Spark记录-Spark作业调试
在本地IDE里直接运行spark程序操作远程集群
一般运行spark作业的方式有两种：
-
本机调试，通过设置master为local模式运行spark作业，这种方式一般用于调试，不用连接远程集群。
-
集群运行。一般本机调试通过后会将作业打成jar包通过spark-submit提交运行。生产环境一般使用这种方式。
操作方法
1.设置master
两种方式：
- 在程序中设置
SparkConf conf = new SparkConf()
.setAppName("helloworld")
.setMaster("spark://192.168.66.66:7077");- 在run configuration中设置
VM options中添加：
-Dspark.master="spark://192.168.66.66:7077"2.设置HDFS
在程序中使用HDFS路径，会出现文件系统不匹配hdfs，可以将集群中的hadoop配置中的core-site.xml和hdfs-site.xml拷贝到项目src/main/resources下
3.发送jar包
如果程序中使用了自定义的算子和依赖的jar包，需要将本项目jar包和依赖的jar包发送到集群中SPARK_HOME/jars目录下，可以用maven-assembly打成带依赖的jar包，spark的jars相当于mvn库。
注意集群中每个节点的jars目录下都要放自己的jar包。
可能遇到的问题
如果遇到了节点间通信问题，可能是jar包没有在所有节点放置好。
incompatible loaded等问题，是依赖的spark版本不匹配，修改dependency。
至此，就可以直接在IDE中运行了
实战操作
1.安装jdk1.8、idea2017，maven3、idea2017安装scala插件
2.新建maven项目-scala
3.配置pom.xml，下载依赖包
<?xml version="1.0" encoding="UTF-8"?>
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"License"); you may not use this file except in compliance
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KIND, either express or implied. See the License for the
specific language governing permissions and limitations
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<!-- $Id: pom.xml 642118 2008-03-28 08:04:16Z reinhard $ -->
<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/maven-v4_0_0.xsd">
<modelVersion>4.0.0</modelVersion>
<packaging>war</packaging>
<name>scala</name>
<groupId>scala</groupId>
<artifactId>scala</artifactId>
<version>1.0-SNAPSHOT</version>
<build>
<plugins>
<plugin>
<groupId>org.mortbay.jetty</groupId>
<artifactId>maven-jetty-plugin</artifactId>
<version>6.1.7</version>
<configuration>
<connectors>
<connector implementation="org.mortbay.jetty.nio.SelectChannelConnector">
<port>8888</port>
<maxIdleTime>30000</maxIdleTime>
</connector>
</connectors>
<webAppSourceDirectory>${project.build.directory}/${pom.artifactId}-${pom.version}</webAppSourceDirectory>
<contextPath>/</contextPath>
</configuration>
</plugin>
</plugins>
</build>
<properties>
<scala.version>2.10.5</scala.version>
<hadoop.version>2.7.3</hadoop.version>
</properties>
<repositories>
<repository>
<id>scala-tools.org</id>
<name>Scala-Tools Maven2 Repository</name>
<url>http://scala-tools.org/repo-releases</url>
</repository>
</repositories>
<dependencies>
<!--dependency>
<groupId>scala</groupId>
<artifactId>[the artifact id of the block to be mounted]</artifactId>
<version>1.0-SNAPSHOT</version>
</dependency-->
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-core_2.10</artifactId>
<version>1.6.3</version>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-sql_2.10</artifactId>
<version>1.6.3</version>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-streaming_2.10</artifactId>
<version>1.6.3</version>
</dependency>
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-client</artifactId>
<version>${hadoop.version}</version>
</dependency>
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-common</artifactId>
<version>${hadoop.version}</version>
</dependency>
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-hdfs</artifactId>
<version>${hadoop.version}</version>
</dependency>
</dependencies>
</project>
4.打开File-Project Structure-Modules，Sources-scala项目src-main新建一个scala目录，设置为Sources
