`
FeiNiBukeZyh
  • 浏览: 12919 次
  • 性别: Icon_minigender_1
  • 来自: 沈阳
社区版块
存档分类
最新评论

Spark on Yarn 模式编写workcount实例

 
阅读更多

import java.util.ArrayList;
import java.util.List;

import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.api.java.function.Function2;
import org.apache.spark.api.java.function.PairFlatMapFunction;

import scala.Tuple2;

public class SparkMain {
    @SuppressWarnings("serial") public static void main(String[] args) {
        SparkConf conf = new SparkConf().setAppName("Spark");
        /*独立模式
        conf.setMaster("spark://master56:7077");
        conf.set("spark.cores.max", "48");
        */
        /*yarn-client模式*/
        conf.setMaster("yarn-client");
        //设置程序包
        conf.setJars(new String[]{"/home/hadoop/Spark-0.0.1-SNAPSHOT/lib/Spark-0.0.1-SNAPSHOT.jar"});
        //设置SparkHOME
        conf.setSparkHome("/home/hadoop/spark-1.2.0-cdh5.3.2");
        //设置运行资源参数
        conf.set("spark.executor.instances", "30");
        conf.set("spark.executor.cores", "3");
        conf.set("spark.executor.memory", "5G");
        conf.set("spark.driver.memory", "3G");
        conf.set("spark.driver.maxResultSize", "10G");
        JavaSparkContext context = new JavaSparkContext(conf);
        //设置运行资源参数
        JavaRDD<String> rdd = context.textFile("hdfs://nujhadoop/spark.txt");
        List<Tuple2<String, Integer>> result = rdd.flatMapToPair(new PairFlatMapFunction<String, String, Integer>(){
                @Override
                public Iterable<Tuple2<String, Integer>> call(String arg0)
                    throws Exception {
                    ArrayList<Tuple2<String, Integer>> list = new ArrayList<Tuple2<String, Integer>>();
                    String[] array = arg0.split(" ");
                    for (String temper : array) {
                        list.add(new Tuple2<String, Integer>(temper, 1));
                    }
                    return list;
                }
                
            }).reduceByKey(new Function2<Integer, Integer, Integer>(){

                @Override
                public Integer call(Integer arg0, Integer arg1)
                    throws Exception {
                    // TODO Auto-generated method stub
                    return arg0 + arg1;
                }
                
            }).collect();
        //打印结果
        for (Tuple2<String, Integer> temper : result) {
            System.out.println(temper._1+","+temper._2);
        }
        context.stop();
    }
}   
    说明:
 一:上传输入文件到hadoop,本例上传的文件名为spark.txt
 :打包程序,打包名为:Spark-0.0.1-SNAPSHOT.jar
 :上传文件到Spark集群进行部署,如:
  

 

    四:启动程序 sh ./run.sh

    日志结果:

    

Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
15/07/15 16:45:10 INFO SecurityManager: Changing view acls to: hadoop
15/07/15 16:45:10 INFO SecurityManager: Changing modify acls to: hadoop
15/07/15 16:45:10 INFO SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users with view permissions: Set(hadoop); users with modify permissions: Set(hadoop)
15/07/15 16:45:11 INFO Slf4jLogger: Slf4jLogger started
15/07/15 16:45:11 INFO Remoting: Starting remoting
15/07/15 16:45:11 INFO Remoting: Remoting started; listening on addresses :[akka.tcp://sparkDriver@slave63:22597]
15/07/15 16:45:11 INFO Remoting: Remoting now listens on addresses: [akka.tcp://sparkDriver@slave63:22597]
15/07/15 16:45:11 INFO Utils: Successfully started service 'sparkDriver' on port 22597.
15/07/15 16:45:11 INFO SparkEnv: Registering MapOutputTracker
15/07/15 16:45:11 INFO SparkEnv: Registering BlockManagerMaster
15/07/15 16:45:11 INFO DiskBlockManager: Created local directory at /tmp/spark-local-20150715164511-17b9
15/07/15 16:45:11 INFO MemoryStore: MemoryStore started with capacity 1635.9 MB
15/07/15 16:45:12 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
15/07/15 16:45:12 INFO HttpFileServer: HTTP File server directory is /tmp/spark-bd6a9445-0d51-4d1b-9fc5-b4dcbcdd4cd0
15/07/15 16:45:12 INFO HttpServer: Starting HTTP Server
15/07/15 16:45:12 INFO Utils: Successfully started service 'HTTP file server' on port 54673.
15/07/15 16:45:12 INFO Utils: Successfully started service 'SparkUI' on port 4040.
15/07/15 16:45:12 INFO SparkUI: Started SparkUI at http://slave63:4040
15/07/15 16:45:13 INFO SparkContext: Added JAR /home/hadoop/Spark-0.0.1-SNAPSHOT/lib/Spark-0.0.1-SNAPSHOT.jar at http://172.20.10.63:54673/jars/Spark-0.0.1-SNAPSHOT.jar with timestamp 1436949913052
15/07/15 16:45:13 INFO RMProxy: Connecting to ResourceManager at master46/172.20.10.46:8032
15/07/15 16:45:13 INFO Client: Requesting a new application from cluster with 30 NodeManagers
15/07/15 16:45:13 INFO Client: Verifying our application has not requested more than the maximum memory capability of the cluster (8192 MB per container)
15/07/15 16:45:13 INFO Client: Will allocate AM container, with 3456 MB memory including 384 MB overhead
15/07/15 16:45:13 INFO Client: Setting up container launch context for our AM
15/07/15 16:45:13 INFO Client: Preparing resources for our AM container
15/07/15 16:45:14 INFO Client: Uploading resource file:/home/hadoop/Spark-0.0.1-SNAPSHOT/lib/spark-assembly-1.2.0-cdh5.3.2.jar -> hdfs://nujhadoop/user/hadoop/.sparkStaging/application_1434338096593_8055/spark-assembly-1.2.0-cdh5.3.2.jar
15/07/15 16:45:15 INFO Client: Setting up the launch environment for our AM container
15/07/15 16:45:16 INFO SecurityManager: Changing view acls to: hadoop
15/07/15 16:45:16 INFO SecurityManager: Changing modify acls to: hadoop
15/07/15 16:45:16 INFO SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users with view permissions: Set(hadoop); users with modify permissions: Set(hadoop)
15/07/15 16:45:16 INFO Client: Submitting application 8055 to ResourceManager
15/07/15 16:45:16 INFO YarnClientImpl: Submitted application application_1434338096593_8055
15/07/15 16:45:17 INFO Client: Application report for application_1434338096593_8055 (state: ACCEPTED)
15/07/15 16:45:17 INFO Client: 
	 client token: N/A
	 diagnostics: N/A
	 ApplicationMaster host: N/A
	 ApplicationMaster RPC port: -1
	 queue: root.hadoop
	 start time: 1436949916087
	 final status: UNDEFINED
	 tracking URL: http://master46:8088/proxy/application_1434338096593_8055/
	 user: hadoop
15/07/15 16:45:18 INFO Client: Application report for application_1434338096593_8055 (state: ACCEPTED)
15/07/15 16:45:19 INFO Client: Application report for application_1434338096593_8055 (state: ACCEPTED)
15/07/15 16:45:20 INFO Client: Application report for application_1434338096593_8055 (state: ACCEPTED)
