Over the course of generating a large item-item similarity matrix, I need to reduce the amount of data I’m returning to the calling program. In short, i’m computing the similarity between over 20,000 different ‘items’ and that results in a gigantic dataset, to the tune of about 3-4 million elements. I now need to reduce my dataset down to the nearest neighbors for each item and prune irrelevant data.
The real problem is:
Given a list of 20,000 items, each item has a corresponding ‘other’ item and the Jaccard/Tanimoto similarity between the two items, show me the k-closest items for each item in my list.
1 3 .00321 1 4 .00256 1 5 .01019 1 6 .00732 2 1 .02136 ....
I thought of doing this in pig but wasn’t really sure how to limit and sort grouped data. I submitted a question to pig-user and the members were helpful. Since I learned a new trick, I thought I’d document it here in case anyone else is looking to do the same.
Rather than bore everyone with item ids and similarity scores(and violating an NDA and losing lots of friends), I’ll use example data from one of Oracle’s demo tables, emp:
SQL> select empno,ename,job,sal,deptno from emp order by deptno,sal desc; EMPNO ENAME JOB SAL DEPTNO ---------- ---------- --------- ---------- ---------- 7839 KING PRESIDENT 5000 10 7782 CLARK MANAGER 2450 10 7934 MILLER CLERK 1300 10 7788 SCOTT ANALYST 3000 20 7902 FORD ANALYST 3000 20 7566 JONES MANAGER 2975 20 7876 ADAMS CLERK 1100 20 7369 SMITH CLERK 800 20 7698 BLAKE MANAGER 2850 30 7499 ALLEN SALESMAN 1600 30 7844 TURNER SALESMAN 1500 30 7654 MARTIN SALESMAN 1250 30 7521 WARD SALESMAN 1250 30 7900 JAMES CLERK 950 30
From here, I want to limit my data set to the employees with the top 3-highest salaries for each department. The part that was foreign to me was running multiple statements on my data through each iteration of a FOREACH command. Through my brief career using hadoop and pig, I’ve never paid much attention to grouping commands together. After reading pig-user and also looking at some of @TheDataChef‘s examples in sounder, I now recognize the value in doing so.
The code to generate the top-n (in this case 3) top salaries:
The output produced :
(5000,KING,7839,10) (2450,CLARK,7782,10) (1300,MILLER,7934,10) (3000,SCOTT,7788,20) (3000,FORD,7902,20) (2975,JONES,7566,20) (2850,BLAKE,7698,30) (1600,ALLEN,7499,30) (1500,TURNER,7844,30)
This example is easily extended to solve my original problem-reducing the number of similar items found for each item. All I’d need to do is group by the first itemid, sort the items in descending order by their Jaccard/Tanimoto score, and then limit to the top-n similarly-scored items for each original itemid.
Now that my data will be sufficiently limited after all of the similarity scores have been calculated, I can let this process run, generate lots of scores, and not worry about polluting my database with extraneous data. Now time to focus on generating item-item similarity without generating so much useless data in the first place! Would love to hear your suggestions.
