[SPARK-16967] move mesos to module

.travis.yml

@@ -44,7 +44,7 @@ notifications:
 # 5. Run maven install before running lint-java.
 install:
   - export MAVEN_SKIP_RC=1
-  - build/mvn -T 4 -q -DskipTests -Pyarn -Phadoop-2.3 -Pkinesis-asl -Phive -Phive-thriftserver install
+  - build/mvn -T 4 -q -DskipTests -Pmesos -Pyarn -Phadoop-2.3 -Pkinesis-asl -Phive -Phive-thriftserver install
 
 # 6. Run lint-java.
 script:

assembly/pom.xml

@@ -138,6 +138,16 @@
         </dependency>
       </dependencies>
     </profile>
+    <profile>
+      <id>mesos</id>
+      <dependencies>
+        <dependency>
+          <groupId>org.apache.spark</groupId>
+          <artifactId>spark-mesos_${scala.binary.version}</artifactId>
+          <version>${project.version}</version>
+        </dependency>
+      </dependencies>
+    </profile>
     <profile>
       <id>hive</id>
       <dependencies>

core/pom.xml

@@ -215,11 +215,6 @@
       <groupId>org.glassfish.jersey.containers</groupId>
       <artifactId>jersey-container-servlet-core</artifactId>
     </dependency>
-    <dependency>
-      <groupId>org.apache.mesos</groupId>
-      <artifactId>mesos</artifactId>
-      <classifier>${mesos.classifier}</classifier>
-    </dependency>
     <dependency>
       <groupId>io.netty</groupId>
       <artifactId>netty-all</artifactId>

core/src/main/scala/org/apache/spark/SparkContext.scala

@@ -42,7 +42,6 @@ import org.apache.hadoop.mapred.{FileInputFormat, InputFormat, JobConf, Sequence
   TextInputFormat}
 import org.apache.hadoop.mapreduce.{InputFormat => NewInputFormat, Job => NewHadoopJob}
 import org.apache.hadoop.mapreduce.lib.input.{FileInputFormat => NewFileInputFormat}
-import org.apache.mesos.MesosNativeLibrary
 
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.broadcast.Broadcast
@@ -56,7 +55,6 @@ import org.apache.spark.rdd._
 import org.apache.spark.rpc.RpcEndpointRef
 import org.apache.spark.scheduler._
 import org.apache.spark.scheduler.cluster.{CoarseGrainedSchedulerBackend, StandaloneSchedulerBackend}
-import org.apache.spark.scheduler.cluster.mesos.{MesosCoarseGrainedSchedulerBackend, MesosFineGrainedSchedulerBackend}
 import org.apache.spark.scheduler.local.LocalSchedulerBackend
 import org.apache.spark.storage._
 import org.apache.spark.storage.BlockManagerMessages.TriggerThreadDump
@@ -2512,18 +2510,6 @@ object SparkContext extends Logging {
         }
         (backend, scheduler)
 
-      case MESOS_REGEX(mesosUrl) =>
-        MesosNativeLibrary.load()
-        val scheduler = new TaskSchedulerImpl(sc)
-        val coarseGrained = sc.conf.getBoolean("spark.mesos.coarse", defaultValue = true)
-        val backend = if (coarseGrained) {
-          new MesosCoarseGrainedSchedulerBackend(scheduler, sc, mesosUrl, sc.env.securityManager)
-        } else {
-          new MesosFineGrainedSchedulerBackend(scheduler, sc, mesosUrl)
-        }
-        scheduler.initialize(backend)
-        (backend, scheduler)
-
       case masterUrl =>
         val cm = getClusterManager(masterUrl) match {
           case Some(clusterMgr) => clusterMgr
@@ -2545,7 +2531,7 @@ object SparkContext extends Logging {
   private def getClusterManager(url: String): Option[ExternalClusterManager] = {
     val loader = Utils.getContextOrSparkClassLoader
     val serviceLoaders =
-    ServiceLoader.load(classOf[ExternalClusterManager], loader).asScala.filter(_.canCreate(url))
+      ServiceLoader.load(classOf[ExternalClusterManager], loader).asScala.filter(_.canCreate(url))
     if (serviceLoaders.size > 1) {
       throw new SparkException(s"Multiple Cluster Managers ($serviceLoaders) registered " +
           s"for the url $url:")
@@ -2566,8 +2552,6 @@ private object SparkMasterRegex {
   val LOCAL_CLUSTER_REGEX = """local-cluster\[\s*([0-9]+)\s*,\s*([0-9]+)\s*,\s*([0-9]+)\s*]""".r
   // Regular expression for connecting to Spark deploy clusters
   val SPARK_REGEX = """spark://(.*)""".r
-  // Regular expression for connection to Mesos cluster by mesos:// or mesos://zk:// url
-  val MESOS_REGEX = """mesos://(.*)""".r
 }
 
