Hadoop 入门教程
Hadoop 是一个大数据应用平台,提供了大数据存储 (HDFS) 和大数据操作 (Mapreduce) 的支持,本文先介绍了 Hadoop 相关知识,再介绍了 mac 下的 Hadoop 安装和配置使用,最后通过 streaming 使用 python 编写 mapreduce 任务。
动机
Hadoop 作为大数据的平台代表,是每一个从事大数据开发者都值得学习的,刚好我入职后要做的项目是一个 大数据平台相关的内容,所以要提前学习下 Hadoop ,包括 hive 和 MapReduce 等的使用。
目标
Hadoop 自己找资料, 搭建环境,用 streaming,python 写一个 wordcount
Hadoop 介绍
Apache Hadoop is an open-source software framework written in Java for distributed storage and distributed processing of very large data sets on computer clusters built from commodity hardware. All the modules in Hadoop are designed with a fundamental assumption that hardware failures (of individual machines, or racks of machines) are commonplace and thus should be automatically handled in software by the framework.
The term “Hadoop” has come to refer not just to the base modules above, but also to the “ecosystem”, or collection of additional software packages that can be installed on top of or alongside Hadoop, such as Apache Pig, Apache Hive, Apache HBase, Apache Spark, and others.
HDFS(Hadoop Distributed File System)
The Hadoop distributed file system (HDFS) is a distributed, scalable, and portable file-system written in Java for the Hadoop framework. A Hadoop cluster has nominally a single namenode plus a cluster of datanodes, although redundancy options are available for the namenode due to its criticality. Each datanode serves up blocks of data over the network using a block protocol specific to HDFS. The file system uses TCP/IP sockets for communication. Clients use remote procedure call (RPC) to communicate between each other.
MapReduce
Above the file systems comes the MapReduce Engine, which consists of one JobTracker, to which client applications submit MapReduce jobs. The JobTracker pushes work out to available TaskTracker nodes in the cluster, striving to keep the work as close to the data as possible.
过程如下:
Map(k1,v1) → list(k2,v2) Reduce(k2, list (v2)) → list(v3)
Hive
Apache Hive is a data warehouse infrastructure built on top of Hadoop for providing data summarization, query, and analysis. While initially developed by Facebook, Apache Hive is now used and developed by other companies such as Netflix. Amazon maintains a software fork of Apache Hive that is included in Amazon Elastic MapReduce on Amazon Web Services.
It provides an SQL-like language called HiveQL with schema on read and transparently converts queries to map/reduce, Apache Tez and Spark jobs.
Hadoop 安装
使用mac Yosemite(10.10.3)
brew insall hadoop $ hadoop version Hadoop 2.7.0 Subversion https://git-wip-us.apache.org/repos/asf/hadoop.git -r d4c8d4d4d203c934e8074b31289a28724c0842cf Compiled by jenkins on 2015-04-10T18:40Z Compiled with protoc 2.5.0 From source with checksum a9e90912c37a35c3195d23951fd18f This command was run using /usr/local/Cellar/hadoop/2.7.0/libexec/share/hadoop/common/hadoop-common-2.7.0.jar
