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.
SPARK-900 Use coarser grained naming for metrics
see SPARK-900 Use coarser grained naming for metrics.
Now the new metric name is formatted as {XXX.YYY.ZZZ.COUNTER_UNIT}, XXX.YYY.ZZZ represents the group name, which can group several metrics under the same Ganglia view.
Don't allocate Kryo buffers unless needed
I noticed that the Kryo serializer could be slower than the Java one by 2-3x on small shuffles because it spend a lot of time initializing Kryo Input and Output objects. This is because our default buffer size for them is very large. Since the serializer is often used on streams, I made the initialization lazy for that, and used a smaller buffer (auto-managed by Kryo) for input.
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>
Allow users to pass broadcasted Configurations and cache InputFormats across Hadoop file reads.
Note: originally from https://github.com/mesos/spark/pull/942
Currently motivated by Shark queries on Hive-partitioned tables, where there's a JobConf broadcast for every Hive-partition (i.e., every subdirectory read). The only thing different about those JobConfs is the input path - the Hadoop Configuration that the JobConfs are constructed from remain the same.
This PR only modifies the old Hadoop API RDDs, but similar additions to the new API might reduce computation latencies a little bit for high-frequency FileInputDStreams (which only uses the new API right now).
As a small bonus, added InputFormats caching, to avoid reflection calls for every RDD#compute().
Few other notes:
Added a general soft-reference hashmap in SparkHadoopUtil because I wanted to avoid adding another class to SparkEnv.
SparkContext default hadoopConfiguration isn't cached. There's no equals() method for Configuration, so there isn't a good way to determine when configuration properties have changed.
SPARK-920/921 - JSON endpoint updates
920 - Removal of duplicate scheme part of Spark URI, it was appearing as spark://spark//host:port in the JSON field.
JSON now delivered as:
url:spark://127.0.0.1:7077
921 - Adding the URL of the Main Application UI will allow custom interfaces (that use the JSON output) to redirect from the standalone UI.
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.
The previous names were confusing because the components weren't just
used in Standalone mode -- in fact, the scheduler used for Standalone
mode is called SparkDeploySchedulerBackend. So, the previous names
were misleading.
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.
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 194ba4b8), 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 194ba4b8.
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.
Forthcoming:
Documentation, tests (! - only ad hoc testing has been performed so far)
I do not intend for this commit to be merged until tests are added, but this patch should
still be mostly reviewable until then.
Implements a basic form of Standalone Scheduler fault recovery. In particular,
this allows faults to be manually recovered from by means of restarting the
Master process on the same machine. This is the majority of the code necessary
for general fault tolerance, which will first elect a leader and then recover
the Master state.
In order to enable fault recovery, the Master will persist a small amount of state related
to the registration of Workers and Applications to disk. If the Master is started and
sees that this state is still around, it will enter Recovery mode, during which time it
will not schedule any new Executors on Workers (but it does accept the registration of
new Clients and Workers).
At this point, the Master attempts to reconnect to all Workers and Client applications
that were registered at the time of failure. After confirming either the existence
or nonexistence of all such nodes (within a certain timeout), the Master will exit
Recovery mode and resume normal scheduling.
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.
some minor fixes to MemoryStore
This is a repeat of #5, moved to its own branch in my repo.
This makes all updates to on ; it skips on synchronizing the reads where it can get away with it.
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.