 /**

core/src/main/scala/org/apache/spark/TaskState.scala

@@ -17,8 +17,6 @@
 
 package org.apache.spark
 
-import org.apache.mesos.Protos.{TaskState => MesosTaskState}
-
 private[spark] object TaskState extends Enumeration {
 
   val LAUNCHING, RUNNING, FINISHED, FAILED, KILLED, LOST = Value
@@ -30,22 +28,4 @@ private[spark] object TaskState extends Enumeration {
   def isFailed(state: TaskState): Boolean = (LOST == state) || (FAILED == state)
 
   def isFinished(state: TaskState): Boolean = FINISHED_STATES.contains(state)
-
-  def toMesos(state: TaskState): MesosTaskState = state match {
-    case LAUNCHING => MesosTaskState.TASK_STARTING
-    case RUNNING => MesosTaskState.TASK_RUNNING
-    case FINISHED => MesosTaskState.TASK_FINISHED
-    case FAILED => MesosTaskState.TASK_FAILED
-    case KILLED => MesosTaskState.TASK_KILLED
-    case LOST => MesosTaskState.TASK_LOST
-  }
-
-  def fromMesos(mesosState: MesosTaskState): TaskState = mesosState match {
-    case MesosTaskState.TASK_STAGING | MesosTaskState.TASK_STARTING => LAUNCHING
-    case MesosTaskState.TASK_RUNNING | MesosTaskState.TASK_KILLING => RUNNING
-    case MesosTaskState.TASK_FINISHED => FINISHED
-    case MesosTaskState.TASK_FAILED => FAILED
-    case MesosTaskState.TASK_KILLED => KILLED
-    case MesosTaskState.TASK_LOST | MesosTaskState.TASK_ERROR => LOST
-  }
 }

core/src/test/scala/org/apache/spark/SparkContextSchedulerCreationSuite.scala

@@ -22,7 +22,6 @@ import org.scalatest.PrivateMethodTester
 import org.apache.spark.internal.Logging
 import org.apache.spark.scheduler.{SchedulerBackend, TaskScheduler, TaskSchedulerImpl}
 import org.apache.spark.scheduler.cluster.StandaloneSchedulerBackend
-import org.apache.spark.scheduler.cluster.mesos.{MesosCoarseGrainedSchedulerBackend, MesosFineGrainedSchedulerBackend}
 import org.apache.spark.scheduler.local.LocalSchedulerBackend
 
 
@@ -130,31 +129,4 @@ class SparkContextSchedulerCreationSuite
       case _ => fail()
     }
   }
-
-  def testMesos(master: String, expectedClass: Class[_], coarse: Boolean) {
-    val conf = new SparkConf().set("spark.mesos.coarse", coarse.toString)
-    try {
-      val sched = createTaskScheduler(master, "client", conf)
-      assert(sched.backend.getClass === expectedClass)
-    } catch {
-      case e: UnsatisfiedLinkError =>
-        assert(e.getMessage.contains("mesos"))
-        logWarning("Mesos not available, could not test actual Mesos scheduler creation")
-      case e: Throwable => fail(e)
-    }
-  }
-
-  test("mesos fine-grained") {
-    testMesos("mesos://localhost:1234", classOf[MesosFineGrainedSchedulerBackend], coarse = false)
-  }
-
-  test("mesos coarse-grained") {
-    testMesos("mesos://localhost:1234", classOf[MesosCoarseGrainedSchedulerBackend], coarse = true)
-  }
-
-  test("mesos with zookeeper") {
-    testMesos("mesos://zk://localhost:1234,localhost:2345",
-      classOf[MesosFineGrainedSchedulerBackend], coarse = false)
-  }
-
 }

dev/create-release/release-build.sh

@@ -80,7 +80,7 @@ NEXUS_PROFILE=d63f592e7eac0 # Profile for Spark staging uploads
 BASE_DIR=$(pwd)
 
