Hello everybody, we have a kerberized HDP (Hortonworks) cluster, we can run Spark jobs from Spark-Submit (CLI), Talend Big Data, but not from Eclipse.
We have a Windows client machine where Eclipse is installed and MIT windows Kerberos Client is confgiured (TGT Configuration). The goal is to run Spark job using eclipse. Portion of the java code related with Spark is operational and tested via CLI. Below is mentioned part of the code for the job.
private void setConfigurationProperties()
{
try{
sConfig.setAppName("abcd-name");
sConfig.setMaster("yarn-client");
sConfig.set("spark.serializer", "org.apache.spark.serializer.KryoSerializer");
sConfig.set("spark.hadoop.yarn.resourcemanager.address", "rs.abcd.com:8032"); sConfig.set("spark.hadoop.yarn.resourcemanager.scheduler.address","rs.abcd.com:8030");
sConfig.set("spark.hadoop.mapreduce.jobhistory.address","rs.abcd.com:10020");
sConfig.set("spark.hadoop.yarn.app.mapreduce.am.staging-dir", "/dir");
sConfig.set("spark.executor.memory", "2g");
sConfig.set("spark.executor.cores", "4");
sConfig.set("spark.executor.instances", "24");
sConfig.set("spark.yarn.am.cores", "24");
sConfig.set("spark.yarn.am.memory", "16g");
sConfig.set("spark.eventLog.enabled", "true");
sConfig.set("spark.eventLog.dir", "hdfs:///spark-history");
sConfig.set("spark.shuffle.memoryFraction", "0.4");
sConfig.set("spark.hadoop." + "mapreduce.application.framework.path","/hdp/apps/version/mapreduce/mapreduce.tar.gz#mr-framework");
sConfig.set("spark.local.dir", "/tmp");
sConfig.set("spark.hadoop.yarn.resourcemanager.principal", "rm/_HOST#ABCD.COM");
sConfig.set("spark.hadoop.mapreduce.jobhistory.principal", "jhs/_HOST#ABCD.COM");
sConfig.set("spark.hadoop.dfs.namenode.kerberos.principal", "nn/_HOST#ABCD.COM");
sConfig.set("spark.hadoop.fs.defaultFS", "hdfs://hdfs.abcd.com:8020");
sConfig.set("spark.hadoop.dfs.client.use.datanode.hostname", "true"); }
}
When we run the code the following error pops up:
17/04/05 23:37:06 INFO Remoting: Starting remoting
17/04/05 23:37:06 INFO Remoting: Remoting started; listening on addresses :[akka.tcp://sparkDriverActorSystem#1.1.1.1:54356]
17/04/05 23:37:06 INFO Utils: Successfully started service 'sparkDriverActorSystem' on port 54356.
17/04/05 23:37:06 INFO SparkEnv: Registering MapOutputTracker
17/04/05 23:37:06 INFO SparkEnv: Registering BlockManagerMaster
17/04/05 23:37:06 INFO DiskBlockManager: Created local directory at C:\tmp\blockmgr-baee2441-1977-4410-b52f-4275ff35d6c1
17/04/05 23:37:06 INFO MemoryStore: MemoryStore started with capacity 2.4 GB
17/04/05 23:37:06 INFO SparkEnv: Registering OutputCommitCoordinator
17/04/05 23:37:07 INFO Utils: Successfully started service 'SparkUI' on port 4040.
17/04/05 23:37:07 INFO SparkUI: Started SparkUI at http://1.1.1.1:4040
17/04/05 23:37:07 INFO RMProxy: Connecting to ResourceManager at rs.abcd.com/1.1.1.1:8032
17/04/05 23:37:07 ERROR SparkContext: Error initializing SparkContext.
org.apache.hadoop.security.AccessControlException: SIMPLE authentication is not enabled. Available:[TOKEN, KERBEROS]
Caused by: org.apache.hadoop.ipc.RemoteException(org.apache.hadoop.security.AccessControlException): SIMPLE authentication is not enabled. Available:[TOKEN, KERBEROS]
17/04/05 23:37:07 INFO SparkUI: Stopped Spark web UI at http://1.1.1.1:4040
Please guide us how to specify in java code Kerberos authentication method instead of SIMPLE. Or how to instruct the client for Kerberos authentication request. And whole what should the process look like and what would be the right approach
Thank you
Related
Spark 2.1.1 built for Hadoop 2.7.3
Scala 2.11.11
Cluster has 3 Linux RHEL 7.3 Azure VM's, running Spark Standalone Deploy Mode (no YARN or Mesos, yet)
I have created a very simple SparkStreaming job using IntelliJ, written in Scala. I'm using Maven and building the job into a fat/uber jar that contains all dependencies.
When I run the job locally it works fine. If I copy the jar to the cluster and run it with a master of local[2] it also works fine. However, if I submit the job to the cluster master it's like it does not want to schedule additional work beyond the first task. The job starts up, grabs however many events are in the Azure Event Hub, processes them successfully, then never does anymore work. It does not matter if I submit the job to the master as just an application or if it's submitted using supervised cluster mode, both do the same thing.
I've looked through all the logs I know of (master, driver (where applicable), and executor) and I am not seeing any errors or warnings that seem actionable. I've altered the log level, shown below, to show ALL/INFO/DEBUG and sifted through those logs without finding anything that seems relevant.
It may be worth noting that I had previously created several jobs that connect to Kafka, instead of the Azure Event Hub, using Java and those jobs run in supervised cluster mode without an issue on this same cluster. This leads me to believe that the cluster configuration isn't an issue, it's either something with my code (below) or the Azure Event Hub.
Any thoughts on where I might check next to isolate this issue? Here is the code for my simple job.
Thanks in advance.
Note: conf.{name} indicates values I'm loading from a config file. I've tested loading and hard-coding them, both with the same result.
package streamingJob
import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.streaming.{Seconds, StreamingContext}
import org.apache.spark.streaming.eventhubs.EventHubsUtils
import org.joda.time.DateTime
object TestJob {
def main(args: Array[String]): Unit = {
val sparkConf = new SparkConf()
sparkConf.setAppName("TestJob")
// Uncomment to run locally
//sparkConf.setMaster("local[2]")
val sparkContext = new SparkContext(sparkConf)
sparkContext.setLogLevel("ERROR")
val streamingContext: StreamingContext = new StreamingContext(sparkContext, Seconds(1))
val readerParams = Map[String, String] (
"eventhubs.policyname" -> conf.policyname,
"eventhubs.policykey" -> conf.policykey,
"eventhubs.namespace" -> conf.namespace,
"eventhubs.name" -> conf.name,
"eventhubs.partition.count" -> conf.partitionCount,
"eventhubs.consumergroup" -> conf.consumergroup
)
val eventData = EventHubsUtils.createDirectStreams(
streamingContext,
conf.namespace,
conf.progressdir,
Map("name" -> readerParams))
eventData.foreachRDD(r => {
r.foreachPartition { p => {
p.foreach(d => {
println(DateTime.now() + ": " + d)
}) // end of EventData
}} // foreachPartition
}) // foreachRDD
streamingContext.start()
streamingContext.awaitTermination()
}
}
Here is a set of logs from when I run this as an application, not cluster/supervised.
