Hadoop-2.2.0集群安装配置实践

Hadoop 2.x和1.x已经大不相同了，应该说对于存储计算都更加通用了。Hadoop 2.x实现了用来管理集群资源的YARN框架，可以面向任何需要使用基于HDFS存储来计算的需要，当然MapReduce现在已经作为外围的插件式的计算框架，你可以根据需要开发或者选择合适的计算框架。目前，貌似对MapReduce支持还是比较好的，毕竟MapReduce框架已经还算成熟。其他一些基于YARN框架的标准也在开发中。
YARN框架的核心是资源的管理和分配调度，它比Hadoop 1.x中的资源分配的粒度更细了，也更加灵活了，它的前景应该不错。由于极大地灵活性，所以在使用过程中由于这些配置的灵活性，可能使用的难度也加大了一些。另外，我个人觉得，YARN毕竟还在发展之中，也有很多不成熟的地方，各种问题频频出现，资料也相对较少，官方文档有时更新也不是很及时，如果我选择做海量数据处理，可能YARN还不能满足生产环境的需要。如果完全使用MapReduce来做计算，还是选择相对更加成熟的Hadoop 1.x版本用于生产环境。
下面使用4台机器，操作系统为CentOS 6.4 64位，一台做主节点，另外三台做从节点，实践集群的安装配置。

主机配置规划

修改/etc/hosts文件，增加如下地址映射：

1	10.95.3.48 m1

2	10.95.3.54 s1

3	10.95.3.59 s2

4	10.95.3.66 s3

每台机器配置对应的hostname，修改/etc/sysconfig/network文件，例如s1节点内容配置为：

1	NETWORKING=yes

2	HOSTNAME=s1

m1为集群主节点，s1、s2、s3为集群从节点。
关于主机资源的配置，我们这里面使用VMWare工具，创建了4个虚拟机，具体置情况如下所示：

• 一个主节点有1个核（core）
• 一个主节点内存1G
• 每个从节点有1个核（core）
• 每个从节点内存2G

目录规划

Hadoop程序存放目录为/home/shirdrn/cloud/programs/hadoop-2.2.0，相关的数据目录，包括日志、存储等指定为/home/shirdrn/cloud/storage/hadoop-2.2.0。将程序和数据目录分开，可以更加方便的进行配置的同步。
具体目录的准备与配置如下所示：

• 在每个节点上创建程序存储目录/home/shirdrn/cloud/programs/hadoop-2.2.0，用来存放Hadoop程序文件
• 在每个节点上创建数据存储目录/home/shirdrn/cloud/storage/hadoop-2.2.0/hdfs，用来存放集群数据
• 在主节点m1上创建目录/home/shirdrn/cloud/storage/hadoop-2.2.0/hdfs/name，用来存放文件系统元数据
• 在每个从节点上创建目录/home/shirdrn/cloud/storage/hadoop-2.2.0/hdfs/data，用来存放真正的数据
• 所有节点上的日志目录为/home/shirdrn/cloud/storage/hadoop-2.2.0/logs
• 所有节点上的临时目录为/home/shirdrn/cloud/storage/hadoop-2.2.0/tmp

下面配置涉及到的目录，都参照这里的目录规划。

环境变量配置

首先，使用Sun的JDK，修改~/.bashrc文件，配置如下：

1	export JAVA_HOME=/usr/java/jdk1.6.0_45/

2	export PATH=$PATH:$JAVA_HOME/bin

3	export CLASSPATH=$JAVA_HOME/lib/*.jar:$JAVA_HOME/jre/lib/*.jar

然后配置Hadoop安装目录，相关环境变量：

1	export HADOOP_HOME=/home/shirdrn/cloud/programs/hadoop-2.2.0

2	export PATH=$PATH:$HADOOP_HOME/bin

3	export PATH=$PATH:$HADOOP_HOME/sbin

4	export HADOOP_LOG_DIR=/home/shirdrn/cloud/storage/hadoop-2.2.0/logs

5	export YARN_LOG_DIR=$HADOOP_LOG_DIR

免密码登录配置

在每各节点上，执行如下命令：

1	ssh-keygen

然后点击回车一直下去即可。
在主节点m1上，执行命令：

1

ssh m1

保证不需要密码即可登录本机m1节点。
将m1的公钥，添加到s1、s2、s3的~/.ssh/authorized_keys文件中，并且需要查看~/.ssh/authorized_keys的权限，不能对同组用户具有写权限，如果有，则执行下面命令：

