Spark on yarn mode end with "Exit status: -100. Diagnostics: Container released on a *lost* node"
I am trying to load a database with 1TB data to spark on AWS using the latest EMR. And the running time is so long that it doesn't finished in even 6 hours, but after running 6h30m , I get some error announcing that Container released on a lost node and then the job failed. Logs are like this:
16/07/01 22:45:43 WARN scheduler.TaskSetManager: Lost task 144178.0 in stage 0.0 (TID 144178, ip-10-0-2-176.ec2.internal): ExecutorLostFailure (executor 5 exited caused by one of the running tasks) Reason: Container marked as failed: container_1467389397754_0001_01_000006 on host: ip-10-0-2-176.ec2.internal. Exit status: -100. Diagnostics: Container released on a *lost* node
16/07/01 22:45:43 WARN scheduler.TaskSetManager: Lost task 144181.0 in stage 0.0 (TID 144181, ip-10-0-2-176.ec2.internal): ExecutorLostFailure (executor 5 exited caused by one of the running tasks) Reason: Container marked as failed: container_1467389397754_0001_01_000006 on host: ip-10-0-2-176.ec2.internal. Exit status: -100. Diagnostics: Container released on a *lost* node
16/07/01 22:45:43 WARN scheduler.TaskSetManager: Lost task 144175.0 in stage 0.0 (TID 144175, ip-10-0-2-176.ec2.internal): ExecutorLostFailure (executor 5 exited caused by one of the running tasks) Reason: Container marked as failed: container_1467389397754_0001_01_000006 on host: ip-10-0-2-176.ec2.internal. Exit status: -100. Diagnostics: Container released on a *lost* node
16/07/01 22:45:43 WARN scheduler.TaskSetManager: Lost task 144213.0 in stage 0.0 (TID 144213, ip-10-0-2-176.ec2.internal): ExecutorLostFailure (executor 5 exited caused by one of the running tasks) Reason: Container marked as failed: container_1467389397754_0001_01_000006 on host: ip-10-0-2-176.ec2.internal. Exit status: -100. Diagnostics: Container released on a *lost* node
16/07/01 22:45:43 INFO scheduler.DAGScheduler: Executor lost: 5 (epoch 0)
16/07/01 22:45:43 WARN cluster.YarnSchedulerBackend$YarnSchedulerEndpoint: Container marked as failed: container_1467389397754_0001_01_000007 on host: ip-10-0-2-173.ec2.internal. Exit status: -100. Diagnostics: Container released on a *lost* node
16/07/01 22:45:43 INFO storage.BlockManagerMasterEndpoint: Trying to remove executor 5 from BlockManagerMaster.
16/07/01 22:45:43 INFO storage.BlockManagerMasterEndpoint: Removing block manager BlockManagerId(5, ip-10-0-2-176.ec2.internal, 43922)
16/07/01 22:45:43 INFO storage.BlockManagerMaster: Removed 5 successfully in removeExecutor
16/07/01 22:45:43 ERROR cluster.YarnClusterScheduler: Lost executor 6 on ip-10-0-2-173.ec2.internal: Container marked as failed: container_1467389397754_0001_01_000007 on host: ip-10-0-2-173.ec2.internal. Exit status: -100. Diagnostics: Container released on a *lost* node
16/07/01 22:45:43 INFO spark.ExecutorAllocationManager: Existing executor 5 has been removed (new total is 41)
16/07/01 22:45:43 WARN scheduler.TaskSetManager: Lost task 144138.0 in stage 0.0 (TID 144138, ip-10-0-2-173.ec2.internal): ExecutorLostFailure (executor 6 exited caused by one of the running tasks) Reason: Container marked as failed: container_1467389397754_0001_01_000007 on host: ip-10-0-2-173.ec2.internal. Exit status: -100. Diagnostics: Container released on a *lost* node
16/07/01 22:45:43 WARN scheduler.TaskSetManager: Lost task 144185.0 in stage 0.0 (TID 144185, ip-10-0-2-173.ec2.internal): ExecutorLostFailure (executor 6 exited caused by one of the running tasks) Reason: Container marked as failed: container_1467389397754_0001_01_000007 on host: ip-10-0-2-173.ec2.internal. Exit status: -100. Diagnostics: Container released on a *lost* node
16/07/01 22:45:43 WARN scheduler.TaskSetManager: Lost task 144184.0 in stage 0.0 (TID 144184, ip-10-0-2-173.ec2.internal): ExecutorLostFailure (executor 6 exited caused by one of the running tasks) Reason: Container marked as failed: container_1467389397754_0001_01_000007 on host: ip-10-0-2-173.ec2.internal. Exit status: -100. Diagnostics: Container released on a *lost* node
16/07/01 22:45:43 WARN scheduler.TaskSetManager: Lost task 144186.0 in stage 0.0 (TID 144186, ip-10-0-2-173.ec2.internal): ExecutorLostFailure (executor 6 exited caused by one of the running tasks) Reason: Container marked as failed: container_1467389397754_0001_01_000007 on host: ip-10-0-2-173.ec2.internal. Exit status: -100. Diagnostics: Container released on a *lost* node
16/07/01 22:45:43 WARN cluster.YarnSchedulerBackend$YarnSchedulerEndpoint: Container marked as failed: container_1467389397754_0001_01_000035 on host: ip-10-0-2-173.ec2.internal. Exit status: -100. Diagnostics: Container released on a *lost* node
16/07/01 22:45:43 INFO scheduler.DAGScheduler: Executor lost: 6 (epoch 0)
16/07/01 22:45:43 INFO storage.BlockManagerMasterEndpoint: Trying to remove executor 6 from BlockManagerMaster.
