Hi Stuart, Join is a higher level logical operation while map/reduce is a technique that could be used implement it. Specifically, in relational algebra, the join construct specifies how to form a single output row from 2 rows arising from two input streams. There are very many ways of implementing this logical operation and traditional database systems have a number of such implementations. Map/reduce being a system that essential allows you to cluster data by doing a distributed sort, is amenable to the sort based techinque for doing the join. A particular implementation of the reducer gets a combined stream of data from the two or more input streams such that they match on the key. It then proceeds to generate the cartesian product of the rows from the imput streams. In order to implement a join, you need to implement this join reducer yourself which is what org.apache.hadoop.mapred.join does. I hope that clears up the confusion.
Cheers, Ashish -----Original Message----- From: [EMAIL PROTECTED] on behalf of Stuart Sierra Sent: Thu 7/3/2008 7:54 AM To: core-user@hadoop.apache.org Subject: Difference between joining and reducing Hello all, After recent talk about joins, I have a (possibly) stupid question: What is the difference between the "join" operations in o.a.h.mapred.join and the standard merge step in a MapReduce job? I understand that doing a join in the Mapper would be much more efficient if you're lucky enough to have your input pre-sorted and -partitioned. But how is a join operation in the Reducer any different from the shuffle/sort/merge that the MapReduce framework already does? Be gentle. Thanks, -Stuart
