• Hadoop3.0大数据处理学习4（案例：数据清洗、数据指标统计、任务脚本封装、Sqoop导出Mysql）

    案例需求分析

    直播公司每日都会产生海量的直播数据，为了更好地服务主播与用户，提高直播质量与用户粘性，往往会对大量的数据进行分析与统计，从中挖掘商业价值，我们将通过一个实战案例，来使用Hadoop技术来实现对直播数据的统计与分析。下面是简化的日志文件，详细的我会更新在Gitee hadoop_study/hadoopDemo1 · Huathy/study-java/

    {"id":"1580089010000","uid":"12001002543","nickname":"jack2543","gold":561,"watchnumpv":1697,"follower":1509,"gifter":2920,"watchnumuv":5410,"length":3542,"exp":183}
{"id":"1580089010001","uid":"12001001853","nickname":"jack1853","gold":660,"watchnumpv":8160,"follower":1781,"gifter":551,"watchnumuv":4798,"length":189,"exp":89}
{"id":"1580089010002","uid":"12001003786","nickname":"jack3786","gold":14,"watchnumpv":577,"follower":1759,"gifter":2643,"watchnumuv":8910,"length":1203,"exp":54}

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    原始数据清洗代码

    1. 清理无效记录：由于原始数据是通过日志方式进行记录的，在使用日志采集工具采集到HDFS后，还需要对数据进行清洗过滤，丢弃缺失字段的数据，针对异常字段值进行标准化处理。
    2. 清除多余字段：由于计算时不会用到所有的字段。

    编码

    DataCleanMap

    package dataClean;

import com.alibaba.fastjson.JSON;
import com.alibaba.fastjson.JSONObject;
import org.apache.commons.lang3.StringUtils;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;

import java.io.IOException;

/**
 * @author Huathy
 * @date 2023-10-22 22:15
 * @description 实现自定义map类，在里面实现具体的清洗逻辑
 */
public class DataCleanMap extends Mapper<LongWritable, Text, Text, Text> {
    /**
     * 1. 从原始数据中过滤出来需要的字段
     * 2. 针对核心字段进行异常值判断
     *
     * @param key
     * @param value
     * @param context
     * @throws IOException
     * @throws InterruptedException
     */
    @Override
    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
        String valStr = value.toString();
        // 将json字符串数据转换成对象
        JSONObject jsonObj = JSON.parseObject(valStr);
        String uid = jsonObj.getString("uid");
        // 这里建议使用getIntValue（返回0）而不是getInt（异常）。
        int gold = jsonObj.getIntValue("gold");
        int watchnumpv = jsonObj.getIntValue("watchnumpv");
        int follower = jsonObj.getIntValue("follower");
        int length = jsonObj.getIntValue("length");
        // 过滤异常数据
        if (StringUtils.isNotBlank(valStr) && (gold * watchnumpv * follower * length) >= 0) {
            // 组装k2，v2
            Text k2 = new Text();
            k2.set(uid);
            Text v2 = new Text();
            v2.set(gold + "\t" + watchnumpv + "\t" + follower + "\t" + length);
            context.write(k2, v2);
        }
    }
}

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    DataCleanJob

    package dataClean;

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

/**
 * @author Huathy
 * @date 2023-10-22 22:02
 * @description 数据清洗作业
 * 1. 从原始数据中过滤出来需要的字段
 * uid gold watchnumpv(总观看)、follower（粉丝关注数量）、length（总时长）
 * 2. 针对以上五个字段进行判断，都不应该丢失或为空，否则任务是异常记录，丢弃。
 * 若个别字段丢失，则设置为0.
 * 
    
