MapReduce

Hadoop - Globally sort mean and when is happen in MapReduce

由 早过忘川 提交于 2019-12-13 13:22:39
问题 I am using Hadoop streaming JAR for WordCount , I want to know how can I get Globally Sort , according to answer on another question in SO, I found that when we use of just one reducer we can get Globally sort but in my result with numReduceTasks=1 (one reducer) it is not sort. For example, my input to mapper is: file 1: A long time ago in a galaxy far far away file 2: Another episode for Star Wars Result is: A 1 a 1 Star 1 ago 1 for 1 far 2 away 1 time 1 Wars 1 long 1 Another 1 in 1 episode

分布式资源调度——YARN框架

由 柔情痞子 提交于 2019-12-13 13:16:19
YARN产生背景 YARN是Hadoop2.x才有的，所以在介绍YARN之前，我们先看一下MapReduce1.x时所存在的问题： 单点故障 节点压力大 不易扩展 MapReduce1.x时的架构如下： 可以看到，1.x时也是Master/Slave这种主从结构，在集群上的表现就是一个JobTracker带多个TaskTracker。 JobTracker：负责资源管理和作业调度 TaskTracker：定期向JobTracker汇报本节点的健康状况、资源使用情况以及作业执行情况。还可以接收来自JobTracker的命令，例如启动任务或结束任务等。 那么这种架构存在哪些问题呢： 整个集群中只有一个JobTracker，就代表着会存在单点故障的情况 JobTracker节点的压力很大，不仅要接收来自客户端的请求，还要接收大量TaskTracker节点的请求 由于JobTracker是单节点，所以容易成为集群中的瓶颈，而且也不易域扩展 JobTracker承载的职责过多，基本整个集群中的事情都是JobTracker来管理 1.x版本的整个集群只支持MapReduce作业，其他例如Spark的作业就不支持了 由于1.x版本不支持其他框架的作业，所以导致我们需要根据不同的框架去搭建多个集群。这样就会导致资源利用率比较低以及运维成本过高，因为多个集群会导致服务环境比较复杂。如下图：

Is Wikipedia's explanation of Map Reduce's reduce incorrect?

由 谁说胖子不能爱 提交于 2019-12-13 13:10:03
问题 MongoDB's explanation of the reduce phase says: The map/reduce engine may invoke reduce functions iteratively; thus, these functions must be idempotent. This is how I always understood reduce to work in a general map reduce environment. Here you could sum values across N machines by reducing the values on each machine, then sending those outputs to another reducer. Wikipedia says: The framework calls the application's Reduce function once for each unique key in the sorted order. The Reduce

Deciding key value pair for deduplication using hadoop mapreduce

由 此生再无相见时 提交于 2019-12-13 12:34:01
问题 I want to implement deduplication of files using Hadoop Mapreduce. I plan to do it by calculating MD5 sum of all the files present in the input directory in my mapper function. These MD5 hash would be the key to the reducer, so files with the same hash would go to the same reducer. The default for the mapper in Hadoop is that the key is the line number and the value is the content of the file. Also I read that if the file is big, then it is split into chunks of 64 MB, which is the maximum

解密淘宝推荐实战，打造 “比你还懂你” 的个性化APP

由 十年热恋 提交于 2019-12-13 11:17:21
【推荐】2019 Java 开发者跳槽指南.pdf(吐血整理) >>> 手淘推荐简介 手淘推荐的快速发展源于2014年阿里“All in 无线”战略的提出。在无线时代，手机屏幕变小，用户无法同时浏览多个视窗，交互变得困难，在这样的情况下，手淘借助个性化推荐来提升用户在无线端的浏览效率。经过近几年的发展，推荐已经成为手淘上面最大的流量入口，每天服务数亿用户，成交量仅次于搜索，成为了手淘成交量第二大入口。 今天的推荐不仅仅包含商品，还包含了直播、店铺、品牌、UGC，PGC等，手淘整体的推荐物种十分丰富，目前手淘的整体推荐场景有上百个。推荐与搜索不同，搜索中用户可以主动表达需求，推荐很少和用户主动互动，或者和用户互动的是后台的算法模型，所以推荐从诞生开始就是大数据+AI的产品。 手淘推荐特点 相比于其他推荐产品，手淘推荐也有自身的如下特点： 1. 购物决策周期 ：手淘推荐的主要价值是挖掘用户潜在需求和帮助用户购买决策，用户的购物决策周期比较长，需要经历需求发现,信息获取，商品对比和下单决策的过程，电商推荐系统需要根据用户购物状态来做出推荐决策。 2. 时效性 ：我们一生会在淘宝购买很多东西，但是这些需求通常是低频和只在很短的时间窗口有效，比如手机1~2才买一次但决策周期只有几小时到几天，因此需要非常强的时效性，需要快速地感知和捕获用户的实时兴趣和探索未知需求,因此

