MapReduce

how to limit the number of mappers

由 一个人想着一个人 提交于 2020-01-11 09:52:22
问题 I explicitly specify the number of mappers within my java program using conf.setNumMapTasks() , but when the job ends, the counter shows that the number of launched map tasks were more than the specified value. How to limit the number of mapper to the specified value? 回答1: According to the Hadoop API Jonf.setNumMapTasks is just a hint to the Hadoop runtime. The total number of map tasks equals to the number of blocks in the input data to be processed. Although, it should be possible to

how to limit the number of mappers

由 妖精的绣舞 提交于 2020-01-11 09:51:22
问题 I explicitly specify the number of mappers within my java program using conf.setNumMapTasks() , but when the job ends, the counter shows that the number of launched map tasks were more than the specified value. How to limit the number of mapper to the specified value? 回答1: According to the Hadoop API Jonf.setNumMapTasks is just a hint to the Hadoop runtime. The total number of map tasks equals to the number of blocks in the input data to be processed. Although, it should be possible to

How do the hive sql queries are submitted as mr job from hive cli

由 时光总嘲笑我的痴心妄想 提交于 2020-01-11 09:41:32
问题 I have deployed a CDH-5.9 cluster with MR as hive execution engine. I have a hive table named "users" with 50 rows. Whenever I execute the query select * from users works fine as follows : hive> select * from users; OK Adam 1 38 ATK093 CHEF Benjamin 2 24 ATK032 SERVANT Charles 3 45 ATK107 CASHIER Ivy 4 30 ATK384 SERVANT Linda 5 23 ATK132 ASSISTANT . . . Time taken: 0.059 seconds, Fetched: 50 row(s) But issuing select max(age) from users failed after submitting as mr job. The container log

Java mapToInt vs Reduce with map

由 一个人想着一个人 提交于 2020-01-11 09:06:10
问题 I've been reading up on reduce and have just found out that there is a 3 argument version that can essentially perform a map reduce like this: String[] strarr = {"abc", "defg", "vwxyz"}; System.out.println(Arrays.stream(strarr).reduce(0, (l, s) -> l + s.length(), (s1, s2) -> s1 + s2)); However I can't see the advantage of this over a mapToInt with a reduce. System.out.println(Arrays.stream(strarr).mapToInt(s -> s.length()).reduce(0, (s1, s2) -> s1 + s2)); Both produce the correct answer of 12

Java mapToInt vs Reduce with map

由 淺唱寂寞╮ 提交于 2020-01-11 09:06:00
问题 I've been reading up on reduce and have just found out that there is a 3 argument version that can essentially perform a map reduce like this: String[] strarr = {"abc", "defg", "vwxyz"}; System.out.println(Arrays.stream(strarr).reduce(0, (l, s) -> l + s.length(), (s1, s2) -> s1 + s2)); However I can't see the advantage of this over a mapToInt with a reduce. System.out.println(Arrays.stream(strarr).mapToInt(s -> s.length()).reduce(0, (s1, s2) -> s1 + s2)); Both produce the correct answer of 12

Why there is a mapreduce.jobtracker.address configuration on YARN?

由 偶尔善良 提交于 2020-01-11 07:09:36
问题 YARN is the Hadoop second generation that not use the jobtracker daemon anymore, and substitute it with resource manager. But why, on mapred-site.xml hadoop 2 there is an mapreduce.jobtracker.address property? 回答1: You are correct. In YARN, jobtracker no longer exists. So as part of client configuration you don't have to to specify the property mapreduce.jobtracker.address . In YARN, you should specify the property mapreduce.framework.name to yarn in the config file. Instead of setting up

where does combiners combine mapper outputs - in map phase or reduce phase in a Map-reduce job?

