sure that was a bit more helpful, thanks :)
i was still wondering to what other use cases would that apply. This
is a good article (best so far i guess):
http://code.google.com/edu/parallel/mapreduce-tutorial.html
The thing is that reduce has to aggregate data or it would be
impractical. So i am
Hi Artur,
in your IPs examples, lets supouse you have ten access log files (from
ten different servers),
there you already have the mapping part done.
Then you reduce each log into anonther new file, indicating the IP
address and the times it's repeated.
At this stage you have a reduced version
yeah i think that would make sense.
if you find more good examples from different areas let me know ... i
think i get the basic idea ... will try to apply it some time :)
cheers :)
art
On 30 October 2010 18:58, Andrés G. Montañez andresmonta...@gmail.com wrote:
Hi Artur,
in your IPs
Hi there guys and girls
Have anyone came across any reasonable explanation / articles on how
hadoop and map reduce work in practice?
i have read a few articles now and then and i must say i am puzzled
am i stupid or they just cant find an easy way to explain it? :P
What i would hope for is
Hi Artur,
Here is an article on wikipedia: http://en.wikipedia.org/wiki/MapReduce
And here are the native implementations in php:
http://www.php.net/manual/en/function.array-map.php
http://www.php.net/manual/en/function.array-reduce.php
The basic idea is to gather a lot of data, from several
hehe sorry but this does not help :-) i can google for wikipedia
definitions.
I was hoping for some really good articles/examples that would put it
into enough context. I would like to have good idea when it could be
useful.
So far had no luck with that. Its like with design patterns ...
Imagine you have to get track of some kind of traffic, for example,
ad impressions;
lets supose that you have millions of those hits; you will have to
have a few servers to
receive the notifications of the impression of an ad.
After the end of the day, you will have that info across a bunch of
