How to limit the number of retries on Spark job failure?
spark max task failures
spark application master
spark-submit --properties file
spark executor failure
is there any way to get spark application id, while running a job?
We are running a Spark job via
spark-submit, and I can see that the job will be re-submitted in the case of failure.
How can I stop it from having attempt #2 in case of yarn container failure or whatever the exception be?
This happened due to lack of memory and "GC overhead limit exceeded" issue.
How to limit the number of retries on Spark job failure?, There are two settings that control the number of retries (i.e. the maximum number of ApplicationMaster registration attempts with YARN is The number of retries is controlled by the following settings (i.e. the maximum number of ApplicationMaster registration attempts with YARN is considered failed and hence the entire Spark application): spark.yarn.maxAppAttempts - Spark's own setting. Have a look on MAX_APP_ATTEMPTS: private [spark] val MAX_APP_ATTEMPTS = ConfigBuilder ("spark.yarn.maxAppAttempts")
An API/programming language-agnostic solution would be to set the yarn max attempts as a command line argument:
spark-submit --conf spark.yarn.maxAppAttempts=1 <application_name>
See @code 's answer
How to limit the number of retries on Spark job failure, How to limit the number of retries on Spark job failure. | Quote Date : 16 Jul, 2019 Views:160. We are running a Spark job via spark-submit, and I'd like to stop Spark from retrying a Spark application in case some particular exception is thrown. I only want to limit the number of retries in case certain conditions are met. Otherwise, I want default number of retries. Note that there is only one Spark job which a Spark application runs.
Add the property
yarn.resourcemanager.am.max-attempts to your yarn-default.xml file. It specifies the maximum number of application attempts.
For more details look into this link
Configuration - Spark 3.0.0 Documentation, deploy.maxExecutorRetries" property can be set to fix the maximum retries of an executor. ,. I'm running spark 1.6.1 in standalone mode. org.apache.spark.SparkException: Job aborted due to stage failure: Total size of serialized results of x tasks (y MB) is bigger than spark.driver.maxResultSize (z MB) Resolution : Increase the Spark Drive Max Result Size value by modifying the value of --conf spark.driver.maxResultSize in the Spark Submit Command Line Options on the Analyze page.
but in general in which cases - it would fail once and recover at the second time - in case of cluster or queue too busy I guess
I am running jobs using oozie coordinators - I was thinking to set to
1 - it it fails it will run at the next materialization -
Spark executor failure retries in spark 1.6.1 Standalone mode,how to , Is there any way to increase the number of re-tries ? best,. /Shahab. Daniel Darabos · Reply | Threaded. I'm running spark 1.6.1 in standalone mode. I wanted to know how to manage the executor failure retries. In spark 2.1.0 version documentation, I see "spark.deploy.maxExecutorRetries" property can be set to fix the maximum retries of an executor.
Increasing the number of retry in case of job failure, SparkException: Task failed while writing rows at error after the maximum number of retries. It would be pretty easy to put in a limited number of retries per stage. Though again, we encounter issues with keeping things resilient. Theoretically one stage could have many retries, but due to failures in different stages further downstream, so we might need to track the cause of each retry as well to still have the desired behavior.
Troubleshooting Spark Issues, Managing, Tuning, and Securing Spark, YARN, and HDFS Sam R. Alapati should retry a failed map task by setting the mapred.map.max.attempts parameter, 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.
Expert Hadoop Administration: Managing, Tuning, and Securing , However, if the driver fails then by design all the executors fail and the computed data number of retries set in configuration yarn.resourcemanager.am.max-attempts. Similarly in Spark standalone mode one should submit the Spark job with "Number of resubmits on Job Failure" is a relatively new feature where we automatically resubmit the job after job failure. It results in new job (with same options as of older job) altogether. However the other setting " Enable Number of Retries" defines the allowed number of attempts within a job itself.
- Since it appears we can use either option to set the max attempts to 1 (since a minimum is used), is one preferable over the other, or would it be a better practice to set both to 1?