QuartzJob 调用 hadoop mapreduce 报错 1C

Error: java.io.IOException: com.mysql.jdbc.Driver
at org.apache.hadoop.mapreduce.lib.db.DBOutputFormat.getRecordWriter(DBOutputFormat.java:185)
at org.apache.hadoop.mapred.ReduceTask$NewTrackingRecordWriter.(ReduceTask.java:540)
at org.apache.hadoop.mapred.ReduceTask.runNewReducer(ReduceTask.java:614)
at org.apache.hadoop.mapred.ReduceTask.run(ReduceTask.java:389)
at org.apache.hadoop.mapred.YarnChild$2.run(YarnChild.java:164)
at java.security.AccessController.doPrivileged(Native Method)
at javax.security.auth.Subject.doAs(Subject.java:422)
at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1693)
at org.apache.hadoop.mapred.YarnChild.main(YarnChild.java:158)

2个回答

看看mysql的配置是否正确

应该是没有mysql驱动包?看下依赖包吧

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Instead, use dfs.metrics.session-idrn2018-04-10 20:11:22,820 INFO [main] jvm.JvmMetrics (JvmMetrics.java:init(76)) - Initializing JVM Metrics with processName=JobTracker, sessionId=rn2018-04-10 20:11:24,310 WARN [main] mapreduce.JobResourceUploader (JobResourceUploader.java:uploadFiles(64)) - Hadoop command-line option parsing not performed. Implement the Tool interface and execute your application with ToolRunner to remedy this.rn2018-04-10 20:11:24,330 WARN [main] mapreduce.JobResourceUploader (JobResourceUploader.java:uploadFiles(171)) - No job jar file set. User classes may not be found. See Job or Job#setJar(String).rn2018-04-10 20:11:24,354 INFO [main] input.FileInputFormat (FileInputFormat.java:listStatus(283)) - Total input paths to process : 1rn2018-04-10 20:11:24,800 INFO [main] mapreduce.JobSubmitter (JobSubmitter.java:submitJobInternal(198)) - number of splits:1rn2018-04-10 20:11:25,532 INFO [main] mapreduce.JobSubmitter (JobSubmitter.java:printTokens(287)) - Submitting tokens for job: job_local607403828_0001rn2018-04-10 20:11:26,056 INFO [main] mapreduce.Job (Job.java:submit(1294)) - The url to track the job: http://localhost:8080/rn2018-04-10 20:11:26,057 INFO [main] mapreduce.Job (Job.java:monitorAndPrintJob(1339)) - Running job: job_local607403828_0001rn2018-04-10 20:11:26,066 INFO [Thread-18] mapred.LocalJobRunner (LocalJobRunner.java:createOutputCommitter(471)) - OutputCommitter set in config nullrn2018-04-10 20:11:26,081 INFO [Thread-18] output.FileOutputCommitter (FileOutputCommitter.java:(108)) - File Output Committer Algorithm version is 1rn2018-04-10 20:11:26,088 INFO [Thread-18] mapred.LocalJobRunner (LocalJobRunner.java:createOutputCommitter(489)) - OutputCommitter is org.apache.hadoop.mapreduce.lib.output.FileOutputCommitterrn2018-04-10 20:11:26,245 INFO [Thread-18] mapred.LocalJobRunner (LocalJobRunner.java:runTasks(448)) - Waiting for map tasksrn2018-04-10 20:11:26,247 INFO [LocalJobRunner Map Task Executor #0] mapred.LocalJobRunner (LocalJobRunner.java:run(224)) - Starting task: attempt_local607403828_0001_m_000000_0rn2018-04-10 20:11:26,425 INFO [LocalJobRunner Map Task Executor #0] output.FileOutputCommitter (FileOutputCommitter.java:(108)) - File Output Committer Algorithm version is 1rn2018-04-10 20:11:26,474 INFO [LocalJobRunner Map Task Executor #0] mapred.Task (Task.java:initialize(612)) - Using ResourceCalculatorProcessTree : [ ]rn2018-04-10 20:11:26,483 INFO [LocalJobRunner Map Task Executor #0] mapred.MapTask (MapTask.java:runNewMapper(756)) - Processing split: file:/home/hadoop/simple/source.txt:0+13rn2018-04-10 20:11:26,916 INFO [LocalJobRunner Map Task Executor #0] mapred.MapTask (MapTask.java:setEquator(1205)) - (EQUATOR) 0 kvi 26214396(104857584)rn2018-04-10 20:11:26,916 INFO [LocalJobRunner Map Task Executor #0] mapred.MapTask (MapTask.java:init(998)) - mapreduce.task.io.sort.mb: 100rn2018-04-10 20:11:26,916 INFO [LocalJobRunner Map Task Executor #0] mapred.MapTask (MapTask.java:init(999)) - soft limit at 83886080rn2018-04-10 20:11:26,916 INFO [LocalJobRunner Map Task Executor #0] mapred.MapTask (MapTask.java:init(1000)) - bufstart = 0; bufvoid = 104857600rn2018-04-10 20:11:26,916 INFO [LocalJobRunner Map Task Executor #0] mapred.MapTask (MapTask.java:init(1001)) - kvstart = 26214396; length = 6553600rn2018-04-10 20:11:26,932 INFO [LocalJobRunner Map Task Executor #0] mapred.MapTask (MapTask.java:createSortingCollector(403)) - Map output collector class = org.apache.hadoop.mapred.MapTask$MapOutputBufferrn2018-04-10 20:11:26,996 INFO [LocalJobRunner Map Task Executor #0] mapred.MapTask (MapTask.java:flush(1460)) - Starting flush of map outputrn2018-04-10 20:11:27,020 INFO [Thread-18] mapred.LocalJobRunner (LocalJobRunner.java:runTasks(456)) - map task executor complete.rn2018-04-10 20:11:27,044 WARN [Thread-18] mapred.LocalJobRunner (LocalJobRunner.java:run(560)) - job_local607403828_0001rnjava.lang.Exception: java.io.IOException: Type mismatch in key from map: expected org.apache.hadoop.io.Text, received org.apache.hadoop.io.LongWritablern at org.apache.hadoop.mapred.LocalJobRunner$Job.runTasks(LocalJobRunner.java:462)rn at org.apache.hadoop.mapred.LocalJobRunner$Job.run(LocalJobRunner.java:522)rnCaused by: java.io.IOException: Type mismatch in key from map: expected org.apache.hadoop.io.Text, received org.apache.hadoop.io.LongWritablern at org.apache.hadoop.mapred.MapTask$MapOutputBuffer.collect(MapTask.java:1074)rn at org.apache.hadoop.mapred.MapTask$NewOutputCollector.write(MapTask.java:715)rn at org.apache.hadoop.mapreduce.task.TaskInputOutputContextImpl.write(TaskInputOutputContextImpl.java:89)rn at org.apache.hadoop.mapreduce.lib.map.WrappedMapper$Context.write(WrappedMapper.java:112)rn at com.mapreduce.WoldCountMapper.map(WoldCountMapper.java:20)rn at com.mapreduce.WoldCountMapper.map(WoldCountMapper.java:1)rn at org.apache.hadoop.mapreduce.Mapper.run(Mapper.java:146)rn at org.apache.hadoop.mapred.MapTask.runNewMapper(MapTask.java:787)rn at org.apache.hadoop.mapred.MapTask.run(MapTask.java:341)rn at org.apache.hadoop.mapred.LocalJobRunner$Job$MapTaskRunnable.run(LocalJobRunner.java:243)rn at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:511)rn at java.util.concurrent.FutureTask.run(FutureTask.java:266)rn at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)rn at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)rn at java.lang.Thread.run(Thread.java:748)rn2018-04-10 20:11:27,072 INFO [main] mapreduce.Job (Job.java:monitorAndPrintJob(1360)) - Job job_local607403828_0001 running in uber mode : falsern2018-04-10 20:11:27,085 INFO [main] mapreduce.Job (Job.java:monitorAndPrintJob(1367)) - map 0% reduce 0%rn2018-04-10 20:11:27,096 INFO [main] mapreduce.Job (Job.java:monitorAndPrintJob(1380)) - Job job_local607403828_0001 failed with state FAILED due to: NArn2018-04-10 20:11:27,248 INFO [main] mapreduce.Job (Job.java:monitorAndPrintJob(1385)) - Counters: 0
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1.MapReduce的类型 Hadoop的MapReduce函数遵循如下常规格式: --map:(K1, V1) -> list(K2, V2) --combine:(K2, list(V2)) -> list(K2, V2) --partition:(K2, V2) -> integer --reduce:(K2, list(V2)) -> list(K3...
