无法在hadoop群集上运行作业。仅使用LocalJobRunner运行

我已经在CDH5 Beta 2上使用hadoop jar命令和以下命令提交了一份MR作业

hadoop jar ./hadoop-examples-0.0.1-SNAPSHOT.jar com.aravind.learning.hadoop.mapred.join.ReduceSideJoinDriver tech_talks/users.csv tech_talks/ratings.csv tech_talks/output/ReduceSideJoinDriver/

我还尝试过如下显式提供fs名称和job tracker url，但没有成功

hadoop jar ./hadoop-examples-0.0.1-SNAPSHOT.jar com.aravind.learning.hadoop.mapred.join.ReduceSideJoinDriver -Dfs.default.name=hdfs://abc.com:8020 -Dmapreduce.job.tracker=x.x.x.x:8021 tech_talks/users.csv tech_talks/ratings.csv tech_talks/output/ReduceSideJoinDriver/

作业成功运行，但正在使用LocalJobRunner而不是提交到集群。输出被写入HDFS，并且是正确的。我不确定我做错了什么，所以感谢你的意见。我还尝试显式指定fs和job tracker，如下所示，但结果相同

14/04/16 20:35:44 INFO Configuration.deprecation: session.id is deprecated. Instead, use dfs.metrics.session-id
14/04/16 20:35:44 INFO jvm.JvmMetrics: Initializing JVM Metrics with processName=JobTracker, sessionId=
14/04/16 20:35:45 WARN mapreduce.JobSubmitter: No job jar file set.  User classes may not be found. See Job or Job#setJar(String).
14/04/16 20:35:45 INFO input.FileInputFormat: Total input paths to process : 2
14/04/16 20:35:45 INFO mapreduce.JobSubmitter: number of splits:2
14/04/16 20:35:46 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_local1427968352_0001
14/04/16 20:35:46 WARN conf.Configuration: file:/tmp/hadoop-ird2/mapred/staging/ird21427968352/.staging/job_local1427968352_0001/job.xml:an attempt to override final parameter: mapreduce.job.end-notification.max.retry.interval;  Ignoring.
14/04/16 20:35:46 WARN conf.Configuration: file:/tmp/hadoop-ird2/mapred/staging/ird21427968352/.staging/job_local1427968352_0001/job.xml:an attempt to override final parameter: mapreduce.job.end-notification.max.attempts;  Ignoring.
14/04/16 20:35:46 WARN conf.Configuration: file:/tmp/hadoop-ird2/mapred/local/localRunner/ird2/job_local1427968352_0001/job_local1427968352_0001.xml:an attempt to override final parameter: mapreduce.job.end-notification.max.retry.interval;  Ignoring.
14/04/16 20:35:46 WARN conf.Configuration: file:/tmp/hadoop-ird2/mapred/local/localRunner/ird2/job_local1427968352_0001/job_local1427968352_0001.xml:an attempt to override final parameter: mapreduce.job.end-notification.max.attempts;  Ignoring.
14/04/16 20:35:46 INFO mapreduce.Job: The url to track the job: http://localhost:8080/
14/04/16 20:35:46 INFO mapreduce.Job: Running job: job_local1427968352_0001
14/04/16 20:35:46 INFO mapred.LocalJobRunner: OutputCommitter set in config null
14/04/16 20:35:46 INFO mapred.LocalJobRunner: OutputCommitter is org.apache.hadoop.mapreduce.lib.output.FileOutputCommitter
14/04/16 20:35:46 INFO mapred.LocalJobRunner: Waiting for map tasks
14/04/16 20:35:46 INFO mapred.LocalJobRunner: Starting task: attempt_local1427968352_0001_m_000000_0
14/04/16 20:35:46 INFO mapred.Task:  Using ResourceCalculatorProcessTree : [ ]
14/04/16 20:35:46 INFO mapred.MapTask: Processing split: hdfs://...:8020/user/ird2/tech_talks/ratings.csv:0+4388258
14/04/16 20:35:46 INFO mapred.MapTask: Map output collector class = org.apache.hadoop.mapred.MapTask$MapOutputBuffer
14/04/16 20:35:46 INFO mapred.MapTask: (EQUATOR) 0 kvi 26214396(104857584)
14/04/16 20:35:46 INFO mapred.MapTask: mapreduce.task.io.sort.mb: 100
14/04/16 20:35:46 INFO mapred.MapTask: soft limit at 83886080
14/04/16 20:35:46 INFO mapred.MapTask: bufstart = 0; bufvoid = 104857600
14/04/16 20:35:46 INFO mapred.MapTask: kvstart = 26214396; length = 6553600
14/04/16 20:35:47 INFO mapreduce.Job: Job job_local1427968352_0001 running in uber mode : false
14/04/16 20:35:47 INFO mapreduce.Job:  map 0% reduce 0%
14/04/16 20:35:48 INFO mapred.LocalJobRunner:
14/04/16 20:35:48 INFO mapred.MapTask: Starting flush of map output
14/04/16 20:35:48 INFO mapred.MapTask: Spilling map output
14/04/16 20:35:48 INFO mapred.MapTask: bufstart = 0; bufend = 6485388; bufvoid = 104857600
