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一.执行命令:设置MR参数
yarn jar hdp-jar-with-dependencies.jar \
cn.tl.WordCount \
-Dmapred.output.compress=true \
-Dmapred.output.compression.codec=org.apache.hadoop.io.compress.GzipCodec \
-Dmapred.reduce.tasks=2 \
/user/input /user/output1
2.自定义partition
/**
* 自定义Partition的定义
* 自定义规则:单词首字母在a - p为一个分区,q - z为另一个分区
*/
public static class MyHashPartitioner<K, V> extends Partitioner<K, V> {
public int getPartition(K key, V value, int numReduceTasks) {
return (key.toString().charAt(0) < 'q' ? 0 : 1) % numReduceTasks;
}
}
3.wordcount二次排序
package cn.tl.secondsort;
import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.io.WritableComparable;
import org.apache.hadoop.io.WritableComparator;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Partitioner;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.util.GenericOptionsParser;
// 启动mr的driver类
public class SecondSort {
/**
* 自定义的newKey
*/
public static class NewKeyWritable implements
WritableComparable<NewKeyWritable> {
// 组合key,key1是分区key,key2是二次排序key
private String key1;
private int key2;
public NewKeyWritable() {
}
public NewKeyWritable(String key1, int key2) {
this.set(key1, key2);
}
// 一次性将两个key设置成完
public void set(String key1, int key2) {
this.key1 = key1;
this.key2 = key2;
}
// 当map端写出的时候的序列化方法,即map如何将对象写出去,保证与读取的顺序一致
@Override
public void write(DataOutput arg0) throws IOException {
arg0.writeUTF(key1);
arg0.writeInt(key2);
}
// 在reducer读取数据时候的反序列化方法,即reduce如何将对象读取出来,保证与写入的顺序一致
@Override
public void readFields(DataInput arg0) throws IOException {
this.key1 = arg0.readUTF();
this.key2 = arg0.readInt();
}
// 自定义比较器方法,先比较key1,确定分区号。在分区号相同的情况下,去比较key2
// 就不需要单独写一个Comparator了
public int compareTo(NewKeyWritable o) {
int compare = this.key1.compareTo(o.key1);
if (compare != 0) {
return compare;
} else {
// 降序排列,故将o放到前边即可
return Integer.valueOf(o.key2).compareTo(
Integer.valueOf(this.getkey2()));
}
}
public int getkey2() {
return key2;
}
public void setkey2(int key2) {
this.key2 = key2;
}
public String getkey1() {
return key1;
}
public void setkey1(String key1) {
this.key1 = key1;
}
}
// map类,实现map函数
public static class LineProcessMapper extends
Mapper<Object, Text, NewKeyWritable, IntWritable> {
// 暂存每个传过来的词的值,省掉重复申请空间
private NewKeyWritableoutputKey = new NewKeyWritable();
private IntWritable outputValue = new IntWritable();
// 核心map方法的具体实现,逐个<key,value>对去处理
public void map(Object key, Text value, Context context)
throws IOException, InterruptedException {
// 通过context对象,将map的输出逐个输出
String tempLine = value.toString();
if (tempLine != null && tempLine.trim().length() > 0) {
String[] columnArray = tempLine.split("\\s");
outputKey.set(columnArray[0], Integer.parseInt(columnArray[1]));
outputValue.set(Integer.parseInt(columnArray[1]));
context.write(outputKey, outputValue);
}
}
}
/**
* 自定义分区类,包证同key的记录,如S1,S2等,能映射到相同的reduce端去处理
*/
public static class SecondPartitioner extends
Partitioner<NewKeyWritable, IntWritable> {
// 采集默认的HashPartiton实现即可
@Override
public int getPartition(NewKeyWritablekey, IntWritable value,
int numPartitions) {
/*
* 默认的实现 (key.hashCode() & Integer.MAX_VALUE) % numPartitions
* 让key中first字段作为分区依据
*/
return (key.getkey1().hashCode() & Integer.MAX_VALUE)
% numPartitions;
}
}
/**
* 在shuffle阶段的sort全局排序完成后,如何对数据记录进行分组
