YARN MapReduce任务流程
在大数据领域,MapReduce是一种常用的分布式计算模型,用于处理大规模数据集。YARN(Yet Another Resource Negotiator)是Apache Hadoop的资源管理系统,用于调度和分配集群资源。本文将介绍YARN中MapReduce任务的流程,并提供相关的代码示例。
1. YARN架构
在理解YARN MapReduce任务流程之前,先来了解一下YARN的架构。YARN主要由两个核心组件组成:
-
ResourceManager(RM):负责集群资源的管理和分配。它接收客户端的任务请求,并将任务分配给可用的NodeManager。
-
NodeManager(NM):负责单个节点上的资源管理。它接收来自ResourceManager的任务分配,并负责启动和监控任务的执行。
MapReduce任务是在YARN环境中执行的,YARN提供了高度可伸缩性和容错性的计算框架。
2. MapReduce任务流程
MapReduce任务包括两个主要的阶段:Map阶段和Reduce阶段。下面是整个MapReduce任务的流程图:
sequenceDiagram
participant Client
participant ResourceManager
participant NodeManager
participant ApplicationMaster
participant MapTask
participant ReduceTask
Client->>ResourceManager: 提交MapReduce任务
ResourceManager-->>Client: 返回Application ID
ResourceManager->>NodeManager: 分配Container
NodeManager-->>ResourceManager: 确认Container分配成功
ResourceManager->>ApplicationMaster: 启动MapReduce任务
ApplicationMaster->>NodeManager: 启动MapTask
NodeManager-->>ApplicationMaster: 返回MapTask状态
ApplicationMaster->>NodeManager: 启动ReduceTask
NodeManager-->>ApplicationMaster: 返回ReduceTask状态
ApplicationMaster-->>Client: 返回任务执行结果
下面将详细介绍每个阶段的流程和代码示例。
2.1 提交任务
首先,客户端通过YarnClient API提交MapReduce任务。以下是一个示例代码:
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.yarn.client.api.YarnClient;
import org.apache.hadoop.yarn.client.api.YarnClientApplication;
import org.apache.hadoop.yarn.conf.YarnConfiguration;
public class MapReduceClient {
public static void main(String[] args) throws Exception {
Configuration conf = new YarnConfiguration();
YarnClient yarnClient = YarnClient.createYarnClient();
yarnClient.init(conf);
yarnClient.start();
YarnClientApplication app = yarnClient.createApplication();
Job job = Job.getInstance(conf);
job.setJarByClass(MapReduceClient.class);
job.setJobName("MyMapReduceJob");
// 设置任务的输入和输出路径等信息
job.setInputPath(new Path("input"));
job.setOutputPath(new Path("output"));
// 提交任务到ResourceManager
app.submitApplication();
}
}
2.2 任务调度和分配资源
ResourceManager接收到任务请求后,会根据集群中的可用资源决定分配给该任务的Container。以下是一个示例代码:
import org.apache.hadoop.yarn.api.records.ApplicationId;
import org.apache.hadoop.yarn.api.records.ContainerLaunchContext;
import org.apache.hadoop.yarn.api.records.Resource;
import org.apache.hadoop.yarn.api.records.YarnApplicationState;
import org.apache.hadoop.yarn.client.api.YarnClient;
import org.apache.hadoop.yarn.client.api.YarnClientApplication;
import org.apache.hadoop.yarn.conf.YarnConfiguration;
import org.apache.hadoop.yarn.util.Records;
public class ResourceManagerClient {
public static void main(String[] args) throws Exception {
Configuration conf = new YarnConfiguration();
YarnClient yarnClient = YarnClient.createYarnClient();
yarnClient.init(conf);
yarnClient.start();
YarnClientApplication app = yarnClient.createApplication();
ApplicationId appId = app.getNewApplicationResponse().getApplicationId();
// 设置任务所需资源
Resource capability = Records.newRecord(Resource.class);
capability.setMemory(1024);
capability.setVirtualCores(1);
// 创建ContainerLaunchContext
ContainerLaunchContext amContainer = Records.newRecord(ContainerLaunchContext.class);
amContainer.setApplicationId(appId);
amContainer.setResource(capability);
// 提交任务请求
yarnClient.submitApplication(appId, amContainer);
// 等待任务完成
while (true) {
YarnApplicationState state = yarnClient.getApplicationReport(appId).getYarnApplicationState();
if (state == Yarn