How to adapt this query to use window functions - sql

When I started tackling this problem, I thought, "This will be a great query to learn about Window Functions." I wasn't able to end up getting it to work with window functions, but I was able to get what I wanted using a join.
How would you adapt this query to use window functions:
SELECT
day,
COUNT(i.project) as num_open
FROM generate_series(0, 364) as t(day)
LEFT JOIN issues i on (day BETWEEN i.closed_days_ago AND i.created_days_ago)
GROUP BY day
ORDER BY day;
The query above takes a list of issues that have a range represented by created_days_ago and closed_days ago and for the last 365 days, it'll count the number of issues that were created but not yet closed for that specific day.
http://sqlfiddle.com/#!15/663f6/2
The issues table looks like:
CREATE TABLE issues (
id SERIAL,
project VARCHAR(255),
created_days_ago INTEGER,
closed_days_ago INTEGER);
What I was thinking was that the partition for a given day should include all the rows in issues where day is between the created and closed days ago. Something like SELECT day, COUNT(i.project) OVER (PARTITION day BETWEEN created_days_ago AND closed_days_ago) ...
I've never used window functions before, so I might be missing something basic, but it seemed like this was just the type of query that makes window functions so awesome.

The fact that you use generate_series() to create a full range of days, including those days with no changes, and thus no rows in table issues, does not rule out the use of window functions.
In fact, this query runs 50 times faster than the query in the Q in my local test:
SELECT t.day
, COALESCE(sum(a.created) OVER (ORDER BY t.day DESC), 0)
- COALESCE(sum(b.closed) OVER (ORDER BY t.day DESC), 0) AS open_tickets
FROM generate_series(0, 364) t(day)
LEFT JOIN (SELECT created_days_ago AS day, count(*) AS created
FROM issues GROUP BY 1) a USING (day)
LEFT JOIN (SELECT closed_days_ago AS day, count(*) AS closed
FROM issues GROUP BY 1) b USING (day)
ORDER BY 1;
It is also correct, as opposed to the query in the question, which results in 17 open tickets on day 0, although all of them have been closed.
The error is due to BETWEEN in your join condition, which includes upper and lower border. This way tickets are still counted as "open" on the day they are closed.
Each row in the result reflects the number of open tickets at the end of the day.
Explain
The query combines window functions with aggregate functions.
Subquery a counts the number of created tickets per day. This results in a single row per day, making the rest easier.
Subquery b does the same for closed tickets.
Use LEFT JOINs to join to the generated list of days in subquery t.
Be wary of joining to multiple unaggregated tables! That could trigger a CROSS JOIN among the joined tables for multiple matches per row, generating incorrect results. Compare:
Two SQL LEFT JOINS produce incorrect result
Finally use two window functions to compute the running total of created versus closed tickets.
An alternative would be to use this in the outer SELECT
sum(COALESCE(a.created, 0)
- COALESCE(b.closed, 0)) OVER (ORDER BY t.day DESC) AS open_tickets
Performs the same in my tests.
-> SQLfiddle demo.
Aside: I would never store "days_ago" in a table, but the absolute date / timestamp. Looks like a simplification for the purpose of this question.

Related

How to write an SQL query to get max number of counts for the most number of travelling of a user within a month

I have been given a task by my manager to write a SQL query to select the max number of counts (no of records) for a user who has travelled the most within a month provided that if the user travels multiple places on the same date, then it should be counted as one. For instance, if you look at the following table design; according to this scenario, my query must return me a count of 2. Although traveller_id "1" has traveled three times within a month, but he traveled to Thailand and USA on the same date, that is why its count is reduced to 2.
I have also developed my logic for this query but I am unable to write it due to lack of syntax knowledge. I split up this query into 3 parts:
Select All records from the table within a month using the MONTH function of SQL
Select All distinct DateTime records from the above result so that the same DateTime gets eliminated.
Select max number of counts for the traveller who visited most places.
Please help me in completing my query. You can also use a different approach from mine.
You can use the count aggregation in a cte then select top(1):
with u as
(select traveller_id,
count(distinct visit_date) as n
from travellers_log
where visit_date between '2022-03-01' and '2022-03-31'
group by traveller_id)
select top(1) traveller_id, name, n from u inner join table_travellers
on u.traveller_id = table_travellers.id
order by n desc;

