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in Big Data Hadoop & Spark by (11.4k points)

I am doing some memory tuning on my Spark job on YARN and I notice different settings would give different results and affect the outcome of the Spark job run. However, I am confused and do not understand completely why it happens and would appreciate if someone can provide me with some guidance and explanation.

I will provide some background information and describe the cases that I have experienced after them below.

My environment setting were as below:

  • Memory 20G, 20 VCores per node (3 nodes in total)
  • Hadoop 2.6.0
  • Spark 1.4.0

My code recursively filters an RDD to make it smaller (removing examples as part of an algorithm), then does mapToPair and collect to gather the results and save them within a list.

First Case

/bin/spark-submit --class <class name> --master yarn-cluster --driver-memory 7g --executor-memory 1g --num-executors 3 --executor-cores 1 --jars <jar file>

If I run my program with any driver memory less than 11g, I will get the error below which is the SparkContext being stopped or a similar error which is a method being called on a stopped SparkContext. From what I have gathered, this is related to memory not being enough.


Second Case

/bin/spark-submit --class <class name> --master yarn-cluster --driver-memory 7g --executor-memory 3g --num-executors 3 --executor-cores 1 --jars <jar file>

If I run the program with the same driver memory but higher executor memory, the job runs longer (about 3-4 minutes) than the first case and then it will encounter a different error from earlier which is a Container requesting/using more memory than allowed and is being killed because of that. Although I find it weird since the executor memory is increased and this error occurs instead of the error in the first case.


Third Case

/bin/spark-submit --class <class name> --master yarn-cluster --driver-memory 11g --executor-memory 1g --num-executors 3 --executor-cores 1 --jars <jar file>

Any setting with driver memory greater than 10g will lead to the job being able to run successfully.

Fourth Case

/bin/spark-submit --class <class name> --master yarn-cluster --driver-memory 2g --executor-memory 1g --conf spark.yarn.executor.memoryOverhead=1024 --conf spark.yarn.driver.memoryOverhead=1024 --num-executors 3 --executor-cores 1 --jars <jar file>

 The third and fourth case succeeds and I understand that it is because I am giving more memory which solves the memory problems. However, in the third case,

spark.driver.memory + spark.yarn.driver.memoryOverhead = the memory that YARN will create a JVM

= 11g + (driverMemory * 0.07, with minimum of 384m) = 11g + 1.154g = 12.154g

So, from the formula, I can see that my job requires MEMORY_TOTAL of around 12.154g to run successfully which explains why I need more than 10g for the driver memory setting.

But for the fourth case,

spark.driver.memory + spark.yarn.driver.memoryOverhead = the memory that YARN will create a JVM

= 2 + (driverMemory * 0.07, with minimum of 384m) = 2g + 0.524g = 2.524g

It seems that just by increasing the memory overhead by a small amount of 1024(1g) it leads to the successful run of the job with driver memory of only 2g and the MEMORY_TOTAL is only 2.524g! Whereas without the overhead configuration, driver memory less than 11g fails but it doesn't make sense from the formula which is why I am confused.

1 Answer

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by (32.3k points)

All your cases use

--executor-cores 1

It is said to be the best practice to go above 1 and not to go above 5. From my past experience and from Spark developers recommendation:

We can make a rough guess that at most five tasks per executor 

can achieve full write throughput, so it’s good if you keep 

the number of cores per executor below that number.

When you run multiple tasks in the same executor you get an ability to share some common memory regions so it actually saves memory.

Start with --executor-cores 2, double --executor-memory (because --executor-cores tells also how many tasks one executor will run concurrently), and see what it does for you.

Now, In terms of available memory your environment is compact, so going to 3 or 4 will give you even better memory utilization.

We use Spark 1.5 and stopped using --executor-cores 1 quite some time ago as it was giving GC problems; it looks also like a Spark bug, because just giving more memory wasn't helping as much as switching to more tasks per container. I guess tasks in the same executor may peak its memory consumption at different times, so you don't waste/don't have to overprovision memory just to make it work.

Another benefit is that Spark's shared variables (accumulators and broadcast variables) will have just one copy per executor, not per task - so switching to multiple tasks per executor is a direct memory saving right there. Even if you don't use Spark shared variables explicitly, Spark very likely creates them internally anyway. For example, if you join two tables through Spark SQL, Spark's CBO may decide to broadcast a smaller table (or a smaller dataframe) across to make join run faster.


For in-depth information regarding tuning, check out this link:

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