【办公oa 源码】【DNF扫描源码】【声呐雷达源码】hadoop mapreduce源码
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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
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ããPythonçMapReduce代ç
ãã使ç¨Pythonç¼åMapReduce代ç çæ巧就å¨äºæ们使ç¨äº HadoopStreaming æ¥å¸®å©æ们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 为HadoopStreamingä¼å¸®æ们åå¥½å ¶ä»äºãè¿æ¯ççï¼å«ä¸ç¸ä¿¡ï¼
ããMap: mapper.py
ããå°ä¸åç代ç ä¿åå¨/home/hadoop/mapper.pyä¸ï¼ä»å°ä»STDIN读åæ°æ®å¹¶å°åè¯æè¡åéå¼ï¼çæä¸ä¸ªå表æ å°åè¯ä¸åç次æ°çå ³ç³»ï¼
ãã注æï¼è¦ç¡®ä¿è¿ä¸ªèæ¬æ足å¤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
ããprint '%s\\t%s' % (word, 1)å¨è¿ä¸ªèæ¬ä¸ï¼å¹¶ä¸è®¡ç®åºåè¯åºç°çæ»æ°ï¼å®å°è¾åº "<word> 1" è¿ éå°ï¼å°½ç®¡<word>å¯è½ä¼å¨è¾å ¥ä¸åºç°å¤æ¬¡ï¼è®¡ç®æ¯çç»åæ¥çReduceæ¥éª¤ï¼æå«åç¨åºï¼æ¥å®ç°ãå½ç¶ä½ å¯ä»¥æ¹åä¸ç¼ç é£æ ¼ï¼å®å ¨å°éä½ çä¹ æ¯ã
ããReduce: reducer.py
ããå°ä»£ç åå¨å¨/home/hadoop/reducer.py ä¸ï¼è¿ä¸ªèæ¬çä½ç¨æ¯ä»mapper.py çSTDINä¸è¯»åç»æï¼ç¶å计ç®æ¯ä¸ªåè¯åºç°æ¬¡æ°çæ»åï¼å¹¶è¾åºç»æå°STDOUTã
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 ç代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»¥å å¾ä¸å°ä»»ä½è¿åç»æ
ããè¿éæä¸äºå»ºè®®ï¼å ³äºå¦ä½æµè¯ä½ çMapåReduceçåè½ï¼
ããââââââââââââââââââââââââââââââââââââââââââââââ
ãã\r\n
ãã# very basic test
ããhadoop@ubuntu:~$ echo "foo foo quux labs foo bar quux" | /home/hadoop/mapper.py
ããfoo 1
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¬
ãã为äºè¿ä¸ªä¾åï¼æ们å°éè¦ä¸ç§çµå书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
ããä¸è½½ä»ä»¬ï¼å¹¶ä½¿ç¨us-asciiç¼ç åå¨ è§£ååçæ件ï¼ä¿åå¨ä¸´æ¶ç®å½ï¼æ¯å¦/tmp/gutenberg.
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
ããå¨æ们è¿è¡MapReduce job åï¼æ们éè¦å°æ¬å°çæ件å¤å¶å°HDFSä¸ï¼
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
ããç°å¨ï¼ä¸ååå¤å°±ç»ªï¼æ们å°å¨è¿è¡Python MapReduce job å¨Hadoopé群ä¸ãåæä¸é¢æ说çï¼æ们使ç¨çæ¯
ããHadoopStreaming 帮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
ããå¨è¿è¡ä¸ï¼å¦æä½ æ³æ´æ¹Hadoopçä¸äºè®¾ç½®ï¼å¦å¢å Reduceä»»å¡çæ°éï¼ä½ å¯ä»¥ä½¿ç¨â-jobconfâé项ï¼
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¯å ³äºHadoop does not honor mapred.map.tasks
ããè¿ä¸ªä»»å¡å°ä¼è¯»åHDFSç®å½ä¸çgutenberg并å¤çä»ä»¬ï¼å°ç»æåå¨å¨ç¬ç«çç»ææ件ä¸ï¼å¹¶åå¨å¨HDFSç®å½ä¸ç
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 0%
ãã[...] INFO streaming.StreamJob: map % reduce 0%
ãã[...] INFO streaming.StreamJob: map % reduce %
ãã[...] INFO streaming.StreamJob: map % reduce %
ãã[...] INFO streaming.StreamJob: map % reduce %
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$
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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及之后的办公oa 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DNF扫描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
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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。声呐雷达源码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，死亡转盘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。西点自用源码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。- 上一条：php在线考试源码_php在线考试源码下载
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