PIG- Aggregations based on multiple columns - hadoop

Question

My Input data set has 3 columns and schema looks like below:
ActivityDate, EventId, EventDate
Now, using pig i need to derive multiple variables like below in one output file:
1) All Event Ids after ActivityDate >= EventDate -30 days
2) All Event Ids after ActivityDate >= EventDate -60 days
3) All Event Ids after ActivityDate >= EventDate -90 days
I have more than 30 variables like this. If it is one variable, we can use simple FILTER to filter the data.
I am thinking about any UDF implementation which takes bag as input and returns count of Event IDs based on above criteria for each parameter.
What is the best way to aggregate the data on multiple columns in pig ?

Answer 1

I would suggest creating another file with all of your thresholds and cross joining with the file.
so you would have a file containing:
30
60
90
etc
read it like this:
grouping = load 'grouping.txt' using PigStorage(',') as (groups:double);
Then do:
data_with_grouping = cross data, grouping;
Then have this binary condition:
data_with_binary_condition = foreach data_with_grouping generate ActivityDate, EventId, EventDate, groups, (ActivityDate >= EventDate - groups ? 1 : 0) as binary_condition;
Now you will have one column with the threshold and one column with a binary variable that tells you whether the ID follows the condition or not.
you can do a filter out all of the zeros from the binary_condition and then group on the groups column:
data_with_binary_condition_filtered = filter data_with_binary_condition by (binary_condition != 0);
grouped_by_threshold = group data_with_binary_condition_filtered by groups;
count_of_IDS = foreach grouped_by_threshold generate group, COUNT(data_with_binary_condition.EventId);
I hope this works. Obviously, I didn't debug it for you since I don't have your files.
This code will take a tad more time to run, but it will produce the output you need without a UDF.

Answer 2

If I understand your question correctly, you want to divide the difference between EventDate and ActivityDate in 30 days blocks (e.g. 1 to 30, 31 to 60, 61 to 90 and so on) and then count the frequency of each block.
In this case, I would just rearrange the above equation to create the variable 'range' as below:
// assuming input contains 3 columns ActivityDate, EventId, EventDate
// dividing the difference between ED and AD by 30 and casting it to int, so that 1 block is represented by 1 integer.
input1 = FOREACH input GENERATE (int)((EventDate - ActivityDate) / 30) as range;
output1 = GROUP input1 BY range;
output2 = FOREACH output1 GENERATE group AS range, COUNT(range) as count;
Hope this helps.