Hi Gerard,
SPARK-4286 is the ticket I am working on, which besides supporting shuffle
service it also supports the executor scaling callbacks (kill/request
total) for coarse grain mode.
I created SPARK-4940 to discuss more about the distribution problem, and
let's bring our discussions there.
Hi,
After facing issues with the performance of some of our Spark Streaming
jobs, we invested quite some effort figuring out the factors that affect
the performance characteristics of a Streaming job. We defined an
empirical model that helps us reason about Streaming jobs and applied it to
tune
Hi Gerard,
Really nice guide!
I'm particularly interested in the Mesos scheduling side to more evenly
distribute cores across cluster.
I wonder if you are using coarse grain mode or fine grain mode?
I'm making changes to the spark mesos scheduler and I think we can propose a
best way to
Hi Tim,
That would be awesome. We have seen some really disparate Mesos allocations
for our Spark Streaming jobs. (like (7,4,1) over 3 executors for 4 kafka
consumer instead of the ideal (3,3,3,3))
For network dependent consumers, achieving an even deployment would
provide a reliable and