1	chmod g-w ~/.ssh/authorized_keys

这时，在m1节点上，应该保证执行如下命令不需要输入密码：

1

ssh s1

2

ssh s2

3

ssh s3

Hadoop配置文件

配置文件所在目录为/home/shirdrn/programs/hadoop-2.2.0/etc/hadoop，可以修改对应的配置文件。

• 配置文件core-site.xml内容

01	<?xml version="1.0" encoding="UTF-8"?>

02	<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>

03

04	<configuration>

05	<property>

06	<name>fs.defaultFS</name>

07	<value>hdfs://m1:9000/</value>

08	<description>The name of the default file system. A URI whose scheme

09	and authority determine the FileSystem implementation. The uri's

10	scheme determines the config property (fs.SCHEME.impl) naming the

11	FileSystem implementation class. The uri's authority is used to

12	determine the host, port, etc. for a filesystem.</description>

13	</property>

14	<property>

15	<name>dfs.replication</name>

16	<value>3</value>

17	</property>

18	<property>

19	<name>hadoop.tmp.dir</name>

20	<value>/home/shirdrn/cloud/storage/hadoop-2.2.0/tmp/hadoop-${user.name}</value>

21	<description>A base for other temporary directories.</description>

22	</property>

23	</configuration>

• 配置文件hdfs-site.xml内容

01	<?xml version="1.0" encoding="UTF-8"?>

02	<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>

03

04	<configuration>

05	<property>

06	<name>dfs.namenode.name.dir</name>

07	<value>/home/shirdrn/cloud/storage/hadoop-2.2.0/hdfs/name</value>

08	<description>Path on the local filesystem where the NameNode stores

09	the namespace and transactions logs persistently.</description>

10	</property>

11	<property>

12	<name>dfs.datanode.data.dir</name>

13	<value>/home/shirdrn/cloud/storage/hadoop-2.2.0/hdfs/data</value>

14	<description>Comma separated list of paths on the local filesystem of a DataNode where it should store its blocks.</description>

15	</property>

16	<property>

17	<name>dfs.permissions</name>

18	<value>false</value>

19	</property>

20	</configuration>

• 配置文件yarn-site.xml内容

01	<?xml version="1.0"?>

02

03	<configuration>

04	<property>

05	<name>yarn.resourcemanager.resource-tracker.address</name>

06	<value>m1:8031</value>

07	<description>host is the hostname of the resource manager and

08	port is the port on which the NodeManagers contact the Resource Manager.

09	</description>

10	</property>

11	<property>

12	<name>yarn.resourcemanager.scheduler.address</name>

13	<value>m1:8030</value>

14	<description>host is the hostname of the resourcemanager and port is

15

the port

16	on which the Applications in the cluster talk to the Resource Manager.

17	</description>

18	</property>

19	<property>

20	<name>yarn.resourcemanager.scheduler.class</name>

21	<value>org.apache.hadoop.yarn.server.resourcemanager.scheduler.capacity.CapacityScheduler</value>

22	<description>In case you do not want to use the default scheduler</description>

23	</property>

24	<property>

25	<name>yarn.resourcemanager.address</name>

26	<value>m1:8032</value>

27	<description>the host is the hostname of the ResourceManager and the

28	port is the port on

29	which the clients can talk to the Resource Manager.

30	</description>

31	</property>

32	<property>

33	<name>yarn.nodemanager.local-dirs</name>

34	<value>${hadoop.tmp.dir}/nodemanager/local</value>

35	<description>the local directories used by the nodemanager</description>

36	</property>

37	<property>

38	<name>yarn.nodemanager.address</name>

39	<value>0.0.0.0:8034</value>

40	<description>the nodemanagers bind to this port</description>

41	</property>

42	<property>

43	<name>yarn.nodemanager.resource.cpu-vcores</name>

44	<value>1</value>

45	<description></description>

46	</property>

47	<property>

48	<name>yarn.nodemanager.resource.memory-mb</name>

49	<value>2048</value>

50	<description>Defines total available resources on the NodeManager to be made available to running containers</description>

51	</property>

52	<property>

53	<name>yarn.nodemanager.remote-app-log-dir</name>

54	<value>${hadoop.tmp.dir}/nodemanager/remote</value>

55	<description>directory on hdfs where the application logs are moved to </description>

