Merge pull request #217 from aarondav/mesos-urls
Re-enable zk:// urls for Mesos SparkContexts This was broken in PR #71 when we explicitly disallow anything that didn't fit a mesos:// url. Although it is not really clear that a zk:// url should match Mesos, it is what the docs say and it is necessary for backwards compatibility. Additionally added a unit test for the creation of all types of TaskSchedulers. Since YARN and Mesos are not necessarily available in the system, they are allowed to pass as long as the YARN/Mesos code paths are exercised.
This commit is contained in:
Reynold Xin
commit
58d9bbcfec
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@ -153,120 +153,7 @@ class SparkContext(
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executorEnvs("SPARK_USER") = sparkUser
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executorEnvs("SPARK_USER") = sparkUser
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// Create and start the scheduler
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// Create and start the scheduler
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private[spark] var taskScheduler: TaskScheduler = {
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private[spark] var taskScheduler = SparkContext.createTaskScheduler(this, master, appName)
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// Regular expression used for local[N] master format
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val LOCAL_N_REGEX = """local\[([0-9]+)\]""".r
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// Regular expression for local[N, maxRetries], used in tests with failing tasks
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val LOCAL_N_FAILURES_REGEX = """local\[([0-9]+)\s*,\s*([0-9]+)\]""".r
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// Regular expression for simulating a Spark cluster of [N, cores, memory] locally
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val LOCAL_CLUSTER_REGEX = """local-cluster\[\s*([0-9]+)\s*,\s*([0-9]+)\s*,\s*([0-9]+)\s*]""".r
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// Regular expression for connecting to Spark deploy clusters
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val SPARK_REGEX = """spark://(.*)""".r
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// Regular expression for connection to Mesos cluster
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val MESOS_REGEX = """mesos://(.*)""".r
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// Regular expression for connection to Simr cluster
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val SIMR_REGEX = """simr://(.*)""".r
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master match {
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case "local" =>
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new LocalScheduler(1, 0, this)
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case LOCAL_N_REGEX(threads) =>
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new LocalScheduler(threads.toInt, 0, this)
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case LOCAL_N_FAILURES_REGEX(threads, maxFailures) =>
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new LocalScheduler(threads.toInt, maxFailures.toInt, this)
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case SPARK_REGEX(sparkUrl) =>
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val scheduler = new ClusterScheduler(this)
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val masterUrls = sparkUrl.split(",").map("spark://" + _)
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val backend = new SparkDeploySchedulerBackend(scheduler, this, masterUrls, appName)
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scheduler.initialize(backend)
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scheduler
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case SIMR_REGEX(simrUrl) =>
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val scheduler = new ClusterScheduler(this)
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val backend = new SimrSchedulerBackend(scheduler, this, simrUrl)
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scheduler.initialize(backend)
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scheduler
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case LOCAL_CLUSTER_REGEX(numSlaves, coresPerSlave, memoryPerSlave) =>
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// Check to make sure memory requested <= memoryPerSlave. Otherwise Spark will just hang.
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val memoryPerSlaveInt = memoryPerSlave.toInt
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if (SparkContext.executorMemoryRequested > memoryPerSlaveInt) {
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throw new SparkException(
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"Asked to launch cluster with %d MB RAM / worker but requested %d MB/worker".format(
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memoryPerSlaveInt, SparkContext.executorMemoryRequested))
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}
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val scheduler = new ClusterScheduler(this)
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val localCluster = new LocalSparkCluster(
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numSlaves.toInt, coresPerSlave.toInt, memoryPerSlaveInt)
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val masterUrls = localCluster.start()
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val backend = new SparkDeploySchedulerBackend(scheduler, this, masterUrls, appName)
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scheduler.initialize(backend)
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backend.shutdownCallback = (backend: SparkDeploySchedulerBackend) => {
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localCluster.stop()
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}
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scheduler
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case "yarn-standalone" =>
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val scheduler = try {
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val clazz = Class.forName("org.apache.spark.scheduler.cluster.YarnClusterScheduler")
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val cons = clazz.getConstructor(classOf[SparkContext])
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cons.newInstance(this).asInstanceOf[ClusterScheduler]
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} catch {
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// TODO: Enumerate the exact reasons why it can fail
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// But irrespective of it, it means we cannot proceed !
