hadoop系列四:mapreduce的使用(二)

转载请在页首明显处注明作者与出处

 

 

一:说明

此为大数据系列的一些博文,有空的话会陆续更新,包含大数据的一些内容,如hadoop,spark,storm,机器学习等。

当前使用的hadoop版本为2.6.4

 

此为mapreducer的第二章节

这一章节中有着 计算共同好友,推荐可能认识的人

 

上一篇:hadoop系列三:mapreduce的使用(一)

 

 

一:说明
二:在开发工具在运行mapreducer
2.1:本地模式运行mapreducer
2.2:在开发工具中运行在yarn中
三:mapreduce实现join
3.1:sql数据库中的示例
3.2:mapreduce的实现思路
3.3:创建相应的javabean
3.4:创建mapper
3.5:创建reduce
3.6:完整代码
3.7:数据倾斜的问题
四:查找共同好友,计算可能认识的人
4.1:准备数据
4.2:计算指定用户是哪些人的好友
4.3:计算共同好友
五:使用GroupingComparator分组计算最大值
5.1:定义一个javabean
5.2:定义一个GroupingComparator
5.3:map代码
5.4:reduce的代码
5.5:启动类
六:自定义输出位置
6.1:自定义FileOutputFormat
七:自定义输入数据
八:全局计数器
九:多个job串联,定义执行顺序
十:mapreduce的参数优化
10.1:资源相关参数
10.2:容错相关参数
10.3:本地运行mapreduce作业
10.4:效率和稳定性相关参数

 

 

 

二:在开发工具在运行mapreducer

之前我们一直是在开发工具中写好了代码,然后打包成jar包在服务器中以hadoop jar的形式运行,当然这个极其麻烦,毕竟上传这个部署还是很麻烦的,其次就是每改一次代码,都需要重新打包到服务器中。还有一个最大的缺点就是没有办法打断点调试一些业务代码,这对于定位代码问题极其困难。这里也有两个办法。

 

2.1:本地模式运行mapreducer

何为本地模式,就是不是运行在yarn上面,仅仅是以运行在本地的一个模式。

首先既然是运行在本地,就需要有所有mapreducer的class文件,先在hadoop官网中下载hadoop的代码,然后编译成相应的操作系统版本,以笔者在windows中开发的环境,肯定是编译windows版本的,然后设置相应的环境变量

 

HADOOP_HOME=E:\software\hadoop-2.6.2

 

然后增加path

%HADOOP_HOME%\bin

然后看一下main方法,其实代码什么都不用改,conf的配置全部可以不写,直接运行就是本地模式,至于为什么在服务器根据hadoop jar运行时,会运行到jar中,因为hadoop jar命令加载了配置文件。

 

        Configuration conf = new Configuration();
        //这个默认值就是local,其实可以不写
        conf.set("mapreduce.framework.name", "local");
        //本地模式运行mr程序时,输入输出可以在本地,也可以在hdfs中,具体需要看如下的两行参数
        //这个默认值 就是本地,其实可以不配
        //conf.set("fs.defaultFS","file:///");
        //conf.set("fs.defaultFS","hdfs://server1:9000/");



        Job job = Job.getInstance(conf);

 

那实际上,需要使用本地模式的时候,这里面的配置可以什么都不写,因为默认的参数就是本地模式,所以这个时候直接运行就行了,当然,在后面我们接收了两个参数,分别是数据的的来源和存储位置,所以我们运行的时候的时候,直接提交参数就行了,以idea为例

 

 像在这里就传了两个参数,地址就在D盘中。

 

当然,其实也是支持挂在hdfs中的,如下配置

 

        Configuration conf = new Configuration();
        //这个默认值就是local,其实可以不写
        conf.set("mapreduce.framework.name", "local");
        //本地模式运行mr程序时,输入输出可以在本地,也可以在hdfs中,具体需要看如下的两行参数
        //其实是可以本地模式也可以使用hdfs中的数据的
        //conf.set("fs.defaultFS","file:///");
        conf.set("fs.defaultFS","hdfs://server1:9000/");

也就是说,即使是本地模式,不仅仅可以使用在硬盘中,也可以使用在hdfs中

 

 

其实我们还需要加上一个日志文件,不然等下出错了,也看不到错误信息,仅仅是一片空白,那就尴尬了

 

在src/main/resource中添加一个log4j.properties文件,内容如下

 

log4j.rootLogger=info, stdout, R
log4j.appender.stdout=org.apache.log4j.ConsoleAppender
log4j.appender.stdout.layout=org.apache.log4j.PatternLayout
log4j.appender.stdout.layout.ConversionPattern=%d [%t] %-5p %c - %m%n
log4j.appender.R=org.apache.log4j.RollingFileAppender
log4j.appender.R.File=example.log
log4j.appender.R.MaxFileSize=100KB
log4j.appender.R.MaxBackupIndex=1
log4j.appender.R.layout=org.apache.log4j.PatternLayout
log4j.appender.R.layout.ConversionPattern=%p %t %c - %m%n

 

打印所有的info信息

 

 

 

 

 

 

2.2:在开发工具中运行在yarn中

上一部分中,我们是运行在本地模式,但是使用开发工具,可以更好的debug,这次我们在开发工具在,运行在服务器中的yarn上面。

想要运行在yarn上面,我们可以进行如下的配置

 

        Configuration conf = new Configuration();
        //运行在yarn的集群模式
        conf.set("mapreduce.framework.name","yarn");
        conf.set("yarn.resourcemanager.hostname","server1");//这行配置,使得该main方法会寻找该机器的mr环境
        conf.set("fs.defaultFS","hdfs://server1:9000/");

 

通过之前的代码,我们知道我们要设置一个参数,使得mr环境能找到该代码的jar包,然后复制到所有的mr机器中去运行,但是我们这里要换一种方式,因为开发工具运行的时候,是直接运行class文件,而不是jar包

        Job job = Job.getInstance(conf);
        //使得hadoop可以根据类包,找到jar包在哪里,如果是在开发工具中运行,那么则是找不到的
        //job.setJarByClass(WordCountDriver.class);
        job.setJar("c:/xx.jar");

所以,如果我们要执行如下的代码,我们还需要先对程序进行打包才行。

仅仅修改完如上的一点代码,我们开始运行。

同样的,先配置启动参数,因为我们没改别的代码,mr的输入与输出都是从启动参数中读取的

 

 

 

 然后执行main方法,如果server1有配置在hosts文中的话,那么见证奇迹.....哦,见证错误吧

在这里会看到一个错误,啥,没权限,对的,而且我们看到一个Administrator的用户,这个其实是我windows系统的用户,说明mapreduce运行的时候,拿的用户是当前登陆的用户,而在服务器中,如果看过之前的文章,我们给的目录权限是hadoop用户,所以我们要设置hadoop的用户。

我们要怎么做呢?还有要怎么设置用户为hadoop呢?我们来看一段hadoop的核心代码

if (!isSecurityEnabled() && (user == null)) {
  String envUser = System.getenv(HADOOP_USER_NAME);
  if (envUser == null) {
    envUser = System.getProperty(HADOOP_USER_NAME);
  }
  user = envUser == null ? null : new User(envUser);
}

这段代码是获取用户的代码,这个时候我们就知道该怎么设置用户名了,常量名称为:HADOOP_USER_NAME

 

        System.setProperty("HADOOP_USER_NAME","hadoop");
        Configuration conf = new Configuration();
        //运行在yarn的集群模式
        conf.set("mapreduce.framework.name","yarn");
        conf.set("yarn.resourcemanager.hostname","server1");//这行配置,使得该main方法会寻找该机器的mr环境
        conf.set("fs.defaultFS","hdfs://server1:9000/");

可以看到红色区域,设置了hadoop的用户,此时,我们再运行一下代码,见证下一个错误,ps:一定要配置日志文件,不然看不到错误信息

从完整的日志中,其实是可以看到,它是运行在yarn中了,不过出错了,图中是错误信息

有点让我吃惊的这竟然是中文的日志哈,如果是英文的日志,则是这样的

 

意思差不多哈,看到这个错误,我们要怎么解决呢?

这是hadoop的一个bug,新版本中已经解决,并且这个bug只会在windwos系统中出现,也就是意味着,如果你用的是linux的图形化界面,在这里面使用开发工具运行,也是不会有问题的。

先看一下问题是怎么产生的吧。先关联源码。

我们先找到org.apache.hadoop.mapred.YARNRunner这个类,并且在492行打上注释,可能位置会不一样,不过只需要找到environment变量即可,然后查看这个变量的名称

经过debug后,进入断点,查看environment变量,把内容最长的一段复制出来到记事本中查看。

很明显,最后的代码是执行在linux中的,但是这段环境却有问题。

首先就是%HADOOP_CONF_DIR%这种环境变量,对linux熟悉的可能知道,linux的环境变量是$JAVA_HOME$的这种形式,这是一个问题。

其次就是斜杠windows与linux也是不同的。

最后,环境变量的相隔,在linux中是冒号,而在windows中是分号。

 

这下应该知道问题了,不过我们要怎么改呢?只能改源代码了,千万不要对改源代码抱有害怕的心里,如果认真想想,这种类型的代码,就算是一个刚学会java基础的人也会修改,并没有什么可怕的。当然,等会也会贴出改完后的完整代码,不想改的同学直接复制就行了。