Hadoop 配置
配置 JAVA_HOME
在.bashrc
或.zshrc
中加入 JAVA_HOME
设置:
# set java home [ -f /usr/libexec/java_home ] && export JAVA_HOME=$(/usr/libexec/java_home)
使设置生效:
source ~/.bashrc # source ~/.zshrc
配置 ssh
1.生成公钥(如果已经生成,就不用了)
ssh-keygen -t rsa
2.设置 Mac 允许远程登录
“System Preferences” -> “Sharing”. Check “Remote Login”
3.设置免密码登录
cat ~/.ssh/id_rsa.pub >> ~/.ssh/authorized_keys
4.测试本地登录
$ ssh localhost Last login: Fri Jun 19 16:30:53 2015 $ exit
Hadoop 配置单节点使用
这里使用单节点做学习使用,配置文件目录 /usr/local/Cellar/hadoop/2.7.0/libexec/etc/hadoop
下
配置 hdfs-site.xml
设置副本数为 1:
<configuration> <property> <name>dfs.replication</name> <value>1</value> </property> </configuration>
配置 core-site.xml
设置文件系统访问的端口:
<configuration> <property> <name>fs.defaultFS</name> <value>hdfs://localhost:9000</value> </property> </configuration>
配置 mapred-site.xml
设置 MapReduce 使用的框架:
<configuration> <property> <name>mapreduce.framework.name</name> <value>yarn</value> </property> </configuration>
配置 yarn-site.xml
<configuration> <property> <name>yarn.nodemanager.aux-services</name> <value>mapreduce_shuffle</value> </property> </configuration>
Hadoop 运行
加入启动和停止 Hadoop 的 alias
alias hstart="start-dfs.sh ; start-yarn.sh" alias hstop="stop-yarn.sh ; stop-dfs.sh"
格式化文件系统
$ hdfs namenode -format
启动 Hadoop
hstart
建立用户空间
hdfs dfs -mkdir /user hdfs dfs -mkdir /user/$(whoami) # 这里是用户
查看 Hadoop 启动的进程情况
jps
正常情况如下:
$ jps 24610 NameNode 24806 SecondaryNameNode 24696 DataNode 25018 NodeManager 24927 ResourceManager 25071 Jps
前面是进程号,后面是进程名
关闭 Hadoop
hstop
Hadoop 实例
运行实例时,当前目录设置为 /usr/local/Cellar/hadoop/2.7.0/libexec
1.上传测试文件到 HDFS 中
hdfs dfs -put etc/hadoop input
把本地 etc/hadoop
下的一些文件上传到 HDFS的 input
中。
可以在刚才建立的用户下查看上传的文件: /user/$(whoami)/input
2.在上传的数据中运行 Hadoop 提供的例子
hadoop jar share/hadoop/mapreduce/hadoop-mapreduce-examples-2.7.0.jar grep input output 'dfs[a-z.]+'
在上传的数据中使用 MapReduce 运行 grep
, 计算以dfs
开头的单词出现的次数,结果保存到 output
中。
3.查看运行结果
hdfs dfs -cat output/part-r-00000 # 文件名可以从[Browse Directory](http://localhost:50070/explorer.html#/)中看到: 4 dfs.class 4 dfs.audit.logger 3 dfs.server.namenode. 2 dfs.period 2 dfs.audit.log.maxfilesize 2 dfs.audit.log.maxbackupindex 1 dfsmetrics.log 1 dfsadmin 1 dfs.servers 1 dfs.replication 1 dfs.file
4.删除刚才生成的文件
hdfs dfs -rm -r /user/$(whoami)/input hdfs dfs -rm -r /user/$(whoami)/output
使用 python 通过 streaming 完成 wordcount
虽然 Hadoop 是使用 Java 开发的,不过支持其它语言开发 MapReduce 程序:
- Hadoop Streaming is a utility which allows users to create and run jobs with any executables (e.g. shell utilities) as the mapper and/or the reducer.
- Hadoop Pipes is a SWIG-compatible C++ API to implement MapReduce applications (non JNI™ based).