56	</property>

57	<property>

58	<name>yarn.nodemanager.log-dirs</name>

59	<value>${hadoop.tmp.dir}/nodemanager/logs</value>

60	<description>the directories used by Nodemanagers as log directories</description>

61	</property>

62	<property>

63	<name>yarn.application.classpath</name>

64	<value>$HADOOP_HOME,$HADOOP_HOME/share/hadoop/common/*,

65	$HADOOP_HOME/share/hadoop/common/lib/*,

66	$HADOOP_HOME/share/hadoop/hdfs/*,$HADOOP_HOME/share/hadoop/hdfs/lib/*,

67	$HADOOP_HOME/share/hadoop/yarn/*,$HADOOP_HOME/share/hadoop/yarn/lib/*,

68	$HADOOP_HOME/share/hadoop/mapreduce/*,$HADOOP_HOME/share/hadoop/mapreduce/lib/*</value>

69	<description>Classpath for typical applications.</description>

70	</property>

71	<!-- Use mapreduce_shuffle instead of mapreduce.suffle (YARN-1229)-->

72	<property>

73	<name>yarn.nodemanager.aux-services</name>

74	<value>mapreduce_shuffle</value>

75	<description>shuffle service that needs to be set for Map Reduce to run </description>

76	</property>

77	<property>

78	<name>yarn.nodemanager.aux-services.mapreduce.shuffle.class</name>

79	<value>org.apache.hadoop.mapred.ShuffleHandler</value>

80	</property>

81	<property>

82	<name>yarn.scheduler.minimum-allocation-mb</name>

83	<value>256</value>

84	</property>

85	<property>

86	<name>yarn.scheduler.maximum-allocation-mb</name>

87	<value>6144</value>

88	</property>

89	<property>

90	<name>yarn.scheduler.minimum-allocation-vcores</name>

91	<value>1</value>

92	</property>

93	<property>

94	<name>yarn.scheduler.maximum-allocation-vcores</name>

95	<value>3</value>

96	</property>

97	</configuration>

• 配置mapred-site.xml文件

01	<?xml version="1.0"?>

02	<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>

03

04	<configuration>

05	<property>

06	<name>mapreduce.framework.name</name>

07	<value>yarn</value>

08	<description>Execution framework set to Hadoop YARN.</description>

09	</property>

10	<property>

11	<name>mapreduce.map.memory.mb</name>

12	<value>512</value>

13	<description>Larger resource limit for maps. default 1024M</description>

14	</property>

15	<property>

16	<name>mapreduce.map.cpu.vcores</name>

17	<value>1</value>

18	<description></description>

19	</property>

20	<property>

21	<name>mapreduce.reduce.memory.mb</name>

22	<value>512</value>

23	<description>Larger resource limit for reduces.</description>

24	</property>

25	<property>

26	<name>mapreduce.reduce.shuffle.parallelcopies</name>

27	<value>5</value>

28	<description>Higher number of parallel copies run by reduces to fetch outputs from very large number of maps.</description>

29	</property>

30	<property>

31	<name>mapreduce.jobhistory.address</name>

32	<value>m1:10020</value>

33	<description>MapReduce JobHistory Server host:port, default port is 10020.</description>

34	</property>

35	<property>

36	<name>mapreduce.jobhistory.webapp.address</name>

37	<value>m1:19888</value>

38	<description>MapReduce JobHistory Server Web UI host:port, default port is 19888.</description>

39	</property>

40	</configuration>

• 配置hadoop-env.sh、yarn-env.sh、mapred-env.sh脚本文件

修改每个脚本文件的JAVA_HOME变量即可，如下所示：

1	export JAVA_HOME=/usr/java/jdk1.6.0_45/

• 配置slaves文件

1

s1

2

s2

3

s3

同步分发程序文件
在主节点m1上将上面配置好的程序文件，复制分发到各个从节点上：

1	scp -r /home/shirdrn/cloud/programs/hadoop-2.2.0 shirdrn@s1:/home/shirdrn/cloud/programs/

2	scp -r /home/shirdrn/cloud/programs/hadoop-2.2.0 shirdrn@s2:/home/shirdrn/cloud/programs/