16/07/01 22:45:43 INFO storage.BlockManagerMasterEndpoint: Removing block manager BlockManagerId(6, ip-10-0-2-173.ec2.internal, 43593)
16/07/01 22:45:43 INFO storage.BlockManagerMaster: Removed 6 successfully in removeExecutor
16/07/01 22:45:43 ERROR cluster.YarnClusterScheduler: Lost executor 30 on ip-10-0-2-173.ec2.internal: Container marked as failed: container_1467389397754_0001_01_000035 on host: ip-10-0-2-173.ec2.internal. Exit status: -100. Diagnostics: Container released on a *lost* node
16/07/01 22:45:43 WARN scheduler.TaskSetManager: Lost task 144162.0 in stage 0.0 (TID 144162, ip-10-0-2-173.ec2.internal): ExecutorLostFailure (executor 30 exited caused by one of the running tasks) Reason: Container marked as failed: container_1467389397754_0001_01_000035 on host: ip-10-0-2-173.ec2.internal. Exit status: -100. Diagnostics: Container released on a *lost* node
16/07/01 22:45:43 INFO spark.ExecutorAllocationManager: Existing executor 6 has been removed (new total is 40)
16/07/01 22:45:43 WARN scheduler.TaskSetManager: Lost task 144156.0 in stage 0.0 (TID 144156, ip-10-0-2-173.ec2.internal): ExecutorLostFailure (executor 30 exited caused by one of the running tasks) Reason: Container marked as failed: container_1467389397754_0001_01_000035 on host: ip-10-0-2-173.ec2.internal. Exit status: -100. Diagnostics: Container released on a *lost* node
16/07/01 22:45:43 WARN scheduler.TaskSetManager: Lost task 144170.0 in stage 0.0 (TID 144170, ip-10-0-2-173.ec2.internal): ExecutorLostFailure (executor 30 exited caused by one of the running tasks) Reason: Container marked as failed: container_1467389397754_0001_01_000035 on host: ip-10-0-2-173.ec2.internal. Exit status: -100. Diagnostics: Container released on a *lost* node
16/07/01 22:45:43 WARN scheduler.TaskSetManager: Lost task 144169.0 in stage 0.0 (TID 144169, ip-10-0-2-173.ec2.internal): ExecutorLostFailure (executor 30 exited caused by one of the running tasks) Reason: Container marked as failed: container_1467389397754_0001_01_000035 on host: ip-10-0-2-173.ec2.internal. Exit status: -100. Diagnostics: Container released on a *lost* node
16/07/01 22:45:43 INFO scheduler.DAGScheduler: Executor lost: 30 (epoch 0)
16/07/01 22:45:43 WARN cluster.YarnSchedulerBackend$YarnSchedulerEndpoint: Container marked as failed: container_1467389397754_0001_01_000024 on host: ip-10-0-2-173.ec2.internal. Exit status: -100. Diagnostics: Container released on a *lost* node
I am pretty sure that my network setting works because I have tried to run this script on the same environment on a much smaller table.
Also, I am aware that somebody posted a question 6 months ago asking for the same issue:spark-job-error-yarnallocator-exit-status-100-diagnostics-container-released but I still have to ask because nobody was answering this question.
Answer
Looks like other peoples has the same issue as well, so I just post an answer instead of writing a comment. I am not sure that this would solve the issue but this should be an idea.
If you use spot instance, you should know that spot instance will be shut down if the price is higher than your input, and you will hit this issue. Even if you are just using a spot instance as a slave. So my solution is not using any spot instance for long term running job.
Another idea is to slice the job into many independent steps, so you can save the result of each step as a file on S3. If any error happened, just start from that step by the cached files.