MapReduce | 易学教程

Why is the right number of reduces in Hadoop 0.95 or 1.75?

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问题 The hadoop documentation states: The right number of reduces seems to be 0.95 or 1.75 multiplied by ( * mapred.tasktracker.reduce.tasks.maximum). With 0.95 all of the reduces can launch immediately and start transferring map outputs as the maps finish. With 1.75 the faster nodes will finish their first round of reduces and launch a second wave of reduces doing a much better job of load balancing. Are these values pretty constant? What are the results when you chose a value between these

How to Join two tables in Hbase

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问题 Problem: I am new to Hbase and I came across a situation where I need to join two tables. Let us suppose I have Employee table and Department table both are created in Hbase. By reading Hbase in action , I got to know that we cannot join tables in Hbase. Solution: I found out a solution that by writing MapReduce Code using Hbase classes and Interfaces we can achieve this task. Also if someone can help me with the coding that would be very helpful 回答1: The easiest way would be to load your

各大云平台物联网相关产品对比分析

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概述本文主要对阿里云、腾讯云、百度云和华为云提供的相关物联网产品进行对比，用于分析各大云平台在物联网方面的布局和实现。硬件开发及组网对比项阿里云腾讯云百度云华为云嵌入式OS 提供AliOS Things嵌入式系统。无无 Huawei LiteOS 轻量级物联网操作系统。设备组网提供物联网络管理平台 ,使用LoRaWAN协议。提供LPWA 物联网络产品，支持 LoRaWAN/CLAA（China LoRa Application Alliance）标准协议。无无边缘计算对比项阿里云腾讯云（无边缘计算产品）百度云华为云设备接入设备接入是Link IoT Edge提供的基础能力，设备接入模块在Link IoT Edge中称为驱动（driver）或设备接入驱动。所有连接到Link IoT Edge的设备都需要通过驱动实现接入。不限制通信协议，但需开发对应驱动，在驱动程序中转换为阿里云Iot物模型的数据格式。 - MQTT协议支持MQTT协议、modbus协议和OPC-UA协议等接入。数据处理边缘计算节点提供流数据分析、函数计算引擎，方便场景编排和业务扩展。 - 提供本地函数计算模块，基于MQTT消息机制。支持流处理云端管理，边缘侧运行，提供实时流处理能力。数据流转提供消息路由功能，可实现：<br>设备至IoT Hub；<br

Is the input format responsible for implementing data locality in Hadoop's MapReduce?

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问题 I am trying to understand data locality as it relates to Hadoop's Map/Reduce framework. In particular I am trying to understand what component handles data locality (i.e. is it the input format?) Yahoo's Developer Network Page states "The Hadoop framework then schedules these processes in proximity to the location of data/records using knowledge from the distributed file system." This seems to imply that the HDFS input format will perhaps query the name node to determine which nodes contain

How do I specify multiple libpath in oozie job?

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问题 My oozie job uses 2 jars x.jar and y.jar and following is my job.properties file. oozie.libpath=/lib oozie.use.system.libpath=true This works perfectly when both the jars are present at same location on HDFS at /lib/x.jar and /lib/y.jar Now I have 2 jars placed at different locations /lib/1/x.jar and /lib/2/y.jar . How can I re-write my code such that both the jars are used while running the map reduce job? Note: I have already refernced the answer How to specify multiple jar files in oozie

MapReduce 计算流程（重点）

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（1）程序员所编写的MR代码，一旦运行就可以称之为一个Job （2）Job在启动之后，会首先向RM注册相关信息（3）如果注册通过则向共享文件系统（HDFS）拷贝先关资源的信息（4）提交完成的Job信息给RM （5）拿到Job信息，根据Job的情况，消耗资源连接到某个节点的上NodeManager去启动MR AppMaster （6）MR AppMaster 首先会初始化Job （7）去共享文件系统中获取输入切片相关的信息（8）MR AppMaster向RM申请资源去进行计算（9）拿到资源后，连接到某个NodeManager去启动Yarn Child （10）Yarn Child去共享文件系统获取完成的Job信息（11）Yarn Child根据任务阶段启动MapTask或者ReduceTask进程进行真正的计算任务的执行，直至计算任务完成，此两个进程完全关闭，客户端停止等待，结束运行。来源： CSDN 作者：乄〇〇链接： https://blog.csdn.net/weixin_44311552/article/details/103463605

What is the usage of Configured class in Hadoop programs?

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问题 Most of Hadoop MapReduce programs are like this: public class MyApp extends Configured Implements Tool { @Override public int run(String[] args) throws Exception { Job job = new Job(getConf()); /* process command line options */ return job.waitForCompletion(true) ? 0 : 1; } public static void main(String[] args) throws Exception { int exitCode = ToolRunner.run(new MyApp(), args); System.exit(exitCode); } } What is the usage of Configured ? As Tool and Configured both have getConf() and

How to share a variable in Mapper and Reducer class?

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问题 I have a requirement like I wanna share a variable between mapper and reducer class. Scenario is as follows:- Suppose my input records are of type A, B and C. I'm processing these records and accordingly generating the key and value for output.collect in map function. But at the same time I've also declared 3 static int variables in mapper class to keep the count of type of record A, B and C. Now these variables will be updated by various map threads. When all the map tasks are done I wanna

Hadoop webuser: No such user

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问题 While running a hadoop multi-node cluster , i got below error message on my master logs , can some advise what to do..? do i need to create a new user or can i gave my existing Machine user name over here 2013-07-25 19:41:11,765 WARN org.apache.hadoop.security.UserGroupInformation: No groups available for user webuser 2013-07-25 19:41:11,778 WARN org.apache.hadoop.security.ShellBasedUnixGroupsMapping: got exception trying to get groups for user webuser org.apache.hadoop.util.Shell

大数据教程（8.5）mapreduce原理之并行度

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上一篇博客介绍了mapreduce的移动流量分析的实战案例，本篇将继续分享mapreduce的并行度原理。一、mapTask并行度的决定机制一个job的map阶段并行度由客户端在提交Job是决定，而客户端对map阶段并行度的规划的基本逻辑为：将待处理数据执行逻辑切片（即按照一个特定切片大小，将待处理数据划分成逻辑上的多个split），然后每一个split分配一个mapTask并行实例处理；这段逻辑及形成的切片规划描述文件，由FileInputFormat实现类的getSplits()方法完成，其过程如下图：（1）FileInputFormat切片机制切片定义在InputFormat类中的getSplit()方法；FileInputFormat中默认的切片机制：a.简单地按照文件的内容长度进行切片,b.切片大小，默认等于block大小，c.切片时不考虑数据集整体，而是逐个针对每一个文件单独切片比如待处理数据有两个文件： file1.txt 320M file2.txt 10M 经过FileInputFormat的切片机制运算后，形成的切片信息如下： file1.txt.split1-- 0~128 file1.txt.split2-- 128~256 file1.txt.split3-- 256~320 file2.txt.split1-- 0~10M （2

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