Changes between Version 9 and Version 10 of PDAD_Applications

Timestamp:

Jan 14, 2010, 2:31:51 PM (14 years ago)

Author:

cristina.basescu

Comment:

--

PDAD_Applications

@@ -18 +18 @@
 b. We have tried two approaches here. The first one is to get the Mapper compute the jobs duration, giving the same key to each pair, and the Reducer will sum up all these values and compute the medium. Unfortunately, no Combiner can be specified here, as the Reducer would not know afterwards how many elements the Mapper generated.
 
-The second approach is to compute the medium on chunks having a fixed size of elements, and then the result would be the medium of all these mediums. Although this is appropiate for specifying a Combiner, it will give an approximate value of the medium, depending on the distribution of values in each of the chunks. In this case, the Mapper will generate a new key for durations at each chunk number of pairs, the Combiner will make the medium for each chunk and output mediums having the same key, and the reduce will compute the medium like it did in the previous example.
+The second approach is to compute the medium on chunks having a fixed size of elements, and then the result would be the medium of all these mediums. Although this is appropriate for specifying a Combiner, it will give an approximate value of the medium, depending on the distribution of values in each of the chunks. In this case, the Mapper will generate a new key for durations at each chunk number of pairs, the Combiner will make the medium for each chunk and output mediums having the same key, and the reduce will compute the medium like it did in the previous example.
 
-d. 
+d. The Mapper classifies inputs and emits keys 'duration classification - reason' having the value of 1, while the Reducer counts the values and if they exceed 1000, they are outputted. The Combiner does basically the same thing as the Reducer.
+
+f. This is a more interesting application, as it requires a natural join between the node table and the event_trace table on platform_id and node_id fields. In order to do that, we concatenate the two join columns. The idea here is that the Mapper will read both files and will emit for a platform_id;node_id key values consisting both of values 1 (meaning the failures on that node), but also location. So after the map phase, we should have for a platform_id;node_id key 
   
 TODO Claudiu for his apps