spark.local.dir configuration parameter

Purpose and Usage

While Spark jobs are running, temporary files, shuffles, and intermediate data are written to the local disk for efficient processing. The spark.local.dir parameter defines the directory path where Spark writes these temporary files.

Default Value

The default value of spark.local.dir is set to /tmp/spark/scratch. This directory location is used for temporary storage on the local disk of each Spark executor node.

Overriding the Default Value

In the Analytics Engine service, you have the ability to override the default value of spark.local.dir when submitting your Spark applications:

Example

Application Payload:

{
 	"application_details": {
 		"application": "/opt/ibm/spark/examples/src/main/python/wordcount.py",
 		"application_arguments": ["cos://mycos-bucket.object-storage/people.csv"],
 		"conf": {
 			"spark.app.name": "MyJob",
 			"spark.local.dir": "/myTempSpace",
 			"spark.hadoop.fs.cos.object-storage.endpoint": "s3.direct.us-south.cloud-object-storage.appdomain.cloud",
 			"spark.hadoop.fs.cos.object-storage.secret.key": "xxxx",
 			"spark.hadoop.fs.cos.object-storage.access.key": "xxxx"
 		}
 	},
 	"volumes": [{
 		"name": "temp-vol",
 		"mount_path": "/myTempSpace",
 		"source_sub_path": ""
 	}]
 }

By overriding the default value of spark.local.dir, you can tailor the temporary file storage location to your specific requirements, such as utilizing a high-performance disk or directing temporary files to a shared network storage location.

Remember to consider the available disk space, permissions, and accessibility of the chosen directory path when selecting a custom value for spark.local.dir.

In notebooks you can override the Spark conf spark.local.dir while initializing the spark context. For example:

from pyspark import SparkContent, SparkConf
import os

# Create a SparkConf
spark_conf = SparkConf().setAppName("DataProcessing").setMaster("local")
spark_conf.set("spark.local.dir", "/home/spark/shared/spark-events/")
sc.stop()
# Create a new SparkContext
sc = SparkContext(conf=spark_conf)

Parent topic: Submitting Spark jobs via API