15/07/15 16:45:21 INFO Client: Application report for application_1434338096593_8055 (state: ACCEPTED)
15/07/15 16:45:22 INFO Client: Application report for application_1434338096593_8055 (state: ACCEPTED)
15/07/15 16:45:22 INFO YarnClientSchedulerBackend: ApplicationMaster registered as Actor[akka.tcp://sparkYarnAM@slave28:55325/user/YarnAM#945036977]
15/07/15 16:45:22 INFO YarnClientSchedulerBackend: Add WebUI Filter. org.apache.hadoop.yarn.server.webproxy.amfilter.AmIpFilter, Map(PROXY_HOSTS -> master46, PROXY_URI_BASES -> http://master46:8088/proxy/application_1434338096593_8055), /proxy/application_1434338096593_8055
15/07/15 16:45:22 INFO JettyUtils: Adding filter: org.apache.hadoop.yarn.server.webproxy.amfilter.AmIpFilter
15/07/15 16:45:23 INFO Client: Application report for application_1434338096593_8055 (state: RUNNING)
15/07/15 16:45:23 INFO Client: 
	 client token: N/A
	 diagnostics: N/A
	 ApplicationMaster host: slave28
	 ApplicationMaster RPC port: 0
	 queue: root.hadoop
	 start time: 1436949916087
	 final status: UNDEFINED
	 tracking URL: http://master46:8088/proxy/application_1434338096593_8055/
	 user: hadoop
15/07/15 16:45:23 INFO YarnClientSchedulerBackend: Application application_1434338096593_8055 has started running.
15/07/15 16:45:23 INFO NettyBlockTransferService: Server created on 50871
15/07/15 16:45:23 INFO BlockManagerMaster: Trying to register BlockManager
15/07/15 16:45:23 INFO BlockManagerMasterActor: Registering block manager slave63:50871 with 1635.9 MB RAM, BlockManagerId(<driver>, slave63, 50871)
15/07/15 16:45:23 INFO BlockManagerMaster: Registered BlockManager
15/07/15 16:45:28 INFO YarnClientSchedulerBackend: Registered executor: Actor[akka.tcp://sparkExecutor@slave52:23892/user/Executor#469935313] with ID 1
15/07/15 16:45:28 INFO RackResolver: Resolved slave52 to /rack2
15/07/15 16:45:29 INFO BlockManagerMasterActor: Registering block manager slave52:36246 with 2.6 GB RAM, BlockManagerId(1, slave52, 36246)
15/07/15 16:45:33 INFO YarnClientSchedulerBackend: Registered executor: Actor[akka.tcp://sparkExecutor@slave63:19749/user/Executor#-1474529488] with ID 4
15/07/15 16:45:33 INFO RackResolver: Resolved slave63 to /rack2
15/07/15 16:45:34 INFO YarnClientSchedulerBackend: Registered executor: Actor[akka.tcp://sparkExecutor@slave28:51624/user/Executor#1260742083] with ID 28
15/07/15 16:45:34 INFO RackResolver: Resolved slave28 to /rack3
15/07/15 16:45:34 INFO BlockManagerMasterActor: Registering block manager slave63:64068 with 2.6 GB RAM, BlockManagerId(4, slave63, 64068)
15/07/15 16:45:35 INFO BlockManagerMasterActor: Registering block manager slave28:17967 with 2.6 GB RAM, BlockManagerId(28, slave28, 17967)
15/07/15 16:45:36 INFO YarnClientSchedulerBackend: Registered executor: Actor[akka.tcp://sparkExecutor@slave23:57756/user/Executor#-1426187042] with ID 16
15/07/15 16:45:36 INFO RackResolver: Resolved slave23 to /rack3
15/07/15 16:45:37 INFO YarnClientSchedulerBackend: Registered executor: Actor[akka.tcp://sparkExecutor@slave36:35348/user/Executor#-1773874771] with ID 3
15/07/15 16:45:37 INFO RackResolver: Resolved slave36 to /rack1
15/07/15 16:45:37 INFO BlockManagerMasterActor: Registering block manager slave23:62605 with 2.6 GB RAM, BlockManagerId(16, slave23, 62605)
15/07/15 16:45:38 INFO BlockManagerMasterActor: Registering block manager slave36:23663 with 2.6 GB RAM, BlockManagerId(3, slave36, 23663)
15/07/15 16:45:39 INFO YarnClientSchedulerBackend: Registered executor: Actor[akka.tcp://sparkExecutor@slave15:43551/user/Executor#-576231312] with ID 11
15/07/15 16:45:39 INFO RackResolver: Resolved slave15 to /rack3
15/07/15 16:45:40 INFO YarnClientSchedulerBackend: Registered executor: Actor[akka.tcp://sparkExecutor@slave37:40681/user/Executor#-1501756719] with ID 29
15/07/15 16:45:40 INFO RackResolver: Resolved slave37 to /rack1