14/04/16 20:35:48 INFO mapred.MapTask: kvstart = 26214396(104857584); kvend = 24860980(99443920); length = 1353417/6553600
14/04/16 20:35:49 INFO mapred.MapTask: Finished spill 0
14/04/16 20:35:49 INFO mapred.Task: Task:attempt_local1427968352_0001_m_000000_0 is done. And is in the process of committing
14/04/16 20:35:49 INFO mapred.LocalJobRunner: map
14/04/16 20:35:49 INFO mapred.Task: Task 'attempt_local1427968352_0001_m_000000_0' done.
14/04/16 20:35:49 INFO mapred.LocalJobRunner: Finishing task: attempt_local1427968352_0001_m_000000_0
14/04/16 20:35:49 INFO mapred.LocalJobRunner: Starting task: attempt_local1427968352_0001_m_000001_0
14/04/16 20:35:49 INFO mapred.Task:  Using ResourceCalculatorProcessTree : [ ]
14/04/16 20:35:49 INFO mapred.MapTask: Processing split: hdfs://...:8020/user/ird2/tech_talks/users.csv:0+186304
14/04/16 20:35:49 INFO mapred.MapTask: Map output collector class = org.apache.hadoop.mapred.MapTask$MapOutputBuffer
14/04/16 20:35:49 INFO mapred.MapTask: (EQUATOR) 0 kvi 26214396(104857584)
14/04/16 20:35:49 INFO mapred.MapTask: mapreduce.task.io.sort.mb: 100
14/04/16 20:35:49 INFO mapred.MapTask: soft limit at 83886080
14/04/16 20:35:49 INFO mapred.MapTask: bufstart = 0; bufvoid = 104857600
14/04/16 20:35:49 INFO mapred.MapTask: kvstart = 26214396; length = 6553600
14/04/16 20:35:49 INFO mapred.LocalJobRunner:
14/04/16 20:35:49 INFO mapred.MapTask: Starting flush of map output
14/04/16 20:35:49 INFO mapred.MapTask: Spilling map output
14/04/16 20:35:49 INFO mapred.MapTask: bufstart = 0; bufend = 209667; bufvoid = 104857600
14/04/16 20:35:49 INFO mapred.MapTask: kvstart = 26214396(104857584); kvend = 26192144(104768576); length = 22253/6553600
14/04/16 20:35:49 INFO mapred.MapTask: Finished spill 0
14/04/16 20:35:49 INFO mapred.Task: Task:attempt_local1427968352_0001_m_000001_0 is done. And is in the process of committing
14/04/16 20:35:49 INFO mapred.LocalJobRunner: map
14/04/16 20:35:49 INFO mapred.Task: Task 'attempt_local1427968352_0001_m_000001_0' done.
14/04/16 20:35:49 INFO mapred.LocalJobRunner: Finishing task: attempt_local1427968352_0001_m_000001_0
14/04/16 20:35:49 INFO mapred.LocalJobRunner: map task executor complete.
14/04/16 20:35:49 INFO mapred.LocalJobRunner: Waiting for reduce tasks
14/04/16 20:35:49 INFO mapred.LocalJobRunner: Starting task: attempt_local1427968352_0001_r_000000_0
14/04/16 20:35:49 INFO mapred.Task:  Using ResourceCalculatorProcessTree : [ ]
14/04/16 20:35:49 INFO mapred.ReduceTask: Using ShuffleConsumerPlugin: org.apache.hadoop.mapreduce.task.reduce.Shuffle@5116331d
14/04/16 20:35:49 INFO reduce.MergeManagerImpl: MergerManager: memoryLimit=652528832, maxSingleShuffleLimit=163132208, mergeThreshold=430669056, ioSortFactor=10, memToMemMergeOutputsThreshold=10
14/04/16 20:35:49 INFO reduce.EventFetcher: attempt_local1427968352_0001_r_000000_0 Thread started: EventFetcher for fetching Map Completion Events
14/04/16 20:35:49 INFO reduce.LocalFetcher: localfetcher#1 about to shuffle output of map attempt_local1427968352_0001_m_000001_0 decomp: 220797 len: 220801 to MEMORY
14/04/16 20:35:49 INFO reduce.InMemoryMapOutput: Read 220797 bytes from map-output for attempt_local1427968352_0001_m_000001_0
14/04/16 20:35:49 INFO reduce.MergeManagerImpl: closeInMemoryFile -> map-output of size: 220797, inMemoryMapOutputs.size() -> 1, commitMemory -> 0, usedMemory ->220797
14/04/16 20:35:49 INFO reduce.LocalFetcher: localfetcher#1 about to shuffle output of map attempt_local1427968352_0001_m_000000_0 decomp: 7162100 len: 7162104 to MEMORY
14/04/16 20:35:49 INFO reduce.InMemoryMapOutput: Read 7162100 bytes from map-output for attempt_local1427968352_0001_m_000000_0
14/04/16 20:35:49 INFO reduce.MergeManagerImpl: closeInMemoryFile -> map-output of size: 7162100, inMemoryMapOutputs.size() -> 2, commitMemory -> 220797, usedMemory ->7382897
