I\'m deploying a Spark data processing job on an EC2 cluster, the job is small for the cluster (16 cores with 120G RAM in total), the largest RDD has only 76k+ rows. But heavily
I was also getting error
org.apache.spark.shuffle.MetadataFetchFailedException: Missing an output location for shuffle
and looking further in log I found
Container killed on request. Exit code is 143
After searching for the exit code, I realized that's its mainly related to memory allocation. So I checked the amount of memory I have configured for executors. I found that by mistake I had configured 7g to driver and only 1g for executor. After increasing the memory of executor my spark job ran successfully.