高可用Hadoop平台－应用JAR部署

1.概述

　　今天在观察集群时，发现NN节点的负载过高，虽然对NN节点的资源进行了调整，同时对NN节点上的应用程序进行重新打包调整，负载问题暂时得到缓解。但是，我想了想，这样也不是长久之计。通过这个问题，我重新分析了一下以前应用部署架构图，发现了一些问题的所在，之前的部署架构是，将打包的应用直接部署在Hadoop集群上，虽然这没什么不好，但是我们分析得知，若是将应用部署在DN节点，那么时间长了应用程序会不会抢占DN节点的资源，那么如果我们部署在NN节点上，又对NN节点计算任务时造成影响，于是，经过讨论后，我们觉得应用程序不应该对Hadoop集群造成干扰，他们应该是属于一种松耦合的关系，所有的应用应该部署在一个AppServer集群上。下面，我就为大家介绍今天的内容。

2.应用部署剖析

　　由于之前的应用程序直接部署在Hadoop集群上，这堆集群或多或少造成了一些影响。我们知道在本地开发Hadoop应用的时候，都可以直接运行相关Hadoop代码，这里我们只用到了Hadoop的HDFS的地址，那我们为什么不能直接将应用单独部署呢？其实本地开发就可以看作是AppServer集群的一个节点，借助这个思路，我们将应用单独打包后，部署在一个独立的AppServer集群，只需要用到Hadoop集群的HDFS地址即可，这里需要注意的是，保证AppServer集群与Hadoop集群在同一个网段。下面我给出解耦后应用部署架构图，如下图所示：

　　从图中我们可以看出，AppServer集群想Hadoop集群提交作业，两者之间的数据交互，只需用到Hadoop的HDFS地址和Java API。在AppServer上的应用不会影响到Hadoop集群的正常运行。

3.示例

　　下面为大家演示相关示例，以WordCountV2为例子，代码如下所示：

package cn.hadoop.hdfs.main;

import java.io.IOException;
import java.util.Random;
import java.util.StringTokenizer;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
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.slf4j.Logger;
import org.slf4j.LoggerFactory;

import cn.hadoop.hdfs.util.SystemConfig;

/**
 * @Date Apr 23, 2015
 *
 * @Author dengjie
 *
 * @Note Wordcount的例子是一个比较经典的mapreduce例子，可以叫做Hadoop版的hello world。
 *       它将文件中的单词分割取出，然后shuffle，sort（map过程），接着进入到汇总统计
 *       （reduce过程），最后写道hdfs中。基本流程就是这样。
 */
public class WordCountV2 {

    private static Logger logger = LoggerFactory.getLogger(WordCountV2.class);
    private static Configuration conf;

    /**
     * 设置高可用集群连接信息
     */
    static {
        String tag = SystemConfig.getProperty("dev.tag");
        String[] hosts = SystemConfig.getPropertyArray(tag + ".hdfs.host", ",");
        conf = new Configuration();
        conf.set("fs.defaultFS", "hdfs://cluster1");
        conf.set("dfs.nameservices", "cluster1");
        conf.set("dfs.ha.namenodes.cluster1", "nna,nns");
        conf.set("dfs.namenode.rpc-address.cluster1.nna", hosts[0]);
        conf.set("dfs.namenode.rpc-address.cluster1.nns", hosts[1]);
        conf.set("dfs.client.failover.proxy.provider.cluster1",
                "org.apache.hadoop.hdfs.server.namenode.ha.ConfiguredFailoverProxyProvider");    
    }

    public static class TokenizerMapper extends Mapper<Object, Text, Text, IntWritable> {

        private final static IntWritable one = new IntWritable(1);
        private Text word = new Text();

        /**
         * 源文件：a b b
         * 
         * map之后：
         * 
         * a 1
         * 
         * b 1
         * 
         * b 1
         */
        public void map(Object key, Text value, Context context) throws IOException, InterruptedException {
            StringTokenizer itr = new StringTokenizer(value.toString());// 整行读取
            while (itr.hasMoreTokens()) {
                word.set(itr.nextToken());// 按空格分割单词
                context.write(word, one);// 每次统计出来的单词+1
            }
        }
    }