3	scp -r /home/shirdrn/cloud/programs/hadoop-2.2.0 shirdrn@s3:/home/shirdrn/cloud/programs/

启动HDFS集群

经过上面配置以后，可以启动HDFS集群。
为了保证集群启动过程中不会出现问题，需要手动关闭每个节点上的防火墙，执行如下命令：

1	sudo service iptables stop

或者永久关闭防火墙：

1	sudo chkconfig iptables off

2	sudo chkconfig ip6tables off

在主节点m1上，首先进行文件系统格式化操作，执行如下命令：

1	hadoop namenode -format

然后，可以启动HDFS集群，执行如下命令：

1	start-dfs.sh

可以查看启动日志，确认HDFS集群启动是否成功：

1	tail -100f /home/shirdrn/cloud/storage/hadoop-2.2.0/logs/hadoop-shirdrn-namenode-m1.log

2	tail -100f /home/shirdrn/cloud/storage/hadoop-2.2.0/logs/hadoop-shirdrn-secondarynamenode-m1.log

3	tail -100f /home/shirdrn/cloud/storage/hadoop-2.2.0/logs/hadoop-shirdrn-datanode-s1.log

4	tail -100f /home/shirdrn/cloud/storage/hadoop-2.2.0/logs/hadoop-shirdrn-datanode-s2.log

5	tail -100f /home/shirdrn/cloud/storage/hadoop-2.2.0/logs/hadoop-shirdrn-datanode-s3.log

或者，查看对应的进程情况：

1

jps

可以通过登录Web控制台，查看HDFS集群状态，访问如下地址：

1	http://m1:50070/

启动YARN集群

在主节点m1上，执行如下命令：

1	start-yarn.sh

可以查看启动日志，确认YARN集群启动是否成功：

1	tail -100f /home/shirdrn/cloud/storage/hadoop-2.2.0/logs/yarn-shirdrn-resourcemanager-m1.log

2	tail -100f /home/shirdrn/cloud/storage/hadoop-2.2.0/logs/yarn-shirdrn-nodemanager-s1.log

3	tail -100f /home/shirdrn/cloud/storage/hadoop-2.2.0/logs/yarn-shirdrn-nodemanager-s2.log

4	tail -100f /home/shirdrn/cloud/storage/hadoop-2.2.0/logs/yarn-shirdrn-nodemanager-s3.log

或者，查看对应的进程情况：

1

jps

另外，ResourceManager运行在主节点m1上，可以Web控制台查看状态：

1	http://m1:8088/

NodeManager运行在从节点上，可以通过Web控制台查看对应节点的资源状态，例如节点s1：

1	http://s1:8042/

管理JobHistory Server

启动可以JobHistory Server，能够通过Web控制台查看集群计算的任务的信息，执行如下命令：

1	mr-jobhistory-daemon.sh start historyserver

默认使用19888端口。
通过访问http://m1:19888/查看任务执行历史信息。
终止JobHistory Server，执行如下命令：

1	mr-jobhistory-daemon.sh stop historyserver

集群验证

我们使用Hadoop自带的WordCount例子进行验证。
先在HDFS创建几个数据目录：

1	hadoop fs -mkdir -p /data/wordcount

2	hadoop fs -mkdir -p /output/

目录/data/wordcount用来存放Hadoop自带的WordCount例子的数据文件，运行这个MapReduce任务的结果输出到/output/wordcount目录中。
将本地文件上传到HDFS中：

1	hadoop fs -put /home/shirdrn/cloud/programs/hadoop-2.2.0/etc/hadoop/*.xml /data/wordcount/

可以查看上传后的文件情况，执行如下命令：

1	hadoop fs -ls /data/wordcount

可以看到上传到HDFS中的文件。
下面，运行WordCount例子，执行如下命令：

1	hadoop jar /home/shirdrn/cloud/programs/hadoop-2.2.0/share/hadoop/mapreduce/hadoop-mapreduce-examples-2.2.0.jar wordcount /data/wordcount /output/wordcount

可以看到控制台输出程序运行的信息：

01	[shirdrn@m1 hadoop-2.2.0]$ hadoop jar /home/shirdrn/cloud/programs/hadoop-2.2.0/share/hadoop/mapreduce/hadoop-mapreduce-examples-2.2.0.jar wordcount /data/wordcount /output/wordcount

