Spark + EMR using Amazon's “maximizeResourceAllocation” setting does not use all cores/vcores

Question

I\'m running an EMR cluster (version emr-4.2.0) for Spark using the Amazon specific maximizeResourceAllocation flag as documented here. According to those docs, \"

Answer 1

With this setting you should get 1 executor on each instance (except the master), each with 8 cores and about 30GB of RAM.

Is the Spark UI at http://:8088/ not showing that allocation?

I'm not sure that setting is really a lot of value compared to the other one mentioned on that page, "Enabling Dynamic Allocation of Executors". That'll let Spark manage it's own number of instances for a job, and if you launch a task with 2 CPU cores and 3G of RAM per executor you'll get a pretty good ratio of CPU to memory for EMR's instance sizes.

Answer 2

Okay, after a lot of experimentation, I was able to track down the problem. I'm going to report my findings here to help people avoid frustration in the future.

• While there is a discrepancy between the 8 cores asked for and the 16 VCores that YARN knows about, this doesn't seem to make a difference. YARN isn't using cgroups or anything fancy to actually limit how many CPUs the executor can actually use.
• "Cores" on the executor is actually a bit of a misnomer. It is actually how many concurrent tasks the executor will willingly run at any one time; essentially boils down to how many threads are doing "work" on each executor.
• When maximizeResourceAllocation is set, when you run a Spark program, it sets the property spark.default.parallelism to be the number of instance cores (or "vCPUs") for all the non-master instances that were in the cluster at the time of creation. This is probably too small even in normal cases; I've heard that it is recommended to set this at 4x the number of cores you will have to run your jobs. This will help make sure that there are enough tasks available during any given stage to keep the CPUs busy on all executors.
• When you have data that comes from different runs of different spark programs, your data (in RDD or Parquet format or whatever) is quite likely to be saved with varying number of partitions. When running a Spark program, make sure you repartition data either at load time or before a particularly CPU intensive task. Since you have access to the spark.default.parallelism setting at runtime, this can be a convenient number to repartition to.

TL;DR

1. maximizeResourceAllocation will do almost everything for you correctly except...
2. You probably want to explicitly set spark.default.parallelism to 4x number of instance cores you want the job to run on on a per "step" (in EMR speak)/"application" (in YARN speak) basis, i.e. set it every time and...
3. Make sure within your program that your data is appropriately partitioned (i.e. want many partitions) to allow Spark to parallelize it properly

Answer 3

in the EMR version 3.x, this maximizeResourceAllocation was implemented with a reference table: https://github.com/awslabs/emr-bootstrap-actions/blob/master/spark/vcorereference.tsv

it used by a shell script: maximize-spark-default-config, in the same repo, you can take a look how they implemented this.

maybe in the new EMR version 4, this reference table was somehow wrong... i believe you can find all this AWS script in your EC2 instance of EMR, should be located in /usr/lib/spark or /opt/aws or something like this.

anyway, at least, you can write your own bootstrap action scripts for this in EMR 4, with a correct reference table, similar to the implementation in EMR 3.x

moreover, since we are going to use STUPS infrastructure, worth take a look the STUPS appliance for Spark: https://github.com/zalando/spark-appliance

you can explicitly specify the number of cores by setting senza parameter DefaultCores when you deploy your spark cluster

some of highlight of this appliance comparing to EMR are:

able to use it with even t2 instance type, auto-scalable based on roles like other STUPS appliance, etc.

and you can easily deploy your cluster in HA mode with zookeeper, so no SPOF on master node, HA mode in EMR is currently still not possible, and i believe EMR is mainly designed for "large clusters temporarily for ad-hoc analysis jobs", not for "dedicated cluster that is permanently on", so HA mode will not be possible in near further with EMR.