 * 分析：
 * 1. 由于原始数据是json格式，可以使用fastjson对原始数据进行解析，获取指定字段的内容
 * 2. 然后对获取到的数据进行判断，只保留满足条件的数据
 * 3. 由于不需要聚合过程，只是一个简单的过滤操作，所以只需要map阶段即可，不需要reduce阶段
 * 4. 其中map阶段的k1,v1的数据类型是固定的，k2,v2的数据类型是k2存储主播ID，v2存储核心字段
 * 中间用\t制表符分隔即可
 */
public class DataCleanJob {
    public static void main(String[] args) throws Exception {
        System.out.println("inputPath  => " + args[0]);
        System.out.println("outputPath  => " + args[1]);
        String path = args[0];
        String path2 = args[1];

        // job需要的配置参数
        Configuration configuration = new Configuration();
        // 创建job
        Job job = Job.getInstance(configuration, "wordCountJob");
        // 注意：这一行必须设置，否则在集群的时候将无法找到Job类
        job.setJarByClass(DataCleanJob.class);
        // 指定输入文件
        FileInputFormat.setInputPaths(job, new Path(path));
        FileOutputFormat.setOutputPath(job, new Path(path2));

        // 指定map相关配置
        job.setMapperClass(DataCleanMap.class);
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(Text.class);
        // 指定reduce 数量0，表示禁用reduce
        job.setNumReduceTasks(0);

        // 提交任务
        job.waitForCompletion(true);
    }
}

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    运行

    ## 运行命令
[root@cent7-1 hadoop-3.2.4]# hadoop jar hadoopDemo1-0.0.1-SNAPSHOT-jar-with-dependencies.jar dataClean.DataCleanJob hdfs://cent7-1:9000/data/videoinfo/231022 hdfs://cent7-1:9000/data/res231022
inputPath  => hdfs://cent7-1:9000/data/videoinfo/231022
outputPath  => hdfs://cent7-1:9000/data/res231022
2023-10-22 23:16:15,845 INFO client.RMProxy: Connecting to ResourceManager at /0.0.0.0:8032
2023-10-22 23:16:16,856 WARN mapreduce.JobResourceUploader: Hadoop command-line option parsing not performed. Implement the Tool interface and execute your application with ToolRunner to remedy this.
2023-10-22 23:16:17,041 INFO mapreduce.JobResourceUploader: Disabling Erasure Coding for path: /tmp/hadoop-yarn/staging/root/.staging/job_1697985525421_0002
2023-10-22 23:16:17,967 INFO input.FileInputFormat: Total input files to process : 1
2023-10-22 23:16:18,167 INFO mapreduce.JobSubmitter: number of splits:1
2023-10-22 23:16:18,873 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1697985525421_0002
2023-10-22 23:16:18,874 INFO mapreduce.JobSubmitter: Executing with tokens: []
2023-10-22 23:16:19,157 INFO conf.Configuration: resource-types.xml not found
2023-10-22 23:16:19,158 INFO resource.ResourceUtils: Unable to find 'resource-types.xml'.
2023-10-22 23:16:19,285 INFO impl.YarnClientImpl: Submitted application application_1697985525421_0002
2023-10-22 23:16:19,345 INFO mapreduce.Job: The url to track the job: http://cent7-1:8088/proxy/application_1697985525421_0002/
2023-10-22 23:16:19,346 INFO mapreduce.Job: Running job: job_1697985525421_0002
2023-10-22 23:16:31,683 INFO mapreduce.Job: Job job_1697985525421_0002 running in uber mode : false
2023-10-22 23:16:31,689 INFO mapreduce.Job:  map 0% reduce 0%
2023-10-22 23:16:40,955 INFO mapreduce.Job:  map 100% reduce 0%
2023-10-22 23:16:43,012 INFO mapreduce.Job: Job job_1697985525421_0002 completed successfully
2023-10-22 23:16:43,153 INFO mapreduce.Job: Counters: 33
	File System Counters
		FILE: Number of bytes read=0
		FILE: Number of bytes written=238970
		FILE: Number of read operations=0
		FILE: Number of large read operations=0
		FILE: Number of write operations=0
		HDFS: Number of bytes read=24410767
		HDFS: Number of bytes written=1455064
		HDFS: Number of read operations=7
		HDFS: Number of large read operations=0
		HDFS: Number of write operations=2
		HDFS: Number of bytes read erasure-coded=0
	Job Counters 
		Launched map tasks=1
		Data-local map tasks=1
		Total time spent by all maps in occupied slots (ms)=7678
		Total time spent by all reduces in occupied slots (ms)=0
		Total time spent by all map tasks (ms)=7678
		Total vcore-milliseconds taken by all map tasks=7678
		Total megabyte-milliseconds taken by all map tasks=7862272
	Map-Reduce Framework
		Map input records=90000
		Map output records=46990
		Input split bytes=123
		Spilled Records=0
		Failed Shuffles=0
		Merged Map outputs=0
		GC time elapsed (ms)=195
		CPU time spent (ms)=5360
		Physical memory (bytes) snapshot=302153728
		Virtual memory (bytes) snapshot=2588925952
		Total committed heap usage (bytes)=214958080
		Peak Map Physical memory (bytes)=302153728
		Peak Map Virtual memory (bytes)=2588925952
	File Input Format Counters 
		Bytes Read=24410644
	File Output Format Counters 
		Bytes Written=1455064
[root@cent7-1 hadoop-3.2.4]# 

