MapReduce

Combining Multiple Maps together in Pig

由 馋奶兔 提交于 2019-12-11 18:35:00
问题 I am using pig for the first time. I've gotten to the point where I have exactly the answer I want, but in a weirdly nested format: {(price,49),(manages,"1d74426f-2b0a-4777-ac1b-042268cab09c")} I'd like the output to be a single map, without any wrapping: [price#49, manages#"1d74426f-2b0a-4777-ac1b-042268cab09c"] I've managed to use TOMAP to get this far, but I can't figure out how to merge and flatten it away. {([price_specification#{"amount":49,"currency":"USD"}]),([manages#"newest-nodes

Problems with installing Hadoop on Ubuntu 12.04

由 ♀尐吖头ヾ 提交于 2019-12-11 18:27:22
问题 I just set up a new Ubuntu 12.04 VM (Virtualbox) and wanted to test Hadoop on it. I am following this guide: http://hadoop.apache.org/docs/r0.20.2/quickstart.html I think I am doing something wrong with the java installation and the JAVA_HOME path... Right now bin/hadoop always just returns "command not found" Where do I have to extract the hadoop folder? Do I need to set up SSH before? What about SSHD? What are the commands to install the correct java version? What EXACTLY do I have to enter

Get Total Input Path Count in Hadoop Mapper

由 ⅰ亾dé卋堺 提交于 2019-12-11 18:00:19
问题 We are trying to grab the total number of input paths our MapReduce program is iterating through in our mapper. We are going to use this along with a counter to format our value depending on the index. Is there an easy way to pull the total input path count from the mapper? Thanks in advance. 回答1: You could look through the source for FileInputFormat.getSplits() - this pulls back the configuration property for mapred.input.dir and then resolves this CSV to an array of Paths. These paths can

Hadoop Map-reduce job failed

由 谁都会走 提交于 2019-12-11 17:51:57
问题 I am currently using a hadoop 5 node cluster with 5 slaves Each datanode has a capacity of 8.7 TB. I am executing a map reduce job to execute 312 GB of data but got a error of Apllication failed after executing the program. I cant understand the error, firstly the map reduce job started , it got to 11% after that it started again from 1%. 1) Am i executing a bigger dataset that cant be executed in the cluster configuration ? 2) Do i need to configure out my map reduce drivers for running the

MapReduce单词统计

由 六月ゝ 毕业季﹏ 提交于 2019-12-11 17:48:06
WordcountMapper类 package com.sky.mr.wordcount; import org.apache.hadoop.io.IntWritable; import org.apache.hadoop.io.LongWritable; import org.apache.hadoop.io.Text; import org.apache.hadoop.mapreduce.Mapper; import org.junit.Test; import java.io.IOException; public class WordcountMapper extends Mapper<LongWritable,Text, Text, IntWritable> { //由于每读一行文本数据，就要调用一次map方法，为了避免多次创建对象，浪费内存资源，将Text,IntWritable对象创建在 //map方法之外 Text k = new Text(); IntWritable v = new IntWritable(1); @Override protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException { /

浅谈我的转型大数据学习之路

由 微笑、不失礼 提交于 2019-12-11 17:01:20
一、背景介绍 本人目前是一名大数据工程师，项目数据50T，日均数据增长20G左右，个人是从Java后端开发，经过3个月的业余自学成功转型大数据工程师。 二、大数据介绍 大数据本质也是数据，但是又有了新的特征，包括数据来源广、数据格式多样化（结构化数据、非结构化数据、Excel文件、文本文件等）、数据量大（最少也是TB级别的、甚至可能是PB级别）、数据增长速度快等。 针对以上主要的4个特征我们需要考虑以下问题： 数据来源广，该如何采集汇总？，对应出现了Sqoop，Cammel，Datax等工具。 数据采集之后，该如何存储？，对应出现了GFS，HDFS，TFS等分布式文件存储系统。 由于数据增长速度快，数据存储就必须可以水平扩展。 数据存储之后，该如何通过运算快速转化成一致的格式，该如何快速运算出自己想要的结果？ 对应的MapReduce这样的分布式运算框架解决了这个问题；但是写MapReduce需要Java代码量很大，所以出现了Hive，Pig等将SQL转化成MapReduce的解析引擎； 普通的MapReduce处理数据只能一批一批地处理，时间延迟太长，为了实现每输入一条数据就能得到结果，于是出现了Storm/JStorm这样的低时延的流式计算框架； 但是如果同时需要批处理和流处理，按照如上就得搭两个集群，Hadoop集群（包括HDFS+MapReduce+Yarn

