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https://issues.apache.org/jira/browse/SPARK-2089?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14095247#comment-14095247
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Mridul Muralidharan commented on SPARK-2089:
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Since I am not maintaining the code anymore, I dont have strong preference
either way.
I am not sure what the format means btw - I see multiple nodes and racks
mentioned in the same group ...
In general though, I am not convinced it is a good direction to take.
1) It is a workaround for a design issue and has non trivial performance
implications (serializing into this form to immediately deserialize it is
expensive for large inputs : not to mention, it gets shipped to executors for
no reason).
2) It locks us into a format which provides inadequate information - number of
blocks per node, size per block, etc is lost (or maybe I just did not
understand what the format is !).
3) We are currently investigating evolving in the opposite direction - add more
information so that we can be more specific about where to allocate executors.
For example: I can see the fairly near term need to associate executors with
accelerator cards (and break the OFF_HEAP -> tachyon implicit assumption).
A string representation makes it fragile to evolve.
As I mentioned before, the current yarn allocation model in spark is a very
naive implementation - which I did not expect to survive this long : it was
directly from our prototype.
We really should be modifying it to consider cost of data transfer and
prioritize allocation that way (number of blocks on a node/rack, size of
blocks, number of replicas available, etc).
For small datasets on small enough clusters this is not relevant but has
implications as we grow along both axis.
> With YARN, preferredNodeLocalityData isn't honored
> ---------------------------------------------------
>
> Key: SPARK-2089
> URL: https://issues.apache.org/jira/browse/SPARK-2089
> Project: Spark
> Issue Type: Bug
> Components: YARN
> Affects Versions: 1.0.0
> Reporter: Sandy Ryza
> Assignee: Sandy Ryza
> Priority: Critical
>
> When running in YARN cluster mode, apps can pass preferred locality data when
> constructing a Spark context that will dictate where to request executor
> containers.
> This is currently broken because of a race condition. The Spark-YARN code
> runs the user class and waits for it to start up a SparkContext. During its
> initialization, the SparkContext will create a YarnClusterScheduler, which
> notifies a monitor in the Spark-YARN code that . The Spark-Yarn code then
> immediately fetches the preferredNodeLocationData from the SparkContext and
> uses it to start requesting containers.
> But in the SparkContext constructor that takes the preferredNodeLocationData,
> setting preferredNodeLocationData comes after the rest of the initialization,
> so, if the Spark-YARN code comes around quickly enough after being notified,
> the data that's fetched is the empty unset version. The occurred during all
> of my runs.
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