15/07/15 16:45:40 INFO BlockManagerMasterActor: Registering block manager slave15:55745 with 2.6 GB RAM, BlockManagerId(11, slave15, 55745)
15/07/15 16:45:41 INFO YarnClientSchedulerBackend: Registered executor: Actor[akka.tcp://sparkExecutor@slave26:28665/user/Executor#1165917342] with ID 21
15/07/15 16:45:41 INFO RackResolver: Resolved slave26 to /rack3
15/07/15 16:45:41 INFO YarnClientSchedulerBackend: Registered executor: Actor[akka.tcp://sparkExecutor@slave54:37653/user/Executor#587407704] with ID 2
15/07/15 16:45:41 INFO RackResolver: Resolved slave54 to /rack2
15/07/15 16:45:41 INFO BlockManagerMasterActor: Registering block manager slave37:38747 with 2.6 GB RAM, BlockManagerId(29, slave37, 38747)
15/07/15 16:45:42 INFO BlockManagerMasterActor: Registering block manager slave26:46197 with 2.6 GB RAM, BlockManagerId(21, slave26, 46197)
15/07/15 16:45:42 INFO YarnClientSchedulerBackend: Registered executor: Actor[akka.tcp://sparkExecutor@slave17:64410/user/Executor#-1365579611] with ID 19
15/07/15 16:45:42 INFO RackResolver: Resolved slave17 to /rack3
15/07/15 16:45:42 INFO YarnClientSchedulerBackend: Registered executor: Actor[akka.tcp://sparkExecutor@slave35:15510/user/Executor#972094812] with ID 15
15/07/15 16:45:42 INFO RackResolver: Resolved slave35 to /rack1
15/07/15 16:45:42 INFO YarnClientSchedulerBackend: Registered executor: Actor[akka.tcp://sparkExecutor@slave55:36974/user/Executor#-597250789] with ID 26
15/07/15 16:45:42 INFO RackResolver: Resolved slave55 to /rack2
15/07/15 16:45:42 INFO BlockManagerMasterActor: Registering block manager slave54:18807 with 2.6 GB RAM, BlockManagerId(2, slave54, 18807)
15/07/15 16:45:43 INFO YarnClientSchedulerBackend: SchedulerBackend is ready for scheduling beginning after waiting maxRegisteredResourcesWaitingTime: 30000(ms)
15/07/15 16:45:43 INFO BlockManagerMasterActor: Registering block manager slave17:58808 with 2.6 GB RAM, BlockManagerId(19, slave17, 58808)
15/07/15 16:45:43 INFO BlockManagerMasterActor: Registering block manager slave35:29737 with 2.6 GB RAM, BlockManagerId(15, slave35, 29737)
15/07/15 16:45:43 INFO MemoryStore: ensureFreeSpace(261904) called with curMem=0, maxMem=1715396935
15/07/15 16:45:43 INFO MemoryStore: Block broadcast_0 stored as values in memory (estimated size 255.8 KB, free 1635.7 MB)
15/07/15 16:45:43 INFO BlockManagerMasterActor: Registering block manager slave55:29257 with 2.6 GB RAM, BlockManagerId(26, slave55, 29257)
15/07/15 16:45:43 INFO MemoryStore: ensureFreeSpace(21065) called with curMem=261904, maxMem=1715396935
15/07/15 16:45:43 INFO MemoryStore: Block broadcast_0_piece0 stored as bytes in memory (estimated size 20.6 KB, free 1635.7 MB)
15/07/15 16:45:43 INFO BlockManagerInfo: Added broadcast_0_piece0 in memory on slave63:50871 (size: 20.6 KB, free: 1635.9 MB)
15/07/15 16:45:43 INFO BlockManagerMaster: Updated info of block broadcast_0_piece0
15/07/15 16:45:43 INFO SparkContext: Created broadcast 0 from textFile at SparkMain.java:31
15/07/15 16:45:44 INFO FileInputFormat: Total input paths to process : 1
15/07/15 16:45:44 INFO SparkContext: Starting job: collect at SparkMain.java:53
15/07/15 16:45:44 INFO DAGScheduler: Registering RDD 2 (flatMapToPair at SparkMain.java:32)
15/07/15 16:45:44 INFO DAGScheduler: Got job 0 (collect at SparkMain.java:53) with 2 output partitions (allowLocal=false)
15/07/15 16:45:44 INFO DAGScheduler: Final stage: Stage 1(collect at SparkMain.java:53)
15/07/15 16:45:44 INFO DAGScheduler: Parents of final stage: List(Stage 0)
15/07/15 16:45:44 INFO DAGScheduler: Missing parents: List(Stage 0)
15/07/15 16:45:44 INFO DAGScheduler: Submitting Stage 0 (FlatMappedRDD[2] at flatMapToPair at SparkMain.java:32), which has no missing parents
15/07/15 16:45:44 INFO MemoryStore: ensureFreeSpace(3672) called with curMem=282969, maxMem=1715396935
15/07/15 16:45:44 INFO MemoryStore: Block broadcast_1 stored as values in memory (estimated size 3.6 KB, free 1635.7 MB)