Suggestions required in increasing utilization of yarn containers on our discovery cluster

由 瘦欲@ 提交于 2019-12-11 16:57:56
问题 Current Setup we have our 10 node discovery cluster. Each node of this cluster has 24 cores and 264 GB ram Keeping some memory and CPU aside for background processes, we are planning to use 240 GB memory. now, when it comes to container set up, as each container may need 1 core, so max we can have 24 containers, each with 10GB memory. Usually clusters have containers with 1-2 GB memory but we are restricted with the available cores we have with us or maybe I am missing something Problem

MapReduce with paramiko how to print stdout as it streams

由 拜拜、爱过 提交于 2019-12-11 16:16:45
问题 I have created a small Python script using paramiko that allows me to run MapReduce jobs without using PuTTY or cmd windows to initiate the jobs. This works great, except that I don't get to see stdout until the job completes. How can I set this up so that I can see each line of stdout as it is generated, just as I would be able to via cmd window? Here is my script: import paramiko # Define connection info host_ip = 'xx.xx.xx.xx' user = 'xxxxxxxxx' pw = 'xxxxxxxxx' # Commands list_dir = "ls

How to iterate among text in the for loop and find count of a particular text in MapReduce()

由 醉酒当歌 提交于 2019-12-11 16:16:23
问题 So here is a piece of Reduce() code on a particular dataset which has a bunch of designations as 'key' and the salary of designation of a particular named person as 'value' public static class ReduceEmployee extends Reducer<Text, IntWritable, Text, IntWritable> { public void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException { int sum = 0; for (IntWritable val : values) { sum += val.get(); } context.write(key, new IntWritable(sum)); } }

MaxCompute用户初体验

由 瘦欲@ 提交于 2019-12-11 15:11:09
【推荐】2019 Java 开发者跳槽指南.pdf(吐血整理) >>> 作为一名初次使用MaxCompute的用户，我体会颇深。MaxCompute 开箱即用，拥有集成化的操作界面，你不必关心集群搭建、配置和运维工作。仅需简单的点击鼠标，几步操作，就可以在MaxCompute中上传数据，分析数据并得到分析结果。 作为一种快速、完全托管的 TB/PB 级数据仓库解决方案，MaxCompute不仅为我们提供了传统的命令行操作，而且提供了丰富的web端操作界面。对于数据开发，测试，发布，数据流，数据权限管理都非常好用，支持python，java的udf，对于复杂的逻辑查询也支持传统的MapReduce，同时也支持多种机器学习算法，非常好用。 MaxCompute为我们提供了统一的项目管理。实际开发中各个团队都有自己的项目,自己管理自己的项目, 通过项目隔离,有效的防止数据和任务被其他团队修改和删除等问题。除非是pro项目任务出错,否则不会影响到其他业务线的任务,最大程度降低各业务间的影响。 同时，大数据开发套件和 MaxCompute关系紧密，大数据开发套件为 MaxCompute 提供了一站式的数据同步，任务开发，数据工作流开发，数据管理和数据运维等功能。 当需要处理的数据变得非常多，并且数据发展到足够复杂的时候，这些数据往往需要用不同的模式进行处理，除此之外
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