- When a resourceOffers() call has multiple offers, force the TaskSets
to consider them in increasing order of locality levels so that they
get a chance to launch stuff locally across all offers
- Simplify ClusterScheduler.prioritizeContainers
- Add docs on the new configuration options
- Added periodic revival of offers in StandaloneSchedulerBackend
- Replaced task scheduling aggression with multi-level delay scheduling
in ClusterTaskSetManager
- Fixed ZippedRDD preferred locations because they can't currently be
process-local
- Fixed some uses of hostPort
- Replace use of hostPort vs host in Task.preferredLocations with a
TaskLocation class that contains either an executorId and a host or
just a host. This is part of a bigger effort to eliminate hostPort
based data structures and just use executorID, since the hostPort vs
host stuff is confusing (and not checkable with static typing, leading
to ugly debug code), and hostPorts are not provided by Mesos.
- Replaced most hostPort-based data structures and fields as above.
- Simplified ClusterTaskSetManager to deal with preferred locations in a
more concise way and generally be more concise.
- Updated the way ClusterTaskSetManager handles racks: instead of
enqueueing a task to a separate queue for all the hosts in the rack,
which would create lots of large queues, have one queue per rack name.
- Removed non-local fallback stuff in ClusterScheduler that tried to
launch less-local tasks on a node once the local ones were all
assigned. This change didn't work because many cluster schedulers send
offers for just one node at a time (even the standalone and YARN ones
do so as nodes join the cluster one by one). Thus, lots of non-local
tasks would be assigned even though a node with locality for them
would be able to receive tasks just a short time later.
- Renamed MapOutputTracker "generations" to "epochs".
It made the JSON creation slightly more complicated, but reduces one external dependency. The scala library also properly escape "/" (which lift-json doesn't).
These are used all over the place now and they are not specific to memory at all.
memoryBytesToString --> bytesToString
memoryMegabytesToString --> megabytesToString
Ops teams need to ensure that the cluster is functional and performant. Having to scrape the html source for worker state won't work reliably, and will be slow. By exposing the state in the json output, ops teams are able to ensure a fully functional environment by querying for the json output and parsing for dead nodes.
1. Renamed SparkContext.addLocalProperty to setLocalProperty. And allow this function to unset a property.
2. Renamed SparkContext.setDescription to setCurrentJobDescription.
3. Throw an exception if the fair scheduler allocation file is invalid.
1) UI crashed if the executor UI was loaded before any tasks started.
2) The total tasks was incorrectly reported due to using string (rather
than int) arithmetic.
The TaskState class's isFinished() method didn't return true for
KILLED tasks, which means some resources are never reclaimed
for tasks that are killed. This also made it inconsistent with the
isFinished() method used by CoarseMesosSchedulerBackend.
JobConf's constructor loads default config files in some verisons of
Hadoop, which is quite slow, and we only need the Configuration object
to pass the correct ClassLoader.
This commit fixes issues where SparkListeners that take a while to
process events slow the DAGScheduler.
This commit also fixes a bug in the UI where if a user goes to a
web page of a stage that does not exist, they can create a memory
leak (granted, this is not an issue at small scale -- probably only
an issue if someone actively tried to DOS the UI).
One bug caused the UI to crash if you try to look at a job's status
before any of the tasks have finished.
The second bug was a concurrency issue where two different threads
(the scheduling thread and a UI thread) could be reading/updating
the data structures in JobProgressListener concurrently.
The third bug mis-used an Option, also causing the UI to crash
under certain conditions.
Removal of items from ArrayBuffers in the UI code was slow and
significantly impacted scheduler throughput. This commit
improves scheduler throughput by 5x.