由 大城市里の小女人 提交于 2020-01-11 05:20:10
问题 I was under the impression that combiners are just like reducers that act on the local map task, That is it aggregates the results of individual Map task in order to reduce the network bandwidth for output transfer. And from reading Hadoop- The definitive guide 3rd edition , my understanding seems correct. From chapter 2 (page 34) Combiner Functions Many MapReduce jobs are limited by the bandwidth available on the cluster, so it pays to minimize the data transferred between map and reduce

Hadoop优化 第一篇 : HDFS/MapReduce

由 跟風遠走 提交于 2020-01-11 05:00:52
比较惭愧，博客很久（半年）没更新了。最近也自己搭了个博客，wordpress玩的还不是很熟，感兴趣的朋友可以多多交流哈！地址是：http://www.leocook.org/ 另外，我建了个QQ群：305994766，希望对大数据、算法研发、系统架构感兴趣的朋友能够加入进来，大家一起学习，共同进步（进群请说明自己的公司-职业-昵称）。 1.应用程序角度进行优化 1.1.减少不必要的reduce任务 若对于同一份数据需要多次处理，可以尝试先排序、分区，然后自定义InputSplit将某一个分区作为一个Map的输入，在Map中处理数据，将Reduce的个数设置为空。 1.2.外部文件引用 如字典、配置文件等需要在Task之间共享的数据，可使用分布式缓存DistributedCache或者使用-files 1.3.使用Combiner combiner是发生在map端的，作用是归并Map端输出的文件，这样Map端输出的数据量就小了，减少了Map端和reduce端间的数据传输。需要注意的是，Combiner不能影响作业的结果;不是每个MR都可以使用Combiner的，需要根据具体业务来定;Combiner是发生在Map端的，不能垮Map来执行（只有Reduce可以接收多个Map任务的输出数据） 1.4.使用合适的Writable类型 尽可能使用二进制的Writable类型，例如

hadoop 2.7.3 (hadoop2.x)使用ant制作eclipse插件hadoop-eclipse-plugin-2.7.3.jar

由 ≯℡__Kan透↙ 提交于 2020-01-11 04:43:20
　　为了做mapreduce开发,要使用eclipse,并且需要对应的Hadoop插件hadoop-eclipse-plugin-2.7.3.jar,首先说明一下,在hadoop1.x之前官方hadoop安装包中都自带有eclipse的插件,而如今随着程序员的开发工具eclipse版本的增多和差异,hadoop插件也必须要和开发工具匹配,hadoop的插件包也不可能全部兼容.为了简化,如今的hadoop安装包内不会含有eclipse的插件.需要各自根据自己的eclipse自行编译. 1. 环境准备 　　使用ant制作自己的eclipse插件,介绍一下我的环境和工具 ( 安装路径根据自己 ) 　　系统: 64bit Ubuntu 14.04,(系统不重要Win也可以,方法都一样) 　　JDK 版本: jdk-7u80-linux-x64.tar.gz 安装路径： /usr/lib/jvm 　　eclipse 版本: ide工具eclipse-jee-mars-2-linux-gtk-x86_64.tar.gz 安装路径： /home/hadoop/ 　　hadoop 版本: hadoop-2.7.3.tar.gz 安装路径：/usr/local 　　ant(这个也随意,二进制安装或者apt-get安装都可以,配置好环境变量) , 我的 ant 版本是1.9.3 , 有的是 1.9.7

shuffle error:exceeded max_failed_unique_matche : bailing out

由 ♀尐吖头ヾ 提交于 2020-01-11 03:59:07
问题 I am new to hadoop and i am trying to execute the wordcount example. I have a cluster of 4 nodes made by virtual machines on my computer. Every time the job completes the map task but the reduce task at time about 16% shows this error: Shuffle Error: Exceeded MAX_FAILED_UNIQUE_FETCHES; bailing-out. 12/05/24 04:43:12 WARN mapred.JobClient: Error reading task outputmachine3-VirtualBox It looks like the slaves are unable to retrieve data from other slaves. On some links I found that it can come
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