Hadoop链式的MapReduce编程
通过多个Mapper过滤出符合要求的数据 输入数据: computer    5000 SmartPhone    3000 Tablet    15000 TV    5000 Book    18 Clothes    150 Gloves    9 SmartPhone    3000 Tablet    1500 computer    5000 SmartPhone    3000 ...
Hadoop核心组件之MapReduce
MapReduce概述 Google MapReduce的克隆版本 优点:海量数据的离线处理,易开发,易运行 缺点:实时流式计算 Hadoop MapReduce是一个软件框架,用于轻松编写应用程序,以可靠,容错的方式在大型集群(数千个节点)的商用硬件上并行处理大量数据(多TB数据集) MapReduce编程模型 思想:分而治之 MapReduce作业通常将输入数据集拆分为独立的块,这些块由m...
Hadoop之MapReduce工作原理
Map阶段 ①输入分片(inputsplit),这个时候也就是输入数据的时候,这时会进行会通过内部计算对数据进行逻辑上的分片。默认情况下这里的分片与HDFS中文件的分块是一致的。每一个逻辑上的分片也就对应着一个mapper任务。 ②Mapper将切片的数据输入到map函数中进行处理。 ③Buffer函数将结果输出首先放入buffer(缓冲区)中从而为后面的操作(也就是写入硬盘)做准备。这...
Hadoop MapReduce Cookbook
Learn to process large and complex data sets, starting simply, then diving in deep. Solve complex big data problems such as classifications, finding relationships, online marketing and recommendations. More than 50 Hadoop MapReduce recipes, presented in a simple and straightforward manner, with step-by-step instructions and real world examples.
大数据 hadoop mapreduce 词频统计
在hadoop平台上,用mapreduce编程实现大数据的词频统计
Hadoop及Mapreduce入门
Hadoop及Mapreduce入门,简单易懂,让你快速进入hadoop的世界
Hadoop之MapReduce自定义二次排序
一、概述 MapReduce框架对处理结果的输出会根据key值进行默认的排序,这个默认排序可以满足一部分需求,但是也是十分有限的。在我们实际的需求当中,往往有要对reduce输出结果进行二次排序的需求。对于二次排序的实现,网络上已经有很多人分享过了,但是对二次排序的实现的原理以及整个MapReduce框架的处理流程的分析还是有非常大的出入,而且部分分析是没有经过验证的。本文将通过一个实际的Map...
Hadoop深入学习:MapReduce
本节我们主要来学习Hadoop MapReduce分布式计算框架,它主要分为两部分:[b]编程模型和运行时环境[/b]。 [b]MapReduce编程模型为用户提供了简单易用的编程接口,可以让用户像编写不同的程序一向只要实现两个简单的函数(map()和reduce()函数)便实现一个分布式的应用程序,而其他的比价麻烦的和分布式相关的所有操作都由MapReduce的...