 我们复制这样的一个类,包括代码,包名都要一样,直接建立在我们的工程中,java会优先读取本工程中的类

/**
 * Licensed to the Apache Software Foundation (ASF) under one
 * or more contributor license agreements.  See the NOTICE file
 * distributed with this work for additional information
 * regarding copyright ownership.  The ASF licenses this file
 * to you under the Apache License, Version 2.0 (the
 * "License"); you may not use this file except in compliance
 * with the License.  You may obtain a copy of the License at
 *
 *     http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

package org.apache.hadoop.mapred;

import java.io.IOException;
import java.nio.ByteBuffer;
import java.util.ArrayList;
import java.util.Collection;
import java.util.HashMap;
import java.util.HashSet;
import java.util.List;
import java.util.Map;
import java.util.Vector;

import org.apache.commons.logging.Log;
import org.apache.commons.logging.LogFactory;
import org.apache.hadoop.classification.InterfaceAudience.Private;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileContext;
import org.apache.hadoop.fs.FileStatus;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.fs.UnsupportedFileSystemException;
import org.apache.hadoop.io.DataOutputBuffer;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.ipc.ProtocolSignature;
import org.apache.hadoop.mapreduce.Cluster.JobTrackerStatus;
import org.apache.hadoop.mapreduce.ClusterMetrics;
import org.apache.hadoop.mapreduce.Counters;
import org.apache.hadoop.mapreduce.JobContext;
import org.apache.hadoop.mapreduce.JobID;
import org.apache.hadoop.mapreduce.JobStatus;
import org.apache.hadoop.mapreduce.MRJobConfig;
import org.apache.hadoop.mapreduce.QueueAclsInfo;
import org.apache.hadoop.mapreduce.QueueInfo;
import org.apache.hadoop.mapreduce.TaskAttemptID;
import org.apache.hadoop.mapreduce.TaskCompletionEvent;
import org.apache.hadoop.mapreduce.TaskReport;
import org.apache.hadoop.mapreduce.TaskTrackerInfo;
import org.apache.hadoop.mapreduce.TaskType;
import org.apache.hadoop.mapreduce.TypeConverter;
import org.apache.hadoop.mapreduce.protocol.ClientProtocol;
import org.apache.hadoop.mapreduce.security.token.delegation.DelegationTokenIdentifier;
import org.apache.hadoop.mapreduce.v2.LogParams;
import org.apache.hadoop.mapreduce.v2.api.MRClientProtocol;
import org.apache.hadoop.mapreduce.v2.api.protocolrecords.GetDelegationTokenRequest;
import org.apache.hadoop.mapreduce.v2.jobhistory.JobHistoryUtils;
import org.apache.hadoop.mapreduce.v2.util.MRApps;
import org.apache.hadoop.security.Credentials;
import org.apache.hadoop.security.SecurityUtil;
import org.apache.hadoop.security.UserGroupInformation;
import org.apache.hadoop.security.authorize.AccessControlList;
import org.apache.hadoop.security.token.Token;
import org.apache.hadoop.yarn.api.ApplicationConstants;
import org.apache.hadoop.yarn.api.ApplicationConstants.Environment;
import org.apache.hadoop.yarn.api.records.ApplicationAccessType;
import org.apache.hadoop.yarn.api.records.ApplicationId;
import org.apache.hadoop.yarn.api.records.ApplicationReport;
import org.apache.hadoop.yarn.api.records.ApplicationSubmissionContext;
import org.apache.hadoop.yarn.api.records.ContainerLaunchContext;
import org.apache.hadoop.yarn.api.records.LocalResource;
import org.apache.hadoop.yarn.api.records.LocalResourceType;
import org.apache.hadoop.yarn.api.records.LocalResourceVisibility;
import org.apache.hadoop.yarn.api.records.ReservationId;
import org.apache.hadoop.yarn.api.records.Resource;
import org.apache.hadoop.yarn.api.records.URL;
import org.apache.hadoop.yarn.api.records.YarnApplicationState;
import org.apache.hadoop.yarn.conf.YarnConfiguration;
import org.apache.hadoop.yarn.exceptions.YarnException;
import org.apache.hadoop.yarn.factories.RecordFactory;
import org.apache.hadoop.yarn.factory.providers.RecordFactoryProvider;
import org.apache.hadoop.yarn.security.client.RMDelegationTokenSelector;
import org.apache.hadoop.yarn.util.ConverterUtils;

import com.google.common.annotations.VisibleForTesting;
import com.google.common.base.CaseFormat;

/**
 * This class enables the current JobClient (0.22 hadoop) to run on YARN.
 */
@SuppressWarnings("unchecked")
public class YARNRunner implements ClientProtocol {

    private static final Log LOG = LogFactory.getLog(YARNRunner.class);

    private final RecordFactory recordFactory = RecordFactoryProvider.getRecordFactory(null);
    private ResourceMgrDelegate resMgrDelegate;
    private ClientCache clientCache;
    private Configuration conf;
    private final FileContext defaultFileContext;

    /**
     * Yarn runner incapsulates the client interface of yarn
     * 
     * @param conf
     *            the configuration object for the client
     */
    public YARNRunner(Configuration conf) {
        this(conf, new ResourceMgrDelegate(new YarnConfiguration(conf)));
    }

    /**
     * Similar to {@link #YARNRunner(Configuration)} but allowing injecting
     * {@link ResourceMgrDelegate}. Enables mocking and testing.
     * 
     * @param conf
     *            the configuration object for the client
     * @param resMgrDelegate
     *            the resourcemanager client handle.
     */
    public YARNRunner(Configuration conf, ResourceMgrDelegate resMgrDelegate) {
        this(conf, resMgrDelegate, new ClientCache(conf, resMgrDelegate));
    }

    /**
     * Similar to
     * {@link YARNRunner#YARNRunner(Configuration, ResourceMgrDelegate)} but
     * allowing injecting {@link ClientCache}. Enable mocking and testing.
     * 
     * @param conf
     *            the configuration object
     * @param resMgrDelegate
     *            the resource manager delegate
     * @param clientCache
     *            the client cache object.
     */
    public YARNRunner(Configuration conf, ResourceMgrDelegate resMgrDelegate, ClientCache clientCache) {
        this.conf = conf;
        try {
            this.resMgrDelegate = resMgrDelegate;
            this.clientCache = clientCache;
            this.defaultFileContext = FileContext.getFileContext(this.conf);
        } catch (UnsupportedFileSystemException ufe) {
            throw new RuntimeException("Error in instantiating YarnClient", ufe);
        }
    }

    @Private
    /**
     * Used for testing mostly.
     * @param resMgrDelegate the resource manager delegate to set to.
     */
    public void setResourceMgrDelegate(ResourceMgrDelegate resMgrDelegate) {
        this.resMgrDelegate = resMgrDelegate;
    }

    @Override
    public void cancelDelegationToken(Token<DelegationTokenIdentifier> arg0) throws IOException, InterruptedException {
        throw new UnsupportedOperationException("Use Token.renew instead");
    }

    @Override
    public TaskTrackerInfo[] getActiveTrackers() throws IOException, InterruptedException {
        return resMgrDelegate.getActiveTrackers();
    }

    @Override
    public JobStatus[] getAllJobs() throws IOException, InterruptedException {
        return resMgrDelegate.getAllJobs();
    }

    @Override
    public TaskTrackerInfo[] getBlacklistedTrackers() throws IOException, InterruptedException {
        return resMgrDelegate.getBlacklistedTrackers();
    }

    @Override
    public ClusterMetrics getClusterMetrics() throws IOException, InterruptedException {
        return resMgrDelegate.getClusterMetrics();
    }

    @VisibleForTesting
    void addHistoryToken(Credentials ts) throws IOException, InterruptedException {
        /* check if we have a hsproxy, if not, no need */
        MRClientProtocol hsProxy = clientCache.getInitializedHSProxy();
        if (UserGroupInformation.isSecurityEnabled() && (hsProxy != null)) {
            /*
             * note that get delegation token was called. Again this is hack for
             * oozie to make sure we add history server delegation tokens to the
             * credentials
             */
            RMDelegationTokenSelector tokenSelector = new RMDelegationTokenSelector();
            Text service = resMgrDelegate.getRMDelegationTokenService();
            if (tokenSelector.selectToken(service, ts.getAllTokens()) != null) {
                Text hsService = SecurityUtil.buildTokenService(hsProxy.getConnectAddress());
                if (ts.getToken(hsService) == null) {
                    ts.addToken(hsService, getDelegationTokenFromHS(hsProxy));
                }
            }
        }
    }

    @VisibleForTesting
    Token<?> getDelegationTokenFromHS(MRClientProtocol hsProxy) throws IOException, InterruptedException {
        GetDelegationTokenRequest request = recordFactory.newRecordInstance(GetDelegationTokenRequest.class);
        request.setRenewer(Master.getMasterPrincipal(conf));
        org.apache.hadoop.yarn.api.records.Token mrDelegationToken;
        mrDelegationToken = hsProxy.getDelegationToken(request).getDelegationToken();
        return ConverterUtils.convertFromYarn(mrDelegationToken, hsProxy.getConnectAddress());
    }

    @Override
    public Token<DelegationTokenIdentifier> getDelegationToken(Text renewer) throws IOException, InterruptedException {
        // The token is only used for serialization. So the type information
        // mismatch should be fine.
        return resMgrDelegate.getDelegationToken(renewer);
    }

    @Override
    public String getFilesystemName() throws IOException, InterruptedException {
        return resMgrDelegate.getFilesystemName();
    }

    @Override
    public JobID getNewJobID() throws IOException, InterruptedException {
        return resMgrDelegate.getNewJobID();
    }

    @Override
    public QueueInfo getQueue(String queueName) throws IOException, InterruptedException {
        return resMgrDelegate.getQueue(queueName);
    }

    @Override
    public QueueAclsInfo[] getQueueAclsForCurrentUser() throws IOException, InterruptedException {
        return resMgrDelegate.getQueueAclsForCurrentUser();
    }

    @Override
    public QueueInfo[] getQueues() throws IOException, InterruptedException {
        return resMgrDelegate.getQueues();
    }

    @Override
    public QueueInfo[] getRootQueues() throws IOException, InterruptedException {
        return resMgrDelegate.getRootQueues();
    }

    @Override
    public QueueInfo[] getChildQueues(String parent) throws IOException, InterruptedException {
        return resMgrDelegate.getChildQueues(parent);
    }

    @Override
    public String getStagingAreaDir() throws IOException, InterruptedException {
        return resMgrDelegate.getStagingAreaDir();
    }

