【打工网源码】【主力拉高出货源码】【2021卡牌手游源码】hadoop mapreduce源码-皮皮网

【打工网源码】【主力拉高出货源码】【2021卡牌手游源码】hadoop mapreduce源码

2024-11-14 15:21:26 来源：adl腾落指标源码分类：百科

1.å¦ä½ä½¿ç¨Pythonä¸ºHadoopç¼åä¸ä¸ªç®åçMapReduceç¨åº
2.mapreduceåhadoopçå³ç³»
3.hadoop的核心配置文件有哪些
4.Idea 开发Mapreduce遇到的问题，代码不能自动实现方法！搞了很久没搞出来，哪位大牛知道这个？
5.å¦ä½å¨Hadoopä¸ç¼åMapReduceç¨åº
6.Hadoop开源实现

hadoop mapreduce源码

å¦ä½ä½¿ç¨Pythonä¸ºHadoopç¼åä¸ä¸ªç®åçMapReduceç¨åº

æä»¬å°ç¼åä¸ä¸ªç®åç MapReduce ç¨åºï¼ä½¿ç¨çæ¯C-Pythonï¼èä¸æ¯Jythonç¼ååæåæjaråçç¨åºã

ããåå³æ¡ä»¶

ããå¦ä½ä½¿ç¨Hadoop Distributed File System (HDFS)å¨Ubuntu Linux å»ºç«åèç¹ç Hadoop éç¾¤

ããå¦ä½ä½¿ç¨Hadoop Distributed File System (HDFS)å¨Ubuntu Linux å»ºç«å¤èç¹ç Hadoop éç¾¤

ããPythonçMapReduceä»£ç

ããä½¿ç¨Pythonç¼åMapReduceä»£ç çæå·§å°±å¨äºæä»¬ä½¿ç¨äº HadoopStreaming æ¥å¸®å©æä»¬å¨Map å Reduceé´ä¼ éæ°æ®éè¿STDIN (æ åè¾å¥)åSTDOUT (æ åè¾åº).æä»¬ä»ä»ä½¿ç¨Pythonçsys.stdinæ¥è¾å¥æ°æ®ï¼ä½¿ç¨sys.stdoutè¾åºæ°æ®ï¼è¿æ ·åæ¯å ä¸ºHadoopStreamingä¼å¸®æä»¬åå¥½å¶ä»äºãè¿æ¯ççï¼å«ä¸ç¸ä¿¡ï¼

ããMap: mapper.py

ããæ³¨æï¼è¦ç¡®ä¿è¿ä¸ªèæ¬æè¶³å¤æéï¼chmod +x /home/hadoop/mapper.pyï¼ã

ãã#!/usr/bin/env python

ãã

ããimport sys

ãã

ãã# input comes from STDIN (standard input)

ããfor line in sys.stdin:

ãã# remove leading and trailing whitespace

ããline = line.strip()

ãã# split the line into words

ããwords = line.split()

ãã# increase counters

ããfor word in words:

ãã# write the results to STDOUT (standard output);

ãã# what we output here will be the input for the

ãã# Reduce step, i.e. the input for reducer.py

ãã#

ãã# tab-delimited; the trivial word count is 1

ããReduce: reducer.py

ããåæ ·ï¼è¦æ³¨æèæ¬æéï¼chmod +x /home/hadoop/reducer.py

ãã#!/usr/bin/env python

ãã

ããfrom operator import itemgetter

ããimport sys

ãã

ãã# maps words to their counts

ããword2count = { }

ãã

ãã# input comes from STDIN

ããfor line in sys.stdin:

ãã# remove leading and trailing whitespace

ããline = line.strip()

ãã

ãã# parse the input we got from mapper.py

ããword, count = line.split('\\t', 1)

ãã# convert count (currently a string) to int

ããtry:

ããcount = int(count)

ããword2count[word] = word2count.get(word, 0) + count

ããexcept ValueError:

ãã# count was not a number, so silently

ãã# ignore/discard this line

ããpass

ãã

ãã# sort the words lexigraphically;

ãã#

ãã# this step is NOT required, we just do it so that our

ãã# final output will look more like the official Hadoop

ãã# word count examples

ããsorted_word2count = sorted(word2count.items(), key=itemgetter(0))

ãã

ãã# write the results to STDOUT (standard output)

ããfor word, count in sorted_word2count:

ããprint '%s\\t%s'% (word, count)

ããæµè¯ä½ çä»£ç ï¼cat data | map | sort | reduceï¼

ããæå»ºè®®ä½ å¨è¿è¡MapReduce jobæµè¯åå°è¯æå·¥æµè¯ä½ çmapper.py å reducer.pyèæ¬ï¼ä»¥åå¾ä¸å°ä»»ä½è¿åç»æ

ããââââââââââââââââââââââââââââââââââââââââââââââ

ãã\r\n

ãã# very basic test

ããhadoop@ubuntu:~$ echo "foo foo quux labs foo bar quux" | /home/hadoop/mapper.py

ããfoo 1

ããquux 1

ããlabs 1

ããfoo 1

ããbar 1

ããââââââââââââââââââââââââââââââââââââââââââââââ

ããhadoop@ubuntu:~$ echo "foo foo quux labs foo bar quux" | /home/hadoop/mapper.py | sort | /home/hadoop/reducer.py

ããbar 1

ããfoo 3

ããlabs 1

ããââââââââââââââââââââââââââââââââââââââââââââââ

ãã# using one of the ebooks as example input

ãã# (see below on where to get the ebooks)

ããhadoop@ubuntu:~$ cat /tmp/gutenberg/-8.txt | /home/hadoop/mapper.py

ããThe 1

ããProject 1

ããGutenberg 1

ããEBook 1

ããof 1

ãã[...]

ãã(you get the idea)

ããquux 2

ããquux 1

ããââââââââââââââââââââââââââââââââââââââââââââââ

ããå¨Hadoopå¹³å°ä¸è¿è¡Pythonèæ¬

ããä¸ºäºè¿ä¸ªä¾åï¼æä»¬å°éè¦ä¸ç§çµåä¹¦ï¼

ããThe Outline of Science, Vol. 1 (of 4) by J. Arthur Thomson\r\n

ããThe Notebooks of Leonardo Da Vinci\r\n

ããUlysses by James Joyce

ããhadoop@ubuntu:~$ ls -l /tmp/gutenberg/

ããtotal

ãã-rw-r--r-- 1 hadoop hadoop -- : -8.txt

ãã-rw-r--r-- 1 hadoop hadoop -- : 7ldvc.txt

ãã-rw-r--r-- 1 hadoop hadoop -- : ulyss.txt

ããhadoop@ubuntu:~$

ããå¤å¶æ¬å°æ°æ®å°HDFS

ããhadoop@ubuntu:/usr/local/hadoop$ bin/hadoop dfs -copyFromLocal /tmp/gutenberg gutenberg

ããhadoop@ubuntu:/usr/local/hadoop$ bin/hadoop dfs -ls

ããFound 1 items

ãã/user/hadoop/gutenberg <dir>

ããhadoop@ubuntu:/usr/local/hadoop$ bin/hadoop dfs -ls gutenberg

ããFound 3 items

ãã/user/hadoop/gutenberg/-8.txt <r 1>

ãã/user/hadoop/gutenberg/7ldvc.txt <r 1>

ãã/user/hadoop/gutenberg/ulyss.txt <r 1>

ããæ§è¡ MapReduce job

ããHadoopStreaming å¸®å©æä»¬ä¼ éæ°æ®å¨MapåReduceé´å¹¶éè¿STDINåSTDOUTï¼è¿è¡æ ååè¾å¥è¾åºã

ããhadoop@ubuntu:/usr/local/hadoop$ bin/hadoop jar contrib/streaming/hadoop-0..1-streaming.jar

ãã-mapper /home/hadoop/mapper.py -reducer /home/hadoop/reducer.py -input gutenberg/

ãã-output gutenberg-output

ããhadoop@ubuntu:/usr/local/hadoop$ bin/hadoop jar contrib/streaming/hadoop-0..1-streaming.jar

ãã-jobconf mapred.reduce.tasks= -mapper ...

