Resolving package conflicts with hadoop 0.23.9
Hadoop 0.23.9 is having a package conflict with easymock's dependencies.
(cherry picked from commit 023e3fdf00)
Signed-off-by: Reynold Xin <rxin@apache.org>
One major change was the use of messages instead of raw functions as the
parameter of Akka scheduled timers. Since messages are serialized, unlike
raw functions, the behavior is easier to think about and doesn't cause
race conditions when exceptions are thrown.
Another change is to avoid using global pointers that might change without
a lock.
Currently PythonPartitioner determines partition ID by hashing a
byte-array representation of PySpark's key. This PR lets
PythonPartitioner use the actual partition ID, which is required e.g.
for sorting via PySpark.
Improved organization of scheduling packages.
This commit does not change any code -- only file organization.
Please let me know if there was some masterminded strategy behind
the existing organization that I failed to understand!
There are two components of this change:
(1) Moving files out of the cluster package, and down
a level to the scheduling package. These files are all used by
the local scheduler in addition to the cluster scheduler(s), so
should not be in the cluster package. As a result of this change,
none of the files in the local package reference files in the
cluster package.
(2) Moving the mesos package to within the cluster package.
The mesos scheduling code is for a cluster, and represents a
specific case of cluster scheduling (the Mesos-related classes
often subclass cluster scheduling classes). Thus, the most logical
place for it seems to be within the cluster package.
The one thing about the scheduling code that seems a little funny to me
is the naming of the SchedulerBackends. The StandaloneSchedulerBackend
is not just for Standalone mode, but instead is used by Mesos coarse grained
mode and Yarn, and the backend that *is* just for Standalone mode is instead called SparkDeploySchedulerBackend. I didn't change this because I wasn't sure if there
was a reason for this naming that I'm just not aware of.
This commit does not change any code -- only file organization.
There are two components of this change:
(1) Moving files out of the cluster package, and down
a level to the scheduling package. These files are all used by
the local scheduler in addition to the cluster scheduler(s), so
should not be in the cluster package. As a result of this change,
none of the files in the local package reference files in the
cluster package.
(2) Moving the mesos package to within the cluster package.
The mesos scheduling code is for a cluster, and represents a
specific case of cluster scheduling (the Mesos-related classes
often subclass cluster scheduling classes). Thus, the most logical
place for it is within the cluster package.
This change requires adding an extra failure mode: tasks can complete
successfully, but the result gets lost or flushed from the block manager
before it's been fetched.
In MapOutputTrackerSuite, the "remote fetch" test sets spark.driver.port
and spark.hostPort, assuming that they will be cleared by
LocalSparkContext. However, the test never sets sc, so it remains null,
causing LocalSparkContext to skip clearing these properties. Subsequent
tests therefore fail with java.net.BindException: "Address already in
use".
This commit makes LocalSparkContext clear the properties even if sc is
null.
Include the useful tip that if shuffle=true, coalesce can actually
increase the number of partitions.
This makes coalesce more like a generic `RDD.repartition` operation.
(Ideally this `RDD.repartition` could automatically choose either a coalesce or
a shuffle if numPartitions was either less than or greater than, respectively,
the current number of partitions.)
Caching the results of local actions (e.g., rdd.first()) causes the driver to
store entire partitions in its own memory, which may be highly constrained.
This patch simply makes the CacheManager avoid caching the result of all locally-run computations.
This commit makes Spark invocation saner by using an assembly JAR to
find all of Spark's dependencies instead of adding all the JARs in
lib_managed. It also packages the examples into an assembly and uses
that as SPARK_EXAMPLES_JAR. Finally, it replaces the old "run" script
with two better-named scripts: "run-examples" for examples, and
"spark-class" for Spark internal classes (e.g. REPL, master, etc). This
is also designed to minimize the confusion people have in trying to use
"run" to run their own classes; it's not meant to do that, but now at
least if they look at it, they can modify run-examples to do a decent
job for them.
As part of this, Bagel's examples are also now properly moved to the
examples package instead of bagel.
- Use SPARK_PUBLIC_DNS environment variable if set (for EC2)
- Use a non-ephemeral port (3030 instead of 33000) by default
- Updated test to use non-ephemeral port too
- 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
- 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".
These are used all over the place now and they are not specific to memory at all.
memoryBytesToString --> bytesToString
memoryMegabytesToString --> megabytesToString
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.
