Hadoop
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Doug Cutting - blog
Hadoop
包括下面这些子项目:
《Hadoop权威指南(中文版)
》
本人已买且读过部分章节。翻译的语句明显不通,但是该刚接触 Hadoop 挚友的还是很有帮助的。从中文版的内容来看,英文原版的质量非常不错。所以,建议将她和英文版(下载电子版即可,下载地址详见下面,附件也有文件下载),以及 Hadoop 官方文档信息一起结合起来学习和实践。这应该是一种不错的折衷方案吧,毕竟有关 Hadoop 的经典中文书籍少之又少。
《Hadoop: The Definitive Guide
》
从中文版的内容介绍来看,她对 Hadoop 的 HDFS 和 MapReduce 的具体实现细节都介绍地很详细。个人认为她与《Java 编程思想》有的一拼。英文原版下载地址:Oreilly.Hadoop.The.Definitive.Guide.Jun.2009.rar
《云计算的关键技术与应用实例
》
有选择的看了这本书的部分章节,发现她对云计算(包括概念、相关技术)的解释还是颇有深度,且是用通俗易懂的语言阐明非常深奥的知识实属难得。同时也看出作者对云计算的理解还是很有深度的。
The Google File System
Sanjay Ghemawat
,
Howard Gobioff
, and
Shun-Tak Leung
Abstract
We
have designed and implemented the Google File System, a scalable
distributed file system for large distributed data-intensive
applications. It provides fault tolerance while running on inexpensive
commodity hardware, and it delivers high aggregate performance to a
large number of clients.
While
sharing many of the same goals as previous distributed file systems,
our design has been driven by observations of our application workloads
and technological environment, both current and anticipated, that
reflect a marked departure from some earlier file system assumptions.
This has led us to reexamine traditional choices and explore radically
different design points.
The
file system has successfully met our storage needs. It is widely
deployed within Google as the storage platform for the generation and
processing of data used by our service as well as research and
development efforts that require large data sets. The largest cluster
to date provides hundreds of terabytes of storage across thousands of
disks on over a thousand machines, and it is concurrently accessed by
hundreds of clients.
In this
paper, we present file system interface extensions designed to support
distributed applications, discuss many aspects of our design, and
report measurements from both micro-benchmarks and real world use.
Appeared in:
19th ACM Symposium on Operating Systems Principles,
Lake George, NY, October, 2003.
Download: PDF Version
MapReduce: Simplified Data Processing on Large Clusters
Jeffrey Dean
and Sanjay Ghemawat
Abstract
MapReduce
is a programming model and an associated implementation for processing
and generating large data sets. Users specify a map function that
processes a key/value pair to generate a set of intermediate key/value
pairs, and a reduce function that merges all intermediate values
associated with the same intermediate key. Many real world tasks are
expressible in this model, as shown in the paper.
Programs
written in this functional style are automatically parallelized and
executed on a large cluster of commodity machines. The run-time system
takes care of the details of partitioning the input data, scheduling
the program's execution across a set of machines, handling machine
failures, and managing the required inter-machine communication. This
allows programmers without any experience with parallel and distributed
systems to easily utilize the resources of a large distributed system.
Our
implementation of MapReduce runs on a large cluster of commodity
machines and is highly scalable: a typical MapReduce computation
processes many terabytes of data on thousands of machines. Programmers
find the system easy to use: hundreds of MapReduce programs have been
implemented and upwards of one thousand MapReduce jobs are executed on
Google's clusters every day.
Appeared in:
OSDI'04: Sixth Symposium on Operating System Design and Implementation,
San Francisco, CA, December, 2004.
Download: PDF Version
Slides: HTML Slides
想要学习 Google 技术的挚友,不妨时常访问她:
Google Research 技术论文中心
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