## 统计输出文件行数
[root@cent7-1 hadoop-3.2.4]# hdfs dfs -cat hdfs://cent7-1:9000/data/res231022/* | wc -l
46990
## 查看原始数据记录数
[root@cent7-1 hadoop-3.2.4]# hdfs dfs -cat hdfs://cent7-1:9000/data/videoinfo/231022/* | wc -l
90000

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    数据指标统计

    1. 对数据中的金币数量，总观看PV，粉丝关注数量，视频总时长等指标进行统计（涉及四个字段为了后续方便，可以自定义Writable）
    2. 统计每天开播时长最长的前10名主播以及对应的开播时长

    自定义Writeable代码实现

    由于原始数据涉及多个需要统计的字段，可以将这些字段统一的记录在一个自定义的数据类型中，方便使用

    package videoinfo;

import org.apache.hadoop.io.Writable;

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

/**
 * @author Huathy
 * @date 2023-10-22 23:32
 * @description 自定义数据类型，为了保存主播相关核心字段，方便后期维护
 */
public class VideoInfoWriteable implements Writable {
    private long gold;
    private long watchnumpv;
    private long follower;
    private long length;

    public void set(long gold, long watchnumpv, long follower, long length) {
        this.gold = gold;
        this.watchnumpv = watchnumpv;
        this.follower = follower;
        this.length = length;
    }

    public long getGold() {
        return gold;
    }

    public long getWatchnumpv() {
        return watchnumpv;
    }

    public long getFollower() {
        return follower;
    }

    public long getLength() {
        return length;
    }

    @Override
    public void write(DataOutput dataOutput) throws IOException {
        dataOutput.writeLong(gold);
        dataOutput.writeLong(watchnumpv);
        dataOutput.writeLong(follower);
        dataOutput.writeLong(length);
    }

    @Override
    public void readFields(DataInput dataInput) throws IOException {
        this.gold = dataInput.readLong();
        this.watchnumpv = dataInput.readLong();
        this.follower = dataInput.readLong();
        this.length = dataInput.readLong();
    }

    @Override
    public String toString() {
        return gold + "\t" + watchnumpv + "\t" + follower + "\t" + length;
    }
}

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    基于主播维度 videoinfo

    VideoInfoJob

    package videoinfo;

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.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

/**
 * @author Huathy
 * @date 2023-10-22 23:27
 * @description 数据指标统计作业
 * 1. 基于主播进行统计，统计每个主播在当天收到的总金币数量，总观看PV，总粉丝关注量，总视频开播市场
 * 分析
 * 1. 为了方便统计主播的指标数据吗，最好是把这些字段整合到一个对象中，这样维护方便
 * 这样就需要自定义Writeable
 * 2. 由于在这里需要以主播维度进行数据的聚合，所以需要以主播ID作为KEY，进行聚合统计
 * 3. 所以Map节点的是
 * 4. 由于需要聚合，所以Reduce阶段也需要
 */
public class VideoInfoJob {
    public static void main(String[] args) throws Exception {
        System.out.println("inputPath  => " + args[0]);
        System.out.println("outputPath  => " + args[1]);
        String path = args[0];
        String path2 = args[1];

