MapReduce

How to run hadoop multithread way in single JVM?

由 こ雲淡風輕ζ 提交于 2019-12-22 20:43:11
问题 I have 4 core desktop and want to use all my cores for local data processing with hadoop. (i.e. sometimes I have enough power to process data locally sometimes I submit same jobs to cluster). By default hadoop local mode runs only one mapper and one reducer so my local jobs are really slow. I do not want to setup cluster on single machine first because of "painful" configuration and second I have to create jar each time. So perfect solution is to how run embedded Hadoop on a single machine PS

How to run hadoop multithread way in single JVM?

由 穿精又带淫゛_ 提交于 2019-12-22 20:43:10
问题 I have 4 core desktop and want to use all my cores for local data processing with hadoop. (i.e. sometimes I have enough power to process data locally sometimes I submit same jobs to cluster). By default hadoop local mode runs only one mapper and one reducer so my local jobs are really slow. I do not want to setup cluster on single machine first because of "painful" configuration and second I have to create jar each time. So perfect solution is to how run embedded Hadoop on a single machine PS

Mongodb map reduce across 2 collection

由 房东的猫 提交于 2019-12-22 18:15:34
问题 Let say we have user and post collection. In post collection, vote store the user name as a key. db.user.insert({name:'a', age:12}); db.user.insert({name:'b', age:12}); db.user.insert({name:'c', age:22}); db.user.insert({name:'d', age:22}); db.post.insert({Title:'Title1', vote:[a]}); db.post.insert({Title:'Title2', vote:[a,b]}); db.post.insert({Title:'Title3', vote:[a,b,c]}); db.post.insert({Title:'Title4', vote:[a,b,c,d]}); We would like to group by the post.Title and find out the count of

RavenDB: Why do I get null-values for fields in this multi-map/reduce index?

由 蹲街弑〆低调 提交于 2019-12-22 18:09:28
问题 Inspired by Ayende's article https://ayende.com/blog/89089/ravendb-multi-maps-reduce-indexes, I have the following index, that works as such: public class Posts_WithViewCountByUser : AbstractMultiMapIndexCreationTask<Posts_WithViewCountByUser.Result> { public Posts_WithViewCountByUser() { AddMap<Post>(posts => from p in posts select new { ViewedByUserId = (string) null, ViewCount = 0, Id = p.Id, PostTitle = p.PostTitle, }); AddMap<PostView>(postViews => from postView in postViews select new {

Hadoop MultipleOutputs does not write to multiple files when file formats are custom format

由 孤者浪人 提交于 2019-12-22 17:48:45
问题 I am trying to read from cassandra and write the reducers output to multiple output files using MultipleOutputs api (Hadoop version 1.0.3). The file formats in my case are custom output formats extending FileOutputFormat. I have configured my job in a similar manner as shown in MultipleOutputs api. However, when I run the job, I only get one output file named part-r-0000 which is in text output format. If job.setOutputFormatClass() is not set, by default it considers TextOutputFormat to be

Map Reduce with mongo on nested document

由 坚强是说给别人听的谎言 提交于 2019-12-22 17:29:10
问题 I have the following document structure: { "country_id" : 328, "country_name" : "Australien", "cities" : [{ "city_id" : 19398, "city_name" : "Bondi Beach (Sydney)" }, { "city_id" : 31102, "city_name" : "Double Bay (Sydney)" }, { "city_id" : 31101, "city_name" : "Rushcutters Bay (Sydney)" }, { "city_id" : 817, "city_name" : "Sydney" }, { "city_id" : 31022, "city_name" : "Wolly Creek (Sydney)" }, { "city_id" : 18851, "city_name" : "Woollahra" }], "regions" : { "region_id" : 796, "region_name" :

mongodb - retrieve array subset

由 邮差的信 提交于 2019-12-22 17:10:07
问题 what seemed a simple task, came to be a challenge for me. I have the following mongodb structure: { (...) "services": { "TCP80": { "data": [{ "status": 1, "delay": 3.87, "ts": 1308056460 },{ "status": 1, "delay": 2.83, "ts": 1308058080 },{ "status": 1, "delay": 5.77, "ts": 1308060720 }] } }} Now, the following query returns whole document: { 'services.TCP80.data.ts':{$gt:1308067020} } I wonder - is it possible for me to receive only those "data" array entries matching $gt criteria (kind of

Best practice to pass copy of object to all mappers in hadoop

由 不羁岁月 提交于 2019-12-22 14:47:26
问题 Hello I am currently learning Map Reduce and am trying to build a small Job with hadoop 1.0.4. I have a list of stopp words and a list of patterns. Before my files are mapped I want to load the stoppwords in an efficient Datastructure such as a map. I also want to build one regex pattern from my patternlist. Since these are serial tasks I want to do them in front of the mapping and pass every mapper a copy of those to objects which they can read/write on. I thought about simply having a

Hadoop Spark Kylin...你知道大数据框架名字背后的故事吗？

由 放肆的年华 提交于 2019-12-22 14:26:25
对软件命名并不是一件容易的事情，名字要朗朗上口，易于记忆，既不能天马行空，又要代表软件本身的功能和创新。本文将历数几款大数据框架及其创始背后的故事。 Hadoop：最具童心 2004年，Apache Hadoop（以下简称Hadoop）的创始人Doug Cutting和Mike Cafarella受MapReduce编程模型和Google File System等论文的启发，对论文中提及的思想进行了编程实现，Hadoop的名字来源于Doug Cutting儿子的玩具大象。当时Cutting的儿子刚刚两岁，正处在咿呀学语的阶段，经常将自己的黄色玩具大象叫做"Hadoop"，Cutting灵机一动，将自己的大数据项目以此来命名。 Cutting称，软件的名字有时候要听起来“毫无意义”，因为软件会随着时间不断迭代演进，一开始就使用一个与其初始功能紧密相关的名字，日后有可能比较尴尬。 由于Doug Cutting后来加入了雅虎，并在雅虎工作期间支持了大量Hadoop的研发工作，因此Hadoop也经常被认为是雅虎开源的一款大数据框架。时至今日，Hadoop不仅仅是整个大数据领域的先行者和领导者，更形成了一套围绕Hadoop的生态系统，Hadoop和它的生态是绝大多数企业首选的大数据解决方案。 目前，Hadoop的核心组件主要有三个： Hadoop MapReduce

Two equal combine keys do not get to the same reducer

由 半城伤御伤魂 提交于 2019-12-22 13:07:36
问题 I'm making a Hadoop application in Java with the MapReduce framework. I use only Text keys and values for both input and output. I use a combiner to do an extra step of computations before reducing to the final output. But I have the problem that the keys do not go to the same reducer. I create and add the key/value pair like this in the combiner: public static class Step4Combiner extends Reducer<Text,Text,Text,Text> { private static Text key0 = new Text(); private static Text key1 = new Text
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