14/04/16 20:35:49 INFO reduce.EventFetcher: EventFetcher is interrupted.. Returning
14/04/16 20:35:49 INFO mapred.LocalJobRunner: 2 / 2 copied.
14/04/16 20:35:49 INFO reduce.MergeManagerImpl: finalMerge called with 2 in-memory map-outputs and 0 on-disk map-outputs
14/04/16 20:35:49 INFO mapred.Merger: Merging 2 sorted segments
14/04/16 20:35:49 INFO mapred.Merger: Down to the last merge-pass, with 2 segments left of total size: 7382885 bytes
14/04/16 20:35:50 INFO reduce.MergeManagerImpl: Merged 2 segments, 7382897 bytes to disk to satisfy reduce memory limit
14/04/16 20:35:50 INFO reduce.MergeManagerImpl: Merging 1 files, 7382899 bytes from disk
14/04/16 20:35:50 INFO reduce.MergeManagerImpl: Merging 0 segments, 0 bytes from memory into reduce
14/04/16 20:35:50 INFO mapred.Merger: Merging 1 sorted segments
14/04/16 20:35:50 INFO mapred.Merger: Down to the last merge-pass, with 1 segments left of total size: 7382889 bytes
14/04/16 20:35:50 INFO mapred.LocalJobRunner: 2 / 2 copied.
14/04/16 20:35:50 INFO Configuration.deprecation: mapred.skip.on is deprecated. Instead, use mapreduce.job.skiprecords
14/04/16 20:35:50 INFO mapreduce.Job:  map 100% reduce 0%
14/04/16 20:35:51 INFO mapred.Task: Task:attempt_local1427968352_0001_r_000000_0 is done. And is in the process of committing
14/04/16 20:35:51 INFO mapred.LocalJobRunner: 2 / 2 copied.
14/04/16 20:35:51 INFO mapred.Task: Task attempt_local1427968352_0001_r_000000_0 is allowed to commit now
14/04/16 20:35:51 INFO output.FileOutputCommitter: Saved output of task 'attempt_local1427968352_0001_r_000000_0' to hdfs://...:8020/user/ird2/tech_talks/output/ReduceSideJoinDriver/_temporary/0/task_local1427968352_0001_r_000000
14/04/16 20:35:51 INFO mapred.LocalJobRunner: reduce > reduce
14/04/16 20:35:51 INFO mapred.Task: Task 'attempt_local1427968352_0001_r_000000_0' done.
14/04/16 20:35:51 INFO mapred.LocalJobRunner: Finishing task: attempt_local1427968352_0001_r_000000_0
14/04/16 20:35:51 INFO mapred.LocalJobRunner: reduce task executor complete.
14/04/16 20:35:52 INFO mapreduce.Job:  map 100% reduce 100%
14/04/16 20:35:52 INFO mapreduce.Job: Job job_local1427968352_0001 completed successfully
14/04/16 20:35:52 INFO mapreduce.Job: Counters: 38
        File System Counters
                FILE: Number of bytes read=14767932
                FILE: Number of bytes written=29952985
                FILE: Number of read operations=0
                FILE: Number of large read operations=0
                FILE: Number of write operations=0
                HDFS: Number of bytes read=13537382
                HDFS: Number of bytes written=2949787
                HDFS: Number of read operations=28
                HDFS: Number of large read operations=0
                HDFS: Number of write operations=5
        Map-Reduce Framework
                Map input records=343919
                Map output records=343919
                Map output bytes=6695055
                Map output materialized bytes=7382905
                Input split bytes=272
                Combine input records=0
                Combine output records=0
                Reduce input groups=5564
                Reduce shuffle bytes=7382905
                Reduce input records=343919
                Reduce output records=5564
                Spilled Records=687838
                Shuffled Maps =2
                Failed Shuffles=0
                Merged Map outputs=2
                GC time elapsed (ms)=92
                CPU time spent (ms)=0
                Physical memory (bytes) snapshot=0
                Virtual memory (bytes) snapshot=0
                Total committed heap usage (bytes)=1416101888
        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=4574562
        File Output Format Counters
                Bytes Written=2949787