5.打开File-Project Structure-Libraries,添加scala sdk（2.10.5）
6.从集群里复制一个hadoop程序到D盘，再到网上下载https://github.com/srccodes/hadoop-common-2.2.0-bin，解压合并bin文件夹即可，设置用户环境变量HADOOP_HOME。
7.返回主界面，在src-main-scala下新建一个object：sparkPi:
import scala.math.random
import org.apache.spark._
object sparkPi {
def main(args: Array[String]) {
System.setProperty("hadoop.home.dir", "D:\\hadoop")
val conf = new SparkConf().setAppName("Spark Pi").setMaster("spark://192.168.66.66:7077")
.set("spark.executor.memory.","1g")
.set("spark.serializer","org.apache.spark.serializer.KryoSerializer")
.setJars(Seq("D:\\workspace\\scala\\out\\scala.jar"))
val spark = new SparkContext(conf)
val slices = if (args.length > 0) args(0).toInt else 2
println("Time:" + spark.startTime)
val n = math.min(1000L * slices, Int.MaxValue).toInt // avoid overflow
val count = spark.parallelize(1 until n, slices).map { i =>
val x = random * 2 - 1
val y = random * 2 - 1
if (x*x + y*y < 1) 1 else 0
}.reduce(_ + _)
println("Pi is roughly " + 4.0 * count / n)
spark.stop()
}
}
8.打开File-Project Structure-Artifacts,添加JAR-from modules with dependencies,选择好主类-sparkPi，设置好Output，去除不必要的包；
9.Build-Build Artifacts-scala.jar
10.Run-Run sparkPi
"C:\Program Files\Java\jdk1.8.0_121\bin\java" "-javaagent:D:\Program Files (x86)\JetBrains\IntelliJ IDEA 173.3302.5\lib\idea_rt.jar=53908:D:\Program Files (x86)\JetBrains\IntelliJ IDEA 173.3302.5\bin" -Dfile.encoding=UTF-8 -classpath "C:\Program Files\Java\jdk1.8.0_121\jre\lib\charsets.jar;C:\Program Files\Java\jdk1.8.0_121\jre\lib\deploy.jar;C:\Program Files\Java\jdk1.8.0_121\jre\lib\ext\access-bridge-64.jar;C:\Program Files\Java\jdk1.8.0_121\jre\lib\ext\cldrdata.jar;C:\Program Files\Java\jdk1.8.0_121\jre\lib\ext\dnsns.jar;C:\Program Files\Java\jdk1.8.0_121\jre\lib\ext\jaccess.jar;C:\Program Files\Java\jdk1.8.0_121\jre\lib\ext\jfxrt.jar;C:\Program Files\Java\jdk1.8.0_121\jre\lib\ext\localedata.jar;C:\Program Files\Java\jdk1.8.0_121\jre\lib\ext\nashorn.jar;C:\Program Files\Java\jdk1.8.0_121\jre\lib\ext\sunec.jar;C:\Program Files\Java\jdk1.8.0_121\jre\lib\ext\sunjce_provider.jar;C:\Program Files\Java\jdk1.8.0_121\jre\lib\ext\sunmscapi.jar;C:\Program 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pi\2.5\servlet-api-2.5.jar;C:\Users\xinfang\.m2\repository\org\mortbay\jetty\jetty\6.1.26\jetty-6.1.26.jar;C:\Users\xinfang\.m2\repository\org\mortbay\jetty\jetty-util\6.1.26\jetty-util-6.1.26.jar;C:\Users\xinfang\.m2\repository\javax\servlet\jsp\jsp-api\2.1\jsp-api-2.1.jar;C:\Users\xinfang\.m2\repository\com\sun\jersey\jersey-json\1.9\jersey-json-1.9.jar;C:\Users\xinfang\.m2\repository\org\codehaus\jettison\jettison\1.1\jettison-1.1.jar;C:\Users\xinfang\.m2\repository\com\sun\xml\bind\jaxb-impl\2.2.3-1\jaxb-impl-2.2.3-1.jar;C:\Users\xinfang\.m2\repository\org\codehaus\jackson\jackson-jaxrs\1.8.3\jackson-jaxrs-1.8.3.jar;C:\Users\xinfang\.m2\repository\org\codehaus\jackson\jackson-xc\1.8.3\jackson-xc-1.8.3.jar;C:\Users\xinfang\.m2\repository\commons-logging\commons-logging\1.1.3\commons-logging-1.1.3.jar;C:\Users\xinfang\.m2\repository\commons-lang\commons-lang\2.6\commons-lang-2.6.jar;C:\Users\xinfang\.m2\repository\commons-configuration\commons-configuration\1.6\commons-configuration