        // job需要的配置参数
        Configuration configuration = new Configuration();
        // 创建job
        Job job = Job.getInstance(configuration, "VideoInfoJob");
        // 注意：这一行必须设置，否则在集群的时候将无法找到Job类
        job.setJarByClass(VideoInfoJob.class);
        // 指定输入文件
        FileInputFormat.setInputPaths(job, new Path(path));
        FileOutputFormat.setOutputPath(job, new Path(path2));

        // 指定map相关配置
        job.setMapperClass(VideoInfoMap.class);
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(LongWritable.class);
        // 指定reduce
        job.setReducerClass(VideoInfoReduce.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(LongWritable.class);

        // 提交任务
        job.waitForCompletion(true);
    }
}

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    VideoInfoMap

    package videoinfo;

import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;

import java.io.IOException;

/**
 * @author Huathy
 * @date 2023-10-22 23:31
 * @description 实现自定义Map类，在这里实现核心字段的拼接
 */
public class VideoInfoMap extends Mapper<LongWritable, Text, Text, VideoInfoWriteable> {
    @Override
    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
        // 读取清洗后的每一行数据
        String line = value.toString();
        String[] fields = line.split("\t");
        String uid = fields[0];
        long gold = Long.parseLong(fields[1]);
        long watchnumpv = Long.parseLong(fields[1]);
        long follower = Long.parseLong(fields[1]);
        long length = Long.parseLong(fields[1]);

        // 组装K2 V2
        Text k2 = new Text();
        k2.set(uid);

        VideoInfoWriteable v2 = new VideoInfoWriteable();
        v2.set(gold, watchnumpv, follower, length);
        context.write(k2, v2);
    }
}

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    VideoInfoReduce

    package videoinfo;

import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;

import java.io.IOException;

/**
 * @author Huathy
 * @date 2023-10-22 23:31
 * @description 实现自定义Map类，在这里实现核心字段的拼接
 */
public class VideoInfoReduce extends Reducer<Text, VideoInfoWriteable, Text, VideoInfoWriteable> {
    @Override
    protected void reduce(Text key, Iterable<VideoInfoWriteable> values, Context context) throws IOException, InterruptedException {
        // 从v2s中把相同key的value取出来，进行累加求和
        long goldSum = 0;
        long watchNumPvSum = 0;
        long followerSum = 0;
        long lengthSum = 0;
        for (VideoInfoWriteable v2 : values) {
            goldSum += v2.getGold();
            watchNumPvSum += v2.getWatchnumpv();
            followerSum += v2.getFollower();
            lengthSum += v2.getLength();
        }
        // 组装k3 v3
        VideoInfoWriteable videoInfoWriteable = new VideoInfoWriteable();
        videoInfoWriteable.set(goldSum, watchNumPvSum, followerSum, lengthSum);
        context.write(key, videoInfoWriteable);
    }
}

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    基于主播的TOPN计算

    VideoInfoTop10Job

    package top10;

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.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

/**
 * @author Huathy
 * @date 2023-10-23 21:27
 * @description 数据指标统计作业
 * 需求：统计每天开播时长最长的前10名主播以及时长信息
 * 分析：
 * 1. 为了统计每天开播时长最长的前10名主播信息，需要在map阶段获取数据中每个主播的ID和直播时长
 * 2. 所以map阶段的k2 v2 为Text LongWriteable
 * 3. 在reduce阶段对相同主播的时长进行累加求和，将这些数据存储到一个临时的map中
 * 4. 在reduce阶段的cleanup函数(最后执行)中，对map集合的数据进行排序处理
 * 5. 在cleanup函数中把直播时长最长的前10名主播信息写出到文件中
 * setup函数在reduce函数开始执行一次，而cleanup在结束时执行一次
 */
public class VideoInfoTop10Job {
    public static void main(String[] args) throws Exception {
        System.out.println("inputPath  => " + args[0]);
        System.out.println("outputPath  => " + args[1]);
        String path = args[0];
        String path2 = args[1];