15/07/15 16:45:44 INFO MemoryStore: ensureFreeSpace(2190) called with curMem=286641, maxMem=1715396935
15/07/15 16:45:44 INFO MemoryStore: Block broadcast_1_piece0 stored as bytes in memory (estimated size 2.1 KB, free 1635.7 MB)
15/07/15 16:45:44 INFO BlockManagerInfo: Added broadcast_1_piece0 in memory on slave63:50871 (size: 2.1 KB, free: 1635.9 MB)
15/07/15 16:45:44 INFO BlockManagerMaster: Updated info of block broadcast_1_piece0
15/07/15 16:45:44 INFO SparkContext: Created broadcast 1 from broadcast at DAGScheduler.scala:838
15/07/15 16:45:44 INFO DAGScheduler: Submitting 2 missing tasks from Stage 0 (FlatMappedRDD[2] at flatMapToPair at SparkMain.java:32)
15/07/15 16:45:44 INFO YarnClientClusterScheduler: Adding task set 0.0 with 2 tasks
15/07/15 16:45:44 INFO RackResolver: Resolved slave38 to /rack1
15/07/15 16:45:44 INFO TaskSetManager: Starting task 0.0 in stage 0.0 (TID 0, slave63, NODE_LOCAL, 1340 bytes)
15/07/15 16:45:44 INFO TaskSetManager: Starting task 1.0 in stage 0.0 (TID 1, slave63, NODE_LOCAL, 1340 bytes)
15/07/15 16:45:45 INFO BlockManagerInfo: Added broadcast_1_piece0 in memory on slave63:64068 (size: 2.1 KB, free: 2.6 GB)
15/07/15 16:45:45 INFO YarnClientSchedulerBackend: Registered executor: Actor[akka.tcp://sparkExecutor@slave65:57998/user/Executor#-1382810865] with ID 12
15/07/15 16:45:45 INFO RackResolver: Resolved slave65 to /rack2
15/07/15 16:45:45 INFO BlockManagerInfo: Added broadcast_0_piece0 in memory on slave63:64068 (size: 20.6 KB, free: 2.6 GB)
15/07/15 16:45:46 INFO YarnClientSchedulerBackend: Registered executor: Actor[akka.tcp://sparkExecutor@slave53:59085/user/Executor#-1064055348] with ID 13
15/07/15 16:45:46 INFO RackResolver: Resolved slave53 to /rack2
15/07/15 16:45:46 INFO YarnClientSchedulerBackend: Registered executor: Actor[akka.tcp://sparkExecutor@slave62:24319/user/Executor#-139207262] with ID 14
15/07/15 16:45:46 INFO RackResolver: Resolved slave62 to /rack2
15/07/15 16:45:46 INFO BlockManagerMasterActor: Registering block manager slave65:64372 with 2.6 GB RAM, BlockManagerId(12, slave65, 64372)
15/07/15 16:45:47 INFO BlockManagerMasterActor: Registering block manager slave62:53823 with 2.6 GB RAM, BlockManagerId(14, slave62, 53823)
15/07/15 16:45:47 INFO YarnClientSchedulerBackend: Registered executor: Actor[akka.tcp://sparkExecutor@slave32:28461/user/Executor#-2071109973] with ID 20
15/07/15 16:45:47 INFO RackResolver: Resolved slave32 to /rack1
15/07/15 16:45:47 INFO BlockManagerMasterActor: Registering block manager slave53:60055 with 2.6 GB RAM, BlockManagerId(13, slave53, 60055)
15/07/15 16:45:47 INFO YarnClientSchedulerBackend: Registered executor: Actor[akka.tcp://sparkExecutor@slave14:35963/user/Executor#148583350] with ID 22
15/07/15 16:45:47 INFO RackResolver: Resolved slave14 to /rack3
15/07/15 16:45:48 INFO BlockManagerMasterActor: Registering block manager slave32:35445 with 2.6 GB RAM, BlockManagerId(20, slave32, 35445)
15/07/15 16:45:48 INFO YarnClientSchedulerBackend: Registered executor: Actor[akka.tcp://sparkExecutor@slave43:63661/user/Executor#1541284948] with ID 24
15/07/15 16:45:48 INFO RackResolver: Resolved slave43 to /rack1
15/07/15 16:45:48 INFO YarnClientSchedulerBackend: Registered executor: Actor[akka.tcp://sparkExecutor@slave33:46267/user/Executor#-1437439698] with ID 10
15/07/15 16:45:48 INFO RackResolver: Resolved slave33 to /rack1
15/07/15 16:45:48 INFO BlockManagerMasterActor: Registering block manager slave43:34953 with 2.6 GB RAM, BlockManagerId(24, slave43, 34953)
15/07/15 16:45:49 INFO BlockManagerMasterActor: Registering block manager slave14:53473 with 2.6 GB RAM, BlockManagerId(22, slave14, 53473)
15/07/15 16:45:49 INFO YarnClientSchedulerBackend: Registered executor: Actor[akka.tcp://sparkExecutor@slave42:26170/user/Executor#794862330] with ID 5