    @Override
    public String getSystemDir() throws IOException, InterruptedException {
        return resMgrDelegate.getSystemDir();
    }

    @Override
    public long getTaskTrackerExpiryInterval() throws IOException, InterruptedException {
        return resMgrDelegate.getTaskTrackerExpiryInterval();
    }

    @Override
    public JobStatus submitJob(JobID jobId, String jobSubmitDir, Credentials ts) throws IOException, InterruptedException {

        addHistoryToken(ts);

        // Construct necessary information to start the MR AM
        ApplicationSubmissionContext appContext = createApplicationSubmissionContext(conf, jobSubmitDir, ts);

        // Submit to ResourceManager
        try {
            ApplicationId applicationId = resMgrDelegate.submitApplication(appContext);

            ApplicationReport appMaster = resMgrDelegate.getApplicationReport(applicationId);
            String diagnostics = (appMaster == null ? "application report is null" : appMaster.getDiagnostics());
            if (appMaster == null || appMaster.getYarnApplicationState() == YarnApplicationState.FAILED || appMaster.getYarnApplicationState() == YarnApplicationState.KILLED) {
                throw new IOException("Failed to run job : " + diagnostics);
            }
            return clientCache.getClient(jobId).getJobStatus(jobId);
        } catch (YarnException e) {
            throw new IOException(e);
        }
    }

    private LocalResource createApplicationResource(FileContext fs, Path p, LocalResourceType type) throws IOException {
        LocalResource rsrc = recordFactory.newRecordInstance(LocalResource.class);
        FileStatus rsrcStat = fs.getFileStatus(p);
        rsrc.setResource(ConverterUtils.getYarnUrlFromPath(fs.getDefaultFileSystem().resolvePath(rsrcStat.getPath())));
        rsrc.setSize(rsrcStat.getLen());
        rsrc.setTimestamp(rsrcStat.getModificationTime());
        rsrc.setType(type);
        rsrc.setVisibility(LocalResourceVisibility.APPLICATION);
        return rsrc;
    }

    public ApplicationSubmissionContext createApplicationSubmissionContext(Configuration jobConf, String jobSubmitDir, Credentials ts) throws IOException {
        ApplicationId applicationId = resMgrDelegate.getApplicationId();

        // Setup resource requirements
        Resource capability = recordFactory.newRecordInstance(Resource.class);
        capability.setMemory(conf.getInt(MRJobConfig.MR_AM_VMEM_MB, MRJobConfig.DEFAULT_MR_AM_VMEM_MB));
        capability.setVirtualCores(conf.getInt(MRJobConfig.MR_AM_CPU_VCORES, MRJobConfig.DEFAULT_MR_AM_CPU_VCORES));
        LOG.debug("AppMaster capability = " + capability);

        // Setup LocalResources
        Map<String, LocalResource> localResources = new HashMap<String, LocalResource>();

        Path jobConfPath = new Path(jobSubmitDir, MRJobConfig.JOB_CONF_FILE);

        URL yarnUrlForJobSubmitDir = ConverterUtils.getYarnUrlFromPath(defaultFileContext.getDefaultFileSystem().resolvePath(defaultFileContext.makeQualified(new Path(jobSubmitDir))));
        LOG.debug("Creating setup context, jobSubmitDir url is " + yarnUrlForJobSubmitDir);

        localResources.put(MRJobConfig.JOB_CONF_FILE, createApplicationResource(defaultFileContext, jobConfPath, LocalResourceType.FILE));
        if (jobConf.get(MRJobConfig.JAR) != null) {
            Path jobJarPath = new Path(jobConf.get(MRJobConfig.JAR));
            LocalResource rc = createApplicationResource(FileContext.getFileContext(jobJarPath.toUri(), jobConf), jobJarPath, LocalResourceType.PATTERN);
            String pattern = conf.getPattern(JobContext.JAR_UNPACK_PATTERN, JobConf.UNPACK_JAR_PATTERN_DEFAULT).pattern();
            rc.setPattern(pattern);
            localResources.put(MRJobConfig.JOB_JAR, rc);
        } else {
            // Job jar may be null. For e.g, for pipes, the job jar is the
            // hadoop
            // mapreduce jar itself which is already on the classpath.
            LOG.info("Job jar is not present. " + "Not adding any jar to the list of resources.");
        }

        // TODO gross hack
        for (String s : new String[] { MRJobConfig.JOB_SPLIT, MRJobConfig.JOB_SPLIT_METAINFO }) {
            localResources.put(MRJobConfig.JOB_SUBMIT_DIR + "/" + s, createApplicationResource(defaultFileContext, new Path(jobSubmitDir, s), LocalResourceType.FILE));
        }

        // Setup security tokens
        DataOutputBuffer dob = new DataOutputBuffer();
        ts.writeTokenStorageToStream(dob);
        ByteBuffer securityTokens = ByteBuffer.wrap(dob.getData(), 0, dob.getLength());

        // Setup the command to run the AM
        List<String> vargs = new ArrayList<String>(8);
        // vargs.add(MRApps.crossPlatformifyMREnv(jobConf,
        // Environment.JAVA_HOME)
        // + "/bin/java");
        // TODO   此处为修改处
        System.out.println(MRApps.crossPlatformifyMREnv(jobConf, Environment.JAVA_HOME) + "/bin/java");
        vargs.add("$JAVA_HOME/bin/java");

        // TODO: why do we use 'conf' some places and 'jobConf' others?
        long logSize = jobConf.getLong(MRJobConfig.MR_AM_LOG_KB, MRJobConfig.DEFAULT_MR_AM_LOG_KB) << 10;
        String logLevel = jobConf.get(MRJobConfig.MR_AM_LOG_LEVEL, MRJobConfig.DEFAULT_MR_AM_LOG_LEVEL);
        int numBackups = jobConf.getInt(MRJobConfig.MR_AM_LOG_BACKUPS, MRJobConfig.DEFAULT_MR_AM_LOG_BACKUPS);
        MRApps.addLog4jSystemProperties(logLevel, logSize, numBackups, vargs, conf);

        // Check for Java Lib Path usage in MAP and REDUCE configs
        warnForJavaLibPath(conf.get(MRJobConfig.MAP_JAVA_OPTS, ""), "map", MRJobConfig.MAP_JAVA_OPTS, MRJobConfig.MAP_ENV);
        warnForJavaLibPath(conf.get(MRJobConfig.MAPRED_MAP_ADMIN_JAVA_OPTS, ""), "map", MRJobConfig.MAPRED_MAP_ADMIN_JAVA_OPTS, MRJobConfig.MAPRED_ADMIN_USER_ENV);
        warnForJavaLibPath(conf.get(MRJobConfig.REDUCE_JAVA_OPTS, ""), "reduce", MRJobConfig.REDUCE_JAVA_OPTS, MRJobConfig.REDUCE_ENV);
        warnForJavaLibPath(conf.get(MRJobConfig.MAPRED_REDUCE_ADMIN_JAVA_OPTS, ""), "reduce", MRJobConfig.MAPRED_REDUCE_ADMIN_JAVA_OPTS, MRJobConfig.MAPRED_ADMIN_USER_ENV);

        // Add AM admin command opts before user command opts
        // so that it can be overridden by user
        String mrAppMasterAdminOptions = conf.get(MRJobConfig.MR_AM_ADMIN_COMMAND_OPTS, MRJobConfig.DEFAULT_MR_AM_ADMIN_COMMAND_OPTS);
        warnForJavaLibPath(mrAppMasterAdminOptions, "app master", MRJobConfig.MR_AM_ADMIN_COMMAND_OPTS, MRJobConfig.MR_AM_ADMIN_USER_ENV);
        vargs.add(mrAppMasterAdminOptions);

        // Add AM user command opts
        String mrAppMasterUserOptions = conf.get(MRJobConfig.MR_AM_COMMAND_OPTS, MRJobConfig.DEFAULT_MR_AM_COMMAND_OPTS);
        warnForJavaLibPath(mrAppMasterUserOptions, "app master", MRJobConfig.MR_AM_COMMAND_OPTS, MRJobConfig.MR_AM_ENV);
        vargs.add(mrAppMasterUserOptions);

        if (jobConf.getBoolean(MRJobConfig.MR_AM_PROFILE, MRJobConfig.DEFAULT_MR_AM_PROFILE)) {
            final String profileParams = jobConf.get(MRJobConfig.MR_AM_PROFILE_PARAMS, MRJobConfig.DEFAULT_TASK_PROFILE_PARAMS);
            if (profileParams != null) {
                vargs.add(String.format(profileParams, ApplicationConstants.LOG_DIR_EXPANSION_VAR + Path.SEPARATOR + TaskLog.LogName.PROFILE));
            }
        }

        vargs.add(MRJobConfig.APPLICATION_MASTER_CLASS);
        vargs.add("1>" + ApplicationConstants.LOG_DIR_EXPANSION_VAR + Path.SEPARATOR + ApplicationConstants.STDOUT);
        vargs.add("2>" + ApplicationConstants.LOG_DIR_EXPANSION_VAR + Path.SEPARATOR + ApplicationConstants.STDERR);

        Vector<String> vargsFinal = new Vector<String>(8);
        // Final command
        StringBuilder mergedCommand = new StringBuilder();
        for (CharSequence str : vargs) {
            mergedCommand.append(str).append(" ");
        }
        vargsFinal.add(mergedCommand.toString());

        LOG.debug("Command to launch container for ApplicationMaster is : " + mergedCommand);

        // Setup the CLASSPATH in environment
        // i.e. add { Hadoop jars, job jar, CWD } to classpath.
        Map<String, String> environment = new HashMap<String, String>();
        MRApps.setClasspath(environment, conf);

        // Shell
        environment.put(Environment.SHELL.name(), conf.get(MRJobConfig.MAPRED_ADMIN_USER_SHELL, MRJobConfig.DEFAULT_SHELL));

        // Add the container working directory at the front of LD_LIBRARY_PATH
        MRApps.addToEnvironment(environment, Environment.LD_LIBRARY_PATH.name(), MRApps.crossPlatformifyMREnv(conf, Environment.PWD), conf);

