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Hive, mail # user - Rolling MAU computation


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Re: Rolling MAU computation
Igor Tatarinov 2012-10-12, 20:08
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 <[EMAIL PROTECTED]> 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 <[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.
>>
>> igor
>> decide.com
>>
>>
>> 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!
>>>
>>> Tom
>>>
>>>
>>
>