    /**
     * reduce之前：
     * 
     * a 1
     * 
     * b 1
     * 
     * b 1
     * 
     * reduce之后:
     * 
     * a 1
     * 
     * b 2
     */
    public static class IntSumReducer extends Reducer<Text, IntWritable, Text, IntWritable> {
        private IntWritable result = new IntWritable();

        public void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException,
                InterruptedException {
            int sum = 0;
            for (IntWritable val : values) {
                sum += val.get();// 分组累加
            }
            result.set(sum);
            context.write(key, result);// 按相同的key输出
        }
    }

    public static void main(String[] args) {
        try {
            if (args.length < 1) {
                logger.info("args length is 0");
                run("hello.txt");
            } else {
                logger.info("args length is not 0");
                run(args[0]);
            }
        } catch (Exception ex) {
            ex.printStackTrace();
            logger.error(ex.getMessage());
        }
    }

    private static void run(String name) throws Exception {
        long randName = new Random().nextLong();// 重定向输出目录
        logger.info("output name is [" + randName + "]");

        Job job = Job.getInstance(conf);
        job.setJarByClass(WordCountV2.class);
        job.setMapperClass(TokenizerMapper.class);// 指定Map计算的类
        job.setCombinerClass(IntSumReducer.class);// 合并的类
        job.setReducerClass(IntSumReducer.class);// Reduce的类
        job.setOutputKeyClass(Text.class);// 输出Key类型
        job.setOutputValueClass(IntWritable.class);// 输出值类型

        String sysInPath = SystemConfig.getProperty("hdfs.input.path.v2");
        String realInPath = String.format(sysInPath, name);
        String syOutPath = SystemConfig.getProperty("hdfs.output.path.v2");
        String realOutPath = String.format(syOutPath, randName);

        FileInputFormat.addInputPath(job, new Path(realInPath));// 指定输入路径
        FileOutputFormat.setOutputPath(job, new Path(realOutPath));// 指定输出路径

        System.exit(job.waitForCompletion(true) ? 0 : 1);// 执行完MR任务后退出应用
    }
}

　　在本地IDE中运行正常，截图如下所示：

4.应用打包部署

　　然后，我们将WordCountV2应用打包后部署到AppServer1节点，这里由于工程是基于Maven结构的，我们使用Maven命令直接打包，打包命令如下所示：

mvn assembly:assembly

　　然后，我们使用scp命令将打包后的JAR文件上传到AppServer1节点，上传命令如下所示：

scp hadoop-ubas-1.0.0-jar-with-dependencies.jar hadoop@apps:~/

　　接着，我们在AppServer1节点上运行我们打包好的应用，运行命令如下所示：

java -jar hadoop-ubas-1.0.0-jar-with-dependencies.jar

　　但是，这里却很无奈的报错了，错误信息如下所示：

java.io.IOException: No FileSystem for scheme: hdfs
    at org.apache.hadoop.fs.FileSystem.getFileSystemClass(FileSystem.java:2584)
    at org.apache.hadoop.fs.FileSystem.createFileSystem(FileSystem.java:2591)
    at org.apache.hadoop.fs.FileSystem.access$200(FileSystem.java:91)
    at org.apache.hadoop.fs.FileSystem$Cache.getInternal(FileSystem.java:2630)
    at org.apache.hadoop.fs.FileSystem$Cache.get(FileSystem.java:2612)
    at org.apache.hadoop.fs.FileSystem.get(FileSystem.java:370)
    at org.apache.hadoop.fs.FileSystem.get(FileSystem.java:169)
    at org.apache.hadoop.fs.FileSystem.get(FileSystem.java:354)
    at org.apache.hadoop.fs.Path.getFileSystem(Path.java:296)
    at org.apache.hadoop.mapreduce.lib.input.FileInputFormat.addInputPath(FileInputFormat.java:518)
    at cn.hadoop.hdfs.main.WordCountV2.run(WordCountV2.java:134)
    at cn.hadoop.hdfs.main.WordCountV2.main(WordCountV2.java:108)
2015-05-10 23:31:21 ERROR [WordCountV2.main] - No FileSystem for scheme: hdfs