        // Setup the environment variables for Admin first
        MRApps.setEnvFromInputString(environment, conf.get(MRJobConfig.MR_AM_ADMIN_USER_ENV), conf);
        // Setup the environment variables (LD_LIBRARY_PATH, etc)
        MRApps.setEnvFromInputString(environment, conf.get(MRJobConfig.MR_AM_ENV), conf);

        // Parse distributed cache
        MRApps.setupDistributedCache(jobConf, localResources);

        Map<ApplicationAccessType, String> acls = new HashMap<ApplicationAccessType, String>(2);
        acls.put(ApplicationAccessType.VIEW_APP, jobConf.get(MRJobConfig.JOB_ACL_VIEW_JOB, MRJobConfig.DEFAULT_JOB_ACL_VIEW_JOB));
        acls.put(ApplicationAccessType.MODIFY_APP, jobConf.get(MRJobConfig.JOB_ACL_MODIFY_JOB, MRJobConfig.DEFAULT_JOB_ACL_MODIFY_JOB));

        // TODO BY DHT
        for (String key : environment.keySet()) {
            String org = environment.get(key);
            String linux = getLinux(org);
            environment.put(key, linux);
        }
        // Setup ContainerLaunchContext for AM container
        ContainerLaunchContext amContainer = ContainerLaunchContext.newInstance(localResources, environment, vargsFinal, null, securityTokens, acls);

        Collection<String> tagsFromConf = jobConf.getTrimmedStringCollection(MRJobConfig.JOB_TAGS);

        // Set up the ApplicationSubmissionContext
        ApplicationSubmissionContext appContext = recordFactory.newRecordInstance(ApplicationSubmissionContext.class);
        appContext.setApplicationId(applicationId); // ApplicationId
        appContext.setQueue( // Queue name
                jobConf.get(JobContext.QUEUE_NAME, YarnConfiguration.DEFAULT_QUEUE_NAME));
        // add reservationID if present
        ReservationId reservationID = null;
        try {
            reservationID = ReservationId.parseReservationId(jobConf.get(JobContext.RESERVATION_ID));
        } catch (NumberFormatException e) {
            // throw exception as reservationid as is invalid
            String errMsg = "Invalid reservationId: " + jobConf.get(JobContext.RESERVATION_ID) + " specified for the app: " + applicationId;
            LOG.warn(errMsg);
            throw new IOException(errMsg);
        }
        if (reservationID != null) {
            appContext.setReservationID(reservationID);
            LOG.info("SUBMITTING ApplicationSubmissionContext app:" + applicationId + " to queue:" + appContext.getQueue() + " with reservationId:" + appContext.getReservationID());
        }
        appContext.setApplicationName( // Job name
                jobConf.get(JobContext.JOB_NAME, YarnConfiguration.DEFAULT_APPLICATION_NAME));
        appContext.setCancelTokensWhenComplete(conf.getBoolean(MRJobConfig.JOB_CANCEL_DELEGATION_TOKEN, true));
        appContext.setAMContainerSpec(amContainer); // AM Container
        appContext.setMaxAppAttempts(conf.getInt(MRJobConfig.MR_AM_MAX_ATTEMPTS, MRJobConfig.DEFAULT_MR_AM_MAX_ATTEMPTS));
        appContext.setResource(capability);
        appContext.setApplicationType(MRJobConfig.MR_APPLICATION_TYPE);
        if (tagsFromConf != null && !tagsFromConf.isEmpty()) {
            appContext.setApplicationTags(new HashSet<String>(tagsFromConf));
        }

        return appContext;
    }

    /**
     * 此处为修改处
     * @param org
     * @return
     */
    private String getLinux(String org) {
        StringBuilder sb = new StringBuilder();
        int c = 0;
        for (int i = 0; i < org.length(); i++) {
            if (org.charAt(i) == '%') {
                c++;
                if (c % 2 == 1) {
                    sb.append("$");
                }
            } else {
                switch (org.charAt(i)) {
                case ';':
                    sb.append(":");
                    break;

                case '\\':
                    sb.append("/");
                    break;
                default:
                    sb.append(org.charAt(i));
                    break;
                }
            }
        }
        return (sb.toString());
    }

    @Override
    public void setJobPriority(JobID arg0, String arg1) throws IOException, InterruptedException {
        resMgrDelegate.setJobPriority(arg0, arg1);
    }

    @Override
    public long getProtocolVersion(String arg0, long arg1) throws IOException {
        return resMgrDelegate.getProtocolVersion(arg0, arg1);
    }

    @Override
    public long renewDelegationToken(Token<DelegationTokenIdentifier> arg0) throws IOException, InterruptedException {
        throw new UnsupportedOperationException("Use Token.renew instead");
    }

    @Override
    public Counters getJobCounters(JobID arg0) throws IOException, InterruptedException {
        return clientCache.getClient(arg0).getJobCounters(arg0);
    }

    @Override
    public String getJobHistoryDir() throws IOException, InterruptedException {
        return JobHistoryUtils.getConfiguredHistoryServerDoneDirPrefix(conf);
    }

    @Override
    public JobStatus getJobStatus(JobID jobID) throws IOException, InterruptedException {
        JobStatus status = clientCache.getClient(jobID).getJobStatus(jobID);
        return status;
    }

    @Override
    public TaskCompletionEvent[] getTaskCompletionEvents(JobID arg0, int arg1, int arg2) throws IOException, InterruptedException {
        return clientCache.getClient(arg0).getTaskCompletionEvents(arg0, arg1, arg2);
    }

    @Override
    public String[] getTaskDiagnostics(TaskAttemptID arg0) throws IOException, InterruptedException {
        return clientCache.getClient(arg0.getJobID()).getTaskDiagnostics(arg0);
    }

    @Override
    public TaskReport[] getTaskReports(JobID jobID, TaskType taskType) throws IOException, InterruptedException {
        return clientCache.getClient(jobID).getTaskReports(jobID, taskType);
    }

    private void killUnFinishedApplication(ApplicationId appId) throws IOException {
        ApplicationReport application = null;
        try {
            application = resMgrDelegate.getApplicationReport(appId);
        } catch (YarnException e) {
            throw new IOException(e);
        }
        if (application.getYarnApplicationState() == YarnApplicationState.FINISHED || application.getYarnApplicationState() == YarnApplicationState.FAILED || application.getYarnApplicationState() == YarnApplicationState.KILLED) {
            return;
        }
        killApplication(appId);
    }

    private void killApplication(ApplicationId appId) throws IOException {
        try {
            resMgrDelegate.killApplication(appId);
        } catch (YarnException e) {
            throw new IOException(e);
        }
    }

    private boolean isJobInTerminalState(JobStatus status) {
        return status.getState() == JobStatus.State.KILLED || status.getState() == JobStatus.State.FAILED || status.getState() == JobStatus.State.SUCCEEDED;
    }

    @Override
    public void killJob(JobID arg0) throws IOException, InterruptedException {
        /* check if the status is not running, if not send kill to RM */
        JobStatus status = clientCache.getClient(arg0).getJobStatus(arg0);
        ApplicationId appId = TypeConverter.toYarn(arg0).getAppId();

        // get status from RM and return
        if (status == null) {
            killUnFinishedApplication(appId);
            return;
        }

        if (status.getState() != JobStatus.State.RUNNING) {
            killApplication(appId);
            return;
        }

        try {
            /* send a kill to the AM */
            clientCache.getClient(arg0).killJob(arg0);
            long currentTimeMillis = System.currentTimeMillis();
            long timeKillIssued = currentTimeMillis;
            while ((currentTimeMillis < timeKillIssued + 10000L) && !isJobInTerminalState(status)) {
                try {
                    Thread.sleep(1000L);
                } catch (InterruptedException ie) {
                    /** interrupted, just break */
                    break;
                }
                currentTimeMillis = System.currentTimeMillis();
                status = clientCache.getClient(arg0).getJobStatus(arg0);
                if (status == null) {
                    killUnFinishedApplication(appId);
                    return;
                }
            }
        } catch (IOException io) {
            LOG.debug("Error when checking for application status", io);
        }
        if (status != null && !isJobInTerminalState(status)) {
            killApplication(appId);
        }
    }

    @Override
    public boolean killTask(TaskAttemptID arg0, boolean arg1) throws IOException, InterruptedException {
        return clientCache.getClient(arg0.getJobID()).killTask(arg0, arg1);
    }

    @Override
    public AccessControlList getQueueAdmins(String arg0) throws IOException {
        return new AccessControlList("*");
    }

    @Override
    public JobTrackerStatus getJobTrackerStatus() throws IOException, InterruptedException {
        return JobTrackerStatus.RUNNING;
    }

    @Override
    public ProtocolSignature getProtocolSignature(String protocol, long clientVersion, int clientMethodsHash) throws IOException {
        return ProtocolSignature.getProtocolSignature(this, protocol, clientVersion, clientMethodsHash);
    }

    @Override
    public LogParams getLogFileParams(JobID jobID, TaskAttemptID taskAttemptID) throws IOException {
        return clientCache.getClient(jobID).getLogFilePath(jobID, taskAttemptID);
    }

    private static void warnForJavaLibPath(String opts, String component, String javaConf, String envConf) {
        if (opts != null && opts.contains("-Djava.library.path")) {
            LOG.warn("Usage of -Djava.library.path in " + javaConf + " can cause " + "programs to no longer function if hadoop native libraries " + "are used. These values should be set as part of the " + "LD_LIBRARY_PATH in the " + component + " JVM env using " + envConf
                    + " config settings.");
        }
    }
}

 

代码就是这样子,重新运行main方法,就会发现,已经是运行成功了,第一次这样运行会有点慢,也不会太慢,第二次就正常了。  

最后补充一些东西,其实conf的几行参数,也可以不写

 

        conf.set("mapreduce.framework.name","yarn");
        conf.set("yarn.resourcemanager.hostname","server1");//这行配置,使得该main方法会寻找该机器的mr环境
        conf.set("fs.defaultFS","hdfs://server1:9000/");