Hadoop学习笔记————MapReduce
简介 MapReduce是一种编程模型,并且是处理和生成大数据集的相关实现。用户指定一个map函数去处理key/value对,生成一个包含新的key/value对的集合(中间数据);reduce函数合并具有相同key值的中间数据。用户的程序按照这个模式编写,并且在一个集群上运行,这是利用分布式的一个典型的『并行』思想。用户无需关注输入文件的分割、任务在集群上的调度、集群内部的通信以及机器运行的失败...
hadoop MapReduce介绍
hadoop MapReduce介绍 SergeBazhievsky_Introduction_to_Hadoop_MapReduce_v2.pdf 很好的学习hadoop mapreduce计算框架的资料
hadoop mapreduce 原理
mapreduce是hadoop的核心组成,是专门用于数据计算。主要掌握 map、reduce 函数的特点、如何写函数。rnrn我的开发环境是在eclipse,运行程序的时候经常会出现 java 内存不足的情况,需要修改ecplise的jdk使用自己安装的JDK就行。rnrn对于 Hadoop 的 map 函数和 reduce 函数,处理的数据是键值对,也就是说 map 函数接收的数据是键值对,两个参数;输出的也是键值对,两个参数;reduce 函数接收的数和输出的结果也是键值对。我们要做的就是覆盖hadoop的map函数和reduce函数。rnrnmapreduce的执行过程rnrnMapReduce 运行的时候,会通过 Mapper 运行的任务读取 HDFS 中的数据文件,然后调用自己的方法,处理数据,最后输出。Reducer 任务会接收 Mapper 任务输出的数据,作为自己的输入数据,调用自己的方法,最后输出到 HDFS 的文件中。rnrn Mapper任务的执行过程rn每个 Mapper 任务是一个 java 进程,它会读取 HDFS 中的文件,解析成很多的键值对,经过我们覆盖的 map 方法处理后, 转换为很多的键值对再输出rnrn把 Mapper 任务的运行过程分为六个阶段。rnrn第一阶段是把输入文件按照一定的标准分片(InputSplit),每个输入片的大小是固定的。rnrn第二阶段是对输入片中的记录按照一定的规则解析成键值对。rnrn第三阶段是调用 Mapper 类中的 map 方法。rnrn第四阶段是按照一定的规则对第三阶段输出的键值对进行分区。rnrn第五阶段是对每个分区中的键值对进行排序。rnrn第六阶段是对数据进行归约处理,也就是 reduce 处理。键相等的键值对会调用一次reduce 方法。经过这一阶段,数据量会减少。归约后的数据输出到本地的 linxu 文件中。rnrn Reducer任务的执行过程rn每个 Reducer 任务是一个 java 进程。Reducer 任务接收 Mapper 任务的输出,归约处理后写入到 HDFS 中。rnrn可以分为3个阶段rn第一阶段是 Reducer 任务会主动从 Mapper 任务复制其输出的键值对。 Mapper 任务可能会有很多,因此 Reducer 会复制多个 Mapper 的输出。rn第二阶段是把复制到 Reducer 本地数据,全部进行合并,即把分散的数据合并成一个大的数据。再对合并后的数据排序。rn第三阶段是对排序后的键值对调用 reduce 方法。 键相等的键值对调用一次 reduce 方法,每次调用会产生零个或者多个键值对。最后把这些输出的键值对写入到 HDFS 文件中。rn在整个 MapReduce 程序的开发过程中,我们最大的工作量是覆盖 map 函数和覆盖reduce 函数。
hadoop学习笔记之MapReduce特性
1.计数器在MR作业中内置计数器,统计任务状态,用户可以自定义计数器以实现统计目的,这块比较简单,不是什么原理性的东西,直接略过2.排序排序是MR中比较核心的问题,MR中数据是通过排序来进行组织的。排序的效率直接影响着整个作业的运行效率i)部分排序 在Map任务执行完毕之后,写入到磁盘文件之前,对输出数据进行预排序。这样的排序是按照键进行字典排序而成,将键相同的数据组织到一起。预排序完成之后,将数
Hadoop之MapReduce运行原理