        // job需要的配置参数
        Configuration configuration = new Configuration();
        // 从输入路径来获取日期
        String[] fields = path.split("/");
        String tmpdt = fields[fields.length - 1];
        System.out.println("日期：" + tmpdt);
        // 生命周期的配置
        configuration.set("dt", tmpdt);
        // 创建job
        Job job = Job.getInstance(configuration, "VideoInfoTop10Job");
        // 注意：这一行必须设置，否则在集群的时候将无法找到Job类
        job.setJarByClass(VideoInfoTop10Job.class);
        // 指定输入文件
        FileInputFormat.setInputPaths(job, new Path(path));
        FileOutputFormat.setOutputPath(job, new Path(path2));

        job.setMapperClass(VideoInfoTop10Map.class);
        job.setReducerClass(VideoInfoTop10Reduce.class);
        // 指定map相关配置
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(LongWritable.class);
        // 指定reduce
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(LongWritable.class);

        // 提交任务
        job.waitForCompletion(true);
    }
}

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    VideoInfoTop10Map

    package top10;

import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;

import java.io.IOException;

/**
 * @author Huathy
 * @date 2023-10-23 21:32
 * @description 自定义map类，在这里实现核心字段的拼接
 */
public class VideoInfoTop10Map extends Mapper<LongWritable, Text, Text, LongWritable> {
    @Override
    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
        // 读取清洗之后的每一行数据
        String line = key.toString();
        String[] fields = line.split("\t");
        String uid = fields[0];
        long length = Long.parseLong(fields[4]);
        Text k2 = new Text();
        k2.set(uid);
        LongWritable v2 = new LongWritable();
        v2.set(length);
        context.write(k2, v2);
    }
}

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    VideoInfoTop10Reduce

    package top10;

import cn.hutool.core.collection.CollUtil;
import org.apache.commons.collections.CollectionUtils;
import org.apache.commons.collections.MapUtils;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;

import java.io.IOException;
import java.util.*;

/**
 * @author Huathy
 * @date 2023-10-23 21:37
 * @description
 */
public class VideoInfoTop10Reduce extends Reducer<Text, LongWritable, Text, LongWritable> {
    // 保存主播ID和开播时长
    Map<String, Long> map = new HashMap<>();

    @Override
    protected void reduce(Text key, Iterable<LongWritable> values, Context context) throws IOException, InterruptedException {
        String k2 = key.toString();
        long lengthSum = 0;
        for (LongWritable v2 : values) {
            lengthSum += v2.get();
        }
        map.put(k2, lengthSum);
    }

    /**
     * 任务初始化的时候执行一次，一般在里面做一些初始化资源连接的操作。（mysql、redis连接操作）
     *
     * @param context
     * @throws IOException
     * @throws InterruptedException
     */
    @Override
    protected void setup(Context context) throws IOException, InterruptedException {
        System.out.println("setup method running...");
        System.out.println("context: " + context);
        super.setup(context);
    }