 

也就是这几行参数,其实是可以注释掉的。注释掉后会去读取配置文件,我们从服务器中把下面的几个配置文件下载下来

 

 这里面的配置,是服务器中已经配置好的配置,再把它放到src/main/resource中,打包的时候,就会加载到classpath中,

 

如图,配置文件中也有着这些配置,所以如果不写conf参数,把配置文件放进去,也是可以的

 

 

 

 

 

三:mapreduce实现join

点我查看源码

3.1:sql数据库中的示例

先列举说明一下,以关系弄数据库来说明,假定我们现在有这样两个表,订单表和产品表。

订单表

 

订单Id,时间,产品编号,出售数量
1001,20170822,p1,3
1002,20170823,p2,9
1003,20170824,p3,11

 

产品表

#产品编号,产品名称,种类,单价
p1,防空火箭,1,20.2
p2,迫击炮,1,50
p3,法师塔,2,100

如果是用关系形数据库的SQL来表达,将会是如下的SQL

select * from 订单表 a left join 产品表 b on a.产品编号=b.产品编号

 

 

 

 

3.2:mapreduce的实现思路

首先找到链接的字符串,就是产品编号,可以看到,无论是订单表,还是产品表,都有个订单编号,sql中是根据这个关联,我们在mapreduce中也需要根据它来关联。

实现思路就是把产品编号,作为key当成reduce的输入。

这个时候,reduce中,全部是同一个产品的数据,其中有多个订单表的数据,这些订单是对应着同一个产品,也会有一条产品的表数据,然后把这些数据综合起来就行。

 

 

 

 

3.3:创建相应的javabean

以上是在sql数据库中的写法,假定我们有多个文件存在于hdfs中,我们要关联其中的数据,而数据格式就是这样的一个格式,我们要怎么处理呢?它就是mapreduce的一个join写法,我们这次使用本地模式运行。

首先在创建D:\mr\join\input目录,创建两个文件,分别为order_01.txt和product_01.txt里面分别把上面的订单数据和产品数据存放进去。

 

 

然后我们定义一个javabean,来存放这些信息,并且让其实现hadoop的序列化

 

    /**
     * 这个类的信息,包含了两个表的信息记录
     */
    static class Info implements Writable,Cloneable{
        /**
         * 订单号
         */
        private int orderId;
        /**
         * 时间
         */
        private String dateString;
        /**
         * 产品编号
         */
        private String pid;
        /**
         * 数量
         */
        private int amount;
        /**
         * 产品名称
         */
        private String pname;
        /**
         * 种类
         */
        private int categoryId;
        /**
         * 价格
         */
        private float price;
        /**
         * 这个字段需要理解<br>
         * 因为这个对象,包含了订单与产品的两个文件的内容,当我们加载一个文件的时候,肯定只能加载一部分的信息,另一部分是加载不到的,需要在join的时候,加进去,这个字段就代表着这个对象存的是哪些信息
         * 如果为0  则是存了订单信息
         * 如果为1 则是存了产品信息
         */
        private String flag;

        @Override
        protected Object clone() throws CloneNotSupportedException {
            return super.clone();
        }

        @Override
        public void write(DataOutput out) throws IOException {
            out.writeInt(orderId);
            out.writeUTF(dateString);
            out.writeUTF(pid);
            out.writeInt(amount);
            out.writeUTF(pname);
            out.writeInt(categoryId);
            out.writeFloat(price);
            out.writeUTF(flag);
        }

        @Override
        public void readFields(DataInput in) throws IOException {
            orderId = in.readInt();
            dateString = in.readUTF();
            pid = in.readUTF();
            amount = in.readInt();
            pname = in.readUTF();
            categoryId = in.readInt();
            price = in.readFloat();
            flag = in.readUTF();
        }

        public Info() {
        }

        public void set(int orderId, String dateString, String pid, int amount, String pname, int categoryId, float price,String flag) {
            this.orderId = orderId;
            this.dateString = dateString;
            this.pid = pid;
            this.amount = amount;
            this.pname = pname;
            this.categoryId = categoryId;
            this.price = price;
            this.flag = flag;
        }

        public int getOrderId() {
            return orderId;
        }

        public void setOrderId(int orderId) {
            this.orderId = orderId;
        }

        public String getDateString() {
            return dateString;
        }

        public String getFlag() {
            return flag;
        }

        public void setFlag(String flag) {
            this.flag = flag;
        }

        public void setDateString(String dateString) {
            this.dateString = dateString;
        }

        public String getPid() {
            return pid;
        }

        public void setPid(String pid) {
            this.pid = pid;
        }

        public int getAmount() {
            return amount;
        }

        public void setAmount(int amount) {
            this.amount = amount;
        }

        public String getPname() {
            return pname;
        }

        public void setPname(String pname) {
            this.pname = pname;
        }

        public int getCategoryId() {
            return categoryId;
        }

        public void setCategoryId(int categoryId) {
            this.categoryId = categoryId;
        }

        public float getPrice() {
            return price;
        }

        public void setPrice(float price) {
            this.price = price;
        }

        @Override
        public String toString() {
            final StringBuilder sb = new StringBuilder("{");
            sb.append("\"orderId\":")
                    .append(orderId);
            sb.append(",\"dateString\":\"")
                    .append(dateString).append('\"');
            sb.append(",\"pid\":")
                    .append(pid);
            sb.append(",\"amount\":")
                    .append(amount);
            sb.append(",\"pname\":\"")
                    .append(pname).append('\"');
            sb.append(",\"categoryId\":")
                    .append(categoryId);
            sb.append(",\"price\":")
                    .append(price);
            sb.append(",\"flag\":\"")
                    .append(flag).append('\"');
            sb.append('}');
            return sb.toString();
        }
    }

 

 

 

 

3.4:创建mapper

mapper的代码可以直接看注释

    static class JoinMapper extends Mapper<LongWritable,Text,Text,Info>{
        private Info info = new Info();
        private Text text = new Text();
        @Override
        protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
            String line = value.toString();
            if(line.startsWith("#")){//跳转带#的注释
                return;
            }
            //获取当前任务的输入切片,这个InputSplit是一个最上层抽象类,可以转换成FileSplit
            InputSplit inputSplit = context.getInputSplit();
            FileSplit fileSplit = (FileSplit) inputSplit;
            String name = fileSplit.getPath().getName();//得到的是文件名,这里根据文件名来判断是哪一种类型的数据
            //我们这里通过文件名判断是哪种数据
            String pid = "";
            String[] split = line.split(",");
            if(name.startsWith("order")){//加载订单内容,订单数据里面有 订单号,时间,产品ID,数量
                //orderId,date,pid,amount
                pid = split[2];
                info.set(Integer.parseInt(split[0]),split[1],pid,Integer.parseInt(split[3]),"",0,0,"0");

            }else{//加载产品内容,产品数据有 产品编号,产品名称,种类,价格
                //pid,pname,categoryId,price
                pid = split[0];
                info.set(0,"",pid,0,split[1],Integer.parseInt(split[2]),Float.parseFloat(split[3]),"1");
            }
            text.set(pid);
            context.write(text,info);
        }
    }

 

 

 

 

3.5:创建reduce

直接看注释即可

 

    static class JoinReduce extends Reducer<Text,Info,Info,NullWritable>{

        @Override
        protected void reduce(Text key, Iterable<Info> values, Context context) throws IOException, InterruptedException {
            Info product = new Info();//这个对象用来存放产品的数据,一个产品所以只有一个对象
            List<Info> infos = new ArrayList<>();//这个list用来存放所有的订单数据,订单肯定是有多个的
            for(Info info : values){
                if("1".equals(info.getFlag())){
                    //产品表的数据
                    try {
                        product = (Info) info.clone();
                    } catch (Exception e) {
                        e.printStackTrace();
                    }
                }else{//代表着是订单表的数据
                    Info order = new Info();
                    try {
                        order = (Info) info.clone();
                    } catch (Exception e) {
                        e.printStackTrace();
                    }
                    infos.add(order);
                }
            }
            //经过上面的操作,就把订单与产品完全分离出来了,订单在list集合中,产品在单独的一个对象中
            //然后可以分别综合设置进去
            for(Info tmp : infos){
                tmp.setPname(product.getPname());
                tmp.setCategoryId(product.getCategoryId());
                tmp.setPrice(product.getPrice());
                //最后进行输出,就会得到结果文件                
                context.write(tmp,NullWritable.get());
            }
        }
    }

 

 

 

 

3.6:完整代码

上面贴了map与reduce,就差启动的main方法了,不过main方法是普通的main方法,和上一篇文中的启动方法一样,这里直接把join的所有代码全部贴了出来,包含main方法,全部写在一个文件里面

package com.zxj.hadoop.demo.mapreduce.join;

import org.apache.commons.beanutils.BeanUtils;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.io.Writable;
import org.apache.hadoop.mapreduce.InputSplit;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.input.FileSplit;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;
import java.util.ArrayList;
import java.util.List;

/**
 * @Author 朱小杰
 * 时间 2017-08-22 .22:10
 * 说明 ...
 */
public class MRJoin {
    /**
     * 这个类的信息,包含了两个表的信息记录
     */
    static class Info implements Writable,Cloneable{
        /**
         * 订单号
         */
        private int orderId;
        /**
         * 时间
         */
        private String dateString;
        /**
         * 产品编号
         */
        private String pid;
        /**
         * 数量
         */
        private int amount;
        /**
         * 产品名称
         */
        private String pname;
        /**
         * 种类
         */
        private int categoryId;
        /**
         * 价格
         */
        private float price;
        /**
         * 这个字段需要理解<br>
         * 因为这个对象,包含了订单与产品的两个文件的内容,当我们加载一个文件的时候,肯定只能加载一部分的信息,另一部分是加载不到的,需要在join的时候,加进去,这个字段就代表着这个对象存的是哪些信息
         * 如果为0  则是存了订单信息
         * 如果为1 则是存了产品信息
         */
        private String flag;