设置 Streaming 变量(方便后面使用)
streaming 在 brew 中的目录是:/usr/local/Cellar/hadoop/2.7.0/libexec/share/hadoop/tools/lib/hadoop-streaming-2.7.0.jar
, 通过命令查找:
find ./ -type f -name "*streaming*"
设置为一个变量,方便后面使用:
export STREAM="/usr/local/Cellar/hadoop/2.7.0/libexec/share/hadoop/tools/lib/hadoop-streaming-2.7.0.jar"
编写 Map 和 Reduce 程序
默认是从标准输入中读取数据,输出到标准输出中,调用时可以使用输入输出重定向就可以实现和 Hadoop 交互了, 这应该也就是 Streaming 的含义了,自己写的程序也可以通过管道自己调试。
mapper.py
#!/usr/bin/env python # filename: mapper.py import sys for line in sys.stdin: line = line.strip() words = line.split() for word in words: print '%s\t%s' % (word, 1)
给程序加可执行权限:
chmod +x mapper.py
测试下:
$ echo "this is a test " | ./mapper.py this 1 is 1 a 1 test 1
reducer.py
#!/usr/bin/env python # filename:reducer.py import sys current_word = None current_count = 0 word = None for line in sys.stdin: line = line.strip() word, count = line.split('\t', 1) try: count = int(count) except ValueError: continue if current_word == word: current_count += count else: if current_word: print '%s\t%s' % (current_word, current_count) current_count = count current_word = word if current_word == word: print '%s\t%s' % (current_word, current_count)
给程序加可执行权限:
chmod +x reducer.py
测试下:
$ echo "this is a a a test test " | ./mapper.py | sort -k1,1 | ./reducer.py a 3 is 1 test 2 this 1
使用 Hadoop 调用
1.准备数据
- The Outline of Science, Vol. 1 (of 4) by J. Arthur Thomson
- The Notebooks of Leonardo Da Vinci
$ ls -l total 7200 -rwxr-xr-x 1 user staff 165 Jun 19 20:43 mapper.py -rw-r-----@ 1 user staff 674570 Jun 19 21:14 pg20417.txt -rw-r-----@ 1 user staff 1573151 Jun 19 21:14 pg4300.txt -rw-r-----@ 1 user staff 1423803 Jun 19 21:16 pg5000.txt -rwxr-xr-x 1 user staff 539 Jun 19 20:51 reducer.py
2.上传文件到 HDFS
文件要上传到 HDFS 中才能使用 Hadoop 的 MapReduce:
$ hdfs dfs -mkdir /user/$(whoami)/input $ hdfs dfs -put ./*.txt /user/$(whoami)/input #*
3.运行 MapReduce
$ hadoop jar $STREAM \ -files ./mapper.py,./reducer.py \ -mapper ./mapper.py \ -reducer ./reducer.py \ -input /user/$(whoami)/input/pg5000.txt,/user/$(whoami)/input/pg4300.txt,/user/$(whoami)/input/pg20417.txt\ -output /user/$(whoami)/output
4.查看结果
$ hdfs dfs -cat /user/$(whoami)/output/part-00000 | sort -nk 2 | tail with 4686 it 4981 that 6109 is 7401 in 11867 to 12017 a 12064 and 16904 of 23935 the 42074
说明在正常的书中,介词用得真是相当多的,这些词在很多时候就要去除。
5.改进(使用迭代器和生成器)
使用 yield
可以在需要时再提供数据,在大量占用内存的工作时很有效。
改进的 mapper.py :
#!/usr/bin/env python """A more advanced Mapper, using Python iterators and generators.""" import sys def read_input(file): for line in file: # split the line into words yield line.split() def main(separator='\t'): # input comes from STDIN (standard input) data = read_input(sys.stdin) for words in data: # write the results to STDOUT (standard output); # what we output here will be the input for the # Reduce step, i.e. the input for reducer.py # # tab-delimited; the trivial word count is 1 for word in words: print '%s%s%d' % (word, separator, 1) if __name__ == "__main__": main()
改进的 reducer.py :
#!/usr/bin/env python """A more advanced Reducer, using Python iterators and generators.""" from itertools import groupby from operator import itemgetter import sys def read_mapper_output(file, separator='\t'): for line in file: yield line.rstrip().split(separator, 1) def main(separator='\t'): # input comes from STDIN (standard input) data = read_mapper_output(sys.stdin, separator=separator) # groupby groups multiple word-count pairs by word, # and creates an iterator that returns consecutive keys and their group: # current_word - string containing a word (the key) # group - iterator yielding all ["<current_word>", "<count>"] items for current_word, group in groupby(data, itemgetter(0)): try: total_count = sum(int(count) for current_word, count in group) print "%s%s%d" % (current_word, separator, total_count) except ValueError: # count was not a number, so silently discard this item pass if __name__ == "__main__": main()
查看系统状态的 UI
- Resource Manager: http://localhost:50070
- JobTracker: http://localhost:8088
- Specific Node Information: http://localhost:8042
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