You just need to put the join condition in the WHERE clause. That way Hive will do a cartesian product followed by a filter.
On Fri, Oct 12, 2012 at 1:02 PM, Tom Hubina <t...@z2live.com> wrote: > I think I see what you're saying about the temp table with start/end dates > (30x expansion makes sense) and it sounds like it should work. I just need > to figure out a good way to generate the table. Thanks! > > Tom > > On Wed, Oct 10, 2012 at 11:05 PM, Igor Tatarinov <i...@decide.com> wrote: > >> If you have a lot of data, you might have to write a custom reducer (in >> python) to keep track of the moving date window. >> >> If you don't have that much data, you might want to use a temp table >> <start_date, end_date> such that datediff(end_date, start_date) < 30. To >> create such a table, you can self-join a table of unique dates using the >> above condition. Then, you would join your data with that table on >> start_date and group by end_date counting distinct user_ids. Hope I got >> that right :) >> >> The latter approach will essentially multiply the number of rows by 30. >> >> igor >> decide.com >> >> >> On Wed, Oct 10, 2012 at 3:05 PM, Tom Hubina <t...@z2live.com> wrote: >> >>> I'm trying to compute the number of active users in the previous 30 days >>> for each day over a date range. I can't think of any way to do it directly >>> within Hive so I'm wondering if you guys have any ideas. >>> >>> Basically the algorithm is something like: >>> >>> For each day in date range: >>> SELECT day, COUNT(DISTINCT(userid)) FROM logins WHERE day - >>> logins.day < 30; >>> >>> Thanks for your help! >>> >>> Tom >>> >>> >> >