02	13/12/25 22:38:02 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable

03	13/12/25 22:38:03 INFO client.RMProxy: Connecting to ResourceManager at m1/10.95.3.48:8032

04	13/12/25 22:38:04 INFO input.FileInputFormat: Total input paths to process : 7

05	13/12/25 22:38:04 INFO mapreduce.JobSubmitter: number of splits:7

06	13/12/25 22:38:04 INFO Configuration.deprecation: user.name is deprecated. Instead, use mapreduce.job.user.name

07	13/12/25 22:38:04 INFO Configuration.deprecation: mapred.jar is deprecated. Instead, use mapreduce.job.jar

08	13/12/25 22:38:04 INFO Configuration.deprecation: mapred.output.value.class is deprecated. Instead, use mapreduce.job.output.value.class

09	13/12/25 22:38:04 INFO Configuration.deprecation: mapreduce.combine.class is deprecated. Instead, use mapreduce.job.combine.class

10	13/12/25 22:38:04 INFO Configuration.deprecation: mapreduce.map.class is deprecated. Instead, use mapreduce.job.map.class

11	13/12/25 22:38:04 INFO Configuration.deprecation: mapred.job.name is deprecated. Instead, use mapreduce.job.name

12	13/12/25 22:38:04 INFO Configuration.deprecation: mapreduce.reduce.class is deprecated. Instead, use mapreduce.job.reduce.class

13	13/12/25 22:38:04 INFO Configuration.deprecation: mapred.input.dir is deprecated. Instead, use mapreduce.input.fileinputformat.inputdir

14	13/12/25 22:38:04 INFO Configuration.deprecation: mapred.output.dir is deprecated. Instead, use mapreduce.output.fileoutputformat.outputdir

15	13/12/25 22:38:04 INFO Configuration.deprecation: mapred.map.tasks is deprecated. Instead, use mapreduce.job.maps

16	13/12/25 22:38:04 INFO Configuration.deprecation: mapred.output.key.class is deprecated. Instead, use mapreduce.job.output.key.class

17	13/12/25 22:38:04 INFO Configuration.deprecation: mapred.working.dir is deprecated. Instead, use mapreduce.job.working.dir

18	13/12/25 22:38:04 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1388039619930_0002

19	13/12/25 22:38:05 INFO impl.YarnClientImpl: Submitted application application_1388039619930_0002 to ResourceManager at m1/10.95.3.48:8032

20	13/12/25 22:38:05 INFO mapreduce.Job: The url to track the job:http://m1:8088/proxy/application_1388039619930_0002/

21	13/12/25 22:38:05 INFO mapreduce.Job: Running job: job_1388039619930_0002

22	13/12/25 22:38:14 INFO mapreduce.Job: Job job_1388039619930_0002 running in uber mode : false

23	13/12/25 22:38:14 INFO mapreduce.Job: map 0% reduce 0%

24	13/12/25 22:38:22 INFO mapreduce.Job: map 14% reduce 0%

25	13/12/25 22:38:42 INFO mapreduce.Job: map 29% reduce 5%

26	13/12/25 22:38:43 INFO mapreduce.Job: map 43% reduce 5%

27	13/12/25 22:38:45 INFO mapreduce.Job: map 43% reduce 14%

28	13/12/25 22:38:54 INFO mapreduce.Job: map 57% reduce 14%

29	13/12/25 22:38:55 INFO mapreduce.Job: map 71% reduce 19%

30	13/12/25 22:38:56 INFO mapreduce.Job: map 100% reduce 19%

31	13/12/25 22:38:57 INFO mapreduce.Job: map 100% reduce 100%

32	13/12/25 22:38:58 INFO mapreduce.Job: Job job_1388039619930_0002 completed successfully