/spark/bin/spark-submit --class streamingJob.TestJob --master spark://{ip}:7077 --total-executor-cores 1 /spark/job-files/fatjar.jar
Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
17/11/06 17:52:04 INFO SparkContext: Running Spark version 2.1.1
17/11/06 17:52:05 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
17/11/06 17:52:05 INFO SecurityManager: Changing view acls to: root
17/11/06 17:52:05 INFO SecurityManager: Changing modify acls to: root
17/11/06 17:52:05 INFO SecurityManager: Changing view acls groups to:
17/11/06 17:52:05 INFO SecurityManager: Changing modify acls groups to:
17/11/06 17:52:05 INFO SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users with view permissions: Set(root); groups with view permissions: Set(); users with modify permissions: Set(root); groups with modify permissions: Set()
17/11/06 17:52:06 INFO Utils: Successfully started service 'sparkDriver' on port 44384.
17/11/06 17:52:06 INFO SparkEnv: Registering MapOutputTracker
17/11/06 17:52:06 INFO SparkEnv: Registering BlockManagerMaster
17/11/06 17:52:06 INFO BlockManagerMasterEndpoint: Using org.apache.spark.storage.DefaultTopologyMapper for getting topology information
17/11/06 17:52:06 INFO BlockManagerMasterEndpoint: BlockManagerMasterEndpoint up
17/11/06 17:52:06 INFO DiskBlockManager: Created local directory at /tmp/blockmgr-b5e2c0f3-2500-42c6-b057-cf5d368580ab
17/11/06 17:52:06 INFO MemoryStore: MemoryStore started with capacity 366.3 MB
17/11/06 17:52:06 INFO SparkEnv: Registering OutputCommitCoordinator
17/11/06 17:52:06 INFO Utils: Successfully started service 'SparkUI' on port 4040.
17/11/06 17:52:06 INFO SparkUI: Bound SparkUI to 0.0.0.0, and started at http://{ip}:4040
17/11/06 17:52:06 INFO SparkContext: Added JAR file:/spark/job-files/fatjar.jar at spark://{ip}:44384/jars/fatjar.jar with timestamp 1509990726989
17/11/06 17:52:07 INFO StandaloneAppClient$ClientEndpoint: Connecting to master spark://{ip}:7077...
17/11/06 17:52:07 INFO TransportClientFactory: Successfully created connection to /{ip}:7077 after 72 ms (0 ms spent in bootstraps)
17/11/06 17:52:07 INFO StandaloneSchedulerBackend: Connected to Spark cluster with app ID app-20171106175207-0000
17/11/06 17:52:07 INFO Utils: Successfully started service 'org.apache.spark.network.netty.NettyBlockTransferService' on port 44624.
17/11/06 17:52:07 INFO NettyBlockTransferService: Server created on {ip}:44624
17/11/06 17:52:07 INFO BlockManager: Using org.apache.spark.storage.RandomBlockReplicationPolicy for block replication policy
17/11/06 17:52:07 INFO StandaloneAppClient$ClientEndpoint: Executor added: app-20171106175207-0000/0 on worker-20171106173151-{ip}-46086 ({ip}:46086) with 1 cores
17/11/06 17:52:07 INFO BlockManagerMaster: Registering BlockManager BlockManagerId(driver, {ip}, 44624, None)
17/11/06 17:52:07 INFO StandaloneSchedulerBackend: Granted executor ID app-20171106175207-0000/0 on hostPort {ip}:46086 with 1 cores, 1024.0 MB RAM
17/11/06 17:52:07 INFO BlockManagerMasterEndpoint: Registering block manager {ip}:44624 with 366.3 MB RAM, BlockManagerId(driver, {ip}, 44624, None)
17/11/06 17:52:07 INFO BlockManagerMaster: Registered BlockManager BlockManagerId(driver, {ip}, 44624, None)
17/11/06 17:52:07 INFO BlockManager: Initialized BlockManager: BlockManagerId(driver, {ip}, 44624, None)
17/11/06 17:52:07 INFO StandaloneAppClient$ClientEndpoint: Executor updated: app-20171106175207-0000/0 is now RUNNING
17/11/06 17:52:08 INFO StandaloneSchedulerBackend: SchedulerBackend is ready for scheduling beginning after reached minRegisteredResourcesRatio: 0.0
I have some problem with Cloudera VM and Spark. First of all, I'm completely new on Spark, and my boss asked to me to run Spark on Scala in a Virtual Machine for some test.
I have downloaded the Virtual Machine on Virtual Box environment, so I open Eclipse and I had a new Project on Maven.
Obliviously, after I run previously the Cloudera environment and start all services, like Spark, Yarn, Hive and so on.
All services work fine, and all check, in Cloudera services are green. I had do some test with Impala and that works perfectly.
With Eclipse and Scala-Maven environment, the things became worst: that is my very simple code in Scala:
package org.test.spark
import org.apache.spark.SparkConf
import org.apache.spark.SparkContext
import org.apache.spark.sql.SQLContext
object TestSelectAlgorithm {
def main(args: Array[String]) = {
val conf = new SparkConf()
.setAppName("TestSelectAlgorithm")
.setMaster("local")
val sc = new SparkContext(conf)
val sqlContext = new SQLContext(sc)
val df = sqlContext.sql("SELECT * FROM products").show()
}
}
The test is very simple, because the table "products" exist: if I copy-and-paste the same query on Impala, the query works fine!