5.错误分析

　　首先，我们来定位下问题原因，我将打包后的JAR在Hadoop集群上运行，是可以完成良好的运行，并计算出结果信息的，为什么在非Hadoop集群却报错呢？难道是这种架构方式不对？经过仔细的分析错误信息，和我们的Maven依赖环境，问题原因定位出来了，这里我们使用了Maven的assembly插件来打包应用。只是因为当我们使用Maven组件时，它将所有的JARS合并到一个文件中，所有的META-INFO/services/org.apache.hadoop.fs.FileSystem被互相覆盖，仅保留最后一个加入的，在这种情况下FileSystem的列表从Hadoop-Commons重写到Hadoop-HDFS的列表，而DistributedFileSystem就会找不到相应的声明信息。因而，就会出现上述错误信息。在原因找到后，我们剩下的就是去找到解决方法，这里通过分析，我找到的解决办法如下，在Loading相关Hadoop的Configuration时，我们设置相关FileSystem即可，配置代码如下所示：

conf.set("fs.hdfs.impl", org.apache.hadoop.hdfs.DistributedFileSystem.class.getName());
conf.set("fs.file.impl", org.apache.hadoop.fs.LocalFileSystem.class.getName());

　　接下来，我们重新打包应用，然后在AppServer1节点运行该应用，运行正常，并正常统计结果，运行日志如下所示：

[hadoop@apps example]$ java -jar hadoop-ubas-1.0.0-jar-with-dependencies.jar 
2015-05-11 00:08:15 INFO  [SystemConfig.main] - Successfully loaded default properties.
2015-05-11 00:08:15 INFO  [WordCountV2.main] - args length is 0
2015-05-11 00:08:15 INFO  [WordCountV2.main] - output name is [6876390710620561863]
2015-05-11 00:08:16 WARN  [NativeCodeLoader.main] - Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
2015-05-11 00:08:17 INFO  [deprecation.main] - session.id is deprecated. Instead, use dfs.metrics.session-id
2015-05-11 00:08:17 INFO  [JvmMetrics.main] - Initializing JVM Metrics with processName=JobTracker, sessionId=
2015-05-11 00:08:17 WARN  [JobSubmitter.main] - Hadoop command-line option parsing not performed. Implement the Tool interface and execute your application with ToolRunner to remedy this.
2015-05-11 00:08:17 INFO  [FileInputFormat.main] - Total input paths to process : 1
2015-05-11 00:08:18 INFO  [JobSubmitter.main] - number of splits:1
2015-05-11 00:08:18 INFO  [JobSubmitter.main] - Submitting tokens for job: job_local519626586_0001
2015-05-11 00:08:18 INFO  [Job.main] - The url to track the job: http://localhost:8080/
2015-05-11 00:08:18 INFO  [Job.main] - Running job: job_local519626586_0001
2015-05-11 00:08:18 INFO  [LocalJobRunner.Thread-14] - OutputCommitter set in config null
2015-05-11 00:08:18 INFO  [LocalJobRunner.Thread-14] - OutputCommitter is org.apache.hadoop.mapreduce.lib.output.FileOutputCommitter
2015-05-11 00:08:18 INFO  [LocalJobRunner.Thread-14] - Waiting for map tasks
2015-05-11 00:08:18 INFO  [LocalJobRunner.LocalJobRunner Map Task Executor #0] - Starting task: attempt_local519626586_0001_m_000000_0
2015-05-11 00:08:18 INFO  [Task.LocalJobRunner Map Task Executor #0] -  Using ResourceCalculatorProcessTree : [ ]
2015-05-11 00:08:18 INFO  [MapTask.LocalJobRunner Map Task Executor #0] - Processing split: hdfs://cluster1/home/hdfs/test/in/hello.txt:0+24
2015-05-11 00:08:19 INFO  [MapTask.LocalJobRunner Map Task Executor #0] - (EQUATOR) 0 kvi 26214396(104857584)
2015-05-11 00:08:19 INFO  [MapTask.LocalJobRunner Map Task Executor #0] - mapreduce.task.io.sort.mb: 100
2015-05-11 00:08:19 INFO  [MapTask.LocalJobRunner Map Task Executor #0] - soft limit at 83886080
2015-05-11 00:08:19 INFO  [MapTask.LocalJobRunner Map Task Executor #0] - bufstart = 0; bufvoid = 104857600
2015-05-11 00:08:19 INFO  [MapTask.LocalJobRunner Map Task Executor #0] - kvstart = 26214396; length = 6553600
2015-05-11 00:08:19 INFO  [MapTask.LocalJobRunner Map Task Executor #0] - Map output collector class = org.apache.hadoop.mapred.MapTask$MapOutputBuffer
2015-05-11 00:08:19 INFO  [LocalJobRunner.LocalJobRunner Map Task Executor #0] - 