33	13/12/25 22:38:58 INFO mapreduce.Job: Counters: 44

34	File System Counters

35	FILE: Number of bytes read=15339

36	FILE: Number of bytes written=667303

37	FILE: Number of read operations=0

38	FILE: Number of large read operations=0

39	FILE: Number of write operations=0

40	HDFS: Number of bytes read=21904

41	HDFS: Number of bytes written=9717

42	HDFS: Number of read operations=24

43	HDFS: Number of large read operations=0

44	HDFS: Number of write operations=2

45	Job Counters

46	Killed map tasks=2

47	Launched map tasks=9

48	Launched reduce tasks=1

49	Data-local map tasks=9

50	Total time spent by all maps in occupied slots (ms)=457338

51	Total time spent by all reduces in occupied slots (ms)=65832

52	Map-Reduce Framework

53	Map input records=532

54	Map output records=1923

55	Map output bytes=26222

56	Map output materialized bytes=15375

57	Input split bytes=773

58	Combine input records=1923

59	Combine output records=770

60	Reduce input groups=511

61	Reduce shuffle bytes=15375

62	Reduce input records=770

63	Reduce output records=511

64	Spilled Records=1540

65	Shuffled Maps =7

66	Failed Shuffles=0

67	Merged Map outputs=7

68	GC time elapsed (ms)=3951

69	CPU time spent (ms)=22610

70	Physical memory (bytes) snapshot=1598832640

71	Virtual memory (bytes) snapshot=6564274176

72	Total committed heap usage (bytes)=971993088

73	Shuffle Errors

74

BAD_ID=0

75	CONNECTION=0

76	IO_ERROR=0

77	WRONG_LENGTH=0

78	WRONG_MAP=0

79	WRONG_REDUCE=0

80	File Input Format Counters

81	Bytes Read=21131

82	File Output Format Counters

83	Bytes Written=9717

查看结果，执行如下命令：

1	hadoop fs -cat /output/wordcount/part-r-00000 | head

结果数据示例如下：

01	[shirdrn@m1 hadoop-2.2.0]$ hadoop fs -cat /output/wordcount/part-r-00000 | head

02	13/12/25 22:58:55 WARN util.NativeCodeLoader: Unable to load native-hadoop library foryour platform... using builtin-java classes where applicable

03

"*" 17

04

"AS 3

05	"License"); 3

06	"alice,bob 17

07	$HADOOP_HOME/share/hadoop/common/lib/*, 1

08	$HADOOP_HOME/share/hadoop/hdfs/*,$HADOOP_HOME/share/hadoop/hdfs/lib/*, 1

09	$HADOOP_HOME/share/hadoop/mapreduce/*,$HADOOP_HOME/share/hadoop/mapreduce/lib/*</value> 1

10	$HADOOP_HOME/share/hadoop/yarn/*,$HADOOP_HOME/share/hadoop/yarn/lib/*, 1

11

(ASF) 1

12	(YARN-1229)--> 1

13	cat: Unable to write to output stream.

登录到Web控制台，访问链接http://m1:8088/可以看到任务记录情况。
可见，我们的HDFS能够存储数据，而YARN集群也能够运行MapReduce任务。

问题及总结

• 需要知道的默认配置

在Hadoop 2.2.0中，YARN框架有很多默认的参数值，如果你是在机器资源比较不足的情况下，需要修改这些默认值，来满足一些任务需要。
NodeManager和ResourceManager都是在yarn-site.xml文件中配置的，而运行MapReduce任务时，是在mapred-site.xml中进行配置的。
下面看一下相关的参数及其默认值情况：

参数名称	默认值	进程名称	配置文件	含义说明
yarn.nodemanager.resource.memory-mb	8192	NodeManager	yarn-site.xml	从节点所在物理主机的可用物理内存总量
yarn.nodemanager.resource.cpu-vcores	8	NodeManager	yarn-site.xml	节点所在物理主机的可用虚拟CPU资源总数（core）
yarn.nodemanager.vmem-pmem-ratio	2.1	NodeManager	yarn-site.xml	使用1M物理内存，最多可以使用的虚拟内存数量
yarn.scheduler.minimum-allocation-mb	1024	ResourceManager	yarn-site.xml	一次申请分配内存资源的最小数量
yarn.scheduler.maximum-allocation-mb	8192	ResourceManager	yarn-site.xml	一次申请分配内存资源的最大数量
yarn.scheduler.minimum-allocation-vcores	1	ResourceManager	yarn-site.xml	一次申请分配虚拟CPU资源最小数量
yarn.scheduler.maximum-allocation-vcores	8	ResourceManager	yarn-site.xml	一次申请分配虚拟CPU资源最大数量
mapreduce.framework.name	local	MapReduce	mapred-site.xml	取值local、classic或yarn其中之一，如果不是yarn，则不会使用YARN集群来实现资源的分配
mapreduce.map.memory.mb	1024	MapReduce	mapred-site.xml	每个MapReduce作业的map任务可以申请的内存资源数量
mapreduce.map.cpu.vcores	1	MapReduce	mapred-site.xml	每个MapReduce作业的map任务可以申请的虚拟CPU资源的数量
mapreduce.reduce.memory.mb	1024	MapReduce	mapred-site.xml	每个MapReduce作业的reduce任务可以申请的内存资源数量
yarn.nodemanager.resource.cpu-vcores	8	MapReduce	mapred-site.xml	每个MapReduce作业的reduce任务可以申请的虚拟CPU资源的数量