*/
public static class SecondSortGroupComparator extends WritableComparator {
// 对象NewKeyWritable.class注册,让比较器知道该对象并能够初始化
protected SecondSortGroupComparator() {
super(NewKeyWritable.class, true);
}
@Override
public int compare(WritableComparable first, WritableComparable second) {
if (first == null || second == null) {
return 0;
}
NewKeyWritable newKey1 = (NewKeyWritable) first;
NewKeyWritable newKey2 = (NewKeyWritable) second;
// 自定义按原始数据中第一个key分组
return newKey1.getkey1().compareTo(newKey2.getkey1());
}
}
// reduce类,实现reduce函数
public static class SortReducer extends
Reducer<NewKeyWritable, IntWritable, Text, IntWritable> {
private Text outputKey = new Text();
// 核心reduce方法的具体实现,逐个<key,List(v1,v2)>去处理
public void reduce(NewKeyWritable keyPair,
Iterable<IntWritable> values, Context context)
throws IOException, InterruptedException {
// 进来时已经排序完成
outputKey.set(keyPair.getkey1());
for (IntWritable val : values) {
context.write(outputKey, val);
}
}
}
// 启动mr的driver方法
public static void main(String[] args) throws Exception {
// 得到集群配置参数
Configuration conf = new Configuration();
// 参数解析器
GenericOptionsParser optionParser = new GenericOptionsParser(conf, args);
String[] remainingArgs = optionParser.getRemainingArgs();
if ((remainingArgs.length != 2)) {
System.err
.println("Usage: yarn jar jar_path main_class_path -D参数列表 <in> <out>");
System.exit(2);
}
// 设置到本次的job实例中
Job job = Job.getInstance(conf, "mr二次排序");
// 指定本次执行的主类是WordCount
job.setJarByClass(SecondSort.class);
// 指定map类
job.setMapperClass(LineProcessMapper.class);
// 指定partition类
job.setPartitionerClass(SecondPartitioner.class);
job.setGroupingComparatorClass(SecondSortGroupComparator.class);
// 指定reducer类
job.setReducerClass(SortReducer.class);
// 指定job输出的key和value的类型,如果map和reduce输出类型不完全相同,需要重新设置map的output的key和value的class类型
job.setMapOutputKeyClass(NewKeyWritable.class);
job.setMapOutputValueClass(IntWritable.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
// 指定输入数据的路径
FileInputFormat.addInputPath(job, new Path(remainingArgs[0]));
// 指定输出路径,并要求该输出路径一定是不存在的
FileOutputFormat.setOutputPath(job, new Path(remainingArgs[1]));
// 指定job执行模式,等待任务执行完成后,提交任务的客户端才会退出!
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
}
这里的重点是对mr过程的理解。在我看来mr除了分布式、并行之外,它的运行过程的核心就是:对Key进行排序。因此,我们很多mr的变形都要围绕着"如何来构造合适的Key"进行。同时,注意:Partitioner规则、Key排序规则、Group分组规则。
4.传递参数
- Configuration传递:在Job启动时,通过Configuration存储白名单数据,传递给多个Mapper和Reducer。
package cn.tl.mr;
import java.io.BufferedReader;
import java.io.File;
import java.io.FileInputStream;
import java.io.IOException;
import java.io.InputStreamReader;
import java.util.Arrays;
import java.util.HashSet;
import java.util.Set;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
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 org.apache.hadoop.util.GenericOptionsParser;
import org.apache.log4j.Logger;
//启动mr的driver类
public class ConfigSetTransferDriver {
public static Logger logger = Logger.getLogger(ConfigSetTransferDriver.class);
// map类,实现map函数
public static class LineProcessMapper extends Mapper<Object, Text, Text, IntWritable> {
// 暂存每个传过来的词的值,省掉重复申请空间
private Text outputKey = new Text();
private IntWritable outputValue = new IntWritable();
// 过滤whitename的set集合
private Set<String> whiteNameSet = new HashSet<String>();