ããä¸ä¸ªéè¦çå¤å¿æ¯å³äºHadoop does not honor mapred.map.tasks

ããgutenberg-outputç®å½ã

ããä¹åæ§è¡çç»æå¦ä¸ï¼

ããhadoop@ubuntu:/usr/local/hadoop$ bin/hadoop jar contrib/streaming/hadoop-0..1-streaming.jar

ãã-mapper /home/hadoop/mapper.py -reducer /home/hadoop/reducer.py -input gutenberg/

ãã-output gutenberg-output

ãã

ããadditionalConfSpec_:null

ããnull=@@@userJobConfProps_.get(stream.shipped.hadoopstreaming

ããpackageJobJar: [/usr/local/hadoop-datastore/hadoop-hadoop/hadoop-unjar/]

ãã[] /tmp/streamjob.jar tmpDir=null

ãã[...] INFO mapred.FileInputFormat: Total input paths to process : 7

ãã[...] INFO streaming.StreamJob: getLocalDirs(): [/usr/local/hadoop-datastore/hadoop-hadoop/mapred/local]

ãã[...] INFO streaming.StreamJob: Running job: job__

ãã[...]

ãã[...] INFO streaming.StreamJob: map 0% reduce 0%

ãã[...] INFO streaming.StreamJob: map % reduce 0%

ãã[...] INFO streaming.StreamJob: map % reduce %

ãã[...] INFO streaming.StreamJob: Job complete: job__

ãã[...] INFO streaming.StreamJob: Output: gutenberg-output hadoop@ubuntu:/usr/local/hadoop$

mapreduceåhadoopçå³ç³»

hadoopæ¯ä¾æ®mapreduceçåçï¼ç¨Javaè¯è¨å®ç°çåå¸å¼å¤çæºå¶ã

MapReduceåå¸å¼è®¡ç®æ¡æ¶ååï¼

hadoop的核心配置文件有哪些

在Hadoop 1.x版本中，核心组件包括HDFS和MapReduce。而在Hadoop 2.x及之后的打工网源码版本中，核心组件更新为HDFS、Yarn，并且引入了High Availability（高可用性）的概念，允许存在多个NameNode，每个NameNode都具备相同的职能。

以下是关键的Hadoop配置文件及其作用概述：

1. `hadoop-env.sh`：

- 主要设置JDK的安装路径，例如：`export JAVA_HOME=/usr/local/jdk`

2. `core-site.xml`：

- `fs.defaultFS`：指定HDFS的默认名称节点地址，例如：`hdfs://cluster1`

- `hadoop.tmp.dir`：默认的临时文件存储路径，例如：`/export/data/hadoop_tmp`

- `ha.zookeeper.quorum`：ZooKeeper集群的地址和端口，例如：`hadoop:,hadoop:,hadoop:`

- `hadoop.proxyuser.erpmerge.hosts` 和 `hadoop.proxyuser.erpmerge.groups`：用于设置特定用户（如oozie）的代理权限

请注意，配置文件中的主力拉高出货源码路径和地址需要根据实际环境进行相应的修改。

Idea 开发Mapreduce遇到的问题，代码不能自动实现方法！搞了很久没搞出来，哪位大牛知道这个？

项目配置 File ---- Project Structure

1. SDK的配置

2. 加入Hadoop的jar包依赖

3.打包配置

4.开发map-reduce代码

<span style="font-size:px;">import java.io.IOException;

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.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;

public class Dedup {

//map将输入中的value复制到输出数据的key上，并直接输出

public static class Map extends Mapper<Object,Text,Text,Text>{

private static Text line=new Text();//每行数据

//实现map函数

public void map(Object key,Text value,Context context)

throws IOException,InterruptedException{

line=value;

context.write(line, new Text(""));