On the Eclipse environment, otherwise, I have some problem:
Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
16/06/30 05:43:17 INFO SparkContext: Running Spark version 1.6.0
16/06/30 05:43:18 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
16/06/30 05:43:18 WARN Utils: Your hostname, quickstart.cloudera resolves to a loopback address: 127.0.0.1; using 10.0.2.15 instead (on interface eth0)
16/06/30 05:43:18 WARN Utils: Set SPARK_LOCAL_IP if you need to bind to another address
16/06/30 05:43:18 INFO SecurityManager: Changing view acls to: cloudera
16/06/30 05:43:18 INFO SecurityManager: Changing modify acls to: cloudera
16/06/30 05:43:18 INFO SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users with view permissions: Set(cloudera); users with modify permissions: Set(cloudera)
16/06/30 05:43:19 INFO Utils: Successfully started service 'sparkDriver' on port 53730.
16/06/30 05:43:19 INFO Slf4jLogger: Slf4jLogger started
16/06/30 05:43:19 INFO Remoting: Starting remoting
16/06/30 05:43:19 INFO Remoting: Remoting started; listening on addresses :[akka.tcp://sparkDriverActorSystem#10.0.2.15:39288]
16/06/30 05:43:19 INFO Utils: Successfully started service 'sparkDriverActorSystem' on port 39288.
16/06/30 05:43:19 INFO SparkEnv: Registering MapOutputTracker
16/06/30 05:43:19 INFO SparkEnv: Registering BlockManagerMaster
16/06/30 05:43:19 INFO DiskBlockManager: Created local directory at /tmp/blockmgr-7d685fc0-ea88-423a-9335-42ca12db85da
16/06/30 05:43:19 INFO MemoryStore: MemoryStore started with capacity 1619.3 MB
16/06/30 05:43:20 INFO SparkEnv: Registering OutputCommitCoordinator
16/06/30 05:43:20 INFO Utils: Successfully started service 'SparkUI' on port 4040.
16/06/30 05:43:20 INFO SparkUI: Started SparkUI at http://10.0.2.15:4040
16/06/30 05:43:20 INFO Executor: Starting executor ID driver on host localhost
16/06/30 05:43:20 INFO Utils: Successfully started service 'org.apache.spark.network.netty.NettyBlockTransferService' on port 57294.
16/06/30 05:43:20 INFO NettyBlockTransferService: Server created on 57294
16/06/30 05:43:20 INFO BlockManagerMaster: Trying to register BlockManager
16/06/30 05:43:20 INFO BlockManagerMasterEndpoint: Registering block manager localhost:57294 with 1619.3 MB RAM, BlockManagerId(driver, localhost, 57294)
16/06/30 05:43:20 INFO BlockManagerMaster: Registered BlockManager
Exception in thread "main" org.apache.spark.sql.AnalysisException: Table not found: products;
at org.apache.spark.sql.catalyst.analysis.package$AnalysisErrorAt.failAnalysis(package.scala:42)
at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$.getTable(Analyzer.scala:306)
at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$$anonfun$apply$9.applyOrElse(Analyzer.scala:315)
at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$$anonfun$apply$9.applyOrElse(Analyzer.scala:310)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$resolveOperators$1.apply(LogicalPlan.scala:57)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$resolveOperators$1.apply(LogicalPlan.scala:57)
at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:53)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveOperators(LogicalPlan.scala:56)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$1.apply(LogicalPlan.scala:54)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$1.apply(LogicalPlan.scala:54)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:265)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
at scala.collection.Iterator$class.foreach(Iterator.scala:727)
at scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
at scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:48)
at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:103)
at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:47)
at scala.collection.TraversableOnce$class.to(TraversableOnce.scala:273)
at scala.collection.AbstractIterator.to(Iterator.scala:1157)
at scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:265)
at scala.collection.AbstractIterator.toBuffer(Iterator.scala:1157)
at scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:252)
at scala.collection.AbstractIterator.toArray(Iterator.scala:1157)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformChildren(TreeNode.scala:305)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveOperators(LogicalPlan.scala:54)
at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$.apply(Analyzer.scala:310)
at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$.apply(Analyzer.scala:300)
at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1$$anonfun$apply$1.apply(RuleExecutor.scala:83)
at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1$$anonfun$apply$1.apply(RuleExecutor.scala:80)
at scala.collection.LinearSeqOptimized$class.foldLeft(LinearSeqOptimized.scala:111)
at scala.collection.immutable.List.foldLeft(List.scala:84)
at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1.apply(RuleExecutor.scala:80)
at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1.apply(RuleExecutor.scala:72)
at scala.collection.immutable.List.foreach(List.scala:318)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.execute(RuleExecutor.scala:72)
at org.apache.spark.sql.execution.QueryExecution.analyzed$lzycompute(QueryExecution.scala:36)
at org.apache.spark.sql.execution.QueryExecution.analyzed(QueryExecution.scala:36)
at org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:34)
at org.apache.spark.sql.DataFrame.<init>(DataFrame.scala:133)
at org.apache.spark.sql.DataFrame$.apply(DataFrame.scala:52)
at org.apache.spark.sql.SQLContext.sql(SQLContext.scala:817)
at org.test.spark.TestSelectAlgorithm$.main(TestSelectAlgorithm.scala:18)
at org.test.spark.TestSelectAlgorithm.main(TestSelectAlgorithm.scala)
16/06/30 05:43:22 INFO SparkContext: Invoking stop() from shutdown hook
16/06/30 05:43:22 INFO SparkUI: Stopped Spark web UI at http://10.0.2.15:4040
16/06/30 05:43:22 INFO MapOutputTrackerMasterEndpoint: MapOutputTrackerMasterEndpoint stopped!
16/06/30 05:43:22 INFO MemoryStore: MemoryStore cleared
16/06/30 05:43:22 INFO BlockManager: BlockManager stopped
16/06/30 05:43:22 INFO BlockManagerMaster: BlockManagerMaster stopped
16/06/30 05:43:22 INFO OutputCommitCoordinator$OutputCommitCoordinatorEndpoint: OutputCommitCoordinator stopped!
16/06/30 05:43:22 INFO SparkContext: Successfully stopped SparkContext
16/06/30 05:43:22 INFO ShutdownHookManager: Shutdown hook called
16/06/30 05:43:22 INFO ShutdownHookManager: Deleting directory /tmp/spark-29d381e9-b5e7-485c-92f2-55dc57ca7d25
The main error is (for me):
Exception in thread "main" org.apache.spark.sql.AnalysisException: Table not found: products;
I searched on other site and documentation, and I founded that the problem is connected with the Hive table... but I don't use the Hive table, I use SparkSql...
Can anyone help me, please?
Thank you for any reply.