15/07/15 16:45:49 INFO RackResolver: Resolved slave42 to /rack1
15/07/15 16:45:49 INFO YarnClientSchedulerBackend: Registered executor: Actor[akka.tcp://sparkExecutor@slave44:35394/user/Executor#1035079905] with ID 18
15/07/15 16:45:49 INFO RackResolver: Resolved slave44 to /rack1
15/07/15 16:45:49 INFO YarnClientSchedulerBackend: Registered executor: Actor[akka.tcp://sparkExecutor@slave16:52328/user/Executor#1181615525] with ID 30
15/07/15 16:45:49 INFO RackResolver: Resolved slave16 to /rack3
15/07/15 16:45:49 INFO YarnClientSchedulerBackend: Registered executor: Actor[akka.tcp://sparkExecutor@slave13:48403/user/Executor#-1103053012] with ID 27
15/07/15 16:45:49 INFO RackResolver: Resolved slave13 to /rack3
15/07/15 16:45:49 INFO BlockManagerMasterActor: Registering block manager slave42:60923 with 2.6 GB RAM, BlockManagerId(5, slave42, 60923)
15/07/15 16:45:50 INFO BlockManagerMasterActor: Registering block manager slave44:30133 with 2.6 GB RAM, BlockManagerId(18, slave44, 30133)
15/07/15 16:45:50 INFO YarnClientSchedulerBackend: Registered executor: Actor[akka.tcp://sparkExecutor@slave45:63922/user/Executor#-917535710] with ID 6
15/07/15 16:45:50 INFO RackResolver: Resolved slave45 to /rack1
15/07/15 16:45:50 INFO BlockManagerMasterActor: Registering block manager slave16:21970 with 2.6 GB RAM, BlockManagerId(30, slave16, 21970)
15/07/15 16:45:50 INFO BlockManagerMasterActor: Registering block manager slave13:57504 with 2.6 GB RAM, BlockManagerId(27, slave13, 57504)
15/07/15 16:45:50 INFO YarnClientSchedulerBackend: Registered executor: Actor[akka.tcp://sparkExecutor@slave25:18514/user/Executor#-799832935] with ID 25
15/07/15 16:45:50 INFO RackResolver: Resolved slave25 to /rack3
15/07/15 16:45:51 INFO YarnClientSchedulerBackend: Registered executor: Actor[akka.tcp://sparkExecutor@slave27:64380/user/Executor#-520443684] with ID 9
15/07/15 16:45:51 INFO RackResolver: Resolved slave27 to /rack3
15/07/15 16:45:51 INFO BlockManagerMasterActor: Registering block manager slave25:16330 with 2.6 GB RAM, BlockManagerId(25, slave25, 16330)
15/07/15 16:45:51 INFO BlockManagerMasterActor: Registering block manager slave45:63841 with 2.6 GB RAM, BlockManagerId(6, slave45, 63841)
15/07/15 16:45:51 INFO YarnClientSchedulerBackend: Registered executor: Actor[akka.tcp://sparkExecutor@slave24:46357/user/Executor#1463308812] with ID 8
15/07/15 16:45:51 INFO RackResolver: Resolved slave24 to /rack3
15/07/15 16:45:51 INFO TaskSetManager: Finished task 1.0 in stage 0.0 (TID 1) in 7633 ms on slave63 (1/2)
15/07/15 16:45:51 INFO BlockManagerMasterActor: Registering block manager slave33:50916 with 2.6 GB RAM, BlockManagerId(10, slave33, 50916)
15/07/15 16:45:52 INFO TaskSetManager: Finished task 0.0 in stage 0.0 (TID 0) in 7804 ms on slave63 (2/2)
15/07/15 16:45:52 INFO DAGScheduler: Stage 0 (flatMapToPair at SparkMain.java:32) finished in 7.810 s
15/07/15 16:45:52 INFO YarnClientClusterScheduler: Removed TaskSet 0.0, whose tasks have all completed, from pool 
15/07/15 16:45:52 INFO DAGScheduler: looking for newly runnable stages
15/07/15 16:45:52 INFO DAGScheduler: running: Set()
15/07/15 16:45:52 INFO DAGScheduler: waiting: Set(Stage 1)
15/07/15 16:45:52 INFO DAGScheduler: failed: Set()
15/07/15 16:45:52 INFO DAGScheduler: Missing parents for Stage 1: List()
15/07/15 16:45:52 INFO DAGScheduler: Submitting Stage 1 (ShuffledRDD[3] at reduceByKey at SparkMain.java:44), which is now runnable
15/07/15 16:45:52 INFO MemoryStore: ensureFreeSpace(2232) called with curMem=288831, maxMem=1715396935
15/07/15 16:45:52 INFO MemoryStore: Block broadcast_2 stored as values in memory (estimated size 2.2 KB, free 1635.7 MB)
15/07/15 16:45:52 INFO MemoryStore: ensureFreeSpace(1403) called with curMem=291063, maxMem=1715396935
15/07/15 16:45:52 INFO MemoryStore: Block broadcast_2_piece0 stored as bytes in memory (estimated size 1403.0 B, free 1635.7 MB)