}

//reduce将输入中的key复制到输出数据的key上，并直接输出

public static class Reduce extends Reducer<Text,Text,Text,Text>{

//实现reduce函数

public void reduce(Text key,Iterable<Text> values,Context context)

throws IOException,InterruptedException{

context.write(key, new Text(""));

}

public static void main(String[] args) throws Exception{

Configuration conf = new Configuration();

String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs();

Job job = new Job(conf, "Data Deduplication");

job.setJarByClass(Dedup.class);

//设置Map、Combine和Reduce处理类

job.setMapperClass(Map.class);

job.setCombinerClass(Reduce.class);

job.setReducerClass(Reduce.class);

//设置输出类型

job.setOutputKeyClass(Text.class);

job.setOutputValueClass(Text.class);

//设置输入和输出目录

FileInputFormat.addInputPath(job, new Path(otherArgs[0]));

FileOutputForwww.cdxcxgs.com#tOutputPath(job, new Path(otherArgs[1]));

System.exit(job.waitForCompletion(true) ? 0 : 1);

}

}</span>

5.配置编译器

å¦ä½å¨Hadoopä¸ç¼åMapReduceç¨åº

ãã1. æ¦è¿°

ããå¹´ï¼IBMçç ç©¶åE.F.Coddåå£«å¨åç©ãCommunication of the ACMãä¸åè¡¨äºä¸ç¯åä¸ºâA Relational Model of Data for Large Shared Data Banksâçè®ºæï¼æåºäºå³ç³»æ¨¡åçæ¦å¿µï¼æ å¿çå³ç³»æ°æ®åºçè¯çï¼éåå åå¹´ï¼å³ç³»æ°æ®åºåå¶ç»æåæ¥è¯¢è¯è¨SQLæä¸ºç¨åºåå¿é¡»ææ¡çåºæ¬æè½ä¹ä¸ã

ããå¹´4æï¼Jeffrey DeanåSanjay Ghemawatå¨å½éä¼è®®OSDIä¸åè¡¨âMapReduce: Simplified Data Processing on Large Clusterâï¼æ å¿çgoogleçå¤§è§æ¨¡æ°æ®å¤çç³»ç»MapReduceå¬å¼ãåè¿ç¯è®ºæçå¯åï¼å½å¹´ç§å¤©ï¼Hadoop ç± Apache Software Foundation å¬å¸ä½ä¸º Lucene çåé¡¹ç® Nutch çä¸é¨åæ£å¼è¢«å¼å¥ï¼ å¹´ 3 æä»½ï¼MapReduce å Nutch Distributed File System (NDFS) åå«è¢«çº³å¥ç§°ä¸º Hadoop çé¡¹ç®ä¸ãå¦ä»ï¼Hadoopå·²ç»è¢«è¶è¿%çäºèç½å¬å¸ä½¿ç¨ï¼å¶ä»å¾å¤å¬å¸æ£åå¤ä½¿ç¨Hadoopæ¥å¤çæµ·éæ°æ®ï¼éçHadoopè¶æ¥è¶åæ¬¢è¿ï¼ä¹è®¸å¨å°æ¥çææ®µæ¶é´ï¼Hadoopä¼æä¸ºç¨åºåå¿é¡»ææ¡çæè½ä¹ä¸ï¼å¦æçæ¯è¿æ ·çè¯ï¼å¦ä¼å¦ä½å¨Hadoopä¸ç¼åMapReduceç¨åºä¾¿æ¯å¦ä¹ Hadoopçå¼å§ã

ãã2. Hadoop ä½ä¸ææ

ãã2.1 Hadoopä½ä¸æ§è¡æµç¨

ãã2.2 ç¨æ·çå·¥ä½

ããç¨æ·ç¼åMapReduceéè¦å®ç°çç±»æèæ¹æ³æï¼

ããï¼1ï¼ InputFormatæ¥å£

ãã1

ãã2

ãã3

ãã4

ãã5

ãã6

ãã7

ãã8

ãã9

ãã

ããpublic interface InputFormat<K, V> {

ãã

ããInputSplit[] getSplits(JobConf job, int numSplits) throws IOException;