In spark, For impala there is no direct support as hive has .So, You have to load file. If it is csv you can use spark-csv,
val df = sqlContext.read
.format("com.databricks.spark.csv")
.option("header", "true")
.option("inferSchema", "true")
.load("your .csv file location")
import sqlContext.implicits._
import sqlContext._
df.registerTempTable("products")
sqlContext.sql("select * from products").show()
pom dependency for spark-csv
<!-- https://mvnrepository.com/artifact/com.databricks/spark-csv_2.10 -->
<dependency>
<groupId>com.databricks</groupId>
<artifactId>spark-csv_2.10</artifactId>
<version>1.4.0</version>
</dependency>
for avro there is spark-avro
val sqlContext = new SQLContext(sc)
val df = sqlContext.read.avro("your .avro file location")
import sqlContext.implicits._
import sqlContext._
df.registerTempTable("products")
val result= sqlContext.sql("select * from products")
val result.show()
result.write
.format("com.databricks.spark.avro")
.save("Your ouput location")
pom dependency for avro
<!-- http://mvnrepository.com/artifact/com.databricks/spark-avro_2.10 -->
<dependency>
<groupId>com.databricks</groupId>
<artifactId>spark-avro_2.10</artifactId>
<version>2.0.1</version>
</dependency>
and parquet spark has in-build support
val sqlContext = new SQLContext(sc)
val parquetFile = sqlContext.read.parquet("your parquet file location")
parquetFile.registerTempTable("products")
sqlContext.sql("select * from products").show()
Can you check /user/cloudera/.sparkStaging/stagingArea location exist or it contains .avro file?? And please change "Your ouput location" by directory location.
Please check avro github page for more detail. https://github.com/databricks/spark-avro
I start my DataStax cassandra instance with Spark:
dse cassandra -k
I then run this program (from within Eclipse):
import org.apache.spark.sql.SQLContext
import org.apache.spark.SparkConf
import org.apache.spark.SparkContext
object Start {
def main(args: Array[String]): Unit = {
println("***** 1 *****")
val sparkConf = new SparkConf().setAppName("Start").setMaster("spark://127.0.0.1:7077")
println("***** 2 *****")
val sparkContext = new SparkContext(sparkConf)
println("***** 3 *****")
}
}
And I get the following output
***** 1 *****
***** 2 *****
Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
15/12/29 15:27:50 INFO SparkContext: Running Spark version 1.5.2
15/12/29 15:27:51 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
15/12/29 15:27:51 INFO SecurityManager: Changing view acls to: nayan
15/12/29 15:27:51 INFO SecurityManager: Changing modify acls to: nayan
15/12/29 15:27:51 INFO SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users with view permissions: Set(nayan); users with modify permissions: Set(nayan)
15/12/29 15:27:52 INFO Slf4jLogger: Slf4jLogger started
15/12/29 15:27:52 INFO Remoting: Starting remoting
15/12/29 15:27:53 INFO Remoting: Remoting started; listening on addresses :[akka.tcp://sparkDriver#10.0.1.88:55126]
15/12/29 15:27:53 INFO Utils: Successfully started service 'sparkDriver' on port 55126.
15/12/29 15:27:53 INFO SparkEnv: Registering MapOutputTracker
15/12/29 15:27:53 INFO SparkEnv: Registering BlockManagerMaster
15/12/29 15:27:53 INFO DiskBlockManager: Created local directory at /private/var/folders/pd/6rxlm2js10gg6xys5wm90qpm0000gn/T/blockmgr-21a96671-c33e-498c-83a4-bb5c57edbbfb
15/12/29 15:27:53 INFO MemoryStore: MemoryStore started with capacity 983.1 MB
15/12/29 15:27:53 INFO HttpFileServer: HTTP File server directory is /private/var/folders/pd/6rxlm2js10gg6xys5wm90qpm0000gn/T/spark-fce0a058-9264-4f2c-8220-c32d90f11bd8/httpd-2a0efcac-2426-49c5-982a-941cfbb48c88
15/12/29 15:27:53 INFO HttpServer: Starting HTTP Server
15/12/29 15:27:53 INFO Utils: Successfully started service 'HTTP file server' on port 55127.
15/12/29 15:27:53 INFO SparkEnv: Registering OutputCommitCoordinator
15/12/29 15:27:53 INFO Utils: Successfully started service 'SparkUI' on port 4040.
15/12/29 15:27:53 INFO SparkUI: Started SparkUI at http://10.0.1.88:4040
15/12/29 15:27:54 WARN MetricsSystem: Using default name DAGScheduler for source because spark.app.id is not set.
15/12/29 15:27:54 INFO AppClient$ClientEndpoint: Connecting to master spark://127.0.0.1:7077...
15/12/29 15:27:54 WARN ReliableDeliverySupervisor: Association with remote system [akka.tcp://sparkMaster#127.0.0.1:7077] has failed, address is now gated for [5000] ms. Reason: [Disassociated]
15/12/29 15:28:14 ERROR SparkUncaughtExceptionHandler: Uncaught exception in thread Thread[appclient-registration-retry-thread,5,main]
java.util.concurrent.RejectedExecutionException: Task java.util.concurrent.FutureTask#1f22aef0 rejected from java.util.concurrent.ThreadPoolExecutor#176cb4af[Running, pool size = 1, active threads = 1, queued tasks = 0, completed tasks = 0]
So something is happening during the creation of the spark context.
When i look in $DSE_HOME/logs/spark, it is empty. Not sure where else to look.
It turns out that the problem was the spark library version AND the Scala version. DataStax was running Spark 1.4.1 and Scala 2.10.5, while my eclipse project was using 1.5.2 & 2.11.7 respectively.
Note that BOTH the Spark library and Scala appear to have to match. I tried other combinations, but it only worked when both matched.
I am getting pretty familiar with this part of your posted error:
WARN ReliableDeliverySupervisor: Association with remote system [akka.tcp://...
It can have numerous causes, pretty much all related to misconfigured IPs. First I would do whatever zero323 says, then here's my two cents: I have solved my own problems recently by using IP addresses, not hostnames, and the only config I use in a simple standalone cluster is SPARK_MASTER_IP.
SPARK_MASTER_IP in the $SPARK_HOME/conf/spark-env.sh on your master then should lead the master webui to show the IP address you set:
spark://your.ip.address.numbers:7077
And your SparkConf setup can refer to that.
