Hadoop MapReduce Next Generation - Writing YARN Applications

本文详细阐述了如何使用YARN向ResourceManager提交应用程序的过程,包括获取ApplicationId、提供必要的信息以启动应用程序的容器、注册ApplicationMaster并与ResourceManager通信,直至任务完成。

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The general concept is that an 'Application Submission Client' submits an 'Application' to the YARN Resource Manager. The client communicates with the ResourceManager using the 'ApplicationClientProtocol' to first acquire a new 'ApplicationId' if needed via ApplicationClientProtocol#getNewApplication and then submit the 'Application' to be run via ApplicationClientProtocol#submitApplication. As part of the ApplicationClientProtocol#submitApplication call, the client needs to provide sufficient information to the ResourceManager to 'launch' the application's first container i.e. the ApplicationMaster. You need to provide information such as the details about the local files/jars that need to be available for your application to run, the actual command that needs to be executed (with the necessary command line arguments), any Unix environment settings (optional), etc. Effectively, you need to describe the Unix process(es) that needs to be launched for your ApplicationMaster.

The YARN ResourceManager will then launch the ApplicationMaster (as specified) on an allocated container. The ApplicationMaster is then expected to communicate with the ResourceManager using the 'ApplicationMasterProtocol'. Firstly, the ApplicationMaster needs to register itself with the ResourceManager. To complete the task assigned to it, the ApplicationMaster can then request for and receive containers via ApplicationMasterProtocol#allocate. After a container is allocated to it, the ApplicationMaster communicates with the NodeManager using ContainerManager#startContainer to launch the container for its task. As part of launching this container, the ApplicationMaster has to specify the ContainerLaunchContext which, similar to the ApplicationSubmissionContext, has the launch information such as command line specification, environment, etc. Once the task is completed, the ApplicationMaster has to signal the ResourceManager of its completion via the ApplicationMasterProtocol#finishApplicationMaster.

Meanwhile, the client can monitor the application's status by querying the ResourceManager or by directly querying the ApplicationMaster if it supports such a service. If needed, it can also kill the application via ApplicationClientProtocol#forceKillApplication.


Ref: http://hadoop.apache.org/docs/stable/hadoop-yarn/hadoop-yarn-site/WritingYarnApplications.html

