hadoop2.6.0在eclipse上的运行和命令行运行

环境配置：

jdk1.7.0_80

hadoop-2.6.0

eclipse-jee-mars-R-linux-gtk.tar.gz(官网下的目前为止最新的吧)

hadoop2x-eclipse-plugin-master(hadoop在eclipse上的插件，网上一搜一大把）

安装eclipse,解压即可

tar -xvf eclipse-jee-mars-R-linux-gtk.tar.gz

将插件在解压出来有release文件夹下

hadoop-eclipse-kepler-plugin-2.2.0.jar

hadoop-eclipse-kepler-plugin-2.4.1.jar

hadoop-eclipse-plugin-2.6.0.jar

前面两个二选一扔到eclipse的插件plugins下面，第三个不用，因为环境不同，所以要自己编译一个。

编译插件

cd hadoop2x-eclipse-plugin-master/src/contrib/eclipse-plugin
ant jar -Dversion=2.6.0 -Declipse.home=/home/shamrock/eclipse -Dhadoop.home=/home//hadoop-2.6.0

其中/home/shamrock/eclipse是eclipse的安装目录，/home/shamrock/hadoop-2.6.0是hadoop的安装目录

编译完成出现hadoop-eclipse-plugin-2.6.0.jar，将这个扔到eclipse的插件中。

启动eclipse.

然后按照Eclipse下搭建Hadoop2.4.0开发环境 就可以了。

写的程序运行完，如果要想在eclipse的DFS Localtion下的hadoop显示结果，只需要点击这个hadoop选择重连（ReConnect）即可。

接下来，我们想直接命令行运行，该如何运行呢？

首先进入eclipse的workspace找到新建项目的类WordCOunt.java

用vi编辑它，把文件里相对于eclipse的包去掉，比如我的文件只要去掉package myWordCount;

编译java

javac -classpath "$HADOOP_HOME/share/hadoop/common/hadoop-common-2.6.0.jar:$HADOOP_HOME/share/hadoop/mapreduce/hadoop-mapreduce-client-core-2.6.0.jar:$HADOOP_HOME/share/hadoop/common/lib/commons-cli-1.2.jar:$CLASSPATH" WordCount.java