Having said that, I am not familiar with your specific implementation but I notice in the error two occurrences containing:
/private/var/folders/pd/6rxlm2js10gg6xys5wm90qpm0000gn/T/
Have you looked there to see if there's a logs directory? Is that where $DSE_HOME points? Alternatively connect to the driver where it creates it's webui:
INFO SparkUI: Started SparkUI at http://10.0.1.88:4040
and you should see a link to an error log there somewhere.
More on the IP vs. hostname thing, this very old bug is marked as Resolved but I have not figured out what they mean by Resolved, so I just tend toward IP addresses.
I apologize for such an extremely long post, but I wanted to be better understood.
I have built up my cluster, where master in on another machine than workers. Workers are allocated on a quite efficient machine. Between these two machines no firewall is applied.
URL: spark://MASTER_IP:7077
Workers: 10
Cores: 10 Total, 0 Used
Memory: 40.0 GB Total, 0.0 B Used
Applications: 0 Running, 0 Completed
Drivers: 0 Running, 0 Completed
Status: ALIVE
Before launching the app, in the workers logfile is (an example for one worker)
15/03/06 18:52:19 INFO Worker: Registered signal handlers for [TERM, HUP, INT]
15/03/06 18:52:19 INFO SecurityManager: Changing view acls to: szymon
15/03/06 18:52:19 INFO SecurityManager: Changing modify acls to: szymon
15/03/06 18:52:19 INFO SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users with view permissions: Set(szymon); users with modify permissions: Set(szymon)
15/03/06 18:52:20 INFO Slf4jLogger: Slf4jLogger started
15/03/06 18:52:20 INFO Remoting: Starting remoting
15/03/06 18:52:20 INFO Remoting: Remoting started; listening on addresses :[akka.tcp://sparkWorker#WORKER_MACHINE_IP:42240]
15/03/06 18:52:20 INFO Remoting: Remoting now listens on addresses: [akka.tcp://sparkWorker#WORKER_MACHINE_IP:42240]
15/03/06 18:52:20 INFO Utils: Successfully started service 'sparkWorker' on port 42240.
15/03/06 18:52:20 INFO Worker: Starting Spark worker WORKER_MACHINE_IP:42240 with 1 cores, 4.0 GB RAM
15/03/06 18:52:20 INFO Worker: Spark home: /home/szymon/spark
15/03/06 18:52:20 INFO Utils: Successfully started service 'WorkerUI' on port 8081.
15/03/06 18:52:20 INFO WorkerWebUI: Started WorkerWebUI at http://WORKER_MACHINE_IP:8081
15/03/06 18:52:20 INFO Worker: Connecting to master spark://MASTER_IP:7077...
15/03/06 18:52:20 INFO Worker: Successfully registered with master spark://MASTER_IP:7077
I launch my application on a cluster (on the master machine)
./bin/spark-submit --class SimpleApp --master spark://MASTER_IP:7077 --executor-memory 3g --total-executor-cores 10 code/trial_2.11-0.9.jar
My app is then fetched by workers, this is an example of the log output for a worker (#WORKER_MACHINE)
15/03/06 18:07:45 INFO ExecutorRunner: Launch command: "/usr/java/jdk1.8.0_31/bin/java" "-cp" "::/home/machine/spark/conf:/home/machine/spark/assembly/target/scala-2.10/spark-assembly-1.2.1-hadoop2.4.0.jar" "-Dspark.driver.port=56753" "-Dlog4j.configuration=file:////home/machine/spark/conf/log4j.properties" "-Dspark.driver.host=MASTER_IP" "-Xms3072M" "-Xmx3072M" "org.apache.spark.executor.CoarseGrainedExecutorBackend" "akka.tcp://sparkDriver#MASTER_IP:56753/user/CoarseGrainedScheduler" "4" "WORKER_MACHINE_IP" "1" "app-20150306181450-0000" "akka.tcp://sparkWorker#WORKER_MACHINE_IP:45288/user/Worker"
The app wants to connect to localhost at address 127.0.0.1 instead of MASTER_IP (I believe).
How could it be fixed?
15/03/06 18:58:52 ERROR RetryingBlockFetcher: Exception while beginning fetch of 1 outstanding blocks
java.io.IOException: Failed to connect to localhost/127.0.0.1:56545
at org.apache.spark.network.client.TransportClientFactory.createClient(TransportClientFactory.java:191)
The problem is caused by createClient method in TransportClientFactory which is in spark-network-common_2.10-1.2.1-sources.jar, String remoteHost is set up as localhost
/**
* Create a {#link TransportClient} connecting to the given remote host / port.
*
* We maintains an array of clients (size determined by spark.shuffle.io.numConnectionsPerPeer)
* and randomly picks one to use. If no client was previously created in the randomly selected
* spot, this function creates a new client and places it there.
*
* Prior to the creation of a new TransportClient, we will execute all
* {#link TransportClientBootstrap}s that are registered with this factory.
*
* This blocks until a connection is successfully established and fully bootstrapped.
*
* Concurrency: This method is safe to call from multiple threads.
*/
public TransportClient createClient(String remoteHost, int remotePort) throws IOException {
// Get connection from the connection pool first.
// If it is not found or not active, create a new one.
final InetSocketAddress address = new InetSocketAddress(remoteHost, remotePort);
.
.
.
