I Want to Write a Map Reduce Job to Separate Large PDF 1gb File into?

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I want to write a map reduce job to separate large pdf 1GB file into sub pdf files with the give range of page numbers . suggest me how to implement this .. what to write in mappers and reducers?

Note. The world has changed since I initially answered the question. I am updating it to reflect state-of-the-art. - March 5, 2014 Disclaimer. I am a PMC member of Apache Spark. Yes, use Apache Spark™ - Lightning-Fast Cluster Computing Dubbed the leading successor to Hadoop MapReduce, Apache Spark is a cluster compute system that makes data analytics fast -- both fast to run and fast to write. A few factors related to your question. With its general execution graph support and better in-memory storage, programs in Spark can outperform those in Hadoop MapReduce by one or two orders of magnitude. You can express your algorithm in a very concise and understandable manner using Spark's high level, language-integrated APIs. Your program will be 10X shorter than the MapReduce counterparts. There is a new graph computation library called GraphX on Spark to simplify your life. The project features one of the most active open source eco-system in Big Data projects. It has 150+ contributors from 30+ companies. As an example, see one variant of PageRank implementation in Spark. val links = // RDD of (url, neighbors) pairs var ranks = // RDD of (url, rank) pairs for (i <- 1 to ITERATIONS) { val contribs = links.join(ranks).flatMap { case (url, (links, rank)) => links.map(dest => (dest, rank/links.size)) } ranks = contribs.reduceByKey(_ + _) .mapValues(0.15 + 0.85 * _) } ranks.saveAsTextFile(...)

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