Memory error in Spark

Spark is a general purpose cluster computing framework used to process large datasets. It runs on Standalone, Mesos, and YARN clusters.

You often encounter memory issue like something below.

No space left on device.


Spark uses disk space on worker nodes to store shuffle data/files. So if the disk space is not enough to hold the data you’re processing, it will throw error.


I used to get this error on AWS EMR while running spark jobs. By increasing the EBS volume size on core nodes using the below AWS CLI, I was able to fix the memory issue. Another solution is to increase the cluster size so the shuffle file sizes would be smaller.

aws emr create-cluster --release-label emr-5.9.0  --use-default-roles --instance-groups InstanceGroupType=MASTER,InstanceCount=1,InstanceType=d2.xlarge 'InstanceGroupType=CORE,InstanceCount=2,InstanceType=d2.xlarge,EbsConfiguration={EbsOptimized=true,EbsBlockDeviceConfigs=[{VolumeSpecification={VolumeType=gp2,SizeInGB=100}},{VolumeSpecification={VolumeType=io1,SizeInGB=100,Iops=100},VolumesPerInstance=4}]}' --auto-terminate