clientPool.clients[clientIndex] = createClient(address);
}
Here is the file spark-env.sh on the workers site
export SPARK_HOME=/home/szymon/spark
export SPARK_MASTER_IP=MASTER_IP
export SPARK_MASTER_WEBUI_PORT=8081
export SPARK_LOCAL_IP=WORKER_MACHINE_IP
export SPARK_DRIVER_HOST=WORKER_MACHINE_IP
export SPARK_LOCAL_DIRS=/home/szymon/spark/slaveData
export SPARK_WORKER_INSTANCES=10
export SPARK_WORKER_CORES=1
export SPARK_WORKER_MEMORY=4g
export SPARK_WORKER_DIR=/home/szymon/spark/work
And on the master
export SPARK_MASTER_IP=MASTER_IP
export SPARK_LOCAL_IP=MASTER_IP
export SPARK_MASTER_WEBUI_PORT=8081
export SPARK_JAVA_OPTS="-Dlog4j.configuration=file:////home/szymon/spark/conf/log4j.properties -Dspark.driver.host=MASTER_IP"
export SPARK_MASTER_OPTS="-Dspark.deploy.defaultCores=10"
This is the full log output with more details
15/03/06 18:58:50 INFO Worker: Asked to launch executor app-20150306190555-0000/0 for Simple Application
15/03/06 18:58:50 INFO ExecutorRunner: Launch command: "/usr/java/jdk1.8.0_31/bin/java" "-cp" "::/home/szymon/spark/conf:/home/szymon/spark/assembly/target/scala-2.10/spark-assembly-1.2.1-hadoop2.4.0.jar" "-Dspark.driver.port=49407" "-Dlog4j.configuration=file:////home/szymon/spark/conf/log4j.properties" "-Dspark.driver.host=MASTER_IP" "-Xms3072M" "-Xmx3072M" "org.apache.spark.executor.CoarseGrainedExecutorBackend" "akka.tcp://sparkDriver#MASTER_IP:49407/user/CoarseGrainedScheduler" "0" "WORKER_MACHINE_IP" "1" "app-20150306190555-0000" "akka.tcp://sparkWorker#WORKER_MACHINE_IP:42240/user/Worker"
15/03/06 18:58:50 INFO CoarseGrainedExecutorBackend: Registered signal handlers for [TERM, HUP, INT]
15/03/06 18:58:51 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
15/03/06 18:58:51 INFO SecurityManager: Changing view acls to: szymon
15/03/06 18:58:51 INFO SecurityManager: Changing modify acls to: szymon
15/03/06 18:58:51 INFO SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users with view permissions: Set(szymon); users with modify permissions: Set(szymon)
15/03/06 18:58:51 INFO Slf4jLogger: Slf4jLogger started
15/03/06 18:58:51 INFO Remoting: Starting remoting
15/03/06 18:58:51 INFO Remoting: Remoting started; listening on addresses :[akka.tcp://driverPropsFetcher#WORKER_MACHINE_IP:52038]
15/03/06 18:58:51 INFO Utils: Successfully started service 'driverPropsFetcher' on port 52038.
15/03/06 18:58:52 INFO RemoteActorRefProvider$RemotingTerminator: Shutting down remote daemon.
15/03/06 18:58:52 INFO SecurityManager: Changing view acls to: szymon
15/03/06 18:58:52 INFO SecurityManager: Changing modify acls to: szymon
15/03/06 18:58:52 INFO SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users with view permissions: Set(szymon); users with modify permissions: Set(szymon)
15/03/06 18:58:52 INFO RemoteActorRefProvider$RemotingTerminator: Remote daemon shut down; proceeding with flushing remote transports.
15/03/06 18:58:52 INFO Slf4jLogger: Slf4jLogger started
15/03/06 18:58:52 INFO Remoting: Starting remoting
15/03/06 18:58:52 INFO RemoteActorRefProvider$RemotingTerminator: Remoting shut down.
15/03/06 18:58:52 INFO Remoting: Remoting started; listening on addresses :[akka.tcp://sparkExecutor#WORKER_MACHINE_IP:37114]
15/03/06 18:58:52 INFO Utils: Successfully started service 'sparkExecutor' on port 37114.
15/03/06 18:58:52 INFO CoarseGrainedExecutorBackend: Connecting to driver: akka.tcp://sparkDriver#MASTER_IP:49407/user/CoarseGrainedScheduler
15/03/06 18:58:52 INFO WorkerWatcher: Connecting to worker akka.tcp://sparkWorker#WORKER_MACHINE_IP:42240/user/Worker
15/03/06 18:58:52 INFO WorkerWatcher: Successfully connected to akka.tcp://sparkWorker#WORKER_MACHINE_IP:42240/user/Worker
15/03/06 18:58:52 INFO CoarseGrainedExecutorBackend: Successfully registered with driver
15/03/06 18:58:52 INFO Executor: Starting executor ID 0 on host WORKER_MACHINE_IP
15/03/06 18:58:52 INFO SecurityManager: Changing view acls to: szymon
15/03/06 18:58:52 INFO SecurityManager: Changing modify acls to: szymon
15/03/06 18:58:52 INFO SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users with view permissions: Set(szymon); users with modify permissions: Set(szymon)
15/03/06 18:58:52 INFO AkkaUtils: Connecting to MapOutputTracker: akka.tcp://sparkDriver#MASTER_IP:49407/user/MapOutputTracker
15/03/06 18:58:52 INFO AkkaUtils: Connecting to BlockManagerMaster: akka.tcp://sparkDriver#MASTER_IP:49407/user/BlockManagerMaster
15/03/06 18:58:52 INFO DiskBlockManager: Created local directory at /home/szymon/spark/slaveData/spark-b09c3727-8559-4ab8-ab32-1f5ecf7aeaf2/spark-0c892a4d-c8b9-4144-a259-8077f5316b52/spark-89577a43-fb43-4a12-a305-34b267b01f8a/spark-7ad207c4-9d37-42eb-95e4-7b909b71c687
15/03/06 18:58:52 INFO MemoryStore: MemoryStore started with capacity 1589.8 MB
15/03/06 18:58:52 INFO NettyBlockTransferService: Server created on 51205
15/03/06 18:58:52 INFO BlockManagerMaster: Trying to register BlockManager
15/03/06 18:58:52 INFO BlockManagerMaster: Registered BlockManager
15/03/06 18:58:52 INFO AkkaUtils: Connecting to HeartbeatReceiver: akka.tcp://sparkDriver#MASTER_IP:49407/user/HeartbeatReceiver
15/03/06 18:58:52 INFO CoarseGrainedExecutorBackend: Got assigned task 0
15/03/06 18:58:52 INFO Executor: Running task 0.0 in stage 0.0 (TID 0)
15/03/06 18:58:52 INFO Executor: Fetching http://MASTER_IP:57850/jars/trial_2.11-0.9.jar with timestamp 1425665154479
15/03/06 18:58:52 INFO Utils: Fetching http://MASTER_IP:57850/jars/trial_2.11-0.9.jar to /home/szymon/spark/slaveData/spark-b09c3727-8559-4ab8-ab32-1f5ecf7aeaf2/spark-0c892a4d-c8b9-4144-a259-8077f5316b52/spark-411cd372-224e-44c1-84ab-b0c3984a6361/fetchFileTemp7857926599487994869.tmp
15/03/06 18:58:52 INFO Utils: Copying /home/szymon/spark/slaveData/spark-b09c3727-8559-4ab8-ab32-1f5ecf7aeaf2/spark-0c892a4d-c8b9-4144-a259-8077f5316b52/spark-411cd372-224e-44c1-84ab-b0c3984a6361/-19284804851425665154479_cache to /home/szymon/spark/work/app-20150306190555-0000/0/./trial_2.11-0.9.jar
15/03/06 18:58:52 INFO Executor: Adding file:/home/szymon/spark/work/app-20150306190555-0000/0/./trial_2.11-0.9.jar to class loader
