I\'m trying to run the map reduce implementation of quadratic sieve algorithm on Hadoop. For this purpose I\'m using karmasphere Hadoop community plugin with Netbeans. The program works fine using the
I want to simulate in ruby my implementation of the map and reduce functions for a system like hadoop to verify that the idea works at least.
I\'m running into a strange issue. When I run my Hadoop job over a large dataset (>1TB compressed text files), several of the reduce tasks fail, with stacktraces like these:
I\'ve been studying hadoop\'s scheduler mechanism recently. Using 0.20.2(fair&capaci开发者_开发百科ty included)
This might be answered here (or elsewhere) before but I keep getting mixed/no views on the internet. I have never used anything else except SQL like databases and then I came across NoSQL DBs (mongoD
I have a list of strings (from documents in CouchDB). I want to find the minimum prefix length so that all shortened strings (taking the first LEN characters) are unique.
I have a logfile of timestamped values (concurrent users) of different \"zones\" of a chatroom webapp in the format \"Timestamp; Zone; Value\". For each zone exists one value per minute of each day.
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How much of a compute-intensive gain can one expect on GAE MapReduce?The scenario of interest to me is compute intensive, so for example: multiplying a trillion random floats in a single threaded sing
I\'ve got 3 types of documents in my db: { param: \"a\", timestamp: \"t\" } (Type 1) { param: \"b\", partof: \"a\"
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