    /**
     * 任务结束的时候执行一次，做关闭资源连接操作
     *
     * @param context
     * @throws IOException
     * @throws InterruptedException
     */
    @Override
    protected void cleanup(Context context) throws IOException, InterruptedException {
        // 获取日期
        Configuration configuration = context.getConfiguration();
        String date = configuration.get("dt");
        // 排序
        LinkedHashMap<String, Long> sortMap = CollUtil.sortByEntry(map, new Comparator<Map.Entry<String, Long>>() {
            @Override
            public int compare(Map.Entry<String, Long> o1, Map.Entry<String, Long> o2) {
                return -o1.getValue().compareTo(o2.getValue());
            }
        });
        Set<Map.Entry<String, Long>> entries = sortMap.entrySet();
        Iterator<Map.Entry<String, Long>> iterator = entries.iterator();
        // 输出
        int count = 1;
        while (count <= 10 && iterator.hasNext()) {
            Map.Entry<String, Long> entry = iterator.next();
            String key = entry.getKey();
            Long value = entry.getValue();
            // 封装K3 V3
            Text k3 = new Text(date + "\t" + key);
            LongWritable v3 = new LongWritable(value);
            // 统计的时候还应该传入日期来用来输出统计的时间，而不是获取当前时间（可能是统计历史）！
            context.write(k3, v3);
            count++;
        }
    }
}

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    任务定时脚本封装

    任务依赖关系：数据指标统计（top10统计以及播放数据统计）依赖数据清洗作业
    将任务提交命令进行封装，方便调用，便于定时任务调度

    编写任务脚本，并以debug模式执行：sh -x data_clean.sh

    任务执行结果监控

    针对任务执行的结果进行检测，如果执行失败，则重试任务，同时发送告警信息。

    #!/bin/bash
# 建议使用bin/bash形式
# 判读用户是否输入日期，如果没有则默认获取昨天日期。（需要隔几天重跑，灵活的指定日期）
if [ "x$1" = "x" ]; then
  yes_time=$(date +%y%m%d --date="1 days ago")
else
  yes_time=$1
fi

jobs_home=/home/jobs
cleanjob_input=hdfs://cent7-1:9000/data/videoinfo/${yes_time}
cleanjob_output=hdfs://cent7-1:9000/data/videoinfo_clean/${yes_time}
videoinfojob_input=${cleanjob_output}
videoinfojob_output=hdfs://cent7-1:9000/res/videoinfoJob/${yes_time}
top10job_input=${cleanjob_output}
top10job_output=hdfs://cent7-1:9000/res/top10/${yes_time}

# 删除输出目录，为了兼容脚本重跑
hdfs dfs -rm -r ${cleanjob_output}
# 执行数据清洗任务
hadoop jar ${jobs_home}/hadoopDemo1-0.0.1-SNAPSHOT-jar-with-dependencies.jar \
  dataClean.DataCleanJob \
  ${cleanjob_input} ${cleanjob_output}

# 判断数据清洗任务是否成功
hdfs dfs -ls ${cleanjob_output}/_SUCCESS
# echo $? 可以获取上一个命令的执行结果0成功，否则失败
if [ "$?" = "0" ]; then
  echo "clean job execute success ...."
  # 删除输出目录，为了兼容脚本重跑
  hdfs dfs -rm -r ${videoinfojob_output}
  hdfs dfs -rm -r ${top10job_output}
  # 执行指标统计任务1
  echo " execute VideoInfoJob ...."
  hadoop jar ${jobs_home}/hadoopDemo1-0.0.1-SNAPSHOT-jar-with-dependencies.jar \
    videoinfo.VideoInfoJob \
    ${videoinfojob_input} ${videoinfojob_output}
  hdfs dfs -ls ${videoinfojob_output}/_SUCCESS
  if [ "$?" != "0" ]
  then
    echo " VideoInfoJob execute failed .... "
  fi
  # 指定指标统计任务2
  echo " execute VideoInfoTop10Job ...."
  hadoop jar ${jobs_home}/hadoopDemo1-0.0.1-SNAPSHOT-jar-with-dependencies.jar \
    top10.VideoInfoTop10Job \
    ${top10job_input} ${top10job_output}
  hdfs dfs -ls ${top10job_output}/_SUCCESS
  if [ "$?" != "0" ]
  then
    echo " VideoInfoJob execute failed .... "
  fi
else
  echo "clean job execute failed ... date time is ${yes_time}"
  # 给管理员发送短信、邮件
  # 可以在while进行重试
fi

