Unable to get Apache Spark SparkEnv settings via PySpark

To get the same output using PySpark, broadcast the “test” value to the executors so you can perform the map operation on the executors.

Written by Vidhi Khaitan

Last published at: March 18th, 2025

Problem

You’re able to get Apache Spark settings using SparkEnv.get.conf.get() in Scala, but want to use the PySpark equivalent instead. 

 

Scala example

import org.apache.spark.SparkEnv

val res = spark.range(1).rdd.map(_ => SparkEnv.get.conf.get("test", "default")).collect()

 

Cause

PySpark doesn't provide a direct equivalent to Scala's SparkEnv.get.conf.get() that can be safely used on executors. This is due to the differences in how Scala and Python interact with the JVM in Spark. 

 

Solution

Use the following steps to obtain the same output using PySpark. 

  1. Retrieve the value of the configuration parameter "test" from the SparkConf object.
  2. Broadcast test_value to all worker nodes in the Spark cluster. 
  3. Apply the map transformation that replaces each element with the value of the broadcast variable.

 

Example code 

# Get the value of "test" from SparkConf, or use "default" if not set
test_value = sc.getConf().get("test", "default")

# Broadcast the test_value to all worker nodes to perform map operation later
broadcast_test_value = sc.broadcast(test_value)

# Create an RDD with a single element, transform it, and collect the result
res = spark.range(1).rdd.map(lambda _: broadcast_test_value.value).collect()

# Print the result
print(res)