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case th: Throwable => {
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throw new SparkException("YARN mode not available ?", th)
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}
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}
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val backend = new CoarseGrainedSchedulerBackend(scheduler, this.env.actorSystem)
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scheduler.initialize(backend)
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scheduler
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case "yarn-client" =>
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val scheduler = try {
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val clazz = Class.forName("org.apache.spark.scheduler.cluster.YarnClientClusterScheduler")
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val cons = clazz.getConstructor(classOf[SparkContext])
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cons.newInstance(this).asInstanceOf[ClusterScheduler]
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} catch {
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case th: Throwable => {
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throw new SparkException("YARN mode not available ?", th)
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}
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}
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val backend = try {
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val clazz = Class.forName("org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend")
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val cons = clazz.getConstructor(classOf[ClusterScheduler], classOf[SparkContext])
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cons.newInstance(scheduler, this).asInstanceOf[CoarseGrainedSchedulerBackend]
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} catch {
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case th: Throwable => {
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throw new SparkException("YARN mode not available ?", th)
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}
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}
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scheduler.initialize(backend)
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scheduler
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case MESOS_REGEX(mesosUrl) =>
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MesosNativeLibrary.load()
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val scheduler = new ClusterScheduler(this)
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val coarseGrained = System.getProperty("spark.mesos.coarse", "false").toBoolean
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val backend = if (coarseGrained) {
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new CoarseMesosSchedulerBackend(scheduler, this, mesosUrl, appName)
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} else {
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new MesosSchedulerBackend(scheduler, this, mesosUrl, appName)
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}
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scheduler.initialize(backend)
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scheduler
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case _ =>
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throw new SparkException("Could not parse Master URL: '" + master + "'")
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}
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}
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taskScheduler.start()
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taskScheduler.start()
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@volatile private[spark] var dagScheduler = new DAGScheduler(taskScheduler)
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@volatile private[spark] var dagScheduler = new DAGScheduler(taskScheduler)
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@ -1137,6 +1024,124 @@ object SparkContext {
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.map(Utils.memoryStringToMb)
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.map(Utils.memoryStringToMb)
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.getOrElse(512)
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.getOrElse(512)
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}
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}
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// Creates a task scheduler based on a given master URL. Extracted for testing.
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private
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def createTaskScheduler(sc: SparkContext, master: String, appName: String): TaskScheduler = {
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// Regular expression used for local[N] master format
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val LOCAL_N_REGEX = """local\[([0-9]+)\]""".r
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// Regular expression for local[N, maxRetries], used in tests with failing tasks
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val LOCAL_N_FAILURES_REGEX = """local\[([0-9]+)\s*,\s*([0-9]+)\]""".r
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// Regular expression for simulating a Spark cluster of [N, cores, memory] locally
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val LOCAL_CLUSTER_REGEX = """local-cluster\[\s*([0-9]+)\s*,\s*([0-9]+)\s*,\s*([0-9]+)\s*]""".r
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// Regular expression for connecting to Spark deploy clusters
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val SPARK_REGEX = """spark://(.*)""".r
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// Regular expression for connection to Mesos cluster by mesos:// or zk:// url
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val MESOS_REGEX = """(mesos|zk)://.*""".r
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// Regular expression for connection to Simr cluster
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val SIMR_REGEX = """simr://(.*)""".r
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master match {
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case "local" =>
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new LocalScheduler(1, 0, sc)
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case LOCAL_N_REGEX(threads) =>
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new LocalScheduler(threads.toInt, 0, sc)
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case LOCAL_N_FAILURES_REGEX(threads, maxFailures) =>
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new LocalScheduler(threads.toInt, maxFailures.toInt, sc)
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case SPARK_REGEX(sparkUrl) =>
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val scheduler = new ClusterScheduler(sc)
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val masterUrls = sparkUrl.split(",").map("spark://" + _)
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val backend = new SparkDeploySchedulerBackend(scheduler, sc, masterUrls, appName)
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scheduler.initialize(backend)
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scheduler
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case LOCAL_CLUSTER_REGEX(numSlaves, coresPerSlave, memoryPerSlave) =>
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// Check to make sure memory requested <= memoryPerSlave. Otherwise Spark will just hang.
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val memoryPerSlaveInt = memoryPerSlave.toInt
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if (SparkContext.executorMemoryRequested > memoryPerSlaveInt) {
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throw new SparkException(
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"Asked to launch cluster with %d MB RAM / worker but requested %d MB/worker".format(
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memoryPerSlaveInt, SparkContext.executorMemoryRequested))
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}
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val scheduler = new ClusterScheduler(sc)
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val localCluster = new LocalSparkCluster(
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numSlaves.toInt, coresPerSlave.toInt, memoryPerSlaveInt)
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val masterUrls = localCluster.start()
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val backend = new SparkDeploySchedulerBackend(scheduler, sc, masterUrls, appName)
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scheduler.initialize(backend)
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backend.shutdownCallback = (backend: SparkDeploySchedulerBackend) => {
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localCluster.stop()
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}
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scheduler
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case "yarn-standalone" =>
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val scheduler = try {
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val clazz = Class.forName("org.apache.spark.scheduler.cluster.YarnClusterScheduler")
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val cons = clazz.getConstructor(classOf[SparkContext])
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cons.newInstance(sc).asInstanceOf[ClusterScheduler]
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} catch {
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// TODO: Enumerate the exact reasons why it can fail
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// But irrespective of it, it means we cannot proceed !
