I have a query which shows count of messages received based on dates. For Eg:
1 | 1-May-2012
3 | 3-May-2012
4 | 6-May-2012
7 | 7-May-2012
9 | 9-May-2012
5 |
You could achieve this with a left outer join IF you had another table to join to that contains all possible dates.
One option might be to generate the dates in a temp table and join that to your query.
Something like this might do the trick.
CREATE TABLE #TempA (Col1 DateTime)
DECLARE @start DATETIME = convert(datetime, convert(nvarchar(10), getdate(), 121))
SELECT @start
DECLARE @counter INT = 0
WHILE @counter < 50
BEGIN
INSERT INTO #TempA (Col1) VALUES (@start)
SET @start = DATEADD(DAY, 1, @start)
SET @counter = @counter+1
END
That will create a TempTable to hold the dates... I've just generated 50 of them starting from today.
SELECT
a.Col1,
COUNT(b.MessageID)
FROM
TempA a
LEFT OUTER JOIN YOUR_MESSAGE_TABLE b
ON a.Col1 = b.DateColumn
GROUP BY
a.Col1
Then you can left join your message counts to that.
You don't need a separate table for this, you can create what you need in the query. This works for May:
WITH month_may AS (
select to_date('2012-05-01', 'yyyy-mm-dd') + level - 1 AS the_date
from dual
connect by level < 31
)
SELECT *
FROM month_may mm
LEFT JOIN mytable t ON t.some_date = mm.the_date
The date range will depend on how exactly you want to do this and what your range is.
First, it sounds like your application would benefit from a calendar table. A calendar table is a list of dates and information about the dates.
Second, you can do this without using temporary tables. Here is the approach:
with constants as (select min(thedate>) as firstdate from <table>)
dates as (select( <firstdate> + rownum - 1) as thedate
from (select rownum
from <table> cross join constants
where rownum < sysdate - <firstdate> + 1
) seq
)
select dates.thedate, count(t.date)
from dates left outer join
<table> t
on t.date = dates.thedate
group by dates.thedate
Here is the idea. The alias constants records the earliest date in your table. The alias dates then creates a sequence of dates. The inner subquery calculates a sequence of integers, using rownum, and then adds these to the first date. Note this assumes that you have on average at least one transaction per date. If not, you can use a bigger table.
The final part is the join that is used to bring back information about the dates. Note the use of count(t.date) instead of count(*). This counts the number of records in your table, which should be 0 for dates with no data.