Bump on this question – does anyone know what is the effect of spark.driver.cores on the driver's ability to manage larger clusters?
Any tips on setting a correct value? I’m running Spark streaming on Yarn / Hadoop 2.6 / Spark 1.5.1. Thanks, -adrian From: Adrian Tanase Date: Saturday, October 17, 2015 at 10:58 PM To: "user@spark.apache.org<mailto:user@spark.apache.org>" Subject: Spark Streaming scheduler delay VS driver.cores Hi, I’ve recently bumped up the resources for a spark streaming job – and the performance started to degrade over time. it was running fine on 7 nodes with 14 executor cores each (via Yarn) until I bumped executor.cores to 22 cores/node (out of 32 on AWS c3.xlarge, 24 for yarn) The driver has 2 cores and 2 GB ram (usage is at zero). For really low data volume it goes from 1-2 seconds per batch to 4-5 s/batch after about 6 hours, doing almost nothing. I’ve noticed that the scheduler delay is 3-4s, even 5-6 seconds for some tasks. Should be in the low tens of milliseconds. What’s weirder is that under moderate load (thousands of events per second) - the delay is not as obvious anymore. After this I reduced the executor.cores to 20 and bumped driver.cores to 4 and it seems to be ok now. However, this is totally empirical, I have not found any documentation, code samples or email discussion on how to properly set driver.cores. Does anyone know: * If I assign more cores to the driver/application manager, will it use them? * I was looking at the process list with htop and only one of the jvm’s on the driver was really taking up CPU time * What is a decent parallelism factor for a streaming app with 10-20 secs batch time? I found it odd that at 7 x 22 = 154 the driver is becoming a bottleneck * I’ve seen people recommend 3-4 taks/core or ~1000 parallelism for clusters in the tens of nodes Thanks in advance, -adrian