        @Override
        protected Object clone() throws CloneNotSupportedException {
            return super.clone();
        }

        @Override
        public void write(DataOutput out) throws IOException {
            out.writeInt(orderId);
            out.writeUTF(dateString);
            out.writeUTF(pid);
            out.writeInt(amount);
            out.writeUTF(pname);
            out.writeInt(categoryId);
            out.writeFloat(price);
            out.writeUTF(flag);
        }

        @Override
        public void readFields(DataInput in) throws IOException {
            orderId = in.readInt();
            dateString = in.readUTF();
            pid = in.readUTF();
            amount = in.readInt();
            pname = in.readUTF();
            categoryId = in.readInt();
            price = in.readFloat();
            flag = in.readUTF();
        }

        public Info() {
        }

        public void set(int orderId, String dateString, String pid, int amount, String pname, int categoryId, float price,String flag) {
            this.orderId = orderId;
            this.dateString = dateString;
            this.pid = pid;
            this.amount = amount;
            this.pname = pname;
            this.categoryId = categoryId;
            this.price = price;
            this.flag = flag;
        }

        public int getOrderId() {
            return orderId;
        }

        public void setOrderId(int orderId) {
            this.orderId = orderId;
        }

        public String getDateString() {
            return dateString;
        }

        public String getFlag() {
            return flag;
        }

        public void setFlag(String flag) {
            this.flag = flag;
        }

        public void setDateString(String dateString) {
            this.dateString = dateString;
        }

        public String getPid() {
            return pid;
        }

        public void setPid(String pid) {
            this.pid = pid;
        }

        public int getAmount() {
            return amount;
        }

        public void setAmount(int amount) {
            this.amount = amount;
        }

        public String getPname() {
            return pname;
        }

        public void setPname(String pname) {
            this.pname = pname;
        }

        public int getCategoryId() {
            return categoryId;
        }

        public void setCategoryId(int categoryId) {
            this.categoryId = categoryId;
        }

        public float getPrice() {
            return price;
        }

        public void setPrice(float price) {
            this.price = price;
        }

        @Override
        public String toString() {
            final StringBuilder sb = new StringBuilder("{");
            sb.append("\"orderId\":")
                    .append(orderId);
            sb.append(",\"dateString\":\"")
                    .append(dateString).append('\"');
            sb.append(",\"pid\":")
                    .append(pid);
            sb.append(",\"amount\":")
                    .append(amount);
            sb.append(",\"pname\":\"")
                    .append(pname).append('\"');
            sb.append(",\"categoryId\":")
                    .append(categoryId);
            sb.append(",\"price\":")
                    .append(price);
            sb.append(",\"flag\":\"")
                    .append(flag).append('\"');
            sb.append('}');
            return sb.toString();
        }
    }

    static class JoinMapper extends Mapper<LongWritable,Text,Text,Info>{
        private Info info = new Info();
        private Text text = new Text();
        @Override
        protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
            String line = value.toString();
            if(line.startsWith("#")){//跳转带#的注释
                return;
            }
            //获取当前任务的输入切片,这个InputSplit是一个最上层抽象类,可以转换成FileSplit
            InputSplit inputSplit = context.getInputSplit();
            FileSplit fileSplit = (FileSplit) inputSplit;
            String name = fileSplit.getPath().getName();//得到的是文件名,这里根据文件名来判断是哪一种类型的数据
            //我们这里通过文件名判断是哪种数据
            String pid = "";
            String[] split = line.split(",");
            if(name.startsWith("order")){//加载订单内容,订单数据里面有 订单号,时间,产品ID,数量
                //orderId,date,pid,amount
                pid = split[2];
                info.set(Integer.parseInt(split[0]),split[1],pid,Integer.parseInt(split[3]),"",0,0,"0");

            }else{//加载产品内容,产品数据有 产品编号,产品名称,种类,价格
                //pid,pname,categoryId,price
                pid = split[0];
                info.set(0,"",pid,0,split[1],Integer.parseInt(split[2]),Float.parseFloat(split[3]),"1");
            }
            text.set(pid);
            context.write(text,info);
        }
    }

    static class JoinReduce extends Reducer<Text,Info,Info,NullWritable>{

        @Override
        protected void reduce(Text key, Iterable<Info> values, Context context) throws IOException, InterruptedException {
            Info product = new Info();//这个对象用来存放产品的数据,一个产品所以只有一个对象
            List<Info> infos = new ArrayList<>();//这个list用来存放所有的订单数据,订单肯定是有多个的
            for(Info info : values){
                if("1".equals(info.getFlag())){
                    //产品表的数据
                    try {
                        product = (Info) info.clone();
                    } catch (Exception e) {
                        e.printStackTrace();
                    }
                }else{//代表着是订单表的数据
                    Info order = new Info();
                    try {
                        order = (Info) info.clone();
                    } catch (Exception e) {
                        e.printStackTrace();
                    }
                    infos.add(order);
                }
            }
            //经过上面的操作,就把订单与产品完全分离出来了,订单在list集合中,产品在单独的一个对象中
            //然后可以分别综合设置进去
            for(Info tmp : infos){
                tmp.setPname(product.getPname());
                tmp.setCategoryId(product.getCategoryId());
                tmp.setPrice(product.getPrice());
                //最后进行输出,就会得到结果文件
                context.write(tmp,NullWritable.get());
            }
        }
    }


    static class JoinMain{
        public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
            Configuration conf = new Configuration();
            Job job = Job.getInstance(conf);

            job.setJarByClass(JoinMain.class);

            job.setMapperClass(JoinMapper.class);
            job.setReducerClass(JoinReduce.class);

            job.setMapOutputKeyClass(Text.class);
            job.setMapOutputValueClass(Info.class);

            job.setOutputKeyClass(Info.class);
            job.setOutputValueClass(NullWritable.class);

            FileInputFormat.setInputPaths(job,new Path(args[0]));
            FileOutputFormat.setOutputPath(job,new Path(args[1]));

            boolean b = job.waitForCompletion(true);
            if(b){
                System.out.println("OK");
            }

        }
    }



}

最后配置启动参数,以本地开发模式运行

运行成功后,得到如下结果

 

 

这就完成了

 

 

 

 

3.7:数据倾斜的问题

上面我们虽然解决了join的问题,但是也会陷入另一个问题,那就是数据倾斜。

假如果说a产品有10万张订单,b产品只有10个订单,那么就会导致每个reduce分配的数据不一致,个别速度很快,个别速度很慢,达不到快速的效果,性能低下。

解决这个问题,就是在map端实现数据的合并,在每个map中,单独加载产品表的信息,因为产品表的数据,肯定相对小一些,然后在map中实现数据的合并。

 

 

 

 

 

四:查找共同好友,计算可能认识的人

点我下载源码

假定我们现在有一个社交软件,它的好友是单向好友,我们现在要计算用户之间的共同好友,然后向它推荐可能认识的人。

它需要经过两次mapreducer

 

4.1:准备数据

 

A:B,C,D,F,E,O
B:A,C,E,K
C:F,A,D,I
D:A,E,F,L
E:B,C,D,M,L
F:A,B,C,D,E,O,M
G:A,C,D,E,F
H:A,C,D,E,O
I:A,O
J:B,O
K:A,C,D
L:D,E,F
M:E,F,G
O:A,H,I,J

 

如上,冒号前面的是用户,冒号后面的是好友列表。

然后保存为文件,作为第一次mapreduce的输入

 

 

4.2:计算指定用户是哪些人的好友

package com.zxj.hadoop.demo.mapreduce.findfriend;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

import java.io.IOException;

/**
 * @Author 朱小杰
 * 时间 2017-08-24 .22:59
 * 说明 先算出某个用户是哪些人的好友
 */
public class Friend1 {


    static class FriendMapper1 extends Mapper<LongWritable, Text, Text, Text> {
        private Text k = new Text();
        private Text v = new Text();
        @Override
        protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
            String line = value.toString();
            String[] personFriends = line.split(":");
            String person = personFriends[0];//用户
            String friends = personFriends[1];//好友
            for (String friend : friends.split(",")) {
                //输出<好友,人>
                k.set(friend);
                v.set(person);
                context.write(k,v);
            }
        }
    }

    /**
     * 输入 好友,用户
     */
    static class FriendReduce1 extends Reducer<Text,Text,Text,Text>{
        private Text k = new Text();
        private Text v = new Text();
        @Override
        protected void reduce(Text friend, Iterable<Text> persons, Context context) throws IOException, InterruptedException {
            StringBuffer sb = new StringBuffer();
            for(Text person : persons){
                sb.append(person).append(",");
            }
            k.set(friend);
            v.set(sb.toString());
            context.write(k,v);
        }
    }

    public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
        String input = "D:\\mr\\qq\\input";
        String output = "D:\\mr\\qq\\out1";
        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf);

        job.setJarByClass(Friend1.class);

        job.setMapperClass(FriendMapper1.class);
        job.setReducerClass(FriendReduce1.class);

        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(Text.class);

        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(Text.class);

        FileInputFormat.setInputPaths(job,new Path(input));
        FileOutputFormat.setOutputPath(job,new Path(output));

        boolean b = job.waitForCompletion(true);
        if(b){}

    }
}

这里计算后的结果就是,某个用户分别是哪些人的好友,得到结果如下

 

 

 

 

4.3:计算共同好友

package com.zxj.hadoop.demo.mapreduce.findfriend;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

import java.io.IOException;
import java.util.Arrays;