15/03/06 18:58:52 INFO TorrentBroadcast: Started reading broadcast variable 0
15/03/06 18:58:52 ERROR RetryingBlockFetcher: Exception while beginning fetch of 1 outstanding blocks
java.io.IOException: Failed to connect to localhost/127.0.0.1:56545
at org.apache.spark.network.client.TransportClientFactory.createClient(TransportClientFactory.java:191)
at org.apache.spark.network.client.TransportClientFactory.createClient(TransportClientFactory.java:156)
at org.apache.spark.network.netty.NettyBlockTransferService$$anon$1.createAndStart(NettyBlockTransferService.scala:78)
at org.apache.spark.network.shuffle.RetryingBlockFetcher.fetchAllOutstanding(RetryingBlockFetcher.java:140)
at org.apache.spark.network.shuffle.RetryingBlockFetcher.start(RetryingBlockFetcher.java:120)
at org.apache.spark.network.netty.NettyBlockTransferService.fetchBlocks(NettyBlockTransferService.scala:87)
at org.apache.spark.network.BlockTransferService.fetchBlockSync(BlockTransferService.scala:89)
at org.apache.spark.storage.BlockManager$$anonfun$doGetRemote$2.apply(BlockManager.scala:595)
at org.apache.spark.storage.BlockManager$$anonfun$doGetRemote$2.apply(BlockManager.scala:593)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
at org.apache.spark.storage.BlockManager.doGetRemote(BlockManager.scala:593)
at org.apache.spark.storage.BlockManager.getRemoteBytes(BlockManager.scala:587)
at org.apache.spark.broadcast.TorrentBroadcast$$anonfun$org$apache$spark$broadcast$TorrentBroadcast$$readBlocks$1.org$apache$spark$broadcast$TorrentBroadcast$$anonfun$$getRemote$1(TorrentBroadcast.scala:126)
at org.apache.spark.broadcast.TorrentBroadcast$$anonfun$org$apache$spark$broadcast$TorrentBroadcast$$readBlocks$1$$anonfun$1.apply(TorrentBroadcast.scala:136)
at org.apache.spark.broadcast.TorrentBroadcast$$anonfun$org$apache$spark$broadcast$TorrentBroadcast$$readBlocks$1$$anonfun$1.apply(TorrentBroadcast.scala:136)
at scala.Option.orElse(Option.scala:257)
at org.apache.spark.broadcast.TorrentBroadcast$$anonfun$org$apache$spark$broadcast$TorrentBroadcast$$readBlocks$1.apply$mcVI$sp(TorrentBroadcast.scala:136)
at org.apache.spark.broadcast.TorrentBroadcast$$anonfun$org$apache$spark$broadcast$TorrentBroadcast$$readBlocks$1.apply(TorrentBroadcast.scala:119)
at org.apache.spark.broadcast.TorrentBroadcast$$anonfun$org$apache$spark$broadcast$TorrentBroadcast$$readBlocks$1.apply(TorrentBroadcast.scala:119)
at scala.collection.immutable.List.foreach(List.scala:318)
at org.apache.spark.broadcast.TorrentBroadcast.org$apache$spark$broadcast$TorrentBroadcast$$readBlocks(TorrentBroadcast.scala:119)
at org.apache.spark.broadcast.TorrentBroadcast$$anonfun$readBroadcastBlock$1.apply(TorrentBroadcast.scala:174)
at org.apache.spark.util.Utils$.tryOrIOException(Utils.scala:1090)
at org.apache.spark.broadcast.TorrentBroadcast.readBroadcastBlock(TorrentBroadcast.scala:164)
at org.apache.spark.broadcast.TorrentBroadcast._value$lzycompute(TorrentBroadcast.scala:64)
at org.apache.spark.broadcast.TorrentBroadcast._value(TorrentBroadcast.scala:64)
at org.apache.spark.broadcast.TorrentBroadcast.getValue(TorrentBroadcast.scala:87)
at org.apache.spark.broadcast.Broadcast.value(Broadcast.scala:70)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:58)
at org.apache.spark.scheduler.Task.run(Task.scala:56)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:200)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)
Caused by: java.net.ConnectException: Connection refused: localhost/127.0.0.1:56545
at sun.nio.ch.SocketChannelImpl.checkConnect(Native Method)
at io.netty.channel.socket.nio.NioSocketChannel.doFinishConnect(NioSocketChannel.java:208)
at io.netty.channel.nio.AbstractNioChannel$AbstractNioUnsafe.finishConnect(AbstractNioChannel.java:287)
at io.netty.channel.nio.NioEventLoop.processSelectedKey(NioEventLoop.java:528)
at io.netty.channel.nio.NioEventLoop.processSelectedKeysOptimized(NioEventLoop.java:468)
at io.netty.channel.nio.NioEventLoop.processSelectedKeys(NioEventLoop.java:382)
at io.netty.channel.nio.NioEventLoop.run(NioEventLoop.java:354)
at io.netty.util.concurrent.SingleThreadEventExecutor$2.run(SingleThreadEventExecutor.java:116)
... 1 more
15/03/06 18:58:52 INFO RetryingBlockFetcher: Retrying fetch (1/3) for 1 outstanding blocks after 5000 ms
15/03/06 18:58:57 ERROR RetryingBlockFetcher: Exception while beginning fetch of 1 outstanding blocks (after 1 retries)
java.io.IOException: Failed to connect to localhost/127.0.0.1:56545
at org.apache.spark.network.client.TransportClientFactory.createClient(TransportClientFactory.java:191)
at org.apache.spark.network.client.TransportClientFactory.createClient(TransportClientFactory.java:156)
at org.apache.spark.network.netty.NettyBlockTransferService$$anon$1.createAndStart(NettyBlockTransferService.scala:78)
at org.apache.spark.network.shuffle.RetryingBlockFetcher.fetchAllOutstanding(RetryingBlockFetcher.java:140)
at org.apache.spark.network.shuffle.RetryingBlockFetcher.access$200(RetryingBlockFetcher.java:43)
at org.apache.spark.network.shuffle.RetryingBlockFetcher$1.run(RetryingBlockFetcher.java:170)
at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:511)
at java.util.concurrent.FutureTask.run(FutureTask.java:266)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)
Caused by: java.net.ConnectException: Connection refused: localhost/127.0.0.1:56545
at sun.nio.ch.SocketChannelImpl.checkConnect(Native Method)
at sun.nio.ch.SocketChannelImpl.finishConnect(SocketChannelImpl.java:716)
at io.netty.channel.socket.nio.NioSocketChannel.doFinishConnect(NioSocketChannel.java:208)
at io.netty.channel.nio.AbstractNioChannel$AbstractNioUnsafe.finishConnect(AbstractNioChannel.java:287)
at io.netty.channel.nio.NioEventLoop.processSelectedKey(NioEventLoop.java:528)
at io.netty.channel.nio.NioEventLoop.processSelectedKeysOptimized(NioEventLoop.java:468)
at io.netty.channel.nio.NioEventLoop.processSelectedKeys(NioEventLoop.java:382)
at io.netty.channel.nio.NioEventLoop.run(NioEventLoop.java:354)
at io.netty.util.concurrent.SingleThreadEventExecutor$2.run(SingleThreadEventExecutor.java:116)
... 1 more
.