MapReduce就是分而治之的理念,把一个复杂的任务划分为若干个简单的任务分别来做。把一些数据通过map来归类,通过reducer来把同一类的数据进行处理。map的工作就是切分数据,然后给他们分类,分类的方式就是以key,value(键值对) 分类之后,reduce拿到的都是同类数据进行处理
初始hadoop的mapreduce框架
mapreduce是一个集成框架,这个继承框架是处理海量数据的并且是一个分布式的,就是有多个及其共同组成了同一个集群来提供服务。mapreduce是一个用于处理海量数据的分布式计算框架。 他解决了 数据分布式存储 作业调度 容错 机器间通信等复杂问题首先是数据分布式存储,其实hadoop本身是不存储数据的,那么数据其实是存储到hdfs上,hadoop生态的底层是hdfs然后再往上走就是一个
hadoop mapreduce详细过程分析
hadoop  mapreduce详细过程分析 hadoop在工业界目前已经是公认的大数据通用存储和分析平台。hadoop提供了一个可靠的共享存储和分析系统。hadoop最核心的两大部分hdfs和mapreduce。hdfs实现数据的存储,mapreduce实现数据的分析和处理。 现在我来聊一聊mapreduce的基本过程。面对大量的数据,mapreduce采用如下的步骤来对数据进行分析和处理
hadoop的mapreduce任务的执行流程
hadoop2.x的三大核心:mapreduce 、hdfs以及yarn ,其中核心之一mapreduce,利用了分而治之的思想,Map(映射)和 Reduce(归约),分布式多处理然后进行汇总的思想,比如:清点扑克牌把里面的花色都分开,一个人清点那么可能耗时4分钟,如果利用mapreduce的思想,把扑克牌分成4份,每个人对自己的那一份进行清点,然后4个人都清点完成之后把各自的相同花色放一起进行汇
大数据—Hadoop之MapReduce练习
一、单表索引      实现步骤:①map阶段将读入数据分割成child和parent之后,将parent设置成key,child设置成value进行输出,并作为左表;再将同一对child和parent中的child设置成key,parent设置成value进行输出,作为右表②为了区分输出中的左右表,需要在输出的value中再加上左右表的信息,比如在value的String最开始处加上字符...
Hadoop学习笔记(五)MapReduce
MapReduce概述 源自于Google的MapReduce论文,发表于2004年12月 Hadoop MapReduce是Google MapReduce的克隆版 MapReduce优点:海量数据的离线处理、易开发、易运行。 所谓海量数据,说明MapReduce可以处理的数据量非常大,离线处理说明MapReduce跟实时响应不同,用户将作业提交,系统按批次进行处理,由于数据量大,自然非常耗时。所
HADOOP的mapReduce流程解析
1、首先第一个启动的是MRAppMasterk进程,它根据提交的job信息,计算出需要启动mapTask实例的数量,然后向集群申请对应的机器启动相应数量的maptask进程。2、mapTask负责map阶段的数据处理。mapTask进程启动之后,根据给定的数据切片范围进行处理。    主要流程如下:i)根据客户定义的inputformat来获取RecodReader读取数据,形成输入KV对。   ...
Hadoop之MapReduce思维导图
Hadoop之MapReduce思维导图
Hadoop计算框架:MapReduce
文档较详尽的讲述了MR的简介,MR初学分析示例(有代码)、MR特性,MR的执行过程(有代码),MR单元测试介绍(有代码)、HA的架构和配置、同时也向大众推荐了两本书。其中部分有较为详尽的链接以供参考。
Hadoop大数据平台之谷歌MapReduce
谷歌的MapReduce实现,Hadoop大数据平台的开发技术之一MapReduce
Hadoop MapReduce教程.pdf
Hadoop MapReduce教程.pdf
Hadoop下MapReduce编程介绍
详细介绍基于hadoop的mapreduce编程,基本原理。hadoop架构,map的处理方式,reduce的处理输入输出等。
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