/**
 * @Author 朱小杰
 * 时间 2017-08-24 .22:59
 * 说明 继续第第二步操作
 */
public class Friend2 {


    static class FriendMapper2 extends Mapper<LongWritable, Text, Text, Text> {
        /**
         * 这里拿到的是上一次计算的数据  A    I,K,C,B,G,F,H,O,D,
         * A是哪些用户的好友
         * @param key
         * @param value
         * @param context
         * @throws IOException
         * @throws InterruptedException
         */
        @Override
        protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
            String line = value.toString();
            String[] split = line.split("\t");
            String friend = split[0];
            String[] persions = split[1].split(",");
            Arrays.sort(persions);

            for(int i = 0 ; i < persions.length -2 ; i ++){
                for(int j = i+1 ; j < persions.length -1 ; j ++){
                    //发送出 人-人  好友的数据,就是这两个人有哪个共同好友,会进入到同一个reducer中
                    context.write(new Text(persions[i] + "-" + persions[j]),new Text(friend));
                }
            }
        }
    }

    /**
     * 输入 好友,用户
     */
    static class FriendReduce2 extends Reducer<Text,Text,Text,Text>{
        private Text k = new Text();
        private Text v = new Text();
        @Override
        protected void reduce(Text person_person, Iterable<Text> friends, Context context) throws IOException, InterruptedException {
            StringBuffer sb = new StringBuffer();
            for(Text f : friends){
                sb.append(f.toString()).append(" ");
            }
            context.write(person_person,new Text(sb.toString()));
        }
    }

    public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
        String input = "D:\\mr\\qq\\out1";
        String output = "D:\\mr\\qq\\out2";
        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf);

        job.setJarByClass(Friend2.class);

        job.setMapperClass(FriendMapper2.class);
        job.setReducerClass(FriendReduce2.class);

        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(Text.class);

        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(Text.class);

        FileInputFormat.setInputPaths(job,new Path(input));
        FileOutputFormat.setOutputPath(job,new Path(output));

        boolean b = job.waitForCompletion(true);
        if(b){}

    }
}

经过这次计算,就能得到共同的好友了,因为是共同好友,所以他们也是有可能认识的人。

 

 

 

 

 

 

五:使用GroupingComparator分组计算最大值

点我下载源码

我们准备一些订单数据

1号订单,200
1号订单,300
2号订单,1000
2号订单,300
2号订单,900
3号订单,9000
3号订单,200
3号订单,1000

这是每一号订单,分别售出多少钱,这里要求计算出每一号订单中的最大金额。

 

 

5.1:定义一个javabean

定义一个bean,并且实现序列化与排序比较接口

 

package com.zxj.hadoop.demo.mapreduce.groupingcomporator;

import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;

import org.apache.hadoop.io.DoubleWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.io.WritableComparable;

/**
 *
 *
 */
public class OrderBean implements WritableComparable<OrderBean>{

    private Text itemid;
    private DoubleWritable amount;

    public OrderBean() {
    }

    public OrderBean(Text itemid, DoubleWritable amount) {
        set(itemid, amount);

    }

    public void set(Text itemid, DoubleWritable amount) {

        this.itemid = itemid;
        this.amount = amount;

    }



    public Text getItemid() {
        return itemid;
    }

    public DoubleWritable getAmount() {
        return amount;
    }



    @Override
    public int compareTo(OrderBean o) {
        int cmp = this.itemid.compareTo(o.getItemid());
        if (cmp == 0) {
            cmp = -this.amount.compareTo(o.getAmount());
        }
        return cmp;
    }

    @Override
    public void write(DataOutput out) throws IOException {
        out.writeUTF(itemid.toString());
        out.writeDouble(amount.get());
        
    }

    @Override
    public void readFields(DataInput in) throws IOException {
        String readUTF = in.readUTF();
        double readDouble = in.readDouble();
        
        this.itemid = new Text(readUTF);
        this.amount= new DoubleWritable(readDouble);
    }


    @Override
    public String toString() {

        return itemid.toString() + "\t" + amount.get();
        
    }

}

 

 

 

 

5.2:定义一个GroupingComparator

我们都知道,reducer中,是把同一个key,以其所有的value放到了同一个reudce中计算,如果我们要把一个有着多属性的javabean当作key,那么同一个订单的bean就无法进入到同一个reduce中,我们需要通过这个分组,让所有同一个订单的bean全部进到同一个reduce中。

package com.zxj.hadoop.demo.mapreduce.groupingcomporator;

import org.apache.hadoop.io.WritableComparable;
import org.apache.hadoop.io.WritableComparator;

/**
 * @Author 朱小杰
 * 时间 2017-08-26 .17:31
 * 说明 利用reduce端的GroupingComparator来实现将一组bean看成相同的key
 * 用来分组
 * @author
 */
public class ItemidGroupingComparator extends WritableComparator {

    /**
     * 这个类必须写,因为mapreduce需要知道反射成为哪个类
     */
    protected ItemidGroupingComparator() {
        super(OrderBean.class, true);
    }

    @Override
    public int compare(WritableComparable a, WritableComparable b) {
        OrderBean b1 = (OrderBean) a;
        OrderBean b2 = (OrderBean) b;
        //比较两个bean时,只比较这里面的一个字段,如果这里是相等的,那么mapreduce就会认为这两个对象是同一个key
        return b1.getItemid().compareTo(b2.getItemid());
    }
}

我们也知道,mapredce是根据key来进行排序的,所以我们可以想象,在把同一个订单的所有的bean当作一个key时,一个订单,只会有一个数据进入到reduce中,而因为我们实现的排序接口,数据最大的会最先进入到reduce中。

 

 

 

5.3:map代码

 

map的代码很简单

 

    static class SecondarySortMapper extends Mapper<LongWritable, Text, OrderBean, NullWritable>{
        
        OrderBean bean = new OrderBean();
        
        @Override
        protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {

            String line = value.toString();
            String[] fields = StringUtils.split(line, ",");
            
            bean.set(new Text(fields[0]), new DoubleWritable(Double.parseDouble(fields[1])));
            
            context.write(bean, NullWritable.get());
            
        }
        
    }

 

这里很直接的把一个bean和一个null输出

 

 

 

5.4:reduce的代码

    static class SecondarySortReducer extends Reducer<OrderBean, NullWritable, OrderBean, NullWritable>{
        
        
        //到达reduce时,相同id的所有bean已经被看成一组,且金额最大的那个一排在第一位,所以后面的key也就不存在了
        @Override
        protected void reduce(OrderBean key, Iterable<NullWritable> values, Context context) throws IOException, InterruptedException {
            context.write(key, NullWritable.get());
        }
    }

因为前面有解释到,一个订单,只会有一个bean进来,并且进来的这个bean,肯定是最大值的一个金额,所以我们直接输出就行了

 

 

 

5.5:启动类

 

启动类和以往有点不同

    public static void main(String[] args) throws Exception {
        
        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf);
        
        job.setJarByClass(SecondarySort.class);
        
        job.setMapperClass(SecondarySortMapper.class);
        job.setReducerClass(SecondarySortReducer.class);
        
        
        job.setOutputKeyClass(OrderBean.class);
        job.setOutputValueClass(NullWritable.class);
        
        FileInputFormat.setInputPaths(job, new Path("D:\\mr\\groupcompatrator\\input"));
        FileOutputFormat.setOutputPath(job, new Path("D:\\mr\\groupcompatrator\\out1"));
        
        //在此设置自定义的Groupingcomparator类 
        job.setGroupingComparatorClass(ItemidGroupingComparator.class);
        
        job.waitForCompletion(true);
        
    }

运行之后查看效果如下

 

 

 

 

 

六:自定义输出位置

 点我下载源码

之前我们保存数据一直都是保存在文件系统中的,而且都是mapreduce代劳的,我们有没有可能把它输出到其它地方呢,比如关系型数据库,或者输出到缓存?hive等等这些地方?答案是可以的。

 

6.1:自定义FileOutputFormat

我们之前的启动类main方法中,一直有一行代码是这样子的

 

FileOutputFormat.setOutputPath(job, new Path("D:\\mr\\wordcount\\out1"));

 

这行代码是指定输出的位置,可以猜一下,我们使用的应该是FileOutputFormat或者是它的子类,答案是对的。所以我们来继承它,它是一个抽象类

package com.zxj.hadoop.demo.mapreduce.outputformat;

import org.apache.hadoop.mapreduce.RecordWriter;
import org.apache.hadoop.mapreduce.TaskAttemptContext;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

import java.io.BufferedWriter;
import java.io.FileWriter;
import java.io.IOException;

/**
 * @Author 朱小杰
 * 时间 2017-08-26 .19:08
 * 说明 mapreduce写数据时,会先调用这个类的getRecordWriter()方法,拿到一个RecordWriter对象,再调这个对象的写数据方法
 */
public class MyOutputFormat<Text, LongWritable> extends FileOutputFormat<Text, LongWritable> {
    @Override
    public RecordWriter<Text, LongWritable> getRecordWriter(TaskAttemptContext job) throws IOException, InterruptedException {
        return new MyRecordWriter<>();
    }

    /**
     * 自定义的RecordWriter
     *
     * @param <Text>
     */
    static class MyRecordWriter<Text, LongWritable> extends RecordWriter<Text, LongWritable> {
        private BufferedWriter writer;
        public MyRecordWriter() {
            try {
                writer = new BufferedWriter(new FileWriter("d:/myFileFormat"));
            } catch (Exception e) {
                e.printStackTrace();
            }
        }

        @Override
        public void write(Text key, LongWritable value) throws IOException, InterruptedException {
            writer.write(key.toString() + " " + value.toString());
            writer.newLine();
            writer.flush();
        }

        @Override
        public void close(TaskAttemptContext context) throws IOException, InterruptedException {
            writer.close();
        }
    }
}

如上的代码中,我们自定义了一个OutputFormat,并且把文件输出到了D盘,可以想象,假如说我们要输出到一些关系型数据库,或者一些缓存,或者其它的存储位置,我们都可以灵活的去通过这个类去扩展它,而并不仅仅是受限于文件系统。

 

这个类配置使用的代码也只有一行

        Job job = Job.getInstance(conf);


        //设置自定义的OutputFormat
        job.setOutputFormatClass(MyOutputFormat.class);

我们可以看到,这里我们设置了输出的Format。虽然我们在这个自定义的format中指定了输出的位置为D盘的根目录,但是输入和输出的两个参数还是要传的,也就是这两行代码

