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?

Putting aside the whole "Why can't you just run it twice?" thing, the answer is almost certainly no. The InputSplit calculation must be done prior to task launch in order to determine how many tasks, where to place them, etc. The partitioning logic also gets kind of screwy since it becomes a moving target. While one could imagine a framework change to allow this sort of thing, it really won't tell you much about the performance characteristics. At a basic level, changing the number of map tasks being used in the middle of the run will almost certainly have a detrimental impact on disk and/or network IO due to decreased locality.

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So yeah, the number of reducers for these three programs comes out to be like .6/1.13/3/1.07/1.06 respectively. Also, since the word processor is a word processor and not an AI, it doesn't have to do any more work than just dividing the numbers up to the limit. The number of words in this test file came out to be 26.5 and the number of reducers came out to be 26/16.5. Now, the word processor has to divide the number of words by 22 which only ends up giving us 17.5. So yeah, the word processor doesn't waste as much time as the test program with the same file size. So yeah, if you've got a big file to work with for the test program it might be better to just use the test program (i.e. it's faster and less work). But you just need to be aware of what's taking up.