打包

jar -cvf wordcount.jar ./*.calss

执行，如果你之前在eclipse运行过，则有输入文件，如我的是/input，同时把之前运行产生的输出文件/output删除

如果你把hadoop的bin和sbin都加入环境配置了，那就可以直接使用hadoop语句

hadoop jar wordcount.jar WordCount /input /output

15/07/23 16:46:35 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
15/07/23 16:46:36 INFO Configuration.deprecation: session.id is deprecated. Instead, use dfs.metrics.session-id
15/07/23 16:46:36 INFO jvm.JvmMetrics: Initializing JVM Metrics with processName=JobTracker, sessionId=
15/07/23 16:46:36 INFO jvm.JvmMetrics: Cannot initialize JVM Metrics with processName=JobTracker, sessionId= - already initialized
15/07/23 16:46:36 INFO mapred.FileInputFormat: Total input paths to process : 2
15/07/23 16:46:36 INFO mapreduce.JobSubmitter: number of splits:2
15/07/23 16:46:37 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_local1599291624_0001
15/07/23 16:46:37 INFO mapreduce.Job: The url to track the job: http://localhost:8080/ 15/07/23 16:46:37 INFO mapred.LocalJobRunner: OutputCommitter set in config null
15/07/23 16:46:37 INFO mapred.LocalJobRunner: OutputCommitter is org.apache.hadoop.mapred.FileOutputCommitter
15/07/23 16:46:37 INFO mapreduce.Job: Running job: job_local1599291624_0001
15/07/23 16:46:37 INFO mapred.LocalJobRunner: Waiting for map tasks
15/07/23 16:46:37 INFO mapred.LocalJobRunner: Starting task: attempt_local1599291624_0001_m_000000_0
15/07/23 16:46:37 INFO mapred.Task:  Using ResourceCalculatorProcessTree : [ ]
15/07/23 16:46:37 INFO mapred.MapTask: Processing split: hdfs://master:9000/input/readme.txt:0+55
15/07/23 16:46:37 INFO mapred.MapTask: numReduceTasks: 1
15/07/23 16:46:38 INFO mapreduce.Job: Job job_local1599291624_0001 running in uber mode : false
15/07/23 16:46:38 INFO mapred.MapTask: (EQUATOR) 0 kvi 26214396(104857584)
15/07/23 16:46:38 INFO mapred.MapTask: mapreduce.task.io.sort.mb: 100
15/07/23 16:46:38 INFO mapred.MapTask: soft limit at 83886080
15/07/23 16:46:38 INFO mapred.MapTask: bufstart = 0; bufvoid = 104857600
15/07/23 16:46:38 INFO mapred.MapTask: kvstart = 26214396; length = 6553600
15/07/23 16:46:38 INFO mapred.MapTask: Map output collector class = org.apache.hadoop.mapred.MapTask$MapOutputBuffer
15/07/23 16:46:38 INFO mapreduce.Job:  map 0% reduce 0%
I love hadoop.So I hope I can do my best to finish it!哈哈
15/07/23 16:46:38 INFO mapred.LocalJobRunner:
15/07/23 16:46:38 INFO mapred.MapTask: Starting flush of map output
15/07/23 16:46:38 INFO mapred.MapTask: Spilling map output
15/07/23 16:46:38 INFO mapred.MapTask: bufstart = 0; bufend = 107; bufvoid = 104857600
15/07/23 16:46:38 INFO mapred.MapTask: kvstart = 26214396(104857584); kvend = 26214348(104857392); length = 49/6553600
15/07/23 16:46:38 INFO mapred.MapTask: Finished spill 0
15/07/23 16:46:38 INFO mapred.Task: Task:attempt_local1599291624_0001_m_000000_0 is done. And is in the process of committing
15/07/23 16:46:38 INFO mapred.LocalJobRunner: hdfs://master:9000/input/readme.txt:0+55
15/07/23 16:46:38 INFO mapred.Task: Task 'attempt_local1599291624_0001_m_000000_0' done.
15/07/23 16:46:38 INFO mapred.LocalJobRunner: Finishing task: attempt_local1599291624_0001_m_000000_0
15/07/23 16:46:38 INFO mapred.LocalJobRunner: Starting task: attempt_local1599291624_0001_m_000001_0
15/07/23 16:46:38 INFO mapred.Task:  Using ResourceCalculatorProcessTree : [ ]
15/07/23 16:46:38 INFO mapred.MapTask: Processing split: hdfs://master:9000/input/readme2:0+55
15/07/23 16:46:38 INFO mapred.MapTask: numReduceTasks: 1
15/07/23 16:46:39 INFO mapred.MapTask: (EQUATOR) 0 kvi 26214396(104857584)
15/07/23 16:46:39 INFO mapred.MapTask: mapreduce.task.io.sort.mb: 100
15/07/23 16:46:39 INFO mapred.MapTask: soft limit at 83886080
15/07/23 16:46:39 INFO mapred.MapTask: bufstart = 0; bufvoid = 104857600
15/07/23 16:46:39 INFO mapred.MapTask: kvstart = 26214396; length = 6553600
15/07/23 16:46:39 INFO mapred.MapTask: Map output collector class = org.apache.hadoop.mapred.MapTask$MapOutputBuffer
I love hadoop.So I hope I can do my best to finish it!哈哈
15/07/23 16:46:39 INFO mapred.LocalJobRunner:
15/07/23 16:46:39 INFO mapred.MapTask: Starting flush of map output
15/07/23 16:46:39 INFO mapred.MapTask: Spilling map output
15/07/23 16:46:39 INFO mapred.MapTask: bufstart = 0; bufend = 107; bufvoid = 104857600
15/07/23 16:46:39 INFO mapred.MapTask: kvstart = 26214396(104857584); kvend = 26214348(104857392); length = 49/6553600
15/07/23 16:46:39 INFO mapred.MapTask: Finished spill 0
15/07/23 16:46:39 INFO mapred.Task: Task:attempt_local1599291624_0001_m_000001_0 is done. And is in the process of committing
15/07/23 16:46:39 INFO mapred.LocalJobRunner: hdfs://master:9000/input/readme2:0+55
15/07/23 16:46:39 INFO mapred.Task: Task 'attempt_local1599291624_0001_m_000001_0' done.
15/07/23 16:46:39 INFO mapred.LocalJobRunner: Finishing task: attempt_local1599291624_0001_m_000001_0