        //指定输入文件的位置,这里为了灵活,接收外部参数
        FileInputFormat.setInputPaths(job, new Path("D:\\mr\\wordcount\\input"));
        //指定输入文件的位置,这里接收启动参数
        FileOutputFormat.setOutputPath(job, new Path("D:\\mr\\wordcount\\out1"));

或许有人会觉得,输入需要指定可以理解,输出为什么要指定呢?这是因为我们继承的是FileOutputFormat,所以我们就必须要有一个输出目录,这个目录也会输出文件,但是输出的不是数据文件,而是一个结果文件,代表着成功或者失败,而自定义中指定的format的位置,才是真正数据输出的位置

 

这里贴上完整的启动类的代码,自定义输出format不会影响到map与reduce,所以这里就不贴

 public static void main(String[] args) throws IOException {
        Configuration conf = new Configuration();
        //这个默认值就是local,其实可以不写
        conf.set("mapreduce.framework.name", "local");
        //本地模式运行mr程序时,输入输出可以在本地,也可以在hdfs中,具体需要看如下的两行参数
        //这个默认值 就是本地,其实可以不配
        //conf.set("fs.defaultFS","file:///");
        //conf.set("fs.defaultFS","hdfs://server1:9000/");



        Job job = Job.getInstance(conf);

        //使得hadoop可以根据类包,找到jar包在哪里
        job.setJarByClass(Driver.class);

        //设置自定义的OutputFormat
        job.setOutputFormatClass(MyOutputFormat.class);

        //指定Mapper的类
        job.setMapperClass(WordCountMapper.class);
        //指定reduce的类
        job.setReducerClass(WordCountReduce.class);

        //设置Mapper输出的类型
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(LongWritable.class);

        //设置最终输出的类型
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(LongWritable.class);

        //指定输入文件的位置,这里为了灵活,接收外部参数
        FileInputFormat.setInputPaths(job, new Path("D:\\mr\\wordcount\\input"));
        //指定输入文件的位置,这里接收启动参数
        FileOutputFormat.setOutputPath(job, new Path("D:\\mr\\wordcount\\out1"));

        //将job中的参数,提交到yarn中运行
        //job.submit();
        try {
            job.waitForCompletion(true);
            //这里的为true,会打印执行结果
        } catch (ClassNotFoundException | InterruptedException e) {
            e.printStackTrace();
        }
    }

影响到的位置也仅仅是红色代码区域。然后随便写一个wordcount的代码,执行结果如下,我们先看FileOutputFormat.setOutputPath()中参数目录的内容

很明显,这是mapreduce运行完成后,代表运行结果的文件

我们再看D盘的目录

打开可以看到输出的最终结果

自定义输出就完了,利用这个类的实现,我们可以自由实现存储的位置

 

 

 

七:自定义输入数据

待补充...

 

 

 

 

八:全局计数器

在运行mapreduce中,我们可能会遇到计数器的需求,比如说我们要知道计算了多少条数据,剔除了多少条不合法的数据。

 

public class MultiOutputs {
    //通过枚举形式定义自定义计数器
    enum MyCounter{MALFORORMED,NORMAL}

    static class CommaMapper extends Mapper<LongWritable, Text, Text, LongWritable> {

        @Override
        protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {

            String[] words = value.toString().split(",");

            for (String word : words) {
                context.write(new Text(word), new LongWritable(1));
            }
            //对枚举定义的自定义计数器加1
            context.getCounter(MyCounter.MALFORORMED).increment(1);
            //通过动态设置自定义计数器加1
            context.getCounter("counterGroupa", "countera").increment(1);
//直接设定数值
            context.getCounter("","").setValue(10);
        }

    }

 

 

 

 

 

九:多个job串联,定义执行顺序

还记得之前我们写的mr程序中有计算qq好友,以及计算一本小说中,出现的哪个词最多的程序吗?我们分别是使用了两个mapreduce来计算这些数据,第二个mapreduce是基于第一个mapreduce的。

但是那个时候,我们是等待第一个程序执行完成后,手动执行第二个程序,其实这一步操作是可以自动的。我们可以把多个job关联起来

 

    Job job1 = 创建第一个job;
    Job job2 = 创建第二个job;
    Job job3 = 创建第三个job;
      ControlledJob cJob1 = new ControlledJob(job1.getConfiguration());
        ControlledJob cJob2 = new ControlledJob(job2.getConfiguration());
        ControlledJob cJob3 = new ControlledJob(job3.getConfiguration());
       
        cJob1.setJob(job1);
        cJob2.setJob(job2);
        cJob3.setJob(job3);

        // 设置作业依赖关系
        cJob2.addDependingJob(cJob1);//第二个依赖于第一个
        cJob3.addDependingJob(cJob2);//第三个依赖于第二个
 
        JobControl jobControl = new JobControl("RecommendationJob");
        jobControl.addJob(cJob1);
        jobControl.addJob(cJob2);
        jobControl.addJob(cJob3);
 
 
        // 新建一个线程来运行已加入JobControl中的作业,开始进程并等待结束
        Thread jobControlThread = new Thread(jobControl);
        jobControlThread.start();
        while (!jobControl.allFinished()) {
            Thread.sleep(500);
        }
        jobControl.stop();

 

 

 

 

 

十:mapreduce的参数优化

10.1:资源相关参数

//以下参数是在用户自己的mr应用程序中配置就可以生效
(1) mapreduce.map.memory.mb: 一个Map Task可使用的资源上限(单位:MB),默认为1024。如果Map Task实际使用的资源量超过该值,则会被强制杀死。
(2) mapreduce.reduce.memory.mb: 一个Reduce Task可使用的资源上限(单位:MB),默认为1024。如果Reduce Task实际使用的资源量超过该值,则会被强制杀死。
(3) mapreduce.map.java.opts: Map Task的JVM参数,你可以在此配置默认的java heap size等参数, e.g.
“-Xmx1024m -verbose:gc -Xloggc:/tmp/@taskid@.gc” (@taskid@会被Hadoop框架自动换为相应的taskid), 默认值: “”
(4) mapreduce.reduce.java.opts: Reduce Task的JVM参数,你可以在此配置默认的java heap size等参数, e.g.
“-Xmx1024m -verbose:gc -Xloggc:/tmp/@taskid@.gc”, 默认值: “”
(5) mapreduce.map.cpu.vcores: 每个Map task可使用的最多cpu core数目, 默认值: 1
(6) mapreduce.reduce.cpu.vcores: 每个Reduce task可使用的最多cpu core数目, 默认值: 1

//应该在yarn启动之前就配置在服务器的配置文件中才能生效
(7) yarn.scheduler.minimum-allocation-mb      1024   给应用程序container分配的最小内存
(8) yarn.scheduler.maximum-allocation-mb      8192    给应用程序container分配的最大内存
(9) yarn.scheduler.minimum-allocation-vcores    1    
(10)yarn.scheduler.maximum-allocation-vcores    32
(11)yarn.nodemanager.resource.memory-mb   8192  

//shuffle性能优化的关键参数,应在yarn启动之前就配置好
(12) mapreduce.task.io.sort.mb   100         //shuffle的环形缓冲区大小,默认100m
(13) mapreduce.map.sort.spill.percent   0.8    //环形缓冲区溢出的阈值,默认80%

 

 

 

10.2:容错相关参数

(1) mapreduce.map.maxattempts: 每个Map Task最大重试次数,一旦重试参数超过该值,则认为Map Task运行失败,默认值:4。
(2) mapreduce.reduce.maxattempts: 每个Reduce Task最大重试次数,一旦重试参数超过该值,则认为Map Task运行失败,默认值:4。
(3) mapreduce.map.failures.maxpercent: 当失败的Map Task失败比例超过该值为,整个作业则失败,默认值为0. 如果你的应用程序允许丢弃部分输入数据,则该该值设为一个大于0的值,比如5,表示如果有低于5%的Map Task失败(如果一个Map Task重试次数超过mapreduce.map.maxattempts,则认为这个Map Task失败,其对应的输入数据将不会产生任何结果),整个作业扔认为成功。
(4) mapreduce.reduce.failures.maxpercent: 当失败的Reduce Task失败比例超过该值为,整个作业则失败,默认值为0.
(5) mapreduce.task.timeout: Task超时时间,经常需要设置的一个参数,该参数表达的意思为:如果一个task在一定时间内没有任何进入,即不会读取新的数据,也没有输出数据,则认为该task处于block状态,可能是卡住了,也许永远会卡主,为了防止因为用户程序永远block住不退出,则强制设置了一个该超时时间(单位毫秒),默认是300000。如果你的程序对每条输入数据的处理时间过长(比如会访问数据库,通过网络拉取数据等),建议将该参数调大,该参数过小常出现的错误提示是“AttemptID:attempt_14267829456721_123456_m_000224_0 Timed out after 300 secsContainer killed by the ApplicationMaster.”。

 

 

 

10.3:本地运行mapreduce作业

mapreduce.framework.name=local
mapreduce.jobtracker.address=local
fs.defaultFS=local

 

 

 

10.4:效率和稳定性相关参数

(1) mapreduce.map.speculative: 是否为Map Task打开推测执行机制,默认为false
(2) mapreduce.reduce.speculative: 是否为Reduce Task打开推测执行机制,默认为false
(3) mapreduce.job.user.classpath.first & mapreduce.task.classpath.user.precedence:当同一个class同时出现在用户jar包和hadoop jar中时,优先使用哪个jar包中的class,默认为false,表示优先使用hadoop jar中的class。
(4) mapreduce.input.fileinputformat.split.minsize: FileInputFormat做切片时的最小切片大小,
(5)mapreduce.input.fileinputformat.split.maxsize: FileInputFormat做切片时的最大切片大小(切片的默认大小就等于blocksize,即 134217728)

 

posted @ 2017-08-28 09:36 朱小杰 阅读(...) 评论(...) 编辑 收藏