Github user tgravescs commented on a diff in the pull request:
https://github.com/apache/spark/pull/4688#discussion_r25527152
--- Diff:
yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtil.scala ---
@@ -82,6 +93,102 @@ class YarnSparkHadoopUtil extends SparkHadoopUtil {
if (credentials != null) credentials.getSecretKey(new Text(key)) else
null
}
+ override def setPrincipalAndKeytabForLogin(principal: String, keytab:
String): Unit = {
+ loginPrincipal = Option(principal)
+ keytabFile = Option(keytab)
+ }
+
+ private[spark] override def scheduleLoginFromKeytab(
+ callback: (SerializableBuffer) => Unit): Unit = {
+
+ loginPrincipal match {
+ case Some(principal) =>
+ val keytab = keytabFile.get
+ val remoteFs = FileSystem.get(conf)
+ val remoteKeytabPath = new Path(
+ remoteFs.getHomeDirectory, System.getenv("SPARK_STAGING_DIR") +
Path.SEPARATOR + keytab)
+ val localFS = FileSystem.getLocal(conf)
+ // At this point, SparkEnv is likely no initialized, so create a
dir, put the keytab there.
+ val tempDir = Utils.createTempDir()
+ val localURI = new URI(tempDir.getAbsolutePath + Path.SEPARATOR +
keytab)
+ val qualifiedURI = new URI(localFS.makeQualified(new
Path(localURI)).toString)
+ FileUtil.copy(
--- End diff --
why not just use the distributed cache for this? I'm more hesitant about
us manually doing it and messing up permissions and such then allow YARN to do.
You can set it up so it only downloads to AM and not executors.
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