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    使用Sqoop将计算结果导出到MySQL

    Sqoop可以快速的实现hdfs-mysql的导入导出

    快速安装Sqoop工具

    image.png

    image.png

    数据导出功能开发，使用Sqoop将MapReduce计算的结果导出到Mysql中

    1. 导出命令
    sqoop export \
--connect 'jdbc:mysql://192.168.56.101:3306/data?serverTimezone=UTC&useSSL=false' \
--username 'hdp' \
--password 'admin' \
--table 'top10' \
--export-dir '/res/top10/231022' \
--input-fields-terminated-by "\t"

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    1. 导出日志
    [root@cent7-1 sqoop-1.4.7.bin_hadoop-2.6.0]# sqoop export \
> --connect 'jdbc:mysql://192.168.56.101:3306/data?serverTimezone=UTC&useSSL=false' \
> --username 'hdp' \
> --password 'admin' \
> --table 'top10' \
> --export-dir '/res/top10/231022' \
> --input-fields-terminated-by "\t"
Warning: /home/sqoop-1.4.7.bin_hadoop-2.6.0//../hcatalog does not exist! HCatalog jobs will fail.
Please set $HCAT_HOME to the root of your HCatalog installation.
Warning: /home/sqoop-1.4.7.bin_hadoop-2.6.0//../accumulo does not exist! Accumulo imports will fail.
Please set $ACCUMULO_HOME to the root of your Accumulo installation.
2023-10-24 23:42:09,452 INFO sqoop.Sqoop: Running Sqoop version: 1.4.7
2023-10-24 23:42:09,684 WARN tool.BaseSqoopTool: Setting your password on the command-line is insecure. Consider using -P instead.
2023-10-24 23:42:09,997 INFO manager.MySQLManager: Preparing to use a MySQL streaming resultset.
2023-10-24 23:42:10,022 INFO tool.CodeGenTool: Beginning code generation
Loading class `com.mysql.jdbc.Driver'. This is deprecated. The new driver class is `com.mysql.cj.jdbc.Driver'. The driver is automatically registered via the SPI and manual loading of the driver class is generally unnecessary.
2023-10-24 23:42:10,921 INFO manager.SqlManager: Executing SQL statement: SELECT t.* FROM `top10` AS t LIMIT 1
2023-10-24 23:42:11,061 INFO manager.SqlManager: Executing SQL statement: SELECT t.* FROM `top10` AS t LIMIT 1
2023-10-24 23:42:11,084 INFO orm.CompilationManager: HADOOP_MAPRED_HOME is /home/hadoop-3.2.4
注: /tmp/sqoop-root/compile/6d507cd9a1a751990abfd7eef20a60c2/top10.java使用或覆盖了已过时的 API。
注: 有关详细信息, 请使用 -Xlint:deprecation 重新编译。
2023-10-24 23:42:23,932 INFO orm.CompilationManager: Writing jar file: /tmp/sqoop-root/compile/6d507cd9a1a751990abfd7eef20a60c2/top10.jar
2023-10-24 23:42:23,972 INFO mapreduce.ExportJobBase: Beginning export of top10
2023-10-24 23:42:23,972 INFO Configuration.deprecation: mapred.job.tracker is deprecated. Instead, use mapreduce.jobtracker.address
2023-10-24 23:42:24,237 INFO Configuration.deprecation: mapred.jar is deprecated. Instead, use mapreduce.job.jar
2023-10-24 23:42:27,318 INFO Configuration.deprecation: mapred.reduce.tasks.speculative.execution is deprecated. Instead, use mapreduce.reduce.speculative
2023-10-24 23:42:27,325 INFO Configuration.deprecation: mapred.map.tasks.speculative.execution is deprecated. Instead, use mapreduce.map.speculative
2023-10-24 23:42:27,326 INFO Configuration.deprecation: mapred.map.tasks is deprecated. Instead, use mapreduce.job.maps
2023-10-24 23:42:27,641 INFO client.RMProxy: Connecting to ResourceManager at /0.0.0.0:8032
2023-10-24 23:42:29,161 INFO mapreduce.JobResourceUploader: Disabling Erasure Coding for path: /tmp/hadoop-yarn/staging/root/.staging/job_1698153196891_0015
