Replaced the daemon thread started by DAGScheduler with an actor
Remove unnecessary null checking
Put the periodical resubmitFailedStages() call into a scheduled task
Made some changes according to suggestions from @aarondav
spark-assembly.jar fails to authenticate with YARN ResourceManager

sbt-assembly is setup to pick the first META-INF/services/org.apache.hadoop.security.SecurityInfo file instead of merging them. This causes Kerberos authentication to fail, this manifests itself in the "info:null" debug log statement:

    DEBUG SaslRpcClient: Get token info proto:interface org.apache.hadoop.yarn.api.ApplicationClientProtocolPB info:null
    DEBUG SaslRpcClient: Get kerberos info proto:interface org.apache.hadoop.yarn.api.ApplicationClientProtocolPB info:null
    ERROR UserGroupInformation: PriviledgedActionException as:foo@BAR (auth:KERBEROS) cause:org.apache.hadoop.security.AccessControlException: Client cannot authenticate via:[TOKEN, KERBEROS]
    DEBUG UserGroupInformation: PrivilegedAction as:foo@BAR (auth:KERBEROS) from:org.apache.hadoop.ipc.Client$Connection.handleSaslConnectionFailure(Client.java:583)
    WARN Client: Exception encountered while connecting to the server : org.apache.hadoop.security.AccessControlException: Client cannot authenticate via:[TOKEN, KERBEROS]
    ERROR UserGroupInformation: PriviledgedActionException as:foo@BAR (auth:KERBEROS) cause:java.io.IOException: org.apache.hadoop.security.AccessControlException: Client cannot authenticate via:[TOKEN, KERBEROS]

This previously would just contain a single class:

$ unzip -c assembly/target/scala-2.10/spark-assembly-0.9.0-incubating-SNAPSHOT-hadoop2.2.0.jar META-INF/services/org.apache.hadoop.security.SecurityInfo
Archive:  assembly/target/scala-2.10/spark-assembly-0.9.0-incubating-SNAPSHOT-hadoop2.2.0.jar
  inflating: META-INF/services/org.apache.hadoop.security.SecurityInfo

    org.apache.hadoop.security.AnnotatedSecurityInfo

And now has the full list of classes:

$ unzip -c assembly/target/scala-2.10/spark-assembly-0.9.0-incubating-SNAPSHOT-hadoop2.2.0.jar META-INF/services/org.apache.hadoop.security.SecurityInfoArchive:  assembly/target/scala-2.10/spark-assembly-0.9.0-incubating-SNAPSHOT-hadoop2.2.0.jar
  inflating: META-INF/services/org.apache.hadoop.security.SecurityInfo

    org.apache.hadoop.security.AnnotatedSecurityInfo
    org.apache.hadoop.mapreduce.v2.app.MRClientSecurityInfo
    org.apache.hadoop.mapreduce.v2.security.client.ClientHSSecurityInfo
    org.apache.hadoop.yarn.security.client.ClientRMSecurityInfo
    org.apache.hadoop.yarn.security.ContainerManagerSecurityInfo
    org.apache.hadoop.yarn.security.SchedulerSecurityInfo
    org.apache.hadoop.yarn.security.admin.AdminSecurityInfo
    org.apache.hadoop.yarn.server.RMNMSecurityInfoClass
Upgrade to sbt-assembly 0.9.2
Write Spark UI url to driver file on HDFS
Merge pull request #165 from NathanHowell/kerberos-master

spark-assembly.jar fails to authenticate with YARN ResourceManager

The META-INF/services/ sbt MergeStrategy was discarding support for Kerberos, among others. This pull request changes to a merge strategy similar to sbt-assembly's default. I've also included an update to sbt-assembly 0.9.2, a minor fix to it's zip file handling.
Merge pull request #159 from liancheng/dagscheduler-actor-refine

Migrate the daemon thread started by DAGScheduler to Akka actor

`DAGScheduler` adopts an event queue and a daemon thread polling the it to process events sent to a `DAGScheduler`.  This is a classical actor use case.  By migrating this thread to Akka actor, we may benefit from both cleaner code and better performance (context switching cost of Akka actor is much less than that of a native thread).

But things become a little complicated when taking existing test code into consideration.

Code in `DAGSchedulerSuite` is somewhat tightly coupled with `DAGScheduler`, and directly calls `DAGScheduler.processEvent` instead of posting event messages to `DAGScheduler`.  To minimize code change, I chose to let the actor to delegate messages to `processEvent`.  Maybe this doesn't follow conventional actor usage, but I tried to make it apparently correct.

Another tricky part is that, since `DAGScheduler` depends on the `ActorSystem` provided by its field `env`, `env` cannot be null.  But the `dagScheduler` field created in `DAGSchedulerSuite.before` was given a null `env`.  What's more, `BlockManager.blockIdsToBlockManagers` checks whether `env` is null to determine whether to run the production code or the test code (bad smell here, huh?).  I went through all callers of `BlockManager.blockIdsToBlockManagers`, and made sure that if `env != null` holds, then `blockManagerMaster == null` must also hold.  That's the logic behind `BlockManager.scala` [line 896](https://github.com/liancheng/incubator-spark/compare/dagscheduler-actor-refine?expand=1#diff-2b643ea78c1add0381754b1f47eec132L896).

At last, since `DAGScheduler` instances are always `start()`ed after creation, I removed the `start()` method, and starts the `eventProcessActor` within the constructor.
Don't ignore spark.cores.max when using Mesos Coarse mode
Fixed a scaladoc typo in HadoopRDD.scala
Fixed typos in the CDH4 distributions version codes.
Changed local backend to use Akka actor
Merge pull request #171 from RIA-pierre-borckmans/master

Fixed typos in the CDH4 distributions version codes.

Nothing important, but annoying when doing a copy/paste...
Merge pull request #170 from liancheng/hadooprdd-doc-typo

Fixed a scaladoc typo in HadoopRDD.scala
Merge pull request #169 from kayousterhout/mesos_fix

Don't ignore spark.cores.max when using Mesos Coarse mode

totalCoresAcquired is decremented but never incremented, causing Spark to effectively ignore spark.cores.max in coarse grained Mesos mode.
Fix bug where scheduler could hang after task failure.

When a task fails, we need to call reviveOffers() so that the
task can be rescheduled on a different machine. In the current code,
the state in ClusterTaskSetManager indicating which tasks are
pending may be updated after revive offers is called (there's a
race condition here), so when revive offers is called, the task set
manager does not yet realize that there are failed tasks that need
to be relaunched.
Fix bug where scheduler could hang after task failure.

When a task fails, we need to call reviveOffers() so that the
task can be rescheduled on a different machine. In the current code,
the state in ClusterTaskSetManager indicating which tasks are
pending may be updated after revive offers is called (there's a
race condition here), so when revive offers is called, the task set
manager does not yet realize that there are failed tasks that need
to be relaunched.
Don't retry tasks if result wasn't serializable
Don't retry tasks when they fail due to a NotSerializableException