partitions, however, can be adjusted later by using techniques like repartition or coalesce.

The mapPartitions and foreachPartition methods can be used to access data in a given partition. These techniques allow you to apply a function to each partition of the RDD rather than each element. When working with big RDDs, this can be more efficient since it allows for more efficient data processing.

Partitions are smaller, independent bits of data that may be handled in parallel in Spark RDDs. RDDs can be partitioned in a variety of ways, with the number of partitions variable. The methods mapPartitions and foreachPartition make it possible to process partitions quickly.

foreachPartition(f : scala.Function1[scala.Iterator[T], scala.Unit]) : scala.Unit

Spark foreachPartition Vs foreach

Both foreach and foreachPartition are Apache Spark methods that let you to perform an action on each member of an RDD (Resilient Distributed Dataset). The primary distinction between the two is that foreach applies the action to each RDD element, whereas foreachPartition applies the action to each RDD partition.

If you use foreach, the action will be applied to each element of the RDD, which might cause performance concerns if the RDD is huge. ForeachPartition, on the other hand, will perform the action to each partition of the RDD, implying that the action is done to all components of a partition at once. This can result in improved speed when working with big RDDs since it allows for more efficient data processing.

It’s important to note that foreachPartition is run on the worker nodes, whereas foreach is run on the driver.

Spark foreachPartition Vs foreach On RDD

Here’s an example of using foreach and foreachPartition in Scala and PySpark:

Spark Scala:

val rdd = sc.parallelize(1 to 10)

// Using foreach to print each element of the RDD
rdd.foreach(x => println(x))

// Using foreachPartition to print the elements of each partition
rdd.foreachPartition(iter => iter.foreach(x => println(x)))

PySpark foreachPartition Vs foreach:

rdd = sc.parallelize(range(1, 11))

# Using foreach to print each element of the RDD
rdd.foreach(lambda x: print(x))

# Using foreachPartition to print the elements of each partition
rdd.foreachPartition(lambda iter: [print(x) for x in iter])

Both examples will print the same result, however the first foreach prints the items of the RDD one by one, whereas the second foreachPartition displays the partition elements.

Foreach applies the action to each element of the RDD, whereas foreachPartition applies the action to each partition of the RDD, implying that the action is applied to all elements of a partition at the same time.

You can also use these examples to examine the performance difference when working with big RDDs; foreachPartition should be more efficient in circumstances when you need to process the RDD items in bulk, per partition.

Spark foreachPartition Vs foreach On DataFrame And DataSet

Here’s an example of using foreach and foreachPartition on a DataFrame in Scala and PySpark:

Spark Scala

val df = spark.createDataFrame(Seq((1, "a"), (2, "b"), (3, "c")))

// Using foreach to print each row of the DataFrame
df.foreach(row => println(row))

// Using foreachPartition to print the rows of each partition
df.foreachPartition(iter => iter.foreach(row => println(row)))

PySpark foreachPartition Vs foreach:

from pyspark.sql.functions import *
df = spark.createDataFrame([(1, "a"), (2, "b"), (3, "c")], ["id", "name"])

# Using foreach to print each row of the DataFrame
df.foreach(lambda row: print(row))

# Using foreachPartition to print the rows of each partition
df.foreachPartition(lambda iter: [print(row) for row in iter])

Both examples will provide the identical results, however the first foreach prints the rows of the DataFrame one by one, whereas the second foreachPartition displays the rows of the partition.

The action is performed to each row of the DataFrame in the case of foreach, but the action is applied to each partition of the DataFrame in the case of foreachPartition, implying that the action is applied to all rows of a partition at once.

Because of Spark’s query optimization features, foreach and foreachPartition give extra speed benefits when utilising DataFrames. When working with huge datasets, foreach and foreachPartition can push certain calculations down to the worker nodes, resulting in enhanced speed.

Spark foreachPartition Task Not Serializable

in Apache Spark, when you use the foreachPartition method to perform an action on each partition of an RDD, DataFrame, or Dataset, the function that you pass to foreachPartition must be serializable. This means that the function, along with any objects it references, must be able to be converted to a binary format so that it can be sent to the worker nodes for processing.

If the function is not serializable, Spark will throw a “Task not serializable” error. This error occurs because the JVM cannot serialize the non-serializable object and send it to the worker nodes.

Here are a few common reasons why the function passed to foreachPartition might not be serializable:

  • The function references a non-serializable object, such as a file handle or a socket connection.
  • The function references a non-serializable variable, such as a non-serializable object defined in the outer scope of the function.
  • The function references a non-serializable class, such as a class that extends a non-serializable class or interface.

To fix this error, you can make sure that the function passed to foreachPartition is only referencing serializable objects and do not reference any variable outside of the function. Also, you can use mapPartitions instead of foreachPartition when the function you are passing is not serializable.

Spark foreachPartition With Index

The foreachPartition function in Apache Spark lets you to conduct an action on each partition of an RDD, DataFrame, or Dataset. By default, foreachPartition does not return any information about the index of the partition being processed. You may get around this by using the mapPartitionsWithIndex method instead, which has an extra argument that gives you the partition index.

Here is an example of how to use mapPartitionsWithIndex to perform the same functionality as foreachPartition while additionally giving the partition index:

Spark mapPartitionsWithIndex Scala foreachPartition With Index

val rdd = sc.parallelize(1 to 10)

// Using mapPartitionsWithIndex to print each element of the RDD
rdd.mapPartitionsWithIndex { case (index, iter) => 
  iter.map(x => s"Partition $index: $x")
}.foreach(println)

PySpark mapPartitionsWithIndex

rdd = sc.parallelize(range(1, 11))

# Using mapPartitionsWithIndex to print each element of the RDD
rdd.mapPartitionsWithIndex(lambda index, iter: ["Partition {}: {}".format(index, x) for x in iter]).foreach(print)

Both examples will print the same result, however the second one mapPartitionsWithIndex prints the partition items together with the partition index.

The sole difference between mapPartitions and mapPartitionsWithIndex is that it accepts one more parameter, an integer value denoting the partition index. This can be handy when doing actions on data that are reliant on which split the data is in.

Summary

In summary, foreach is best suited for circumstances where each element of the RDD must be processed individually, but foreachPartition is more efficient for cases when the elements of the RDD must be processed in bulk, by partition.

The function passed to Spark foreachPartition must be serializable, otherwise Spark will throw “Task not serializable” error. To fix this error, make sure that the function passed to Spark foreachPartition is only referencing serializable objects and do not reference any variable outside of the function.

foreachPartition does not return the index of the partition being processed. To get this capability, utilise the mapPartitionsWithIndex method, which has an extra parameter that returns the partition index.

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