ãã

ããRecordReader<K, V> getRecordReader(InputSplit split,

ãã

ããJobConf job,

ãã

ããReporter reporter) throws IOException;

ãã

ãã}

ãã

ããå¶ä¸getSplitså½æ°å°ææè¾å¥æ°æ®åænumSplitsä¸ªsplitï¼æ¯ä¸ªsplitäº¤ç»ä¸ä¸ªmap taskå¤çãgetRecordReaderå½æ°æä¾ä¸ä¸ªç¨æ·è§£æsplitçè¿ä»£å¨å¯¹è±¡ï¼å®å°splitä¸çæ¯ä¸ªrecordè§£æækey/valueå¯¹ã

ããHadoopæ¬èº«æä¾äºä¸äºInputFormatï¼

ããï¼2ï¼Mapperæ¥å£

ãã1

ãã2

ãã3

ãã4

ãã5

ãã6

ãã7

ãã8

ãã9

ãã

ããvoid map(K1 key,

ãã

ããV1 value,

ãã

ããOutputCollector<K2,V2> output,

ãã

ããReporter reporter

ãã

ãã) throws IOException

ãã

ããå¶ä¸ï¼<K1 V1>æ¯éè¿Inputformatä¸çRecordReaderå¯¹è±¡è§£æå¤ç çï¼OutputCollectorè·åmap()çè¾åºç»æï¼Reporterä¿åäºå½åtaskå¤çè¿åº¦ã

ããHadoopæ¬èº«æä¾äºä¸äºMapperä¾ç¨æ·ä½¿ç¨ï¼

ããï¼3ï¼Partitioneræ¥å£

ããç¨æ·éç»§æ¿è¯¥æ¥å£å®ç°èªå·±çPartitionerä»¥æå®map taskäº§ççkey/valueå¯¹äº¤ç»åªä¸ªreduce taskå¤çï¼å¥½çPartitionerè½è®©æ¯ä¸ªreduce taskå¤ççæ°æ®ç¸è¿ï¼ä»èè¾¾å°è´è½½åè¡¡ãPartitionerä¸éå®ç°çå½æ°æ¯

ããgetPartition( K2 key, V2 value, int numPartitions)

ããè¯¥å½æ°è¿å<K2 V2>å¯¹åºçreduce task IDã

ããï¼4ï¼Combiner

ããCombinerä½¿å¾map taskä¸reduce taskä¹é´çæ°æ®ä¼ è¾éå¤§å¤§åå°ï¼å¯ææ¾æé«æ§è½ãå¤§å¤æ°æåµä¸ï¼Combinerä¸Reducerç¸åã

ããï¼5ï¼Reduceræ¥å£

ãã1

ãã2

ãã3

ãã4

ãã5

ãã6

ãã7

ãã8

ãã9

ãã

ããvoid reduce(K2 key,

ãã

ããIterator<V2> values,

ãã

ããOutputCollector<K3,V3> output,

ãã

ããReporter reporter

ãã

ãã) throws IOException

ãã

ããHadoopæ¬èº«æä¾äºä¸äºReducerä¾ç¨æ·ä½¿ç¨ï¼

ããï¼6ï¼OutputFormat

ããç¨æ·éè¿OutputFormatæå®è¾åºæä»¶çåå®¹æ ¼å¼ï¼ä¸è¿å®æ²¡æsplitãæ¯ä¸ªreduce taskå°å¶æ°æ®åå¥èªå·±çæä»¶ï¼æä»¶åä¸ºpart-nnnnnï¼å¶ä¸nnnnnä¸ºreduce taskçIDã

ããHadoopæ¬èº«æä¾äºå ä¸ªOutputFormat:

ãã3. åå¸å¼ç¼å

ãã4. å¤è¯è¨ç¼åMapReduceä½ä¸

ããå¦æä½ è¦ç¨C/C++ç¼åMpaReduceä½ä¸ï¼å¯ä½¿ç¨çå·¥å·æHadoop StreamingæèHadoop Pipesã

ããå¦æä½ è¦ç¨Pythonç¼åMapReduceä½ä¸ï¼å¯ä»¥ä½¿ç¨Hadoop StreamingæèPydoopã

ããå¦æä½ è¦ä½¿ç¨å¶ä»è¯è¨ï¼å¦shellï¼phpï¼rubyçï¼å¯ä½¿ç¨Hadoop Streamingã

ããå³äºHadoop Streamingç¼ç¨ï¼å¯åè§æçè¿ç¯åæï¼ãHadoop Streamingç¼ç¨ãï¼/projects/pydoop/

ããå³äºHadoop pipesç¼ç¨ï¼å¯åè§ãHadoop Tutorial 2.2 â Running C++ Programs on Hadoopãã

ãã5. ç¼ç¨æ¹å¼æ¯è¾

ããï¼4ï¼Pydoopãå®æ¯ä¸é¨æ¹ä¾¿pythonç¨åºåç¼åMapReduceä½ä¸è®¾è®¡çï¼å¶åºå±ä½¿ç¨äºHadoop Streamingæ¥å£ålibhdfsåºã

ãã6. æ»ç»

Hadoop开源实现

Hadoop是一个开源的项目，主要由HDFS和MapReduce两个核心组件构成。2021卡牌手游源码HDFS是Google File System（GFS）的开源版本，提供了一个分布式文件系统，用于高效存储和管理海量数据。NameNode和DataNode是HDFS的关键角色，NameNode作为唯一的服务节点，负责管理文件系统元数据，而DataNode则是数据存储节点，用户通过NameNode与之交互，转卡跑分平台源码实现透明的数据存取，其操作与普通文件系统API并无二致。

MapReduce则是Google MapReduce的开源实现，主要由JobTracker节点负责任务分配和用户程序的通信。用户通过继承MapReduceBase，实现Map和Reduce功能，注册Job后，Hadoop将自动进行分布式执行。华为云上哪里看源码HDFS和MapReduce是独立工作的，用户可以在没有HDFS的情况下使用MapReduce进行运算。

Hadoop与云计算项目的目标相似，即处理大规模数据的计算。为了支持这种计算，它引入了Hadoop分布式文件系统（HDFS），作为一个稳定且安全的数据容器。HDFS的通信部分主要依赖org.apache.hadoop.ipc提供的RPC服务，用户需要自定义实现数据读写和NameNode/DataNode之间的通信。

MapReduce的核心实现位于org.apache.hadoop.mapred包中，用户需要实现接口类并管理节点通信，即可进行MapReduce计算。Hadoop的发音为[hædu:p]。

最新发布的版本是2.0.2，Hadoop为开发者提供了强大而灵活的工具，支持Fedora、Ubuntu等Linux平台，广泛应用于数据分析领域，由Hortonworks公司负责后续开发工作，确保了项目的持续发展和创新。

扩展资料

一个分布式系统基础架构，由Apache基金会开发。用户可以在不了解分布式底层细节的情况下，开发分布式程序。充分利用集群的威力高速运算和存储。Hadoop实现了一个分布式文件系统（Hadoop Distributed File System），简称HDFS。HDFS有着高容错性的特点，并且设计用来部署在低廉的（low-cost）硬件上。而且它提供高传输率（high throughput）来访问应用程序的数据，适合那些有着超大数据集（large data set）的应用程序。HDFS放宽了（relax）POSIX的要求（requirements）这样可以流的形式访问（streaming access）文件系统中的数据。

【本文网址：http://8o.net.cn/news/39c47599485.html 欢迎转载】

【打工网源码】【主力拉高出货源码】【2021卡牌手游源码】hadoop mapreduce源码

知识