2023-10-24 23:42:39,216 INFO input.FileInputFormat: Total input files to process : 1
2023-10-24 23:42:39,231 INFO input.FileInputFormat: Total input files to process : 1
2023-10-24 23:42:39,387 INFO mapreduce.JobSubmitter: number of splits:4
2023-10-24 23:42:39,475 INFO Configuration.deprecation: mapred.map.tasks.speculative.execution is deprecated. Instead, use mapreduce.map.speculative
2023-10-24 23:42:40,171 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1698153196891_0015
2023-10-24 23:42:40,173 INFO mapreduce.JobSubmitter: Executing with tokens: []
2023-10-24 23:42:40,660 INFO conf.Configuration: resource-types.xml not found
2023-10-24 23:42:40,660 INFO resource.ResourceUtils: Unable to find 'resource-types.xml'.
2023-10-24 23:42:41,073 INFO impl.YarnClientImpl: Submitted application application_1698153196891_0015
2023-10-24 23:42:41,163 INFO mapreduce.Job: The url to track the job: http://cent7-1:8088/proxy/application_1698153196891_0015/
2023-10-24 23:42:41,164 INFO mapreduce.Job: Running job: job_1698153196891_0015
2023-10-24 23:43:02,755 INFO mapreduce.Job: Job job_1698153196891_0015 running in uber mode : false
2023-10-24 23:43:02,760 INFO mapreduce.Job:  map 0% reduce 0%
2023-10-24 23:43:23,821 INFO mapreduce.Job:  map 25% reduce 0%
2023-10-24 23:43:25,047 INFO mapreduce.Job:  map 50% reduce 0%
2023-10-24 23:43:26,069 INFO mapreduce.Job:  map 75% reduce 0%
2023-10-24 23:43:27,088 INFO mapreduce.Job:  map 100% reduce 0%
2023-10-24 23:43:28,112 INFO mapreduce.Job: Job job_1698153196891_0015 completed successfully
2023-10-24 23:43:28,266 INFO mapreduce.Job: Counters: 33
	File System Counters
		FILE: Number of bytes read=0
		FILE: Number of bytes written=993808
		FILE: Number of read operations=0
		FILE: Number of large read operations=0
		FILE: Number of write operations=0
		HDFS: Number of bytes read=1297
		HDFS: Number of bytes written=0
		HDFS: Number of read operations=19
		HDFS: Number of large read operations=0
		HDFS: Number of write operations=0
		HDFS: Number of bytes read erasure-coded=0
	Job Counters 
		Launched map tasks=4
		Data-local map tasks=4
		Total time spent by all maps in occupied slots (ms)=79661
		Total time spent by all reduces in occupied slots (ms)=0
		Total time spent by all map tasks (ms)=79661
		Total vcore-milliseconds taken by all map tasks=79661
		Total megabyte-milliseconds taken by all map tasks=81572864
	Map-Reduce Framework
		Map input records=10
		Map output records=10
		Input split bytes=586
		Spilled Records=0
		Failed Shuffles=0
		Merged Map outputs=0
		GC time elapsed (ms)=3053
		CPU time spent (ms)=11530
		Physical memory (bytes) snapshot=911597568
		Virtual memory (bytes) snapshot=10326462464
		Total committed heap usage (bytes)=584056832
		Peak Map Physical memory (bytes)=238632960
		Peak Map Virtual memory (bytes)=2584969216
	File Input Format Counters 
		Bytes Read=0
	File Output Format Counters 
		Bytes Written=0
2023-10-24 23:43:28,282 INFO mapreduce.ExportJobBase: Transferred 1.2666 KB in 60.9011 seconds (21.2968 bytes/sec)
2023-10-24 23:43:28,291 INFO mapreduce.ExportJobBase: Exported 10 records.

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  • 原文地址：https://blog.csdn.net/qq_40366738/article/details/134044167