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.-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.-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:)
    at org.apache.hadoop.fs.FileSystem.createFileSystem(FileSystem.java:)
    at org.apache.hadoop.fs.FileSystem.access$(FileSystem.java:)
    at org.apache.hadoop.fs.FileSystem$Cache.getInternal(FileSystem.java:)
    at org.apache.hadoop.fs.FileSystem$Cache.get(FileSystem.java:)
    at org.apache.hadoop.fs.FileSystem.get(FileSystem.java:)
    at org.apache.hadoop.fs.FileSystem.get(FileSystem.java:)
    at org.apache.hadoop.fs.FileSystem.get(FileSystem.java:)
    at org.apache.hadoop.fs.Path.getFileSystem(Path.java:)
    at org.apache.hadoop.mapreduce.lib.input.FileInputFormat.addInputPath(FileInputFormat.java:)
    at cn.hadoop.hdfs.main.WordCountV2.run(WordCountV2.java:)
    at cn.hadoop.hdfs.main.WordCountV2.main(WordCountV2.java:)
-- :: 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.-jar-with-dependencies.jar
-- :: INFO  [SystemConfig.main] - Successfully loaded default properties.
-- :: INFO  [WordCountV2.main] - args length is
-- :: INFO  [WordCountV2.main] - output name is []
-- :: WARN  [NativeCodeLoader.main] - Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
-- :: INFO  [deprecation.main] - session.id is deprecated. Instead, use dfs.metrics.session-id
-- :: INFO  [JvmMetrics.main] - Initializing JVM Metrics with processName=JobTracker, sessionId=
-- :: WARN  [JobSubmitter.main] - Hadoop command-line option parsing not performed. Implement the Tool interface and execute your application with ToolRunner to remedy this.
-- :: INFO  [FileInputFormat.main] - Total input paths to process :
-- :: INFO  [JobSubmitter.main] - number of splits:
-- :: INFO  [JobSubmitter.main] - Submitting tokens for job: job_local519626586_0001
-- :: INFO  [Job.main] - The url to track the job: http://localhost:8080/
-- :: INFO  [Job.main] - Running job: job_local519626586_0001
-- :: INFO  [LocalJobRunner.Thread-] - OutputCommitter set in config null
-- :: INFO  [LocalJobRunner.Thread-] - OutputCommitter is org.apache.hadoop.mapreduce.lib.output.FileOutputCommitter
-- :: INFO  [LocalJobRunner.Thread-] - Waiting for map tasks
-- :: INFO  [LocalJobRunner.LocalJobRunner Map Task Executor #] - Starting task: attempt_local519626586_0001_m_000000_0
-- :: INFO  [Task.LocalJobRunner Map Task Executor #] -  Using ResourceCalculatorProcessTree : [ ]
-- :: INFO  [MapTask.LocalJobRunner Map Task Executor #] - Processing split: hdfs://cluster1/home/hdfs/test/in/hello.txt:0+24
-- :: INFO  [MapTask.LocalJobRunner Map Task Executor #] - (EQUATOR)  kvi ()
-- :: INFO  [MapTask.LocalJobRunner Map Task Executor #] - mapreduce.task.io.sort.mb:
-- :: INFO  [MapTask.LocalJobRunner Map Task Executor #] - soft limit at
-- :: INFO  [MapTask.LocalJobRunner Map Task Executor #] - bufstart = ; bufvoid =
-- :: INFO  [MapTask.LocalJobRunner Map Task Executor #] - kvstart = ; length =
-- :: INFO  [MapTask.LocalJobRunner Map Task Executor #] - Map output collector class = org.apache.hadoop.mapred.MapTask$MapOutputBuffer
-- :: INFO  [LocalJobRunner.LocalJobRunner Map Task Executor #] -
-- :: INFO  [MapTask.LocalJobRunner Map Task Executor #] - Starting flush of map output
-- :: INFO  [MapTask.LocalJobRunner Map Task Executor #] - Spilling map output
-- :: INFO  [MapTask.LocalJobRunner Map Task Executor #] - bufstart = ; bufend = ; bufvoid =
-- :: INFO  [MapTask.LocalJobRunner Map Task Executor #] - kvstart = (); kvend = (); length = /
-- :: INFO  [MapTask.LocalJobRunner Map Task Executor #] - Finished spill
-- :: INFO  [Task.LocalJobRunner Map Task Executor #] - Task:attempt_local519626586_0001_m_000000_0 is done. And is in the process of committing
-- :: INFO  [LocalJobRunner.LocalJobRunner Map Task Executor #] - map
-- :: INFO  [Task.LocalJobRunner Map Task Executor #] - Task 'attempt_local519626586_0001_m_000000_0' done.
-- :: INFO  [LocalJobRunner.LocalJobRunner Map Task Executor #] - Finishing task: attempt_local519626586_0001_m_000000_0
-- :: INFO  [LocalJobRunner.Thread-] - map task executor complete.
