MapReduce | 易学教程

In bash how to transform multimap<K,V> to a map of <K, {V1,V2}>

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问题 I am processing output from a file in bash and need to group values by their keys. For example, I have the 13,47099 13,54024 13,1 13,39956 13,0 17,126223 17,52782 17,4 17,62617 17,0 23,1022724 23,79958 23,80590 23,230 23,1 23,118224 23,0 23,1049 42,72470 42,80185 42,2 42,89199 42,0 54,70344 54,72824 54,1 54,62969 54,1 in a file and group all values from a particular key into a single line as in 13,47099,54024,1,39956,0 17,126223,52782,4,62617,0 23,1022724,79958,80590,230,1,118224,0,1049 42

大数据存储框架之Hive概述

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原文地址：http://www.blog.sun-iot.xyz/2019/12/10/bigdata/hive-interview/ 大数据存储框架之Hive概述之前有写到HBase,那是老夫在开发过程中实际使用的一个存储数据库，HBase和Hive同样作为大数据存储中最优秀的两个存储框架，都有着彼此的优势，HBase更适合实时，Hive更适合离线。这里呢，就先简单的介绍一下HIve的基础架构以及Hive的一些基本安装步骤。认识我们的主角Hive 什么是Hive Hive：由Facebook开源用于解决海量结构化日志的数据统计。 Hive是基于Hadoop的一个数据仓库工具，可以将结构化的数据文件映射为一张表，并提供类SQL查询功能(HQL)。本质是：将HQL转化成MapReduce程序如图所示： Hive处理的数据存储在HDFS Hive分析数据底层的实现是MapReduce 执行程序运行在Yarn上 Hive的优缺点优点操作接口采用类SQL语法，提供快速开发的能力（简单、容易上手）。避免了去写MapReduce，减少开发人员的学习成本。 Hive的执行延迟比较高，因此Hive常用于数据分析，对实时性要求不高的场合。 Hive优势在于处理大数据，对于处理小数据没有优势，因为Hive的执行延迟比较高。 Hive支持用户自定义函数

Running a Hadoop Job From another Java Program

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问题 I am writing a program that receives the source code of the mapper/reducers, dynamically compiles the mappers/reducers and makes a JAR file out of them. It then has to run this JAR file on a hadoop cluster. For the last part, I setup all the required parameters dynamically through my code. However, the problem I am facing now is that the code requires the compiled mapper and reducer classes at the time of compiling. But at the time of compiling, I do not have these classes and they will later

mrjob combiner not working python

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问题 Simple map combine reduce program: Map column-1 with value column-3 and append '+' in each mapper output of same key and append '-' after reduce output of same key. input_1 and input_2 both files contain a 1 2 3 a 4 5 6 Code is from mrjob.job import MRJob import re import sys class MRWordFreqCount(MRJob): def mapper(self, _, line): line=re.sub("\s\s+"," ",line) s1=line.split() yield(s1[0],s1[2]) def combiner(self, accid, eventid): s="+" yield (accid, s.join(eventid)) def reducer(self, accid,

How to check memory footprint of Map Task in Hadoop

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问题 I know I can control the max memory for a map (or reduce) task by setting JVM parameters. But I am wondering if there is a way to see current memory usage of a task? 回答1: enable remote HPROF profiling. HPROF is a profiling tool that comes with the JDK that, although basic, can give valuable information about a program’s CPU and heap usage. To use it, you can try this in your code: conf.setBoolean("mapred.task.profile", true); conf.set("mapred.task.profile.params", "-agentlib:hprof=cpu=samples

Hadoop Number of Reducers Configuration Options Priority

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问题 What are the priorities of the following 3 options for setting number of reduces? In other words, if all three are set, which one will be taken into account? Option1: setNumReduceTasks(2) within the application code Option2: -D mapreduce.job.reduces=2 as command line argument Option3: through $HADOOP_CONF_DIR/mapred-site.xml file <property> <name>mapreduce.job.reduces</name> <value>2</value> </property> 回答1: You have them racked in priority order - option 1 will override 2, and 2 will

Apache Impala简介

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1．Impala基本介绍 impala是cloudera提供的一款高效率的sql查询工具，提供实时的查询效果，官方测试性能比hive快10到100倍，其sql查询比sparkSQL还要更加快速，号称是当前大数据领域最快的查询sql工具 impala是参照谷歌的新三篇论文（Caffeine–网络搜索引擎、Pregel–分布式图计算、Dremel–交互式分析工具）当中的Dremel实现而来，其中旧三篇论文分别是（BigTable，GFS，MapReduce）分别对应我们即将学的HBase和已经学过的HDFS以及MapReduce。 impala是基于hive并使用内存进行计算，兼顾数据仓库，具有实时，批处理，多并发等优点。 2．Impala与Hive关系 impala是基于hive的大数据分析查询引擎，直接使用hive的元数据库metadata，意味着impala元数据都存储在hive的metastore当中，并且impala兼容hive的绝大多数sql语法。所以需要安装impala的话，必须先安装hive，保证hive安装成功，并且还需要启动hive的metastore服务。 Hive元数据包含用Hive创建的database、table等元信息。元数据存储在关系型数据库中，如Derby、MySQL等。客户端连接metastore服务

With Hadoop, can I create a tasktracker on a machine that isn't running a datanode?

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问题 So here's my situation : I have a mapreduce job that uses HBase. My mapper takes one line of text input and updates HBase. I have no reducer, and I'm not writing any output to the disc. I would like the ability to add more processing power to my cluster when I'm expecting a burst of utilization, and then scale back down when utilization decreases. Let's assume for the moment that I can't use Amazon or any other cloud provider; I'm running in a private cluster. One solution would be to add new

Expected consumption of open file descriptors in Hadoop 0.21.0

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问题 Given Hadoop 0.21.0, what assumptions does the framework make regarding the number of open file descriptors relative to each individual map and reduce operation? Specifically, what suboperations cause Hadoop to open a new file descriptor during job execution or spill to disk? (This is deliberately ignoring use of MultipleOutputs, as it very clearly screws with the guarantees provided by the system.) My rationale here is simple: I'd like to ensure each job I write for Hadoop guarantees a

Accessing a mapper's counter from a reducer in Hadoop MapReduce

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问题 I need to access counters from mapper in reducer. I tried to perform this solution. My WordCount code is available below. import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.fs.Path; import org.apache.hadoop.io.IntWritable; import org.apache.hadoop.io.LongWritable; import org.apache.hadoop.io.Text; import org.apache.hadoop.mapreduce.Cluster; import org.apache.hadoop.mapreduce.Job; import org.apache.hadoop.mapreduce.Mapper; import org.apache.hadoop.mapreduce.Reducer; import