15/07/15 16:45:52 INFO BlockManagerInfo: Added broadcast_2_piece0 in memory on slave63:50871 (size: 1403.0 B, free: 1635.9 MB)
15/07/15 16:45:52 INFO BlockManagerMaster: Updated info of block broadcast_2_piece0
15/07/15 16:45:52 INFO SparkContext: Created broadcast 2 from broadcast at DAGScheduler.scala:838
15/07/15 16:45:52 INFO DAGScheduler: Submitting 2 missing tasks from Stage 1 (ShuffledRDD[3] at reduceByKey at SparkMain.java:44)
15/07/15 16:45:52 INFO YarnClientClusterScheduler: Adding task set 1.0 with 2 tasks
15/07/15 16:45:52 INFO TaskSetManager: Starting task 0.0 in stage 1.0 (TID 2, slave63, PROCESS_LOCAL, 1121 bytes)
15/07/15 16:45:52 INFO TaskSetManager: Starting task 1.0 in stage 1.0 (TID 3, slave26, PROCESS_LOCAL, 1121 bytes)
15/07/15 16:45:52 INFO BlockManagerInfo: Added broadcast_2_piece0 in memory on slave63:64068 (size: 1403.0 B, free: 2.6 GB)
15/07/15 16:45:52 INFO MapOutputTrackerMasterActor: Asked to send map output locations for shuffle 0 to sparkExecutor@slave63:19749
15/07/15 16:45:52 INFO MapOutputTrackerMaster: Size of output statuses for shuffle 0 is 147 bytes
15/07/15 16:45:52 INFO BlockManagerMasterActor: Registering block manager slave27:35965 with 2.6 GB RAM, BlockManagerId(9, slave27, 35965)
15/07/15 16:45:52 INFO TaskSetManager: Finished task 0.0 in stage 1.0 (TID 2) in 159 ms on slave63 (1/2)
15/07/15 16:45:52 INFO YarnClientSchedulerBackend: Registered executor: Actor[akka.tcp://sparkExecutor@slave18:54423/user/Executor#495118309] with ID 7
15/07/15 16:45:52 INFO RackResolver: Resolved slave18 to /rack3
15/07/15 16:45:52 INFO BlockManagerMasterActor: Registering block manager slave24:57590 with 2.6 GB RAM, BlockManagerId(8, slave24, 57590)
15/07/15 16:45:53 INFO BlockManagerMasterActor: Registering block manager slave18:51244 with 2.6 GB RAM, BlockManagerId(7, slave18, 51244)
15/07/15 16:45:53 INFO BlockManagerInfo: Added broadcast_2_piece0 in memory on slave26:46197 (size: 1403.0 B, free: 2.6 GB)
15/07/15 16:45:53 INFO MapOutputTrackerMasterActor: Asked to send map output locations for shuffle 0 to sparkExecutor@slave26:28665
15/07/15 16:45:53 INFO TaskSetManager: Finished task 1.0 in stage 1.0 (TID 3) in 1605 ms on slave26 (2/2)
15/07/15 16:45:53 INFO DAGScheduler: Stage 1 (collect at SparkMain.java:53) finished in 1.612 s
15/07/15 16:45:53 INFO YarnClientClusterScheduler: Removed TaskSet 1.0, whose tasks have all completed, from pool 
15/07/15 16:45:53 INFO DAGScheduler: Job 0 finished: collect at SparkMain.java:53, took 9.550722 s
So,1
up.He,1
are,1
got,1
decided,1
bunch,1
his,1
few,1
away,1
backed,1
said��I,1
They,1
air,,1
ripe,1
am,1
never,1
One,1
tried,1
last,1
feeling,1
with,1
day,1
start,,1
One,,1
again,,2
paces,,1
three,,1
they,1
just,1
again,1
still,,1
two,,1
grapes.,1
walked,2
summer,1
walking,1
running,1
up,2
not,1
it,1
He,1
fox,2
orchard.,1
succeeded.,1
was,1
sour.��,1
grapes.The,1
a,4
stopped,1
nose,1
At,1
missed,1
before,1
to,1
back.,1
sure,1
he,5
through,1
thirsty,",1
in,1
could,1
grapes.He,1
of,1
hot,1
juicy."I'm,1
were,1
reach,1
an,1
but,3
jumped,2
and,3
up,,1
give,1
thought.,1
the,3
15/07/15 16:45:53 INFO SparkUI: Stopped Spark web UI at http://slave63:4040
15/07/15 16:45:53 INFO DAGScheduler: Stopping DAGScheduler
15/07/15 16:45:53 INFO YarnClientSchedulerBackend: Shutting down all executors
15/07/15 16:45:53 INFO YarnClientSchedulerBackend: Asking each executor to shut down
15/07/15 16:45:53 INFO YarnClientSchedulerBackend: Stopped
15/07/15 16:45:54 INFO MapOutputTrackerMasterActor: MapOutputTrackerActor stopped!
15/07/15 16:45:54 INFO MemoryStore: MemoryStore cleared
15/07/15 16:45:54 INFO BlockManager: BlockManager stopped
15/07/15 16:45:54 INFO BlockManagerMaster: BlockManagerMaster stopped
15/07/15 16:45:54 INFO SparkContext: Successfully stopped SparkContext
15/07/15 16:45:54 INFO RemoteActorRefProvider$RemotingTerminator: Shutting down remote daemon.