 MVN="build/mvn --force"
-PUBLISH_PROFILES="-Pyarn -Phive -Phive-thriftserver -Phadoop-2.2"
+PUBLISH_PROFILES="-Pmesos -Pyarn -Phive -Phive-thriftserver -Phadoop-2.2"
 PUBLISH_PROFILES="$PUBLISH_PROFILES -Pspark-ganglia-lgpl -Pkinesis-asl"
 
 rm -rf spark
@@ -186,12 +186,13 @@ if [[ "$1" == "package" ]]; then
 
   # We increment the Zinc port each time to avoid OOM's and other craziness if multiple builds
   # share the same Zinc server.
-  make_binary_release "hadoop2.3" "-Psparkr -Phadoop-2.3 -Phive -Phive-thriftserver -Pyarn" "3033" &
-  make_binary_release "hadoop2.4" "-Psparkr -Phadoop-2.4 -Phive -Phive-thriftserver -Pyarn" "3034" &
-  make_binary_release "hadoop2.6" "-Psparkr -Phadoop-2.6 -Phive -Phive-thriftserver -Pyarn" "3035" &
-  make_binary_release "hadoop2.7" "-Psparkr -Phadoop-2.7 -Phive -Phive-thriftserver -Pyarn" "3036" &
-  make_binary_release "hadoop2.4-without-hive" "-Psparkr -Phadoop-2.4 -Pyarn" "3037" &
-  make_binary_release "without-hadoop" "-Psparkr -Phadoop-provided -Pyarn" "3038" &
+  FLAGS="-Psparkr -Phive -Phive-thriftserver -Pyarn -Pmesos"
+  make_binary_release "hadoop2.3" "-Phadoop2.3 $FLAGS" "3033" &
+  make_binary_release "hadoop2.4" "-Phadoop2.4 $FLAGS" "3034" &
+  make_binary_release "hadoop2.6" "-Phadoop2.6 $FLAGS" "3035" &
+  make_binary_release "hadoop2.7" "-Phadoop2.7 $FLAGS" "3036" &
+  make_binary_release "hadoop2.4-without-hive" "-Psparkr -Phadoop-2.4 -Pyarn -Pmesos" "3037" &
+  make_binary_release "without-hadoop" "-Psparkr -Phadoop-provided -Pyarn -Pmesos" "3038" &
   wait
   rm -rf spark-$SPARK_VERSION-bin-*/
 

dev/lint-java

@@ -20,7 +20,7 @@
 SCRIPT_DIR="$( cd "$( dirname "$0" )" && pwd )"
 SPARK_ROOT_DIR="$(dirname $SCRIPT_DIR)"
 
-ERRORS=$($SCRIPT_DIR/../build/mvn -Pkinesis-asl -Pyarn -Phive -Phive-thriftserver checkstyle:check | grep ERROR)
+ERRORS=$($SCRIPT_DIR/../build/mvn -Pkinesis-asl -Pmesos -Pyarn -Phive -Phive-thriftserver checkstyle:check | grep ERROR)
 
 if test ! -z "$ERRORS"; then
     echo -e "Checkstyle checks failed at following occurrences:\n$ERRORS"

dev/mima

@@ -24,7 +24,7 @@ set -e
 FWDIR="$(cd "`dirname "$0"`"/..; pwd)"
 cd "$FWDIR"
 
-SPARK_PROFILES="-Pyarn -Pspark-ganglia-lgpl -Pkinesis-asl -Phive-thriftserver -Phive"
+SPARK_PROFILES="-Pmesos -Pyarn -Pspark-ganglia-lgpl -Pkinesis-asl -Phive-thriftserver -Phive"
 TOOLS_CLASSPATH="$(build/sbt -DcopyDependencies=false "export tools/fullClasspath" | tail -n1)"
 OLD_DEPS_CLASSPATH="$(build/sbt -DcopyDependencies=false $SPARK_PROFILES "export oldDeps/fullClasspath" | tail -n1)"
 

dev/scalastyle

@@ -22,6 +22,7 @@
 ERRORS=$(echo -e "q\n" \
     | build/sbt \
         -Pkinesis-asl \
+        -Pmesos \
         -Pyarn \
         -Phive \
         -Phive-thriftserver \

dev/sparktestsupport/modules.py

@@ -458,6 +458,13 @@ def __hash__(self):
     ]
 )
 
