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PySpark DataFrame | foreach method

Machine Learning
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PySpark
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PySpark DataFrame
schedule Jul 1, 2022
Last updated
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PySpark DataFrame's foreach(~) method loops over each row of the DataFrame as a Row object and applies the given function to the row.

WARNING

The following are some limitations of foreach(~):

  • the foreach(~) method in Spark is invoked in the worker nodes instead of the Driver program. This means that if we perform a print(~) inside our function, we will not be able to see the printed results in our session or notebook because the results are printed in the worker node instead.

  • rows are read-only and so you cannot update values of the rows.

Given these limitations, the foreach(~) method is mainly used for logging some information about each row to the local machine or to an external database.

Parameters

1. f | function

The function to apply to each row (Row) of the DataFrame.

Return Value

Nothing is returned.

Examples

Consider the following PySpark DataFrame:

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

To iterate over each row and apply some custom function:

# This function fires in the worker node
def f(row):
print(row.name)

df.foreach(f)

Here, the row.name is printed in the worker nodes so you would not see any output in the driver program.

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Published by Isshin Inada
Edited by 0 others
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