Hello list,
I have a task where I have compare the entries of a big sequencefile
with the entries of many small sequencefiles.
Basically you could describe it like this:
for entry in bigSequenceFile:
for file in listOfSmallFiles:
for entry2 in file:
compare(entry, entry2)
My approach is to use a map-only job. I put the big file in HDFS in the
directory which will be the inputPath for the job and the small files in
HDFS in another directory.
Then in my mapper I will load all the small files in memory and will
compare them to the records which will be sent to me in map() from the
big file.
Something like this:
map(key, value):
for list in smallFiles:
for entry in list:
compare(value, entry)
As I don't want, on each node, to load the files multiple times wasting
memory, I thought about using a MultithreadedMapper, load the my small
files at setup() (ideally called once at the instantiation of the
object) into a datastructure (i.e. a LinkedList, smallFiles in the
pseudocode) shared between the threads.
So basically each node would create only one task with more threads each
running on one CPU and sharing the in-memory datastructure.
The problem I'm facing is that for each thread the setup() method is
called once (so #threads times in total), which is not what I want as it
will load the smallFiles multiple times on a shared datastructure.
Also I'm afraid I'm not able to control the number of these
MultithreadedMappers ran on each node (I want only one).
Can anybody help me out understanding how I can leverage
MultithreadedMapper to get what I want (there's very little info on the
internet about MultithreadedMapper)?
Thanks!
CM
--
Claudio Martella
Free Software & Open Technologies
Analyst
TIS innovation park
Via Siemens 19 | Siemensstr. 19
39100 Bolzano | 39100 Bozen
Tel. +39 0471 068 123
Fax +39 0471 068 129
claudio.marte...@tis.bz.it http://www.tis.bz.it
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