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Author: Donald Miner
ISBN : B00AB61JMQ
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Format: PDF
Download for free books MapReduce Design Patterns: Building Effective Algorithms and Analytics for Hadoop and Other Systems Free Download from with Mediafire Link Download Link
Until now, design patterns for the MapReduce framework have been scattered among various research papers, blogs, and books. This handy guide brings together a unique collection of valuable MapReduce patterns that will save you time and effort regardless of the domain, language, or development framework you’re using.
Each pattern is explained in context, with pitfalls and caveats clearly identified to help you avoid common design mistakes when modeling your big data architecture. This book also provides a complete overview of MapReduce that explains its origins and implementations, and why design patterns are so important. All code examples are written for Hadoop.
- Summarization patterns: get a top-level view by summarizing and grouping data
- Filtering patterns: view data subsets such as records generated from one user
- Data organization patterns: reorganize data to work with other systems, or to make MapReduce analysis easier
- Join patterns: analyze different datasets together to discover interesting relationships
- Metapatterns: piece together several patterns to solve multi-stage problems, or to perform several analytics in the same job
- Input and output patterns: customize the way you use Hadoop to load or store data
"A clear exposition of MapReduce programs for common data processing patterns—this book is indespensible for anyone using Hadoop."
--Tom White, author of Hadoop: The Definitive Guide
Download latest books on mediafire and other links compilation MapReduce Design Patterns: Building Effective Algorithms and Analytics for Hadoop and Other Systems [Kindle Edition] Free Download
- File Size: 2721 KB
- Print Length: 230 pages
- Simultaneous Device Usage: Unlimited
- Publisher: O'Reilly Media; 1 edition (November 21, 2012)
- Sold by: Amazon Digital Services, Inc.
- Language: English
- ASIN: B00AB61JMQ
- Text-to-Speech: Enabled
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- Lending: Not Enabled
- Amazon Best Sellers Rank: #209,357 Paid in Kindle Store (See Top 100 Paid in Kindle Store)
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MapReduce Design Patterns: Building Effective Algorithms and Analytics for Hadoop and Other Systems Free Download
In the 1990s O'Reilly books had a well-earned reputation for quality. O'Reilly authors such as Simson Garfinkel explained technical topics with precision, clarity, and wit. I proudly kept a whole shelf of O'Reilly books at work, and I imbibed copious java from their tenth anniversary mug. I'm sorry to see that O'Reilly's traditional quality has gone the way of the Internet bubble. MapReduce Design Patterns represents the absolute nadir of technical writing, and it never should have been published in its current form.
One of the most poorly written parts of the book is Appendix A on Bloom filters. As I was writing my original review of the book, I thought it might be helpful to point readers to a better explanation of the topic. Turning to Wikipedia as a potential reference, I was struck by the number of similarities between it and Appendix A. It now appears that this appendix plagiarizes the Wikipedia article "Bloom filter." To see this, compare the opening paragraph of the Wikipedia article (January 19, 2013) to the first two paragraphs of the book's appendix (which you can see in the sample pages here):
Wiki: A Bloom filter, conceived by Burton Howard Bloom in 1970, is a space-efficient probabilistic data structure that is used to test whether an element is a member of a set. (Paragraph 1, sentence 1)
MRDP: Conceived by Burton Howard Bloom in 1970, a Bloom filter is a probabilistic data structure used to test whether a member is an element of a set. (Page 221, paragraph 1, sentence 1)
Wiki: False positive retrieval results are possible, but false negatives are not; i.e. a query returns either "inside set (may be wrong)" or "definitely not in set".
It's a good book that show you a lot of Mapreduce patterns using Hadoop.
But the main trouble it's that you can not trust the examples source code, at all. I Clone the Code Github in my Mac and I've found several bugs..
https://github.com/adamjshook/mapreducepatterns
I'm running the Book's code using a macbook with:
- hadoop-1.0.4
- Mac OS ver 10.6.8
- Java ver "1.6.0_43"
- Eclipse
- Data for running the examples from ( Stack Exchange Data Dump - Dec 2011 _Update_ )
For now, these are the bugs I've found:
Page: 31
The error is in the MedianStdDevCombiner code.
I'm looking for a bug in this full example because when you execute it ,you obtain different result from the previous normal Median and Standard deviation using the same input data. The result obtained is nearly double values from the previous example, when need to be the same results.
Page: 35-36
The error i found is in the Inverted Index Example.
In the Mapper Function if "getWikipediaURL" return a null value then you get a nullpointerException because you need to check if the result of this function is null prior to set the "link" variable value.
Page 117-118
In ReduceSideJoinWithBloomDriver Code from github doesn't exist any reference to load the bloom filter from any argument... [something like DistributedCache.addCacheFile(...... ], this file is nearly a Copy/paste from the previous ReduceSideJoin.java.
Page 122:
In ReplicatedJoinMapper you always get a java.io.FileNotFoundException because this code want to read and decompress a folder , not a concrete "file.gz", inside this folder. You only need to add a index to your files inside the DistributedCache.
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