Each chapter in the book provides illustrative working examples of the core Hadoop concepts, along with their description and technicality aspects. However, the reader should follow the examples from the beginning, to understand the different Hadoop components, as the book uses the same data and use cases from previous chapters. With dedicated chapters for configuring and operating a Hadoop cluster and appendices that detail the installation process with example code - will help the readers of the book gear up on how to install and implement the Hadoop framework.
The book goes on to highlight some of the practical applications of Hadoop in later chapters, that gives the reader a clear understanding of situations, when Hadoop can be used and what is the best possible way to implement it for a given situation. The case-studies are not programming related, so they might not be of interest to few of the coding geeks but they give a clear understanding on how Hadoop is used in practical situations to overcome various parallel processing problems that enterprises encounter.
The best thing about the book is that it does not merely teach about the various Hadoop features but also highlights the guidelines on how one can use them effectively. So, you will not just learn to create and run a MapReduce application but also you can learn how to tune various parameters to optimize performance, how to configure various properties and how to test and debug a MapReduce job.
However, these concepts can be learnt over the time on the job, but the purpose of this book, is to help Hadoop developers speed up on the best practices for writing MapReduce jobs and get a good grasp of the design philosophy of Hadoop MapReduce applications.
The most appreciable part of the book, is that has links to latest online documentation, hints and coding gotchas that a Hadoop developer always looks out for. Try adding this search to your want list. Did you know that since , Biblio has used its profits to build 16 public libraries in rural villages of South America?
Home Price Comparison More Search Options. More Than Words Inc. Seller rating : This seller has earned a 5 of 5 Stars rating from Biblio customers. Show Details Description:.
Add to cart Buy Now Item Price. Seller rating : This seller has earned a 4 of 5 Stars rating from Biblio customers. Better World Books. Coordinating the processes in a large-scale distributed computation is a challenge. MapReduce spares the programmer from having to think about failure, since the implementation detects failed map or reduce tasks and reschedules replacements on machines that are healthy.
MapReduce is able to do this since it is a shared-nothing. This is a slight oversimplification, since the output from mappers is fed to the reducers, but this is under the control of the MapReduce system; in this case, it needs to take more care rerunning a failed reducer than rerunning a failed map, since it has to make sure it can retrieve the necessary map outputs, and if not, regenerate them by running the relevant maps again.
By contrast, MPI programs have to explicitly manage their own check-pointing and recovery, which gives more control to the programmer, but makes them more difficult to write. MapReduce might sound like quite a restrictive programming model, and in a sense it is: you are limited to key and value types that are related in specified ways, and mappers and reducers run with very limited coordination between one another the mappers pass keys and values to reducers. A natural question to ask is: can you do anything useful or nontrivial with it?
The answer is yes. MapReduce was invented by engineers at Google as a system for building production search indexes because they found themselves solving the same problem over and over again and MapReduce was inspired by older ideas from the functional programming, distributed computing, and database communities , but it has since been used for many other applications in many other industries.
It is pleasantly surprising to see the range of algorithms that can be expressed in MapReduce, from. SETI home is the most well-known of many volunteer computing projects; others in-clude the Great Internet Mersenne Prime Search to search for large prime numbers and Folding home to understand protein folding and how it relates to disease.
Volunteer computing projects work by breaking the problem they are trying to solve into chunks called work units , which are sent to computers around the world to be analyzed. For example, a SETI home work unit is about 0. When the analysis is completed, the results are sent back to the server, and the client gets another work unit.
As a precaution to combat cheating, each work unit is sent to three different machines and needs at least two results to agree to be accepted. Although SETI home may be superficially similar to MapReduce breaking a problem into independent pieces to be worked on in parallel , there are some significant differ-ences.
Volunteers are donating CPU cycles, not bandwidth. MapReduce is designed to run jobs that last minutes or hours on trusted, dedicated hardware running in a single data center with very high aggregate bandwidth inter-connects.
By contrast, SETI home runs a perpetual computation on untrusted machines on the Internet with highly variable connection speeds and no data locality. Hadoop was created by Doug Cutting, the creator of Apache Lucene, the widely used text search library. Hadoop has its origins in Apache Nutch, an open source web search engine, itself a part of the Lucene project. The name my kid gave a stuffed yellow elephant.
Short, relatively easy to spell and pronounce, meaningless, and not used elsewhere: those are my naming criteria. Kids are good at generating such.
Smaller components are given more descriptive and therefore more mun-dane names. This is a good principle, as it means you can generally work out what something does from its name. Building a web search engine from scratch was an ambitious goal, for not only is the software required to crawl and index websites complex to write, but it is also a challenge to run without a dedicated operations team, since there are so many moving parts.
Nutch was started in , and a working crawler and search system quickly emerged. In par-ticular, GFS would free up time being spent on administrative tasks such as managing storage nodes. NDFS and the MapReduce implementation in Nutch were applicable beyond the realm of search, and in February they moved out of Nutch to form an independent subproject of Lucene called Hadoop.
At around the same time, Doug Cutting joined Yahoo! This was demonstrated in February when Yahoo! In January , Hadoop was made its own top-level project at Apache, confirming its success and its diverse, active community.
By this time, Hadoop was being used by many other companies besides Yahoo! In April , Hadoop broke a world record to become the fastest system to sort a terabyte of data. In November of the same year, Google reported that its MapReduce implementation sorted one ter-abyte in 68 seconds.
Building Internet-scale search engines requires huge amounts of data and therefore large numbers of machines to process it. The WebMap is a graph that consists of roughly 1 trillion edges each representing a web link and billion nodes each representing distinct URLs.
Creating and analyzing such a large graph requires a large number of computers running for many days. In early , the infra-structure for the WebMap, named Dreadnaught , needed to be redesigned to scale up to more nodes. But How Do It Know? The Hadoop Distributed Filesystem.
0コメント