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case th: Throwable => {
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throw new SparkException("YARN mode not available ?", th)
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}
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}
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val backend = new CoarseGrainedSchedulerBackend(scheduler, sc.env.actorSystem)
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scheduler.initialize(backend)
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scheduler
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case "yarn-client" =>
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val scheduler = try {
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val clazz = Class.forName("org.apache.spark.scheduler.cluster.YarnClientClusterScheduler")
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val cons = clazz.getConstructor(classOf[SparkContext])
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cons.newInstance(sc).asInstanceOf[ClusterScheduler]
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} catch {
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case th: Throwable => {
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throw new SparkException("YARN mode not available ?", th)
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}
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}
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val backend = try {
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val clazz = Class.forName("org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend")
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val cons = clazz.getConstructor(classOf[ClusterScheduler], classOf[SparkContext])
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cons.newInstance(scheduler, sc).asInstanceOf[CoarseGrainedSchedulerBackend]
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} catch {
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case th: Throwable => {
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throw new SparkException("YARN mode not available ?", th)
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}
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}
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scheduler.initialize(backend)
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scheduler
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case mesosUrl @ MESOS_REGEX(_) =>
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MesosNativeLibrary.load()
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val scheduler = new ClusterScheduler(sc)
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val coarseGrained = System.getProperty("spark.mesos.coarse", "false").toBoolean
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val url = mesosUrl.stripPrefix("mesos://") // strip scheme from raw Mesos URLs
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val backend = if (coarseGrained) {
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new CoarseMesosSchedulerBackend(scheduler, sc, url, appName)
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} else {
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new MesosSchedulerBackend(scheduler, sc, url, appName)
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}
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scheduler.initialize(backend)
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scheduler
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case SIMR_REGEX(simrUrl) =>
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val scheduler = new ClusterScheduler(sc)
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val backend = new SimrSchedulerBackend(scheduler, sc, simrUrl)
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scheduler.initialize(backend)
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scheduler
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case _ =>
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throw new SparkException("Could not parse Master URL: '" + master + "'")
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}
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}
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}
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}
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/**
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/**
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@ -74,7 +74,7 @@ class LocalActor(localScheduler: LocalScheduler, private var freeCores: Int)
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}
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}
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}
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}
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private[spark] class LocalScheduler(threads: Int, val maxFailures: Int, val sc: SparkContext)
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private[spark] class LocalScheduler(val threads: Int, val maxFailures: Int, val sc: SparkContext)
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extends TaskScheduler
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extends TaskScheduler
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with ExecutorBackend
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with ExecutorBackend
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with Logging {
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with Logging {
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@ -0,0 +1,140 @@
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/*
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|
* Licensed to the Apache Software Foundation (ASF) under one or more
|
||||||
|
* contributor license agreements. See the NOTICE file distributed with
|
||||||
|
* this work for additional information regarding copyright ownership.
|
||||||
|
* The ASF licenses this file to You under the Apache License, Version 2.0
|
||||||
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* (the "License"); you may not use this file except in compliance with
|
||||||
|
* the License. You may obtain a copy of the License at
|
||||||
|
*
|
||||||
|
* http://www.apache.org/licenses/LICENSE-2.0
|
||||||
|
*
|
||||||
|
* Unless required by applicable law or agreed to in writing, software
|
||||||
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
||||||
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||||
|
* See the License for the specific language governing permissions and
|
||||||
|
* limitations under the License.
|
||||||
|
*/
|
||||||
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package org.apache.spark
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import org.scalatest.{FunSuite, PrivateMethodTester}
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import org.apache.spark.scheduler.TaskScheduler
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import org.apache.spark.scheduler.cluster.{ClusterScheduler, SimrSchedulerBackend, SparkDeploySchedulerBackend}
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import org.apache.spark.scheduler.cluster.mesos.{CoarseMesosSchedulerBackend, MesosSchedulerBackend}
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import org.apache.spark.scheduler.local.LocalScheduler
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class SparkContextSchedulerCreationSuite
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extends FunSuite with PrivateMethodTester with LocalSparkContext with Logging {
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def createTaskScheduler(master: String): TaskScheduler = {
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// Create local SparkContext to setup a SparkEnv. We don't actually want to start() the
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// real schedulers, so we don't want to create a full SparkContext with the desired scheduler.