驾驶员代码

public class ReduceSideJoinDriver extends Configured implements Tool
{
    @Override
    public int run(String[] args) throws Exception
    {
        if (args.length != 3)
        {
            System.err.printf("Usage: %s [generic options] <input> <output>\n", getClass().getSimpleName());
            ToolRunner.printGenericCommandUsage(System.err);
            return -1;
        }

        Path usersFile = new Path(args[0]);
        Path ratingsFile = new Path(args[1]);

        Job job = Job.getInstance(getConf(), "Aravind - Reduce Side Join");

        job.getConfiguration().setStrings(usersFile.getName(), "user");
        job.getConfiguration().setStrings(ratingsFile.getName(), "rating");

        job.setInputFormatClass(TextInputFormat.class);
        job.setOutputFormatClass(TextOutputFormat.class);
        job.setMapOutputKeyClass(IntWritable.class);
        job.setMapOutputValueClass(TagAndRecord.class);

        TextInputFormat.addInputPath(job, usersFile);
        TextInputFormat.addInputPath(job, ratingsFile);

        TextOutputFormat.setOutputPath(job, new Path(args[2]));

        job.setMapperClass(ReduceSideJoinMapper.class);
        job.setReducerClass(ReduceSideJoinReducer.class);
        job.setOutputKeyClass(IntWritable.class);
        job.setOutputValueClass(Text.class);

        return job.waitForCompletion(true) ? 0 : 1;
    }

    public static void main(String args[]) throws Exception
    {
        int exitCode = ToolRunner.run(new Configuration(), new ReduceSideJoinDriver(), args);
        System.exit(exitCode);
    }
}

公共类ReduceSideJoinDriver扩展配置的工具
{
@凌驾
公共int运行（字符串[]args）引发异常
{
如果（参数长度！=3）
{
System.err.printf（“用法：%s[通用选项]\n”，getClass（）.getSimpleName（））；
ToolRunner.printGenericCommandUsage（System.err）；
返回-1；
}
Path usersFile=新路径（参数[0]）；
路径比率文件=新路径（args[1]）；
Job Job=Job.getInstance（getConf（），“Aravind-Reduce-Side-Join”）；
job.getConfiguration（）.setString（usersFile.getName（），“user”）；
job.getConfiguration（）.setString（ratingsFile.getName（），“rating”）；
setInputFormatClass（TextInputFormat.class）；
setOutputFormatClass（TextOutputFormat.class）；
setMapOutputKeyClass（IntWritable.class）；
job.setMapOutputValueClass（TagAndRecord.class）；
TextInputFormat.addInputPath（作业，用户文件）；
TextInputFormat.addInputPath（作业、分级文件）；
setOutputPath（作业，新路径（args[2]）；
setMapperClass（reducesDejoinMapper.class）；
job.setReducerClass（reducesDejoinReducer.class）；
job.setOutputKeyClass（IntWritable.class）；
job.setOutputValueClass（Text.class）；
返回作业。waitForCompletion（true）？0:1；
}
公共静态void main（字符串args[]）引发异常
{
int exitCode=ToolRunner.run（新配置（），新reducesDejoinDriver（），args）；
系统退出（退出代码）；
}
}

确保hadoop类路径中有以下有效的配置文件。默认情况下，配置文件取自目录/etc/hadoop/conf。此活动应作为hadoop客户端节点设置的一部分执行

mapred-site.xml
yarn-site.xml
core-site.xml

如果上述配置文件为空。您必须使用正确的属性来处理上述文件。人口增长可以通过两种方式实现

在Cloudera Manager中，当单击service Thread时，在action部分，有一个选项

部署客户端配置

以及启动、停止等。使用该选项部署客户端配置

有时，如果节点不由CM管理，且节点上未配置纱线网关，则上述选项可能不起作用。使用选项下载客户端配置，而不是部署客户端配置。解压缩下载的zip配置文件（上面的文件）并手动将这些文件复制到/etc/hadoop/conf位置

---

为了执行jar，可以使用

hadoop

或

Thread

。

显然，您只能从指定为网关节点的节点提交hadoop作业。一旦我从网关节点提交了作业，一切都正常。

你能附上作业驱动程序类代码吗？@Rocky111添加了驱动程序类代码