+mesos = Module(
+    name="mesos",
+    dependencies=[],
+    source_file_regexes=["mesos/"],
+    sbt_test_goals=["mesos/test"]
+)
+
 # The root module is a dummy module which is used to run all of the tests.
 # No other modules should directly depend on this module.
 root = Module(

dev/test-dependencies.sh

@@ -29,7 +29,7 @@ export LC_ALL=C
 # TODO: This would be much nicer to do in SBT, once SBT supports Maven-style resolution.
 
 # NOTE: These should match those in the release publishing script
-HADOOP2_MODULE_PROFILES="-Phive-thriftserver -Pyarn -Phive"
+HADOOP2_MODULE_PROFILES="-Phive-thriftserver -Pmesos -Pyarn -Phive"
 MVN="build/mvn"
 HADOOP_PROFILES=(
     hadoop-2.2

docs/building-spark.md

@@ -50,7 +50,7 @@ To create a Spark distribution like those distributed by the
 to be runnable, use `./dev/make-distribution.sh` in the project root directory. It can be configured
 with Maven profile settings and so on like the direct Maven build. Example:
 
-    ./dev/make-distribution.sh --name custom-spark --tgz -Psparkr -Phadoop-2.4 -Phive -Phive-thriftserver -Pyarn
+    ./dev/make-distribution.sh --name custom-spark --tgz -Psparkr -Phadoop-2.4 -Phive -Phive-thriftserver -Pmesos -Pyarn
 
 For more information on usage, run `./dev/make-distribution.sh --help`
 
@@ -105,13 +105,17 @@ By default Spark will build with Hive 1.2.1 bindings.
 
 ## Packaging without Hadoop Dependencies for YARN
 
-The assembly directory produced by `mvn package` will, by default, include all of Spark's 
-dependencies, including Hadoop and some of its ecosystem projects. On YARN deployments, this 
-causes multiple versions of these to appear on executor classpaths: the version packaged in 
+The assembly directory produced by `mvn package` will, by default, include all of Spark's
+dependencies, including Hadoop and some of its ecosystem projects. On YARN deployments, this
+causes multiple versions of these to appear on executor classpaths: the version packaged in
 the Spark assembly and the version on each node, included with `yarn.application.classpath`.
-The `hadoop-provided` profile builds the assembly without including Hadoop-ecosystem projects, 
+The `hadoop-provided` profile builds the assembly without including Hadoop-ecosystem projects,
 like ZooKeeper and Hadoop itself.
 
+## Building with Mesos support
+
+    ./build/mvn -Pmesos -DskipTests clean package
+
 ## Building for Scala 2.10
 To produce a Spark package compiled with Scala 2.10, use the `-Dscala-2.10` property:
 
@@ -263,17 +267,17 @@ The run-tests script also can be limited to a specific Python version or a speci
 
 ## Running R Tests
 
-To run the SparkR tests you will need to install the R package `testthat` 
-(run `install.packages(testthat)` from R shell).  You can run just the SparkR tests using 
+To run the SparkR tests you will need to install the R package `testthat`
+(run `install.packages(testthat)` from R shell).  You can run just the SparkR tests using
 the command:
 
     ./R/run-tests.sh
 
 ## Running Docker-based Integration Test Suites
 
-In order to run Docker integration tests, you have to install the `docker` engine on your box. 
-The instructions for installation can be found at [the Docker site](https://docs.docker.com/engine/installation/). 
-Once installed, the `docker` service needs to be started, if not already running. 
+In order to run Docker integration tests, you have to install the `docker` engine on your box.
+The instructions for installation can be found at [the Docker site](https://docs.docker.com/engine/installation/).
+Once installed, the `docker` service needs to be started, if not already running.
 On Linux, this can be done by `sudo service docker start`.
 
     ./build/mvn install -DskipTests