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sc = new SparkContext("local", "test")
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val createTaskSchedulerMethod = PrivateMethod[TaskScheduler]('createTaskScheduler)
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SparkContext invokePrivate createTaskSchedulerMethod(sc, master, "test")
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}
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test("bad-master") {
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val e = intercept[SparkException] {
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createTaskScheduler("localhost:1234")
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}
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assert(e.getMessage.contains("Could not parse Master URL"))
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}
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test("local") {
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createTaskScheduler("local") match {
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case s: LocalScheduler =>
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assert(s.threads === 1)
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assert(s.maxFailures === 0)
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||||||
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case _ => fail()
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||||||
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}
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||||||
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}
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test("local-n") {
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createTaskScheduler("local[5]") match {
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case s: LocalScheduler =>
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||||||
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assert(s.threads === 5)
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||||||
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assert(s.maxFailures === 0)
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||||||
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case _ => fail()
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||||||
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}
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||||||
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}
|
||||||
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||||||
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test("local-n-failures") {
|
||||||
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createTaskScheduler("local[4, 2]") match {
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||||||
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case s: LocalScheduler =>
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||||||
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assert(s.threads === 4)
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||||||
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assert(s.maxFailures === 2)
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||||||
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case _ => fail()
|
||||||
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}
|
||||||
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}
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||||||
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|
||||||
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test("simr") {
|
||||||
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createTaskScheduler("simr://uri") match {
|
||||||
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case s: ClusterScheduler =>
|
||||||
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assert(s.backend.isInstanceOf[SimrSchedulerBackend])
|
||||||
|
case _ => fail()
|
||||||
|
}
|
||||||
|
}
|
||||||
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|
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|
test("local-cluster") {
|
||||||
|
createTaskScheduler("local-cluster[3, 14, 512]") match {
|
||||||
|
case s: ClusterScheduler =>
|
||||||
|
assert(s.backend.isInstanceOf[SparkDeploySchedulerBackend])
|
||||||
|
case _ => fail()
|
||||||
|
}
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||||||
|
}
|
||||||
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|
||||||
|
def testYarn(master: String, expectedClassName: String) {
|
||||||
|
try {
|
||||||
|
createTaskScheduler(master) match {
|
||||||
|
case s: ClusterScheduler =>
|
||||||
|
assert(s.getClass === Class.forName(expectedClassName))
|
||||||
|
case _ => fail()
|
||||||
|
}
|
||||||
|
} catch {
|
||||||
|
case e: SparkException =>
|
||||||
|
assert(e.getMessage.contains("YARN mode not available"))
|
||||||
|
logWarning("YARN not available, could not test actual YARN scheduler creation")
|
||||||
|
case e: Throwable => fail(e)
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
test("yarn-standalone") {
|
||||||
|
testYarn("yarn-standalone", "org.apache.spark.scheduler.cluster.YarnClusterScheduler")
|
||||||
|
}
|
||||||
|
|
||||||
|
test("yarn-client") {
|
||||||
|
testYarn("yarn-client", "org.apache.spark.scheduler.cluster.YarnClientClusterScheduler")
|
||||||
|
}
|
||||||
|
|
||||||
|
def testMesos(master: String, expectedClass: Class[_]) {
|
||||||
|
try {
|
||||||
|
createTaskScheduler(master) match {
|
||||||
|
case s: ClusterScheduler =>
|
||||||
|
assert(s.backend.getClass === expectedClass)
|
||||||
|
case _ => fail()
|
||||||
|
}
|
||||||
|
} catch {
|
||||||
|
case e: UnsatisfiedLinkError =>
|
||||||
|
assert(e.getMessage.contains("no mesos in"))
|
||||||
|
logWarning("Mesos not available, could not test actual Mesos scheduler creation")
|
||||||
|
case e: Throwable => fail(e)
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
test("mesos fine-grained") {
|
||||||
|
System.setProperty("spark.mesos.coarse", "false")
|
||||||
|
testMesos("mesos://localhost:1234", classOf[MesosSchedulerBackend])
|
||||||
|
}
|
||||||
|
|
||||||
|
test("mesos coarse-grained") {
|
||||||
|
System.setProperty("spark.mesos.coarse", "true")
|
||||||
|
testMesos("mesos://localhost:1234", classOf[CoarseMesosSchedulerBackend])
|
||||||
|
}
|
||||||
|
|
||||||
|
test("mesos with zookeeper") {
|
||||||
|
System.setProperty("spark.mesos.coarse", "false")
|
||||||
|
testMesos("zk://localhost:1234,localhost:2345", classOf[MesosSchedulerBackend])
|
||||||
|
}
|
||||||
|
}
|
||||||
Loading…
Reference in a new issue