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 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.
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.
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.
Allow spark on yarn to be run from HDFS.
Allows the spark.jar, app.jar, and log4j.properties to be put into hdfs. Allows you to specify the files on a different hdfs cluster and it will copy them over. It makes sure permissions are correct and makes sure to put things into public distributed cache so they can be reused amongst users if their permissions are appropriate. Also add a bit of error handling for missing arguments.
Enable stopping and starting a spot cluster
Clusters launched using `--spot-price` contain an on-demand master and spot slaves. Because EC2 does not support stopping spot instances, the spark-ec2 script previously could only destroy such clusters.
This pull request makes it possible to stop and restart a spot cluster.
* The `stop` command works as expected for a spot cluster: the master is stopped and the slaves are terminated.
* To start a stopped spot cluster, the user must invoke `launch --use-existing-master`. This launches fresh spot slaves but resumes the existing master.
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
3 Kryo related changes.
1. Call Kryo setReferences before calling user specified Kryo registrator. This is done so the user specified registrator can override the default setting.
2. Register more internal classes (MapStatus, BlockManagerId).
3. Slightly refactored the internal class registration to allocate less memory.
Add spark-tools assembly to spark-class'ss classpath
This commit adds an assembly for `spark-tools` and adds it to `spark-class`'s classpath, allowing the JavaAPICompletenessChecker to be run against Spark 0.8+ with
./spark-class org.apache.spark.tools.JavaAPICompletenessChecker
Previously, this tool was run through the `run` script. I chose to add this to `run-example` because I didn't want to duplicate code in a `run-tool` script.
Fix secure hdfs access for spark on yarn
https://github.com/apache/incubator-spark/pull/23 broke secure hdfs access. Not sure if it works with secure hdfs on standalone. Fixing it at least for spark on yarn.
The broadcasting of jobconf change also broke secure hdfs access as it didn't take into account things calling the getPartitions before sparkContext is initialized. The DAGScheduler does this as it tries to getShuffleMapStage.
Include appId in executor cmd line args
add the appId back into the executor cmd line args.
I also made a pretty lame regression test, just to make sure it doesn't get dropped in the future. not sure it will run on the build server, though, b/c `ExecutorRunner.buildCommandSeq()` expects to be abel to run the scripts in `bin`.
Add Spark multi-user support for standalone mode and Mesos
This PR add multi-user support for Spark both standalone mode and Mesos (coarse and fine grained ) mode, user can specify the user name who submit app through environment variable `SPARK_USER` or use default one. Executor will communicate with Hadoop using specified user name.
Also I fixed one bug in JobLogger when different user wrote job log to specified folder which has no right file permission.
I separate previous [PR750](https://github.com/mesos/spark/pull/750) into two PRs, in this PR I only solve multi-user support problem. I will try to solve security auth problem in subsequent PR because security auth is a complicated problem especially for Shark Server like long-run app (both Kerberos TGT and HDFS delegation token should be renewed or re-created through app's run time).
