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!
On Wed, Oct 10, 2012 at 11:05 PM, Igor Tatarinov <[EMAIL PROTECTED]> 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.
> On Wed, Oct 10, 2012 at 3:05 PM, Tom Hubina <[EMAIL PROTECTED]> 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!