Support simple rsync schema in the executor uri (apache#12)

* Support simple rsync schema in the executor uri

* Support simple rsync schema in the executor uri

(cherry picked from commit b043fdb)
(cherry picked from commit 6d59ab1)

Author: WenboZhao

cook/src/main/scala/org/apache/spark/scheduler/cluster/cook/CoarseCookSchedulerBackend.scala

@@ -43,21 +43,26 @@ import org.apache.spark.rpc.RpcAddress
 import scala.collection.mutable
 
 object CoarseCookSchedulerBackend {
-  def fetchUri(uri: String): String =
-    Option(URI.create(uri).getScheme).map(_.toLowerCase) match {
-      case Some("http") => s"curl -O $uri"
-      case Some("spark-rsync") =>
-        val regex = "^spark-rsync://".r
-        val cleanURI = regex.replaceFirstIn(uri, "")
-        "RSYNC_CONNECT_PROG=" + "\"" + "knc spark-rsync@%H $SPARK_DRIVER_PULL_PORT" + "\"" +
-          s" rsync $$SPARK_DRIVER_PULL_HOST::spark/${cleanURI} ./"
-      case Some("hdfs") => s"$$HADOOP_COMMAND fs -copyToLocal $uri ."
-      case Some("rover") =>
-        val storage = "/opt/ts/services/storage-client.ts_storage_client/bin/storage"
-        s"$storage -X-Dtwosigma.logdir=$${MESOS_SANDBOX} cp $uri ."
-      case None | Some("file") => s"cp $uri ."
-      case Some(x) => sys.error(s"$x not supported yet")
-    }
+
+  // A collection of regexes for extracting information from an URI
+  private val HTTP_URI_REGEX = """http://(.*)""".r
+  private val RSYNC_URI_REGEX = """rsync://(.*)""".r
+  private val SPARK_RSYNC_URI_REGEX = """spark-rsync://(.*)""".r
+  private val HDFS_URI_REGEX = """hdfs://(.*)""".r
+
+  private[spark] def fetchURI(uri: String): String = uri.toLowerCase match {
+    case HTTP_URI_REGEX(httpURI) =>
+      s"curl -O http://$httpURI"
+    case RSYNC_URI_REGEX(file) =>
+      s"rsync $file ./"
+    case SPARK_RSYNC_URI_REGEX(file) =>
+      "RSYNC_CONNECT_PROG=\"knc spark-rsync@%H $SPARK_DRIVER_PULL_PORT\"" +
+      s" rsync $$SPARK_DRIVER_PULL_HOST::spark/$file ./"
+    case HDFS_URI_REGEX(file) =>
+      s"$$HADOOP_COMMAND fs -copyToLocal hdfs://$file ."
+    case _ =>
+      sys.error(s"$uri not supported yet")
+  }
 
   def apply(scheduler: TaskSchedulerImpl, sc: SparkContext, cookHost: String,
     cookPort: Int): CoarseGrainedSchedulerBackend = {
@@ -176,7 +181,7 @@ class CoarseCookSchedulerBackend(
   override def applicationAttemptId(): Option[String] = Some(applicationId())
 
   def createJob(numCores: Double): Job = {
-    import CoarseCookSchedulerBackend.fetchUri
+    import CoarseCookSchedulerBackend.fetchURI
 
     val jobId = UUID.randomUUID()
     executorUUIDWriter(jobId)
@@ -215,20 +220,20 @@ class CoarseCookSchedulerBackend(
 
     val keystoreUri = conf.getOption("spark.executor.keyStoreFilename")
     val keystorePull = keystoreUri.map { uri =>
-      s"${fetchUri(uri)} && mv $$(basename $uri) spark-executor-keystore"
+      s"${fetchURI(uri)} && mv $$(basename $uri) spark-executor-keystore"
     }
 
     val urisCommand =
       uriValues.map { uri =>
-          s"[ ! -e $$(basename $uri) ] && ${fetchUri(uri)} && tar -xvzf $$(basename $uri)" +
+          s"[ ! -e $$(basename $uri) ] && ${fetchURI(uri)} && tar -xvzf $$(basename $uri)" +
             " || (echo \"ERROR FETCHING\" && exit 1)"
         }
 
     val shippedTarballs: Seq[String] = conf.getOption("spark.cook.shippedTarballs")
       .fold(Seq[String]()){ tgz => tgz.split(",").map(_.trim).toList }
 
     val shippedTarballsCommand = shippedTarballs.map { uri =>
-      s"[ ! -e $$(basename $uri) ] && ${fetchUri(uri)} && tar -xvzf $$(basename $uri)"
+      s"[ ! -e $$(basename $uri) ] && ${fetchURI(uri)} && tar -xvzf $$(basename $uri)"
     }
 
     logDebug(s"command: $commandString")
@@ -244,7 +249,7 @@ class CoarseCookSchedulerBackend(
     val remoteConfFetch = if (remoteHdfsConf.nonEmpty) {
       val name = Paths.get(remoteHdfsConf).getFileName
       Seq(
-        fetchUri(remoteHdfsConf),
+        fetchURI(remoteHdfsConf),
         "mkdir HADOOP_CONF_DIR",
         s"tar --strip-components=1 -xvzf $name -C HADOOP_CONF_DIR",
         // This must be absolute because we cd into the spark directory