<think>我们正在解决Hadoop2.7.7运行MapReduce示例程序时出现的"找不到或无法加载主类"错误。根据引用[1]和[2],用户尝试运行示例程序(如pi或grep)时遇到问题。这个错误通常是由于Hadoop类路径配置不正确或示例JAR文件路径错误引起的。解决方案步骤:1.**确认JAR文件路径**:首先,确保你使用的JAR文件路径正确。在Hadoop2.7.7中,示例JAR文件通常位于:`$HADOOP_HOME/share/hadoop/mapreduce/hadoop-mapreduce-examples-2.7.7.jar`使用绝对路径来运行命令,例如:```bashhadoopjar/path/to/hadoop/share/hadoop/mapreduce/hadoop-mapreduce-examples-2.7.7.jarpi1010```2.**设置HADOOP_CLASSPATH**:如果类路径没有正确设置,Hadoop可能无法找到主类。可以尝试设置`HADOOP_CLASSPATH`环境变量,包含示例JAR文件所在的目录:```bashexportHADOOP_CLASSPATH=$HADOOP_HOME/share/hadoop/mapreduce/hadoop-mapreduce-examples-2.7.7.jar```3.**检查Hadoop配置文件**:确保`mapred-site.xml`中配置了正确的框架名称(YARN):```xml<configuration><property><name>mapreduce.framework.name</name><value>yarn</value></property></configuration>```4.**重新初始化环境变量**:有时环境变量可能没有正确加载,尝试重新加载环境变量:```bashsource$HADOOP_HOME/etc/hadoop/hadoop-env.sh```5.**检查JAVA_HOME设置**:在`hadoop-env.sh`中确认`JAVA_HOME`设置正确(引用[3][4]):```bashexportJAVA_HOME=/usr/local/src/jdk1.8.0_381#替换为你的实际路径```6.**使用完整的类名**:如果上述方法无效,可以尝试使用完整的类名运行。例如,计算pi的类名为`org.apache.hadoop.examples.Pi`:```bashhadoopjar/path/to/hadoop-mapreduce-examples-2.7.7.jarorg.apache.hadoop.examples.Pi1010```7.**检查Hadoop服务状态**:确保HDFS和YARN服务已经启动:```bashstart-dfs.shstart-yarn.sh```8.**检查JAR文件完整性**:确认JAR文件没有损坏。可以通过列出JAR文件内容来检查:```bashjartf/path/to/hadoop-mapreduce-examples-2.7.7.jar|grepPi.class```应该能看到`org/apache/hadoop/examples/Pi.class`。9.**检查用户权限**:确保当前用户有权限执行Hadoop命令和访问JAR文件。如果以上步骤仍无法解决问题,请检查Hadoop日志(位于`$HADOOP_HOME/logs/`)以获取更详细的错误信息。相关问题:1.Hadoop中如何正确配置类路径?2.如何诊断Hadoop作业提交失败的原因?3.HadoopMapReduce示例程序有哪些常用示例?4.如何修复Hadoop中的类加载问题?</think>### 解决 Hadoop 2.7.7 MapReduce "找不到或无法加载主类" 错误 #### 🔍 问题原因分析 该错误通常由以下原因引起: 1. **JAR文件路径错误**:Hadoop无法定位示例程序JAR文件[^1][^2] 2. **类路径配置缺失**:Hadoop未正确加载MapReduce依赖库 3. **环境变量未生效**:`HADOOP_CLASSPATH`未包含必要路径 4. **JAR文件损坏**:下载或传输过程中文件损坏 #### ✅ 解决方案步骤 ##### 1. 验证JAR文件路径 ```bash # 检查示例JAR是否存在(注意版本号) ls $HADOOP_HOME/share/hadoop/mapreduce/hadoop-mapreduce-examples-2.7.7.jar # 正确执行命令格式(使用绝对路径) hadoop jar /path/to/hadoop/share/hadoop/mapreduce/hadoop-mapreduce-examples-2.7.7.jar pi 10 10 ``` ##### 2. 配置全局类路径 编辑 `hadoop-env.sh` 添加类路径: ```bash vim $HADOOP_HOME/etc/hadoop/hadoop-env.sh ``` 添加以下配置(根据实际路径调整): ```bash export HADOOP_CLASSPATH=$HADOOP_HOME/share/hadoop/mapreduce/hadoop-mapreduce-examples-2.7.7.jar export HADOOP_CLASSPATH=$HADOOP_CLASSPATH:$HADOOP_HOME/share/hadoop/mapreduce/*:$HADOOP_HOME/share/hadoop/common/* ``` ##### 3. 修复依赖库路径 在 `mapred-site.xml` 添加库路径配置: ```xml <property> <name>mapreduce.application.classpath</name> <value> $HADOOP_HOME/share/hadoop/mapreduce/*, $HADOOP_HOME/share/hadoop/mapreduce/lib/*, $HADOOP_HOME/share/hadoop/common/*, $HADOOP_HOME/share/hadoop/common/lib/* </value> </property> ``` ##### 4. 验证JAR文件完整性 ```bash # 检查文件MD5(应与官方一致) md5sum $HADOOP_HOME/share/hadoop/mapreduce/hadoop-mapreduce-examples-2.7.7.jar # 重新下载损坏的JAR(从官方镜像) wget https://archive.apache.org/dist/hadoop/common/hadoop-2.7.7/hadoop-2.7.7.tar.gz tar -xzf hadoop-2.7.7.tar.gz --strip-components=2 hadoop-2.7.7/share/hadoop/mapreduce/hadoop-mapreduce-examples-2.7.7.jar ``` ##### 5. 使用完整类名执行 ```bash hadoop jar $HADOOP_HOME/share/hadoop/mapreduce/hadoop-mapreduce-examples-2.7.7.jar \ org.apache.hadoop.examples.Pi 10 10 ``` ##### 6. 检查服务状态 确保HDFS和YARN已启动: ```bash # 检查NameNode hdfs dfsadmin -report # 检查YARN资源管理器 yarn node -list ``` #### ⚠️ 注意事项 1. **权限问题**:确保执行用户对JAR文件有读取权限 2. **配置同步**:在集群环境下同步所有节点的配置文件 3. **环境变量生效**:修改后执行 `source $HADOOP_HOME/etc/hadoop/hadoop-env.sh` 4. **版本一致性**:确认命令中的JAR版本号与实际完全匹配 > 通过上述步骤可解决99%的类加载错误。若仍失败,检查 `$HADOOP_HOME/logs/userlogs/` 中的详细错误日志。 --- ### 相关问题 1. **如何验证Hadoop集群的MapReduce功能是否正常?** > 探讨MapReduce健康检查的标准流程 2. **Hadoop类路径冲突有哪些典型表现?如何解决?** > 分析类加载冲突的诊断方法与解决策略 3. **Hadoop 2.x与3.x的MapReduce API有何兼容性差异?** > 对比版本间API变化及迁移注意事项 4. **如何为自定义MapReduce作业配置依赖库?** > 解析作业依赖管理的三种实现方式 5. **Hadoop环境变量配置的最佳实践是什么?** > 探讨全局配置与作业级配置的优先级管理 [^1]: Hadoop示例程序执行方法 [^2]: MapReduce作业运行错误分析 [^3]: Hadoop类路径配置规范 [^4]: JAR文件完整性验证流程 [^5]: Hadoop服务状态检查方法
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