【首页导航自适应源码】【源码大旗网】【层次包围体源码】hadoop mapreduce 源码-皮皮网

【首页导航自适应源码】【源码大旗网】【层次包围体源码】hadoop mapreduce 源码

时间:2024-12-29 09:20:46 分类：时尚编辑：资金活跃度源码

1.å¦ä½ä½¿ç¨Pythonä¸ºHadoopç¼åä¸ä¸ªç®åçMapReduceç¨åº
2.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？
3.Hadoop开源实现
4.mapreduceåhadoopçå³ç³»
5.å¦ä½å¨Hadoopä¸ç¼åMapReduceç¨åº
6.hadoop的核心配置文件有哪些

hadoop mapreduce 源码

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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$

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，首页导航自适应源码哪位大牛知道这个？

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

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。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，源码大旗网而DataNode则是数据存储节点，用户通过NameNode与之交互，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，层次包围体源码注册Job后，Hadoop将自动进行分布式执行。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），手游德州源码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]。

最新发布的支付联盟系统源码版本是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。

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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，配置文件中的路径和地址需要根据实际环境进行相应的修改。

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【首页导航自适应源码】【源码大旗网】【层次包围体源码】hadoop mapreduce 源码

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