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Are failed tasks automatically resubmitted in Apache Spark to the same or another executor?
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If an executor runs into memory issues, it will fail the task and restart where the last task left off. If that task fails after 3 retries (4 attempts total by default) then that Stage will fail and cause the Spark job as a whole to fail.
And stages are divided into tasks. When you have failed tasks, you need to find the Stage that the tasks belong to. To do this, click on Stages in the Spark UI and then look for the Failed Stages section at the bottom of the page. If an executor runs into memory issues, it will fail the task and restart where the last task left off.
To do this, click on Stages in the Spark UI and then look for the Failed Stages section at the bottom of the page. If an executor runs into memory issues, it will fail the task and restart where the last task left off.
The reason's because of which spark would re-submit your task could be: Executor not responding (no heartbeat signal) or data corrupted etc. which you can not replicate on a single node. What you can try to do is on a hadoop cluster you submit a job and restart one of the node which would make your executor non-responding.
I believe failed tasks are resubmitted because I have seen the same failed task submitted multiple times on the Web UI. However, if the same task fails multiple times, the full job fail:
org.apache.spark.SparkException: Job aborted due to stage failure: Task 120 in stage 91.0 failed 4 times, most recent failure: Lost task 120.3 in stage 91.0
Yes, but there is a parameter set for the max number of failures
spark.task.maxFailures 4 Number of individual task failures before giving up on the job. Should be greater than or equal to 1. Number of allowed retries = this value - 1.
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