finding mean using pig or hadoop - hadoop

Question

I have a huge text file of form
data is saved in directory data/data1.txt, data2.txt and so on
merchant_id, user_id, amount
1234, 9123, 299.2
1233, 9199, 203.2
1234, 0124, 230
and so on..
What I want to do is for each merchant, find the average amount..
so basically in the end i want to save the output in file.
something like
merchant_id, average_amount
1234, avg_amt_1234 a
and so on.
How do I calculate the standard deviation as well?
Sorry for asking such a basic question. :(
Any help would be appreciated. :)

Answer 1

Apache PIG is well adapted for such tasks. See example:
inpt = load '~/pig_data/pig_fun/input/group.txt' as (amnt:double, id:chararray,c2:chararray);
grp = group inpt by id;
mean = foreach grp {
sum = SUM(inpt.amnt);
count = COUNT(inpt);
generate group as id, sum/count as mean, sum as sum, count as count;
};
Pay special attention to the data type of the amnt column as it will influence which implementation of the SUM function PIG is going to invoke.
PIG can also do something that SQL can not, it can put the mean against each input row without using any inner joins. That is useful if you are calculating z-scores using standard deviation.
mean = foreach grp {
sum = SUM(inpt.amnt);
count = COUNT(inpt);
generate FLATTEN(inpt), sum/count as mean, sum as sum, count as count;
};
FLATTEN(inpt) does the trick, now you have access to the original amount that had contributed to the groups average, sum and count.
UPDATE 1:
Calculating variance and standard deviation:
inpt = load '~/pig_data/pig_fun/input/group.txt' as (amnt:double, id:chararray, c2:chararray);
grp = group inpt by id;
mean = foreach grp {
sum = SUM(inpt.amnt);
count = COUNT(inpt);
generate flatten(inpt), sum/count as avg, count as count;
};
tmp = foreach mean {
dif = (amnt - avg) * (amnt - avg) ;
generate *, dif as dif;
};
grp = group tmp by id;
standard_tmp = foreach grp generate flatten(tmp), SUM(tmp.dif) as sqr_sum;
standard = foreach standard_tmp generate *, sqr_sum / count as variance, SQRT(sqr_sum / count) as standard;
It will use 2 jobs. I have not figured out how to do it in one, hmm, need to spend more time on it.

Answer 2

So what do you want? You want the running java code or the abstract map-reduce process? For the second:
The map step:
record -> (merchant_id as key, amount as value)
The reduce step:
(merchant_id, amount) -> (merchant_id, aggregate the value you want)
As in the reduce step, you will be provided with a stream of record having the same key and you can do almost everything you can including the average, variance.

Answer 3

you can calculate the standard deviation just in one step; using the formula
var=E(x^2)-(Ex)^2
inpt = load '~/pig_data/pig_fun/input/group.txt' as (amnt:double, id:chararray, c2:chararray);
grp = group inpt by id;
mean = foreach grp {
sum = SUM(inpt.amnt);
sum2 = SUM(inpt.amnt**2);
count = COUNT(inpt);
generate flatten(inpt), sum/count as avg, count as count, sum2/count- (sum/count)**2 as std;
};
that's it!

Answer 4

I calculated all stats(min, max, mean and standard deviation) in just 1 loop. FILTER_DATA contains data-set.
GROUP_SYMBOL_YEAR = GROUP FILTER_DATA BY (SYMBOL, SUBSTRING(TIMESTAMP,0,4));
STATS_ALL = FOREACH GROUP_SYMBOL_YEAR {
MINIMUM = MIN(FILTER_DATA.CLOSE);
MAXIMUM = MAX(FILTER_DATA.CLOSE);
MEAN = AVG(FILTER_DATA.CLOSE);
CNT = COUNT(FILTER_DATA.CLOSE);
CSQ = FOREACH FILTER_DATA GENERATE CLOSE * CLOSE AS (CC:DOUBLE);
GENERATE group.$0 AS (SYMBOL:CHARARRAY), MINIMUM AS (MIN:DOUBLE), MAXIMUM AS (MAX:DOUBLE), ROUND_TO(MEAN,6) AS (MEAN:DOUBLE), ROUND_TO(SQRT(SUM(CSQ.CC) / (CNT * 1.0) - (MEAN * MEAN)),6) AS (STDDEV:DOUBLE), group.$1 AS (YEAR:INT);
};