2015-05-11 00:08:19 INFO  [MapTask.LocalJobRunner Map Task Executor #0] - Starting flush of map output
2015-05-11 00:08:19 INFO  [MapTask.LocalJobRunner Map Task Executor #0] - Spilling map output
2015-05-11 00:08:19 INFO  [MapTask.LocalJobRunner Map Task Executor #0] - bufstart = 0; bufend = 72; bufvoid = 104857600
2015-05-11 00:08:19 INFO  [MapTask.LocalJobRunner Map Task Executor #0] - kvstart = 26214396(104857584); kvend = 26214352(104857408); length = 45/6553600
2015-05-11 00:08:19 INFO  [MapTask.LocalJobRunner Map Task Executor #0] - Finished spill 0
2015-05-11 00:08:19 INFO  [Task.LocalJobRunner Map Task Executor #0] - Task:attempt_local519626586_0001_m_000000_0 is done. And is in the process of committing
2015-05-11 00:08:19 INFO  [LocalJobRunner.LocalJobRunner Map Task Executor #0] - map
2015-05-11 00:08:19 INFO  [Task.LocalJobRunner Map Task Executor #0] - Task 'attempt_local519626586_0001_m_000000_0' done.
2015-05-11 00:08:19 INFO  [LocalJobRunner.LocalJobRunner Map Task Executor #0] - Finishing task: attempt_local519626586_0001_m_000000_0
2015-05-11 00:08:19 INFO  [LocalJobRunner.Thread-14] - map task executor complete.
2015-05-11 00:08:19 INFO  [LocalJobRunner.Thread-14] - Waiting for reduce tasks
2015-05-11 00:08:19 INFO  [LocalJobRunner.pool-6-thread-1] - Starting task: attempt_local519626586_0001_r_000000_0
2015-05-11 00:08:19 INFO  [Task.pool-6-thread-1] -  Using ResourceCalculatorProcessTree : [ ]
2015-05-11 00:08:19 INFO  [ReduceTask.pool-6-thread-1] - Using ShuffleConsumerPlugin: org.apache.hadoop.mapreduce.task.reduce.Shuffle@16769723
2015-05-11 00:08:19 INFO  [MergeManagerImpl.pool-6-thread-1] - MergerManager: memoryLimit=177399392, maxSingleShuffleLimit=44349848, mergeThreshold=117083600, ioSortFactor=10, memToMemMergeOutputsThreshold=10
2015-05-11 00:08:19 INFO  [EventFetcher.EventFetcher for fetching Map Completion Events] - attempt_local519626586_0001_r_000000_0 Thread started: EventFetcher for fetching Map Completion Events
2015-05-11 00:08:19 INFO  [LocalFetcher.localfetcher#1] - localfetcher#1 about to shuffle output of map attempt_local519626586_0001_m_000000_0 decomp: 50 len: 54 to MEMORY
2015-05-11 00:08:19 INFO  [InMemoryMapOutput.localfetcher#1] - Read 50 bytes from map-output for attempt_local519626586_0001_m_000000_0
2015-05-11 00:08:19 INFO  [MergeManagerImpl.localfetcher#1] - closeInMemoryFile -> map-output of size: 50, inMemoryMapOutputs.size() -> 1, commitMemory -> 0, usedMemory ->50
2015-05-11 00:08:19 INFO  [EventFetcher.EventFetcher for fetching Map Completion Events] - EventFetcher is interrupted.. Returning
2015-05-11 00:08:19 INFO  [LocalJobRunner.pool-6-thread-1] - 1 / 1 copied.
2015-05-11 00:08:19 INFO  [MergeManagerImpl.pool-6-thread-1] - finalMerge called with 1 in-memory map-outputs and 0 on-disk map-outputs
2015-05-11 00:08:19 INFO  [Merger.pool-6-thread-1] - Merging 1 sorted segments
2015-05-11 00:08:19 INFO  [Merger.pool-6-thread-1] - Down to the last merge-pass, with 1 segments left of total size: 46 bytes
2015-05-11 00:08:19 INFO  [MergeManagerImpl.pool-6-thread-1] - Merged 1 segments, 50 bytes to disk to satisfy reduce memory limit
2015-05-11 00:08:19 INFO  [MergeManagerImpl.pool-6-thread-1] - Merging 1 files, 54 bytes from disk
2015-05-11 00:08:19 INFO  [MergeManagerImpl.pool-6-thread-1] - Merging 0 segments, 0 bytes from memory into reduce
2015-05-11 00:08:19 INFO  [Merger.pool-6-thread-1] - Merging 1 sorted segments