// 每个map任务有且仅会执行一次setup方法,用于初始化map函数执行前的所需参数
@Override
protected void setup(Mapper<Object, Text, Text, IntWritable>.Context context)
throws IOException, InterruptedException {
Configuration conf = context.getConfiguration();
String whitelistString = conf.get("whitelist");
String[] whiteNameArray = whitelistString.split("\\s");
whiteNameSet.addAll(Arrays.asList(whiteNameArray));
}
// 核心map方法的具体实现,逐个<key,value>对去处理
public void map(Object key, Text value, Context context) throws IOException, InterruptedException {
// 通过context对象,将map的输出逐个输出
String tempLine = value.toString();
if (tempLine != null && tempLine.trim().length() > 0) {
String[] columnArray = tempLine.split("\\s");
if (whiteNameSet.contains(columnArray[0])) {
outputKey.set(columnArray[0]);
outputValue.set(Integer.parseInt(columnArray[1]));
context.write(outputKey, outputValue);
}
}
}
}
// reduce类,实现reduce函数
public static class SortReducer extends Reducer<Text, IntWritable, Text, IntWritable> {
// 核心reduce方法的具体实现,逐个<key,List(v1,v2)>去处理
public void reduce(Text key, Iterable<IntWritable> values, Context context)
throws IOException, InterruptedException {
// 加强型for,依次获取迭代器中的每个元素值
for (IntWritable val : values) {
// 将计算结果逐条输出
context.write(key, val);
}
}
}
// 读取一个指定本地路径和文件编码的文件内容,转换成字符串
public static String readFile(String filePath, String fileEncoding) {
if (fileEncoding == null) {
fileEncoding = System.getProperty("file.encoding");
}
File file = new File(filePath);
BufferedReader br = null;
String line = null;
StringBuilder sb = new StringBuilder();
try {
br = new BufferedReader(new InputStreamReader(new FileInputStream(file), fileEncoding));
while ((line = br.readLine()) != null) {
sb.append(line + "\n");
}
return sb.toString();
} catch (Exception e) {
logger.info(e.getLocalizedMessage());
} finally {
if (br != null) {
try {
br.close();
} catch (IOException e) {
logger.info(e.getLocalizedMessage());
logger.info("关闭IOUtil流时出现错误!");
}
}
}
return null;
}
// 配置文件读取与值传递
public static void readConfigAndTransfer(Configuration conf, String filePath) {
// 读取本地配置文件
String source = readFile(filePath, "utf-8");
// 将配置文件中的值通过conf set的方式传递 到计算节点中
conf.set("whitelist", source);
// 通过日志打印的方式,将读取到的值,打印出来,如不打印日志,可去除以下代码段
logger.info("whitelist=" + source);
}
// 启动mr的driver方法
public static void main(String[] args) throws Exception {
// 得到集群配置参数
Configuration conf = new Configuration();
// 参数解析器
GenericOptionsParser optionParser = new GenericOptionsParser(conf, args);
String[] remainingArgs = optionParser.getRemainingArgs();
if ((remainingArgs.length < 3)) {
System.err.println("Usage: yarn jar jar_path main_class_path -D参数列表 <in> <out>");
System.exit(2);
}
// 配置参数读取与传递
readConfigAndTransfer(conf, remainingArgs[2]);
// 设置到本次的job实例中
Job job = Job.getInstance(conf, "configuration传参");
// 指定本次执行的主类是WordCount
job.setJarByClass(ConfigSetTransferDriver.class);
// 指定map类
job.setMapperClass(LineProcessMapper.class);
// 指定reducer类
job.setReducerClass(SortReducer.class);
// 指定job输出的key和value的类型,如果map和reduce输出类型不完全相同,需要重新设置map的output的key和value的class类型
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
// 指定输入数据的路径
FileInputFormat.addInputPath(job, new Path(remainingArgs[0]));
// 指定输出路径,并要求该输出路径一定是不存在的
FileOutputFormat.setOutputPath(job, new Path(remainingArgs[1]));
// 指定job执行模式,等待任务执行完成后,提交任务的客户端才会退出!
