Faster and stable/reliable broadcast
HttpBroadcast is noticeably slow, but the alternatives (TreeBroadcast or BitTorrentBroadcast) are notoriously unreliable. The main problem with them is they try to manage the memory for the pieces of a broadcast themselves. Right now, the BroadcastManager does not know which machines the tasks reading from a broadcast variable is running and when they have finished. Consequently, we try to guess and often guess wrong, which blows up the memory usage and kills/hangs jobs.
This very simple implementation solves the problem by not trying to manage the intermediate pieces; instead, it offloads that duty to the BlockManager which is quite good at juggling blocks. Otherwise, it is very similar to the BitTorrentBroadcast implementation (without fancy optimizations). And it runs much faster than HttpBroadcast we have right now.
I've been using this for another project for last couple of weeks, and just today did some benchmarking against the Http one. The following shows the improvements for increasing broadcast size for cold runs. Each line represent the number of receivers.
After the first broadcast is over, i.e., after JVM is wormed up and for HttpBroadcast the server is already running (I think), the following are the improvements for warm runs.
The curves are not as nice as the cold runs, but the improvements are obvious, specially for larger broadcasts and more receivers.
Depending on how it goes, we should deprecate and/or remove old TreeBroadcast and BitTorrentBroadcast implementations, and hopefully, SPARK-889 will not be necessary any more.
Mainly, this occurs if you provide a messed up MASTER url (one that doesn't match one
of our regexes). Previously, we would default to Mesos, fail, and then start the shell
anyway, except that any Spark command would fail.
The interface was used only by the DAG scheduler (so it wasn't necessary
to define the additional interface), and the naming makes it very
confusing when reading the code (because "listener" was used
to describe the DAG scheduler, rather than SparkListeners, which
implement a nearly-identical interface but serve a different
function).
Renamed StandaloneX to CoarseGrainedX.
(as suggested by @rxin here https://github.com/apache/incubator-spark/pull/14)
The previous names were confusing because the components weren't just
used in Standalone mode. The scheduler used for Standalone
mode is called SparkDeploySchedulerBackend, so referring to the base class
as StandaloneSchedulerBackend was misleading.
Refactor BlockId into an actual type
Converts all of our BlockId strings into actual BlockId types. Here are some advantages of doing this now:
+ Type safety
+ Code clarity - it's now obvious what the key of a shuffle or rdd block is, for instance. Additionally, appearing in tuple/map type signatures is a big readability bonus. A Seq[(String, BlockStatus)] is not very clear. Further, we can now use more Scala features, like matching on BlockId types.
+ Explicit usage - we can now formally tell where various BlockIds are being used (without doing string searches); this makes updating current BlockIds a much clearer process, and compiler-supported.
(I'm looking at you, shuffle file consolidation.)
+ It will only get harder to make this change as time goes on.
Downside is, of course, that this is a very invasive change touching a lot of different files, which will inevitably lead to merge conflicts for many.
This is an unfortunately invasive change which converts all of our BlockId
strings into actual BlockId types. Here are some advantages of doing this now:
+ Type safety
+ Code clarity - it's now obvious what the key of a shuffle or rdd block is,
for instance. Additionally, appearing in tuple/map type signatures is a big
readability bonus. A Seq[(String, BlockStatus)] is not very clear.
Further, we can now use more Scala features, like matching on BlockId types.
+ Explicit usage - we can now formally tell where various BlockIds are being used
(without doing string searches); this makes updating current BlockIds a much
clearer process, and compiler-supported.
(I'm looking at you, shuffle file consolidation.)
+ It will only get harder to make this change as time goes on.
Since this touches a lot of files, it'd be best to either get this patch
in quickly or throw it on the ground to avoid too many secondary merge conflicts.
Add an optional closure parameter to HadoopRDD instantiation to use when creating local JobConfs.
Having HadoopRDD accept this optional closure eliminates the need for the HadoopFileRDD added earlier. It makes the HadoopRDD more general, in that the caller can specify any JobConf initialization flow.
Address review comments, move to incubator spark
Also includes a small fix to speculative execution.
<edit> Continued from https://github.com/mesos/spark/pull/914 </edit>
Standalone Scheduler fault tolerance using ZooKeeper
This patch implements full distributed fault tolerance for standalone scheduler Masters.
There is only one master Leader at a time, which is actively serving scheduling
requests. If this Leader crashes, another master will eventually be elected, reconstruct
the state from the first Master, and continue serving scheduling requests.
Leader election is performed using the ZooKeeper leader election pattern. We try to minimize
the use of ZooKeeper and the assumptions about ZooKeeper's behavior, so there is a layer of
retries and session monitoring on top of the ZooKeeper client.
Master failover follows directly from the single-node Master recovery via the file
system (patch d5a96fe), save that the Master state is stored in ZooKeeper instead.
Configuration:
By default, no recovery mechanism is enabled (spark.deploy.recoveryMode = NONE).
By setting spark.deploy.recoveryMode to ZOOKEEPER and setting spark.deploy.zookeeper.url
to an appropriate ZooKeeper URL, ZooKeeper recovery mode is enabled.
By setting spark.deploy.recoveryMode to FILESYSTEM and setting spark.deploy.recoveryDirectory
to an appropriate directory accessible by the Master, we will keep the behavior of from d5a96fe.
Additionally, places where a Master could be specificied by a spark:// url can now take
comma-delimited lists to specify backup masters. Note that this is only used for registration
of NEW Workers and application Clients. Once a Worker or Client has registered with the
Master Leader, it is "in the system" and will never need to register again.
