Question

Each row in my table has a date time stamp, and I wish to query the database from now, to count how many rows are in the last 30 days, the 30 days before that and so on. Until there is a 30 day bin going back to the start of the table.

I have successfully carried out this query by using Python and making several queries. But I’m almost certain that it can be done in one single MySQL query.

Answer 1

No stored procedures, temporary tables, only one query, and an efficient execution plan given an index on the date column:

select

  subdate(
    '2012-12-31',
    floor(dateDiff('2012-12-31', dateStampColumn) / 30) * 30 + 30 - 1
  ) as "period starting",

  subdate(
    '2012-12-31',
    floor(dateDiff('2012-12-31', dateStampColumn) / 30) * 30
  ) as "period ending",

  count(*)

from
  YOURTABLE
group by floor(dateDiff('2012-12-31', dateStampColumn) / 30);

It should be pretty obvious what is happening here, except for this incantation:

floor(dateDiff('2012-12-31', dateStampColumn) / 30)

That expression appears several times, and it evaluates to the number of 30-day periods ago dateStampColumn is. dateDiff returns the difference in days, divide it by 30 to get it in 30-day periods, and feed it all to floor() to round it to an integer. Once we have this number, we can GROUP BY it, and further we do a bit of math to translate this number back into the starting and ending dates of the period.

Replace '2012-12-31' with now() if you prefer. Here’s some sample data:

CREATE TABLE YOURTABLE
    (`Id` int, `dateStampColumn` datetime);

INSERT INTO YOURTABLE
    (`Id`, `dateStampColumn`)
VALUES
    (1, '2012-10-15 02:00:00'),
    (1, '2012-10-17 02:00:00'),
    (1, '2012-10-30 02:00:00'),
    (1, '2012-10-31 02:00:00'),
    (1, '2012-11-01 02:00:00'),
    (1, '2012-11-02 02:00:00'),
    (1, '2012-11-18 02:00:00'),
    (1, '2012-11-19 02:00:00'),
    (1, '2012-11-21 02:00:00'),
    (1, '2012-11-25 02:00:00'),
    (1, '2012-11-25 02:00:00'),
    (1, '2012-11-26 02:00:00'),
    (1, '2012-11-26 02:00:00'),
    (1, '2012-11-24 02:00:00'),
    (1, '2012-11-23 02:00:00'),
    (1, '2012-11-28 02:00:00'),
    (1, '2012-11-29 02:00:00'),
    (1, '2012-11-30 02:00:00'),
    (1, '2012-12-01 02:00:00'),
    (1, '2012-12-02 02:00:00'),
    (1, '2012-12-15 02:00:00'),
    (1, '2012-12-17 02:00:00'),
    (1, '2012-12-18 02:00:00'),
    (1, '2012-12-19 02:00:00'),
    (1, '2012-12-21 02:00:00'),
    (1, '2012-12-25 02:00:00'),
    (1, '2012-12-25 02:00:00'),
    (1, '2012-12-26 02:00:00'),
    (1, '2012-12-26 02:00:00'),
    (1, '2012-12-24 02:00:00'),
    (1, '2012-12-23 02:00:00'),
    (1, '2012-12-31 02:00:00'),
    (1, '2012-12-30 02:00:00'),
    (1, '2012-12-28 02:00:00'),
    (1, '2012-12-28 02:00:00'),
    (1, '2012-12-30 02:00:00');

And the result:

period starting     period ending   count(*)
2012-12-02          2012-12-31      17
2012-11-02          2012-12-01      14
2012-10-03          2012-11-01      5

period endpoints are inclusive.

Play with this in SQL Fiddle.

There’s a bit of potential goofiness in that any 30 day period with zero matching rows will not be included in the result. If you could join this against a table of periods, that could be eliminated. However, MySQL doesn’t have anything like PostgreSQL’s generate_series(), so you’d have to deal with it in your application or try this clever hack.