-1.6.jar;C:\Users\xinfang\.m2\repository\commons-digester\commons-digester\1.8\commons-digester-1.8.jar;C:\Users\xinfang\.m2\repository\commons-beanutils\commons-beanutils\1.7.0\commons-beanutils-1.7.0.jar;C:\Users\xinfang\.m2\repository\commons-beanutils\commons-beanutils-core\1.8.0\commons-beanutils-core-1.8.0.jar;C:\Users\xinfang\.m2\repository\org\codehaus\jackson\jackson-core-asl\1.9.13\jackson-core-asl-1.9.13.jar;C:\Users\xinfang\.m2\repository\org\codehaus\jackson\jackson-mapper-asl\1.9.13\jackson-mapper-asl-1.9.13.jar;C:\Users\xinfang\.m2\repository\org\apache\avro\avro\1.7.4\avro-1.7.4.jar;C:\Users\xinfang\.m2\repository\com\google\protobuf\protobuf-java\2.5.0\protobuf-java-2.5.0.jar;C:\Users\xinfang\.m2\repository\com\google\code\gson\gson\2.2.4\gson-2.2.4.jar;C:\Users\xinfang\.m2\repository\org\apache\hadoop\hadoop-auth\2.7.3\hadoop-auth-2.7.3.jar;C:\Users\xinfang\.m2\repository\org\apache\httpcomponents\httpclient\4.2.5\httpclient-4.2.5.jar;C:\Users\xinfang\.m2\repository\org\apache\httpcomponents\httpcore\4.2.4\httpcore-4.2.4.jar;C:\Users\xinfang\.m2\repository\org\apache\directory\server\apacheds-kerberos-codec\2.0.0-M15\apacheds-kerberos-codec-2.0.0-M15.jar;C:\Users\xinfang\.m2\repository\org\apache\directory\server\apacheds-i18n\2.0.0-M15\apacheds-i18n-2.0.0-M15.jar;C:\Users\xinfang\.m2\repository\org\apache\directory\api\api-asn1-api\1.0.0-M20\api-asn1-api-1.0.0-M20.jar;C:\Users\xinfang\.m2\repository\org\apache\directory\api\api-util\1.0.0-M20\api-util-1.0.0-M20.jar;C:\Users\xinfang\.m2\repository\com\jcraft\jsch\0.1.42\jsch-0.1.42.jar;C:\Users\xinfang\.m2\repository\org\apache\curator\curator-client\2.7.1\curator-client-2.7.1.jar;C:\Users\xinfang\.m2\repository\org\apache\htrace\htrace-core\3.1.0-incubating\htrace-core-3.1.0-incubating.jar;C:\Users\xinfang\.m2\repository\org\apache\zookeeper\zookeeper\3.4.6\zookeeper-3.4.6.jar;C:\Users\xinfang\.m2\repository\org\apache\commons\commons-compress\1.4.1\commons-compress-1.4.1.jar;C:\Users\xinfang\.m2\repository\org\tukaani\xz\1.0\xz-1.0.jar;C:\Users\xinfang\.m2\repository\org\apache\hadoop\hadoop-hdfs\2.7.3\hadoop-hdfs-2.7.3.jar;C:\Users\xinfang\.m2\repository\commons-daemon\commons-daemon\1.0.13\commons-daemon-1.0.13.jar;C:\Users\xinfang\.m2\repository\io\netty\netty\3.6.2.Final\netty-3.6.2.Final.jar;C:\Users\xinfang\.m2\repository\xerces\xercesImpl\2.9.1\xercesImpl-2.9.1.jar;C:\Users\xinfang\.m2\repository\xml-apis\xml-apis\1.3.04\xml-apis-1.3.04.jar;C:\Users\xinfang\.m2\repository\org\fusesource\leveldbjni\leveldbjni-all\1.8\leveldbjni-all-1.8.jar" sparkPi Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties 17/11/07 15:34:22 INFO SparkContext: Running Spark version 1.6.3 17/11/07 15:34:24 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable 17/11/07 15:34:24 INFO SecurityManager: Changing view acls to: xinfang 17/11/07 15:34:24 INFO SecurityManager: Changing modify acls to: xinfang 17/11/07 15:34:24 INFO SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users with view permissions: Set(xinfang); users with modify permissions: Set(xinfang) 17/11/07 15:34:26 INFO Utils: Successfully started service 'sparkDriver' on port 53931. 