SQL query that finds dates between a range and takes values from another query & iterates range over them? - sql

Question

Sorry if the wording for this question is strange. Wasn't sure how to word it, but here's the context:
I'm working on an application that shows some data about the how often individual applications are being used when users make a request from my web server. The way we take data is by every time the start page loads, it increments a data table called WEB_TRACKING at the date of when it loaded. So there are a lot of holes in data, for example, an application might've been used heavily on September 1st but not at all September 2nd. What I want to do, is add those holes with a value on hits of 0. This is what I came up with.
Select HIT_DATA.DATE_ACCESSED, HIT_DATA.APP_ID, HIT_DATA.NAME, WORKDAYS.BENCH_DAYS, NVL(HIT_DATA.HITS, 0) from (
select DISTINCT( TO_CHAR(WEB.ACCESS_TIME, 'MM/DD/YYYY')) as BENCH_DAYS
FROM WEB_TRACKING WEB
) workDays
LEFT join (
SELECT TO_CHAR(WEB.ACCESS_TIME, 'MM/DD/YYYY') as DATE_ACCESSED, APP.APP_ID, APP.NAME,
COUNT(WEB.IP_ADDRESS) AS HITS
FROM WEB_TRACKING WEB
INNER JOIN WEB_APP APP ON WEB.APP_ID = APP.APP_ID
WHERE APP.IS_ENABLED = 1 AND (APP.APP_ID = 1 OR APP.APP_ID = 2)
AND (WEB.ACCESS_TIME > TO_DATE('08/04/2018', 'MM/DD/YYYY')
AND WEB.ACCESS_TIME < TO_DATE('09/04/2018', 'MM/DD/YYYY'))
GROUP BY TO_CHAR(WEB.ACCESS_TIME, 'MM/DD/YYYY'), APP.APP_ID, APP.NAME
ORDER BY TO_CHAR(WEB.ACCESS_TIME, 'MM/DD/YYYY'), app_id DESC
) HIT_DATA ON HIT_DATA.DATE_ACCESSED = WORKDAYS.BENCH_DAYS
ORDER BY WORKDAYS.BENCH_DAYS
It returns all the dates that between the date range and even converts null hits to 0. However, it returns null for app id and app name. Which makes sense, and I understand how to give a default value for 1 application. I was hoping someone could help me figure out how to do it for multiple applications.
Basically, I am getting this (in the case of using just one application):
| APP_ID | NAME | BENCH_DAYS | HITS |
| ------ | ---------- | ---------- | ---- |
| NULL | NULL | 08/04/2018 | 0 |
| 1 | test_app | 08/05/2018 | 1 |
| NULL | NULL | 08/06/2018 | 0 |
But I want this(with multiple applications):
| APP_ID | NAME | BENCH_DAYS | HITS |
| ------ | ---------- | ---------- | ---- |
| 1 | test_app | 08/04/2018 | 0 |<- these 0's are converted from null
| 1 | test_app | 08/05/2018 | 1 |
| 1 | test_app | 08/06/2018 | 0 | <- these 0's are converted from null
| 2 | prod_app | 08/04/2018 | 2 |
| 2 | prod_app | 08/05/2018 | 0 | <- these 0's are converted from null
So again to reiterate the question in this long post. How should I go about populating this query so that it fills up the holes in the dates but also reuses the application names and ids and populates that information as well?

Answer 1

You need a list of dates, that probably comes from a number generator rather than a table (if that table has holes, your report will too)
Example, every date for the past 30 days:
select trunc(sysdate-30) + level as bench_days from dual connect by level < 30
Use TRUNC instead of turning a date into a string in order to cut the time off
Now you have a list of dates, you want to add in repeating app id and name:
select * from
(select trunc(sysdate-30) + level as bench_days from dual connect by level < 30) dat
CROSS JOIN
(select app_id, name from WEB_APP WHERE APP.IS_ENABLED = 1 AND APP_ID in (1, 2) app
Now you have all your dates, crossed with all your apps. 2 apps and 30 days will make a 60 row resultset via a cross join. Left join your stat data onto it, and group/count/sum/aggregate ...
select app.app_id, app.name, dat.artificialday, COALESCE(stat.ct, 0) as hits from
(select trunc(sysdate-30) + level as artificialday from dual connect by level < 30) dat
CROSS JOIN
(select app_id, name from WEB_APP WHERE APP.IS_ENABLED = 1 AND APP_ID in (1, 2) app
LEFT JOIN
(SELECT app_id, trunc(access_time) accdate, count(ip_address) ct from web_tracking group by app_id, trunc(access_time)) stat
ON
stat.app_id = app.app_id AND
stat.accdate = dat.artificialday
You don't have to write the query this way/do your grouping as a subquery, I'm just representing it this way to lead you to thinking about your data in blocks, that you build in isolation and join together later, to build more comprehensive blocks