Processing password protected zip files using Mapreduce [duplicate]

由 白昼怎懂夜的黑 提交于 2019-12-13 10:54:10
问题 This question already has answers here : Recommendations on a free library to be used for zipping files [closed] (9 answers) Closed 5 years ago . I want to process password protected zipped files using Hadoop mapreduce. I was able to process unprotected zip files using ZipFileInputformat. But it doesn't support password protected zips. Is there any Java library that provide stream access to password protected zip files or extract zip files if I can make its byte content available ? Thanks in

Adding concurrency with iterating over a collection, mapping to multiple hash maps and reducing to one

由 纵然是瞬间 提交于 2019-12-13 10:51:35
问题 Have a specific use case and not too sure of the best approach. So the current approach right now is that I'm iterating over a collection of objects (closeable iterator) and mapping them in a hashmap (dealing with conflicts appropriately, comparing by an object's date property). I'm looking for a parallel approach to speed things up and the initial idea was to use Java 8 streams with parallel and forEach utilizing a concurrent hashmap to enable concurrency. The main bottleneck with this

Aggregation in MapReduce [closed]

由 笑着哭i 提交于 2019-12-13 09:04:06
问题 Closed . This question needs to be more focused. It is not currently accepting answers. Want to improve this question? Update the question so it focuses on one problem only by editing this post. Closed 5 years ago . How can we find tha maximum and minimum element of a column in a .csv. What should we pass into context.write(key,value) of mapper. Whether it is each column of that csv file? Solution 回答1: This is a bit broad for an SO question but I'll bite. Your mapper is for mapping values to

hadoop和spark的区别和联系

由 非 Y 不嫁゛ 提交于 2019-12-13 09:00:02
1、hadoop 1）hadoop简介 Hadoop是一个由Apache基金会所开发的分布式系统基础架构。 Hadoop实现了一个分布式文件系统HDFS。HDFS有高容错性的特点，并且设计用来部署在低廉的硬件上；而且它提供高吞吐量来访问应用程序的数据，适合那些有着超大数据集的应用程序。 Hadoop的框架最核心的设计就是：HDFS和MapReduce。HDFS为海量的数据提供了存储，而MapReduce则为海量的数据提供了计算。 2）hadoop优点 Hadoop 以一种可靠、高效、可伸缩的方式进行数据处理。 可靠性: Hadoop将数据存储在多个备份，Hadoop提供高吞吐量来访问应用程序的数据。 高扩展性： Hadoop是在可用的计算机集簇间分配数据并完成计算任务的，这些集簇可以方便地扩展到数以千计的节点中。 高效性： Hadoop以并行的方式工作，通过并行处理加快处理速度。 高容错性： Hadoop能够自动保存数据的多个副本，并且能够自动将失败的任务重新分配。 低成本： Hadoop能够部署在低廉的（low-cost）硬件上。 2、spark 1）spark简介 Spark 是专为大规模数据处理而设计的快速通用的计算引擎。Spark拥有Hadoop MapReduce所具有的优点，Spark在Job中间输出结果可以保存在内存中，从而不再需要读写HDFS

Retrieving nth qualifier in hbase using java

由 不问归期 提交于 2019-12-13 08:31:44
问题 This question is quite out of box but i need it. In list(collection), we can retrieve the nth element in the list by list.get(i); similarly is there any method, in hbase, using java API, where i can get the nth qualifier given the row id and ColumnFamily name. NOTE: I have million qualifiers in single row in single columnFamily. 回答1: Sorry for being unresponsive. Busy with something important. Try this for right now : package org.myorg.hbasedemo; import java.io.IOException; import java.util
订阅 MapReduce