 

 

 

 

分享到:
评论

相关推荐

    Spark on Yarn模式部署.docx

    Spark on Yarn 模式部署是一种常见的 Spark 应用场景,本文将详细介绍 Spark on Yarn 模式部署的步骤和配置过程。 标题解释 Spark on Yarn 模式部署是指将 Spark 应用程序部署在 Yarn 集群上,使得 Spark 能够使用 ...

    Spark实验:On Yarn模式安装部署(带答案)1

    Spark on Yarn是一种将Spark应用程序部署在Hadoop YARN资源管理器上的方法,它允许Spark充分利用YARN的资源管理和调度功能。在这个实验中,我们将详细探讨如何在Yarn模式下安装和部署Spark集群。 首先,我们需要...

    Spark on Yarn模式的电信大数据处理平台.pdf

    此时,Yarn(Yet Another Resource Negotiator)出现了,它是Hadoop 2.0中引入的新资源管理框架,进一步优化了资源管理和任务调度,而Spark on Yarn模式则充分利用了Yarn的资源调度能力和Spark内存计算的优势,显著...

    【讲义-第10期Spark公益大讲堂】Spark on Yarn-.pdf

    **Spark on Yarn** 是指Apache Spark集群管理器与Apache Hadoop YARN资源管理器之间的集成模式。这种模式下,YARN作为资源调度器负责分配资源,而Spark则负责任务的具体执行。这种方式使得Spark能够更好地利用Hadoop...

    03_MapReduce 和Spark on YARN.docx

    在大数据处理领域,Hadoop MapReduce 和 Apache Spark 是两种重要的计算框架,它们都在YARN(Yet Another Resource Negotiator)上运行以实现资源管理和任务调度。本文将深入探讨这两个框架以及YARN的相关概念。 ...

    Sparkonyarn集群搭建详细过程.pdf

    Spark on YARN 集群搭建详细过程 _title_:Spark on YARN 集群搭建详细过程 _description_:本文详细介绍了 Spark on YARN 集群搭建的过程,包括配置主机 hosts 文件、免密码登录、安装 Java、安装 Scala 等步骤。...

    spark初始化源码阅读sparkonyarn的client和cluster区别

    Spark 初始化源码阅读 Spark on YARN 的 Client 和 Cluster 区别 Spark 是一个大数据处理的开源框架,它可以在多种集群管理器上运行,如 YARN、Mesos 和 Standalone。Spark on YARN 是 Spark 在 YARN 集群管理器上...

    spark yarn模式的搭建.docx

    2. 修改 `conf/spark-defaults.conf`,设置 Spark 在 YARN 上运行的相关参数,如 `spark.master` 设为 `yarn`,`spark.executor.instances` 表示执行器实例数量等。 3. 修改 `conf/spark-env.sh`,设置环境变量,如 ...

    Spark on Yarn集群搭建手册

    Spark on Yan集群搭建的详细过程,减少集群搭建的时间

    基于docker搭建spark on yarn及可视化桌面.doc

    基于docker搭建spark on yarn及可视化桌面.doc

    Spark on Yarn之Executor内存管理 - 简书1

    【Spark on Yarn之Executor内存管理】 Spark是一个分布式计算框架,它可以在多个集群管理系统上运行,包括YARN(Hadoop的资源调度器)。Executor是Spark执行任务的基本单元,每个Executor在一个工作节点上运行,...

    Sparkonyarn集群搭建详细过程.docx

    Spark on YARN 集群搭建是一个复杂的过程,涉及到多台服务器的配置和软件的安装。以下是详细步骤和相关知识点: 1. **主机配置与网络通信** - `/etc/hosts` 文件配置至关重要,它用于解析主机名到IP地址的映射。...

    Spark的Yarn模式

    Spark的Yarn模式是将Spark应用部署在Hadoop的YARN资源管理系统上的方式,这种方式无需单独搭建Spark集群,而是利用YARN的资源管理和调度能力。YARN(Yet Another Resource Negotiator)是Hadoop 2.x版本引入的一个...

    基于SparkonYarn的淘宝数据挖掘平台

    基于SparkonYarn的淘宝数据挖掘平台

    SPARK2_ON_YARN-2.4.0.cloudera2.jar

    SPARK2_ON_YARN-2.4.0 jar包下载

    Spark源码系列(七)Sparkonyarn具体实现

    本来不打算写的了,但是真的是闲来无事,整天看美剧也没啥意思。这一章打算讲一下Spark onyarn的实现,1.0.0里面...在第一章《spark-submit提交作业过程》的时候,我们讲过Sparkonyarn的在cluster模式下它的main clas

    java提交spark任务到yarn平台的配置讲解共9页.pdf.zip

    在提交Spark任务前,需要配置Spark的相关属性,如`spark.master`设置为`yarn-client`或`yarn-cluster`,前者用于客户端模式,后者用于集群模式。此外,还需指定Hadoop的配置目录,例如`spark.yarn.conf.archive`。 ...

    基于Spark_on_Yarn的淘宝数据挖掘平台.pdf

    ### 基于Spark_on_Yarn的淘宝数据挖掘平台 #### 一、为什么选择Spark_on_Yarn 在大数据处理领域,随着数据量的急剧增长和技术的发展,传统的数据处理框架如Hadoop MapReduce面临着一系列挑战。淘宝作为中国最大的...

    三种方式的spark on kubernetes对比

    三种方式的spark on kubernetes对比,第一种:spark原生支持Kubernetes资源调度;第二种:google集成的Kubernetes的spark插件sparkoperator;第三种:standalone方式运行spark集群

    Spark&Yarn手动安装指南

    Spark on Yarn是指在Yarn集群中运行Spark应用程序。下面是Spark on Yarn的配置步骤: 1. 修改配置文件:修改spark-defaults.conf和spark-env.sh文件,配置Spark的参数。 2. 提交Spark任务:使用spark-submit命令...

Global site tag (gtag.js) - Google Analytics