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Hadoop 1.x Architecture and Drawbacks

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[数据库(综合) 所属分类 数据库(综合) | 发布者 店小二04 | 时间 2017 | 作者 红领巾 ] 0人收藏点击收藏
Hadoop 1.x Architecture andDrawbacks

February 13, 2017 byNiranjan Tallapalli Leave a comment

Hadoop is built on two whitepapers published by Google, i.e,

HDFS Map Reduce

HDFS: Hadoop Distributed File System

It is different from the normal file system in a way that the data copied on to HDFS is split into ‘n’ blocks and each block is copied on to a different node in the cluster. To achieve this we use master-slave architecture

HDFS Master => Name Node: Takes the client request and responsible for orchestrating the data copy across the cluster HDFS Slave => Data Node: Actually saves the block of data and coordinates with its master

MapReduce: This is the processing engine and is also implemented in master-slave architecture.

MR Master => Job Tracker: Takes the incoming jobs, identifies the available resources across the cluster, divides the job into tasks and submits it to the cluster MR Slave => Task Tracker: Actually runs the task and coordinates with its master. Architecture
Hadoop 1.x Architecture and Drawbacks
Drawbacks Design of JobTracker is done in such a way that its tightly coupled with two important responsibilities “Resource Management” and “MapReduce Task Execution”. Because of this reason the cluster cannot be used for other distributed computing technologies like Spark/Kafka/Storm/… other than Hadoop MapReduce Name Node can maintain metadata of upto 4000-5000 data nodes at maximum. This will limit the cluster scalability to 4k-5k nodes

Addressing these drawbacks hadoop 2.x is released.

本文数据库(综合)相关术语:系统安全软件

主题: HadoopHDFSMapReduceSparkKafka
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