17/11/07 15:34:26 INFO Slf4jLogger: Slf4jLogger started 17/11/07 15:34:26 INFO Remoting: Starting remoting 17/11/07 15:34:27 INFO Remoting: Remoting started; listening on addresses :[akka.tcp://sparkDriverActorSystem@172.20.107.151:53944] 17/11/07 15:34:27 INFO Utils: Successfully started service 'sparkDriverActorSystem' on port 53944. 17/11/07 15:34:27 INFO SparkEnv: Registering MapOutputTracker 17/11/07 15:34:27 INFO SparkEnv: Registering BlockManagerMaster 17/11/07 15:34:27 INFO DiskBlockManager: Created local directory at C:\Users\xinfang\AppData\Local\Temp\blockmgr-d4ba2426-7f9b-47f1-800e-2aad1aa75f70 17/11/07 15:34:27 INFO MemoryStore: MemoryStore started with capacity 1122.0 MB 17/11/07 15:34:27 INFO SparkEnv: Registering OutputCommitCoordinator 17/11/07 15:34:27 INFO Utils: Successfully started service 'SparkUI' on port 4040. 17/11/07 15:34:27 INFO SparkUI: Started SparkUI at http://172.20.107.151:4040 17/11/07 15:34:27 INFO HttpFileServer: HTTP File server directory is C:\Users\xinfang\AppData\Local\Temp\spark-150f8c4e-c5df-4254-a9f0-a8158b4caff0\httpd-b0aaa30f-cd92-4fbd-b064-6284fa604359 17/11/07 15:34:27 INFO HttpServer: Starting HTTP Server 17/11/07 15:34:28 INFO Utils: Successfully started service 'HTTP file server' on port 53947. 17/11/07 15:34:28 INFO SparkContext: Added JAR D:\workspace\scala\out\scala.jar at http://172.20.107.151:53947/jars/scala.jar with timestamp 1510040068032 17/11/07 15:34:28 INFO AppClient$ClientEndpoint: Connecting to master spark://192.168.66.66:7077... 17/11/07 15:34:30 INFO SparkDeploySchedulerBackend: Connected to Spark cluster with app ID app-20171107153300-0010 17/11/07 15:34:30 INFO AppClient$ClientEndpoint: Executor added: app-20171107153300-0010/0 on worker-20171106165832-192.168.66.66-7078 (192.168.66.66:7078) with 2 cores 17/11/07 15:34:30 INFO SparkDeploySchedulerBackend: Granted executor ID app-20171107153300-0010/0 on hostPort 192.168.66.66:7078 with 2 cores, 1024.0 MB RAM 17/11/07 15:34:30 INFO AppClient$ClientEndpoint: Executor updated: app-20171107153300-0010/0 is now RUNNING 17/11/07 15:34:30 INFO Utils: Successfully started service 'org.apache.spark.network.netty.NettyBlockTransferService' on port 53967. 17/11/07 15:34:30 INFO NettyBlockTransferService: Server created on 53967 17/11/07 15:34:30 INFO BlockManagerMaster: Trying to register BlockManager 17/11/07 15:34:30 INFO BlockManagerMasterEndpoint: Registering block manager 172.20.107.151:53967 with 1122.0 MB RAM, BlockManagerId(driver, 172.20.107.151, 53967) 17/11/07 15:34:30 INFO BlockManagerMaster: Registered BlockManager 17/11/07 15:34:31 INFO SparkDeploySchedulerBackend: SchedulerBackend is ready for scheduling beginning after reached minRegisteredResourcesRatio: 0.0 Time:1510040062460 17/11/07 15:34:31 INFO SparkContext: Starting job: reduce at sparkPi.scala:17 17/11/07 15:34:31 INFO DAGScheduler: Got job 0 (reduce at sparkPi.scala:17) with 2 output partitions 17/11/07 15:34:31 INFO DAGScheduler: Final stage: ResultStage 0 (reduce at sparkPi.scala:17) 17/11/07 15:34:31 INFO DAGScheduler: Parents of final stage: List() 17/11/07 15:34:31 INFO DAGScheduler: Missing parents: List() 17/11/07 15:34:32 INFO DAGScheduler: Submitting ResultStage 0 (MapPartitionsRDD[1] at map at sparkPi.scala:13), which has no missing parents 17/11/07 