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-all/
{"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}
原始数据清洗代码
- 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。
- 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);
}
}
}
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.
* <p>
* 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<LongWritable,Text>，k2,v2的数据类型是<Text,Text>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);
}
}
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
数据指标统计
- 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）
- 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;
}
}
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<k2,v2>是<Text，自定义Writeable>
* 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);
}
}
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);
}
}
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);
}
}
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);
}
}
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);
}
}
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++;
}
}
}
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
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
文章来源：https://www.toymoban.com/news/detail-714523.html
文章来源地址https://www.toymoban.com/news/detail-714523.html
数据导出功能开发，使用Sqoop将MapReduce计算的结果导出到Mysql中
- 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"
- 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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