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
}
- DistributedCache:通过DistributedCache传递参数,会将HDFS上的白名单文件自动同步到每个Map任务的本地临时目录中,然后,每个Mapper或Reducer任务可以读取本地临时目录的该文件获取参数。
package cn.tl.mr;
import java.io.BufferedReader;
import java.io.File;
import java.io.FileInputStream;
import java.io.IOException;
import java.io.InputStreamReader;
import java.util.Arrays;
import java.util.HashSet;
import java.util.Set;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
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 org.apache.hadoop.util.GenericOptionsParser;
import org.apache.log4j.Logger;
public class ConfigSetDistributeCacheDriver {
public static Logger logger = Logger.getLogger(ConfigSetDistributeCacheDriver.class);
// map类,实现map函数
public static class LineProcessMapper extends Mapper<Object, Text, Text, IntWritable> {
// 暂存每个传过来的词的值,省掉重复申请空间
private Text outputKey = new Text();
private IntWritable outputValue = new IntWritable();
// 过滤whitename的set集合
private Set<String> whiteNameSet = new HashSet<String>();
public static String readFile(String filePath, String fileEncoding) {
if (fileEncoding == null) {
fileEncoding = System.getProperty("file.encoding");
}
File file = new File(filePath);
BufferedReader br = null;
String line = null;
StringBuilder sb = new StringBuilder();
try {
br = new BufferedReader(new InputStreamReader(new FileInputStream(file), fileEncoding));
while ((line = br.readLine()) != null) {
sb.append(line + "\n");
}
return sb.toString();
} catch (Exception e) {
logger.info(e.getLocalizedMessage());
} finally {
if (br != null) {
try {
br.close();
} catch (IOException e) {
logger.info(e.getLocalizedMessage());
logger.info("关闭IOUtil流时出现错误!");
}
}
}
return null;
}
@Override
protected void setup(Mapper<Object, Text, Text, IntWritable>.Context context)
throws IOException, InterruptedException {
// 通过上下文,获取本地缓存的配置文件列表
Path[] localCacheFiles = context.getLocalCacheFiles();
// 因为只缓存了一个,故直接读取第1个即可
String whiteListSource = readFile(localCacheFiles[0].toString(), "utf-8");
// 将读取的文件路径进行日志打印
logger.info("localCacheFiles=" + Arrays.toString(localCacheFiles));
// 将读出来的内容按空白分隔,形成一个字符串数组
String[] whiteNameArray = whiteListSource.split("\\s");
// 将字符串数组转化成set结构,专门用于去重
// whiteNameSet.addAll(Arrays.asList(whiteNameArray));
for (String str : whiteNameArray) {
whiteNameSet.add(str);
}
}
// 核心map方法的具体实现,逐个<key,value>对去处理
public void map(Object key, Text value, Context context) throws IOException, InterruptedException {
// 通过context对象,将map的输出逐个输出
String tempLine = value.toString();
if (tempLine != null && tempLine.trim().length() > 0) {
String[] columnArray = tempLine.split("\\s");
if (whiteNameSet.contains(columnArray[0])) {
outputKey.set(columnArray[0]);
outputValue.set(Integer.parseInt(columnArray[1]));
context.write(outputKey, outputValue);
}
}
}
}
// reduce类,实现reduce函数
public static class SortReducer extends Reducer<Text, IntWritable, Text, IntWritable>{
// 核心reduce方法的具体实现,逐个<key,List(v1,v2)>去处理
public void reduce(Text key, Iterable<IntWritable> values, Context context)
throws IOException, InterruptedException {
// 加强型for,依次获取迭代器中的每个元素值
for (IntWritable val : values) {
// 将计算结果逐条输出
context.write(key, val);
}
}
}
// 启动mr的driver方法
public static void main(String[] args) throws Exception {
// 得到集群配置参数
Configuration conf = new Configuration();
// 参数解析器
GenericOptionsParser optionParser = new GenericOptionsParser(conf, args);
String[] remainingArgs = optionParser.getRemainingArgs();
if ((remainingArgs.length < 3)) {
System.err.println("Usage: yarn jar jar_path main_class_path -D参数列表 <in> <out>");
System.exit(2);
}
// 设置到本次的job实例中
Job job = Job.getInstance(conf, "DistributeCache传参");
/**
* 将hdfs的路径添加到cache file列表中,框架将会自动将hdfs文件分发到map任务的临时目录中
*/
// DistributeCache传递配置文件,下方代码为2.x的实现
job.addCacheFile(new Path(remainingArgs[2]).toUri());
// 指定本次执行的主类是WordCount
job.setJarByClass(ConfigSetDistributeCacheDriver.class);
// 指定map类
job.setMapperClass(LineProcessMapper.class);
// 指定reducer类
job.setReducerClass(SortReducer.class);
// 指定job输出的key和value的类型,如果map和reduce输出类型不完全相同,需要重新设置map的output的key和value的class类型
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
// 指定输入数据的路径
FileInputFormat.addInputPath(job, new Path(remainingArgs[0]));
// 指定输出路径,并要求该输出路径一定是不存在的
FileOutputFormat.setOutputPath(job, new Path(remainingArgs[1]));
// 指定job执行模式,等待任务执行完成后,提交任务的客户端才会退出!
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
}