• 异常java.io.IOException: Bad connect ack with firstBadLink as 10.95.3.66:50010

详细异常信息，如下所示：

01	[shirdrn@m1 hadoop-2.2.0]$ hadoop fs -put /home/shirdrn/cloud/programs/hadoop-2.2.0/etc/hadoop/*.xml /data/wordcount/

02	13/12/25 21:29:45 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable

03	13/12/25 21:29:46 INFO hdfs.DFSClient: Exception in createBlockOutputStream

04	java.io.IOException: Bad connect ack with firstBadLink as 10.95.3.66:50010

05	at org.apache.hadoop.hdfs.DFSOutputStream$DataStreamer.createBlockOutputStream(DFSOutputStream.java:1166)

06	at org.apache.hadoop.hdfs.DFSOutputStream$DataStreamer.nextBlockOutputStream(DFSOutputStream.java:1088)

07	at org.apache.hadoop.hdfs.DFSOutputStream$DataStreamer.run(DFSOutputStream.java:514)

08	13/12/25 21:29:46 INFO hdfs.DFSClient: Abandoning BP-1906424073-10.95.3.48-1388035628061:blk_1073741825_1001

09	13/12/25 21:29:46 INFO hdfs.DFSClient: Excluding datanode 10.95.3.66:50010

10	13/12/25 21:29:46 INFO hdfs.DFSClient: Exception in createBlockOutputStream

11	java.io.IOException: Bad connect ack with firstBadLink as 10.95.3.59:50010

12	at org.apache.hadoop.hdfs.DFSOutputStream$DataStreamer.createBlockOutputStream(DFSOutputStream.java:1166)

13	at org.apache.hadoop.hdfs.DFSOutputStream$DataStreamer.nextBlockOutputStream(DFSOutputStream.java:1088)

14	at org.apache.hadoop.hdfs.DFSOutputStream$DataStreamer.run(DFSOutputStream.java:514)

15	13/12/25 21:29:46 INFO hdfs.DFSClient: Abandoning BP-1906424073-10.95.3.48-1388035628061:blk_1073741826_1002

16	13/12/25 21:29:46 INFO hdfs.DFSClient: Excluding datanode 10.95.3.59:50010

17	13/12/25 21:29:46 INFO hdfs.DFSClient: Exception in createBlockOutputStream

18	java.net.NoRouteToHostException: No route to host

19	at sun.nio.ch.SocketChannelImpl.checkConnect(Native Method)

20	at sun.nio.ch.SocketChannelImpl.finishConnect(SocketChannelImpl.java:599)

21	at org.apache.hadoop.net.SocketIOWithTimeout.connect(SocketIOWithTimeout.java:206)

22	at org.apache.hadoop.net.NetUtils.connect(NetUtils.java:529)

23	at org.apache.hadoop.hdfs.DFSOutputStream.createSocketForPipeline(DFSOutputStream.java:1305)

24	at org.apache.hadoop.hdfs.DFSOutputStream$DataStreamer.createBlockOutputStream(DFSOutputStream.java:1128)

25	at org.apache.hadoop.hdfs.DFSOutputStream$DataStreamer.nextBlockOutputStream(DFSOutputStream.java:1088)

26	at org.apache.hadoop.hdfs.DFSOutputStream$DataStreamer.run(DFSOutputStream.java:514)

27	13/12/25 21:29:46 INFO hdfs.DFSClient: Abandoning BP-1906424073-10.95.3.48-1388035628061:blk_1073741828_1004

28	13/12/25 21:29:46 INFO hdfs.DFSClient: Excluding datanode 10.95.3.59:50010

29	13/12/25 21:29:46 INFO hdfs.DFSClient: Exception in createBlockOutputStream

主要是由于Hadoop集群内某些节点的防火墙没有关闭，导致无法访问集群内节点。