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How to handle executor failure in apache spark


How to handle executor failure in apache spark

By : user2185546
Date : November 22 2020, 04:01 AM
hop of those help? You cannot handle executor failures programmatically in your application, if thats what you are asking. You can configure spark configuration properties which guides the actual job execution including how YARN would schedule jobs and handle task and executor failures.
https://spark.apache.org/docs/latest/configuration.html#scheduling
code :


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How to set Apache Spark Executor memory

How to set Apache Spark Executor memory


By : user2629885
Date : March 29 2020, 07:55 AM
Does that help Since you are running Spark in local mode, setting spark.executor.memory won't have any effect, as you have noticed. The reason for this is that the Worker "lives" within the driver JVM process that you start when you start spark-shell and the default memory used for that is 512M. You can increase that by setting spark.driver.memory to something higher, for example 5g. You can do that by either:
code :
spark.driver.memory              5g
$ ./bin/spark-shell --driver-memory 5g
512 MB * 0.6 * 0.9 ~ 265.4 MB
Apache Spark executor uses more than one core despite of spark.executor.cores=1

Apache Spark executor uses more than one core despite of spark.executor.cores=1


By : user3399331
Date : March 29 2020, 07:55 AM
fixed the issue. Will look into that further OK, at the end I have found:
Actually I had just wrong understanding of 'CPU load' here. More than 100% means only execution queue length. CPU cores allocation was correct. 1 core was allocated per executor which was demanded. Based on GC profiling enabled on Spark executors and then job code review it was found root cause was just non-optimal copying of some objects. Code was corrected to be more lean in term of memory management.
How can I assign a unique integer key to every Apache Spark Executor within an Apache Spark Java Application?

How can I assign a unique integer key to every Apache Spark Executor within an Apache Spark Java Application?


By : David Wu
Date : March 29 2020, 07:55 AM
I think the issue was by ths following , The id of the partition might be useful, as all elements of a single partition will always be on one executor.
mapPartitionsWithIndex can help:
code :
val spark = SparkSession.builder.master("local[*]").appName("partitionIndex").getOrCreate()
import spark.implicits._

val ds = spark.createDataset(Seq.range(1, 21)).repartition(4)
ds.rdd
  .mapPartitionsWithIndex((partitionIndex, it) => {
    println("processing partition " + partitionIndex)
    it.toList.map(i => new String("partition " + partitionIndex + " contains number " + i)).iterator
  })
  .foreach(println)
processing partition 1
processing partition 0
processing partition 2
processing partition 3
partition 1 contains number 3
partition 2 contains number 4
partition 2 contains number 9
partition 2 contains number 14
partition 2 contains number 19
partition 0 contains number 2
...
partition 3 contains number 1
partition 3 contains number 5
...
spark executor lost failure

spark executor lost failure


By : Dheeraj Singh
Date : March 29 2020, 07:55 AM
wish of those help LBFGS uses dense vector for storing betas (feature weights) internally and everything is in memory. So regardless of sparsity of features in training set, the total count of features is something to be mindful about.
So to solve this user should either increase executor memory or limit total count of features in the training set.
Get all Apache Spark executor logs

Get all Apache Spark executor logs


By : Ratnakar Shukla
Date : March 29 2020, 07:55 AM
With these it helps Perhaps there is a better way, but we use a script to sync executor logs to S3 every 5 seconds
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