15:34:32 INFO MemoryStore: Block broadcast_0 stored as values in memory (estimated size 1848.0 B, free 1122.0 MB) 17/11/07 15:34:32 INFO MemoryStore: Block broadcast_0_piece0 stored as bytes in memory (estimated size 1206.0 B, free 1122.0 MB) 17/11/07 15:34:32 INFO BlockManagerInfo: Added broadcast_0_piece0 in memory on 172.20.107.151:53967 (size: 1206.0 B, free: 1122.0 MB) 17/11/07 15:34:32 INFO SparkContext: Created broadcast 0 from broadcast at DAGScheduler.scala:1006 17/11/07 15:34:32 INFO DAGScheduler: Submitting 2 missing tasks from ResultStage 0 (MapPartitionsRDD[1] at map at sparkPi.scala:13) 17/11/07 15:34:32 INFO TaskSchedulerImpl: Adding task set 0.0 with 2 tasks 17/11/07 15:34:36 INFO SparkDeploySchedulerBackend: Registered executor NettyRpcEndpointRef(null) (xinfang:53977) with ID 0 17/11/07 15:34:36 INFO TaskSetManager: Starting task 0.0 in stage 0.0 (TID 0, xinfang, partition 0,PROCESS_LOCAL, 2130 bytes) 17/11/07 15:34:36 INFO TaskSetManager: Starting task 1.0 in stage 0.0 (TID 1, xinfang, partition 1,PROCESS_LOCAL, 2130 bytes) 17/11/07 15:34:37 INFO BlockManagerMasterEndpoint: Registering block manager xinfang:64456 with 511.1 MB RAM, BlockManagerId(0, xinfang, 64456) 17/11/07 15:34:42 INFO BlockManagerInfo: Added broadcast_0_piece0 in memory on xinfang:64456 (size: 1206.0 B, free: 511.1 MB) 17/11/07 15:34:44 INFO TaskSetManager: Finished task 1.0 in stage 0.0 (TID 1) in 7589 ms on xinfang (1/2) 17/11/07 15:34:44 INFO TaskSetManager: Finished task 0.0 in stage 0.0 (TID 0) in 7654 ms on xinfang (2/2) 17/11/07 15:34:44 INFO TaskSchedulerImpl: Removed TaskSet 0.0, whose tasks have all completed, from pool 17/11/07 15:34:44 INFO DAGScheduler: ResultStage 0 (reduce at sparkPi.scala:17) finished in 11.849 s 17/11/07 15:34:44 INFO DAGScheduler: Job 0 finished: reduce at sparkPi.scala:17, took 12.553598 s Pi is roughly 3.15 17/11/07 15:34:44 INFO SparkUI: Stopped Spark web UI at http://172.20.107.151:4040 17/11/07 15:34:44 INFO SparkDeploySchedulerBackend: Shutting down all executors 17/11/07 15:34:44 INFO SparkDeploySchedulerBackend: Asking each executor to shut down 17/11/07 15:34:44 INFO MapOutputTrackerMasterEndpoint: MapOutputTrackerMasterEndpoint stopped! 17/11/07 15:34:44 INFO MemoryStore: MemoryStore cleared 17/11/07 15:34:44 INFO BlockManager: BlockManager stopped 17/11/07 15:34:44 INFO BlockManagerMaster: BlockManagerMaster stopped 17/11/07 15:34:44 INFO OutputCommitCoordinator$OutputCommitCoordinatorEndpoint: OutputCommitCoordinator stopped! 17/11/07 15:34:44 INFO SparkContext: Successfully stopped SparkContext 17/11/07 15:34:44 INFO RemoteActorRefProvider$RemotingTerminator: Shutting down remote daemon. 17/11/07 15:34:44 INFO RemoteActorRefProvider$RemotingTerminator: Remote daemon shut down; proceeding with flushing remote transports. 17/11/07 15:34:44 INFO ShutdownHookManager: Shutdown hook called 17/11/07 15:34:44 INFO ShutdownHookManager: Deleting directory C:\Users\xinfang\AppData\Local\Temp\spark-150f8c4e-c5df-4254-a9f0-a8158b4caff0\httpd-b0aaa30f-cd92-4fbd-b064-6284fa604359 17/11/07 15:34:44 INFO ShutdownHookManager: Deleting directory C:\Users\xinfang\AppData\Local\Temp\spark-150f8c4e-c5df-4254-a9f0-a8158b4caff0 Process finished with exit code 0