15/07/23 16:46:39 INFO mapred.LocalJobRunner: map task executor complete.
15/07/23 16:46:39 INFO mapred.LocalJobRunner: Waiting for reduce tasks
15/07/23 16:46:39 INFO mapred.LocalJobRunner: Starting task: attempt_local1599291624_0001_r_000000_0
15/07/23 16:46:39 INFO mapred.Task:  Using ResourceCalculatorProcessTree : [ ]
15/07/23 16:46:39 INFO mapred.ReduceTask: Using ShuffleConsumerPlugin: org.apache.hadoop.mapreduce.task.reduce.Shuffle@d3ec2f
15/07/23 16:46:39 INFO reduce.MergeManagerImpl: MergerManager: memoryLimit=334154944, maxSingleShuffleLimit=83538736, mergeThreshold=220542272, ioSortFactor=10, memToMemMergeOutputsThreshold=10
15/07/23 16:46:39 INFO reduce.EventFetcher: attempt_local1599291624_0001_r_000000_0 Thread started: EventFetcher for fetching Map Completion Events
15/07/23 16:46:39 INFO reduce.LocalFetcher: localfetcher#1 about to shuffle output of map attempt_local1599291624_0001_m_000001_0 decomp: 119 len: 123 to MEMORY
15/07/23 16:46:39 INFO reduce.InMemoryMapOutput: Read 119 bytes from map-output for attempt_local1599291624_0001_m_000001_0
15/07/23 16:46:39 INFO reduce.MergeManagerImpl: closeInMemoryFile -> map-output of size: 119, inMemoryMapOutputs.size() -> 1, commitMemory -> 0, usedMemory ->119
15/07/23 16:46:39 INFO reduce.LocalFetcher: localfetcher#1 about to shuffle output of map attempt_local1599291624_0001_m_000000_0 decomp: 119 len: 123 to MEMORY
15/07/23 16:46:39 INFO reduce.InMemoryMapOutput: Read 119 bytes from map-output for attempt_local1599291624_0001_m_000000_0
15/07/23 16:46:39 INFO reduce.MergeManagerImpl: closeInMemoryFile -> map-output of size: 119, inMemoryMapOutputs.size() -> 2, commitMemory -> 119, usedMemory ->238
15/07/23 16:46:39 INFO reduce.EventFetcher: EventFetcher is interrupted.. Returning
15/07/23 16:46:39 INFO mapred.LocalJobRunner: 2 / 2 copied.
15/07/23 16:46:39 INFO reduce.MergeManagerImpl: finalMerge called with 2 in-memory map-outputs and 0 on-disk map-outputs
15/07/23 16:46:39 INFO mapred.Merger: Merging 2 sorted segments
15/07/23 16:46:39 INFO mapred.Merger: Down to the last merge-pass, with 2 segments left of total size: 230 bytes
15/07/23 16:46:39 INFO reduce.MergeManagerImpl: Merged 2 segments, 238 bytes to disk to satisfy reduce memory limit
15/07/23 16:46:39 INFO reduce.MergeManagerImpl: Merging 1 files, 240 bytes from disk
15/07/23 16:46:39 INFO reduce.MergeManagerImpl: Merging 0 segments, 0 bytes from memory into reduce
15/07/23 16:46:39 INFO mapred.Merger: Merging 1 sorted segments
15/07/23 16:46:39 INFO mapred.Merger: Down to the last merge-pass, with 1 segments left of total size: 232 bytes
15/07/23 16:46:39 INFO mapred.LocalJobRunner: 2 / 2 copied.
15/07/23 16:46:39 INFO mapreduce.Job:  map 100% reduce 0%
15/07/23 16:46:40 INFO mapred.Task: Task:attempt_local1599291624_0001_r_000000_0 is done. And is in the process of committing
15/07/23 16:46:40 INFO mapred.LocalJobRunner: 2 / 2 copied.
15/07/23 16:46:40 INFO mapred.Task: Task attempt_local1599291624_0001_r_000000_0 is allowed to commit now
15/07/23 16:46:40 INFO output.FileOutputCommitter: Saved output of task 'attempt_local1599291624_0001_r_000000_0' to hdfs://master:9000/output/_temporary/0/task_local1599291624_0001_r_000000
15/07/23 16:46:40 INFO mapred.LocalJobRunner: reduce > reduce
15/07/23 16:46:40 INFO mapred.Task: Task 'attempt_local1599291624_0001_r_000000_0' done.
15/07/23 16:46:40 INFO mapred.LocalJobRunner: Finishing task: attempt_local1599291624_0001_r_000000_0
15/07/23 16:46:40 INFO mapred.LocalJobRunner: reduce task executor complete.
15/07/23 16:46:40 INFO mapreduce.Job:  map 100% reduce 100%
15/07/23 16:46:40 INFO mapreduce.Job: Job job_local1599291624_0001 completed successfully
15/07/23 16:46:40 INFO mapreduce.Job: Counters: 38
File System Counters
FILE: Number of bytes read=18012
FILE: Number of bytes written=751783
FILE: Number of read operations=0
FILE: Number of large read operations=0
FILE: Number of write operations=0
HDFS: Number of bytes read=275
HDFS: Number of bytes written=73
HDFS: Number of read operations=25
HDFS: Number of large read operations=0
HDFS: Number of write operations=5
Map-Reduce Framework
Map input records=2
Map output records=26
Map output bytes=214
Map output materialized bytes=246
Input split bytes=171
Combine input records=26
Combine output records=22
Reduce input groups=11
Reduce shuffle bytes=246
Reduce input records=22
Reduce output records=11
Spilled Records=44
Shuffled Maps =2
Failed Shuffles=0
Merged Map outputs=2
GC time elapsed (ms)=0
CPU time spent (ms)=0
Physical memory (bytes) snapshot=0
Virtual memory (bytes) snapshot=0
Total committed heap usage (bytes)=806354944
Shuffle Errors
BAD_ID=0
CONNECTION=0
IO_ERROR=0
WRONG_LENGTH=0
WRONG_MAP=0
WRONG_REDUCE=0
File Input Format Counters
Bytes Read=110
File Output Format Counters
Bytes Written=73

到此结束！！！！！