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From "Lalwani, Jayesh" <jlalw...@amazon.com.INVALID>
Subject Re: Recovery when two spark nodes out of 6 fail
Date Fri, 25 Jun 2021 15:23:04 GMT
Spark replicates the partitions among multiple nodes. If one executor fails, it moves the processing
over to other executor. However, if the data is lost, it re-executes the processing that generated
the data, and might have to go back to the source.

In case of failure, there will be delay in getting results. The amount of delay depends on
how much reprocessing Spark needs to do.

When the driver executes an action, it submits a job to the Cluster Manager. The Cluster Manager
starts submitting tasks to executors and monitoring them. In case, executors dies, the Cluster
Manager does the work of reassigning the tasks. While all of this is going on, the driver
is just sitting there waiting for the action to complete. SO, driver does nothing, really.
The Cluster Manager is doing most of the work of managing the workload

Spark, by itself doesn’t add executors when executors fail. It just moves the tasks to other
executors. If you are installing plain vanilla Spark on your own cluster, you need to figure
out how to bring back executors. Most of the popular platforms built on top of Spark (Glue,
EMR, Kubernetes) will replace failed nodes. You need to look into the capabilities of your
chosen platform.

If the driver dies, the Spark job dies. There’s no recovering from that. The only way to
recover is to run the job again. Batch jobs do not have benchmarking. So, they will need to
reprocess everything from the beginning. You need to write your jobs to be idempotent; ie;
rerunning them shouldn’t change the outcome. Streaming jobs have benchmarking, and they
will start from the last microbatch. This means that they might have to repeat the last microbatch.

From: "ashok34668@yahoo.com.INVALID" <ashok34668@yahoo.com.INVALID>
Date: Friday, June 25, 2021 at 10:38 AM
To: "user@spark.apache.org" <user@spark.apache.org>
Subject: [EXTERNAL] Recovery when two spark nodes out of 6 fail


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Greetings,

This is a scenario that we need to come up with a comprehensive answers to fulfil please.

If we have 6 spark VMs each running two executors via spark-submit.


  1.   we have two VMs failures at H/W level, rack failure
  2.  we lose 4 executors of spark out of 12
  3.  Happening half way through the spark-submit job
  4.
So my humble questions are:


  1.  Will there be any data lost from the final result due to missing nodes?
  2.  How will RDD lineage will handle this?
  3.  Will there be any delay in getting the final result?
  4.  How the driver will handle these two nodes failure
  5.  Will there be additional executors added to the existing nodes or the existing executors
will handle the job of 4 failing executors.
  6.  If running in client mode and the node holding the driver dies?
  7.  If running in cluster mode happens

Did search in Google no satisfactory answers gurus, hence turning to forum.

Best

A.K.
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