-- :: INFO  [LocalJobRunner.Thread-] - Waiting for reduce tasks
-- :: INFO  [LocalJobRunner.pool--thread-] - Starting task: attempt_local519626586_0001_r_000000_0
-- :: INFO  [Task.pool--thread-] -  Using ResourceCalculatorProcessTree : [ ]
-- :: INFO  [ReduceTask.pool--thread-] - Using ShuffleConsumerPlugin: org.apache.hadoop.mapreduce.task.reduce.Shuffle@
-- :: INFO  [MergeManagerImpl.pool--thread-] - MergerManager: memoryLimit=, maxSingleShuffleLimit=, mergeThreshold=, ioSortFactor=, memToMemMergeOutputsThreshold=
-- :: INFO  [EventFetcher.EventFetcher for fetching Map Completion Events] - attempt_local519626586_0001_r_000000_0 Thread started: EventFetcher for fetching Map Completion Events
-- :: INFO  [LocalFetcher.localfetcher#] - localfetcher# about to shuffle output of map attempt_local519626586_0001_m_000000_0 decomp:  len:  to MEMORY
-- :: INFO  [InMemoryMapOutput.localfetcher#] - Read  bytes from map-output for attempt_local519626586_0001_m_000000_0
-- :: INFO  [MergeManagerImpl.localfetcher#] - closeInMemoryFile -> map-output of size: , inMemoryMapOutputs.size() -> , commitMemory -> , usedMemory ->
-- :: INFO  [EventFetcher.EventFetcher for fetching Map Completion Events] - EventFetcher is interrupted.. Returning
-- :: INFO  [LocalJobRunner.pool--thread-] -  /  copied.
-- :: INFO  [MergeManagerImpl.pool--thread-] - finalMerge called with  in-memory map-outputs and  on-disk map-outputs
-- :: INFO  [Merger.pool--thread-] - Merging  sorted segments
-- :: INFO  [Merger.pool--thread-] - Down to the last merge-pass, with  segments left of total size:  bytes
-- :: INFO  [MergeManagerImpl.pool--thread-] - Merged  segments,  bytes to disk to satisfy reduce memory limit
-- :: INFO  [MergeManagerImpl.pool--thread-] - Merging  files,  bytes from disk
-- :: INFO  [MergeManagerImpl.pool--thread-] - Merging  segments,  bytes from memory into reduce
-- :: INFO  [Merger.pool--thread-] - Merging  sorted segments
-- :: INFO  [Merger.pool--thread-] - Down to the last merge-pass, with  segments left of total size:  bytes
-- :: INFO  [LocalJobRunner.pool--thread-] -  /  copied.
-- :: INFO  [deprecation.pool--thread-] - mapred.skip.on is deprecated. Instead, use mapreduce.job.skiprecords
-- :: INFO  [Job.main] - Job job_local519626586_0001 running in uber mode : false
-- :: INFO  [Job.main] -  map % reduce %
-- :: INFO  [Task.pool--thread-] - Task:attempt_local519626586_0001_r_000000_0 is done. And is in the process of committing
-- :: INFO  [LocalJobRunner.pool--thread-] -  /  copied.
-- :: INFO  [Task.pool--thread-] - Task attempt_local519626586_0001_r_000000_0 is allowed to commit now
-- :: INFO  [FileOutputCommitter.pool--thread-] - 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
-- :: INFO  [LocalJobRunner.pool--thread-] - reduce > reduce
-- :: INFO  [Task.pool--thread-] - Task 'attempt_local519626586_0001_r_000000_0' done.
-- :: INFO  [LocalJobRunner.pool--thread-] - Finishing task: attempt_local519626586_0001_r_000000_0
-- :: INFO  [LocalJobRunner.Thread-] - reduce task executor complete.
-- :: INFO  [Job.main] -  map % reduce %
-- :: INFO  [Job.main] - Job job_local519626586_0001 completed successfully
-- :: INFO  [Job.main] - Counters:
    File System Counters
        FILE: Number of bytes read=
        FILE: Number of bytes written=
        FILE: Number of read operations=
        FILE: Number of large read operations=
        FILE: Number of write operations=
        HDFS: Number of bytes read=
        HDFS: Number of bytes written=
        HDFS: Number of read operations=
        HDFS: Number of large read operations=
        HDFS: Number of write operations=
    Map-Reduce Framework
        Map input records=
        Map output records=
        Map output bytes=
        Map output materialized bytes=
        Input split bytes=
        Combine input records=
        Combine output records=
        Reduce input groups=
        Reduce shuffle bytes=
        Reduce input records=
        Reduce output records=
        Spilled Records=
        Shuffled Maps =
        Failed Shuffles=
        Merged Map outputs=
        GC time elapsed (ms)=
        CPU time spent (ms)=
        Physical memory (bytes) snapshot=
        Virtual memory (bytes) snapshot=
        Total committed heap usage (bytes)=
    Shuffle Errors
        BAD_ID=
        CONNECTION=
        IO_ERROR=
        WRONG_LENGTH=
        WRONG_MAP=
        WRONG_REDUCE=
    File Input Format Counters
        Bytes Read=
    File Output Format Counters
        Bytes Written=

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！