.
.
15/03/06 19:00:22 INFO RetryingBlockFetcher: Retrying fetch (1/3) for 1 outstanding blocks after 5000 ms
15/03/06 19:00:24 ERROR CoarseGrainedExecutorBackend: Driver Disassociated [akka.tcp://sparkExecutor#WORKER_MACHINE_IP:37114] -> [akka.tcp://sparkDriver#MASTER_IP:49407] disassociated! Shutting down.
15/03/06 19:00:24 WARN ReliableDeliverySupervisor: Association with remote system [akka.tcp://sparkDriver#MASTER_IP:49407] has failed, address is now gated for [5000] ms. Reason is: [Disassociated].
15/03/06 19:00:24 INFO Worker: Asked to kill executor app-20150306190555-0000/0
15/03/06 19:00:24 INFO ExecutorRunner: Runner thread for executor app-20150306190555-0000/0 interrupted
15/03/06 19:00:24 INFO ExecutorRunner: Killing process!
15/03/06 19:00:25 INFO Worker: Executor app-20150306190555-0000/0 finished with state KILLED exitStatus 1
15/03/06 19:00:25 INFO Worker: Cleaning up local directories for application app-20150306190555-0000
15/03/06 19:00:25 WARN ReliableDeliverySupervisor: Association with remote system [akka.tcp://sparkExecutor#WORKER_MACHINE_IP:37114] has failed, address is now gated for [5000] ms. Reason is: [Disassociated].
15/03/06 19:00:25 INFO LocalActorRef: Message [akka.remote.transport.ActorTransportAdapter$DisassociateUnderlying] from Actor[akka://sparkWorker/deadLetters] to Actor[akka://sparkWorker/system/transports/akkaprotocolmanager.tcp0/akkaProtocol-tcp%3A%2F%2FsparkWorker%40WORKER_MACHINE_IP%3A45806-2#1549100100] was not delivered. [1] dead letters encountered. This logging can be turned off or adjusted with configuration settings 'akka.log-dead-letters' and 'akka.log-dead-letters-during-shutdown'.
There is a warning, which I believe is not the case at this issue
15/03/06 18:07:46 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
You can try to set conf.set("spark.driver.host",""), the client host is a host where you start spark-shell or other script.
I want to use machine A where I will submit my Spark job to the cluster, A has no spark environment, just java. When I launch the jar, there is a HTTP server starts:
[steven#bj-230 ~]$ java -jar helloCluster.jar SimplyApp
log4j:WARN No appenders could be found for logger (akka.event.slf4j.Slf4jLogger).
log4j:WARN Please initialize the log4j system properly.
log4j:WARN See http://logging.apache.org/log4j/1.2/faq.html#noconfig for more info.
14/06/10 16:54:54 INFO SparkEnv: Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
14/06/10 16:54:54 INFO SparkEnv: Registering BlockManagerMaster
14/06/10 16:54:54 INFO DiskBlockManager: Created local directory at /tmp/spark-local-20140610165454-4393
14/06/10 16:54:54 INFO MemoryStore: MemoryStore started with capacity 1055.1 MB.
14/06/10 16:54:54 INFO ConnectionManager: Bound socket to port 59981 with id = ConnectionManagerId(bj-230,59981)
14/06/10 16:54:54 INFO BlockManagerMaster: Trying to register BlockManager
14/06/10 16:54:54 INFO BlockManagerMasterActor$BlockManagerInfo: Registering block manager bj-230:59981 with 1055.1 MB RAM
14/06/10 16:54:54 INFO BlockManagerMaster: Registered BlockManager
14/06/10 16:54:54 INFO HttpServer: Starting HTTP Server
14/06/10 16:54:54 INFO HttpBroadcast: Broadcast server started at http://10.10.10.230:59233
14/06/10 16:54:54 INFO SparkEnv: Registering MapOutputTracker
14/06/10 16:54:54 INFO HttpFileServer: HTTP File server directory is /tmp/spark-bfdd02f1-3c02-4233-854f-af89542b9acf
14/06/10 16:54:54 INFO HttpServer: Starting HTTP Server
14/06/10 16:54:54 INFO SparkUI: Started Spark Web UI at http://bj-230:4040
14/06/10 16:54:54 INFO SparkContext: Added JAR hdfs://master:8020/tmp/helloCluster.jar at hdfs://master:8020/tmp/helloCluster.jar with timestamp 1402390494838
14/06/10 16:54:54 INFO AppClient$ClientActor: Connecting to master spark://master:7077...
So, what's the meaning of this server? And if I am behind a NAT, is it possible to use this machine A to submit my job to remote cluster?
By the way, the result of this execution is failed. Error log:
14/06/10 16:55:05 INFO SparkDeploySchedulerBackend: Executor app-20140610165321-0005/7 removed: Command exited with code 1
14/06/10 16:55:05 ERROR AppClient$ClientActor: Master removed our application: FAILED; stopping client
14/06/10 16:55:05 WARN SparkDeploySchedulerBackend: Disconnected from Spark cluster! Waiting for reconnection...
14/06/10 16:55:11 WARN TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient memory
The spark driver starts few HTTP endpoints:
It provides a Web console that shows the job progress. This http endpoint has a default port of 4040 and can be changed with the configuration option: spark.ui.port. Then, you connect to it with your browser: http://your_host:4040 and you will be able to follow the job. It's only alive the time the driver runs.
There's an additional HTTP endpoint to provide a file download service for the jars declared as dependencies. The workers will contact the driver to download the list of dependencies. This is a random assigned port. Therefore, the driver must be on a routable network from the Spark workers.