2015-05-11 00:08:19 INFO  [Merger.pool-6-thread-1] - Down to the last merge-pass, with 1 segments left of total size: 46 bytes
2015-05-11 00:08:19 INFO  [LocalJobRunner.pool-6-thread-1] - 1 / 1 copied.
2015-05-11 00:08:19 INFO  [deprecation.pool-6-thread-1] - mapred.skip.on is deprecated. Instead, use mapreduce.job.skiprecords
2015-05-11 00:08:19 INFO  [Job.main] - Job job_local519626586_0001 running in uber mode : false
2015-05-11 00:08:19 INFO  [Job.main] -  map 100% reduce 0%
2015-05-11 00:08:19 INFO  [Task.pool-6-thread-1] - Task:attempt_local519626586_0001_r_000000_0 is done. And is in the process of committing
2015-05-11 00:08:19 INFO  [LocalJobRunner.pool-6-thread-1] - 1 / 1 copied.
2015-05-11 00:08:19 INFO  [Task.pool-6-thread-1] - Task attempt_local519626586_0001_r_000000_0 is allowed to commit now
2015-05-11 00:08:19 INFO  [FileOutputCommitter.pool-6-thread-1] - Saved output of task 'attempt_local519626586_0001_r_000000_0' to hdfs://cluster1/home/hdfs/test/out/6876390710620561863/_temporary/0/task_local519626586_0001_r_000000
2015-05-11 00:08:19 INFO  [LocalJobRunner.pool-6-thread-1] - reduce > reduce
2015-05-11 00:08:19 INFO  [Task.pool-6-thread-1] - Task 'attempt_local519626586_0001_r_000000_0' done.
2015-05-11 00:08:19 INFO  [LocalJobRunner.pool-6-thread-1] - Finishing task: attempt_local519626586_0001_r_000000_0
2015-05-11 00:08:19 INFO  [LocalJobRunner.Thread-14] - reduce task executor complete.
2015-05-11 00:08:20 INFO  [Job.main] -  map 100% reduce 100%
2015-05-11 00:08:20 INFO  [Job.main] - Job job_local519626586_0001 completed successfully
2015-05-11 00:08:20 INFO  [Job.main] - Counters: 38
    File System Counters
        FILE: Number of bytes read=77813788
        FILE: Number of bytes written=78928898
        FILE: Number of read operations=0
        FILE: Number of large read operations=0
        FILE: Number of write operations=0
        HDFS: Number of bytes read=48
        HDFS: Number of bytes written=24
        HDFS: Number of read operations=13
        HDFS: Number of large read operations=0
        HDFS: Number of write operations=4
    Map-Reduce Framework
        Map input records=2
        Map output records=12
        Map output bytes=72
        Map output materialized bytes=54
        Input split bytes=108
        Combine input records=12
        Combine output records=6
        Reduce input groups=6
        Reduce shuffle bytes=54
        Reduce input records=6
        Reduce output records=6
        Spilled Records=12
        Shuffled Maps =1
        Failed Shuffles=0
        Merged Map outputs=1
        GC time elapsed (ms)=53
        CPU time spent (ms)=0
        Physical memory (bytes) snapshot=0
        Virtual memory (bytes) snapshot=0
        Total committed heap usage (bytes)=241442816
    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=24
    File Output Format Counters 
        Bytes Written=24

6.总结

　　这里需要注意的是，我们应用部署架构没问题，思路是正确的，问题出在打包上，在打包的时候需要特别注意，另外，有些同学使用IDE的Export导出时也要注意一下，相关依赖是否存在，还有常见的第三方打包工具Fat，这个也是需要注意的。

7.结束语

　　这篇博客就和大家分享到这里，如果大家在研究学习的过程当中有什么问题，可以加群进行讨论或发送邮件给我，我会尽我所能为您解答，与君共勉！

posted @ 2015-05-12 15:37 哥不是小萝莉阅读(2066) 评论(3) 编辑收藏举报

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哥不是小萝莉

高可用Hadoop平台－应用JAR部署

1.概述

2.应用部署剖析

3.示例

4.应用打包部署

5.错误分析

6.总结

7.结束语

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作者：哥不是小萝莉［关于我］［犒赏］

出处：http://www.cnblogs.com/smartloli/

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