comparing usage of inner join and where in

with two tables - all_data and selected_place_day_hours
all_data has place_id, day, hour, metric
selected_place_day_hours has fields place_id, day, hour
I need to subset all_data such that only records with place_id, day, hour in selected_place_day_hours are selected.
I can go two ways about it
1.Use inner join
select a.*
from all_data as a
inner join selected_place_day_hours as b
on (a.place_id = b.place_id)
and ( a.day = b.day)
and ( a.hour = b.hour)
;
2.Use where in
select *
from all_data
where
place_id in (select place_id from selected_place_day_hours)
and day in (select day from selected_place_day_hours)
and hour in (select day from selected_place_day_hours)
;
I want to get some idea on why, when, if you would choose one over the other from a functional and performance perspective ?
One thought is that in #2 above, probably sub-selects is not performance friendly and also longer code.
The two are semantically different.
The IN does a semi-join, meaning that it returns one from all_data regardless of how many rows are matched in selected_place_day_hours.
The JOIN can return multiple rows.
So, the first piece of advice is to use the version that is correct for what you want to accomplish.
Assuming the data in select_place_day_hours guarantees at most one match, then you have an issue with performance. The first piece of advice is to try both queries on your data and on your system. However, often JOIN is optimized at least as well as IN, so that would usually be a safe choice.
These days, SQL tends to ignore what you say and do its own thing.
This is why SQL is a declarative language, not a programming language: you tell it what you want, not how to do it. The SQL interpreter will work out what you want and devise its own plan for how to get the results.
In this case, the 2 versions will probably produce an identical plan, regardless of how you write it. In any case, the plan chosen will be the most efficient one.
The reasons to prefer the join syntax over the older where syntax are:
to look cool: you don’t want anybody catching you with code that is old-fashioned
the join syntax is easy to adapt to outer joins
the join syntax allows you to separate the join part from additional filter by distinguishing between join and where
The reasons do not include whether one is better, because the interpreter will handle that.
These are some more notes that are too long for a comment.
First it should be showed that your two queries is different. (Maybe the 2nd query you wrote is a wrong query)
For example:
all_data
place_id day hour other_cols...
1 4 3 ....
selected_place_day_hours
place_id day hour
1 4 9
4444 4444 6
Then your 1st query will get no row in return, and your 2nd will return (1, 4, 6)
One more note is that if (place_id, day, hour) is unique, your first query is in same purpose of following query
SELECT *
FROM all_data
WHERE
(place_id, day, hour) IN (
SELECT place_id, day, hour
FROM selected_place_day_hours
);

Monthly Moving Average of User Activity in SQL Server Using Window Functions

Let's say I have a table UserActivity in SQL Server 2012 with two columns:
ActivityDateTime
UserID
I want to calculate number of distinct users with any activity in a 30-day period (my monthly active users) on a daily basis. (So I have a 30-day window that increments a day at a time. How do I do this efficiently using window functions in SQL Server?
The output would look like this:
Date,NumberActiveUsersInPrevious30Days
01-01-2010,13567
01-02-2010,14780
01-03-2010,13490
01-04-2010,15231
01-05-2010,15321
01-06-2010,14513
...
SQL Server doesn't support COUNT(DISTINCT ... ) OVER () or a numeric value (30 PRECEDING) in conjunction with RANGE
I wouldn't bother trying to coerce window functions into doing this. Because of the COUNT(DISTINCT UserID) requirement it is always going to have to re-examine the entire 30 day window for each date.
You can create a calendar table with a row for each date and use
SELECT C.Date,
NumberActiveUsersInPrevious30Days
FROM Calendar C
CROSS APPLY (SELECT COUNT(DISTINCT UserID)
FROM UserActivity
WHERE ActivityDateTime >= DATEADD(DAY, -30, C.[Date])
AND ActivityDateTime < C.[Date]) CA(NumberActiveUsersInPrevious30Days)
WHERE C.Date BETWEEN '2010-01-01' AND '2010-01-06'
Option 1: For (while) loop though each day and select 30 days backward for each (obviously quite slow).
Option 2: A separate table with a row for each day and join on the original table (again quite slow).
Option 3: Recursive CTEs or stored procs (still not doing much better).
Option 4: For (while) loop in combination with cursors (efficient, but requires some advanced SQL knowledge). With this solution you will step through each day and each row in order and keep track of the average (you'll need some sort of wrap-around array to know what value to subtract when a day moves out of range).
Option 5: Option 3 in a general-purpose / scripting programming language (C++ / Java / PHP) (easy to do with basic knowledge of one of those languages, efficient).
Some related questions.

Is there a way to handle immutability that's robust and scalable?

Since bigquery is append-only, I was thinking about stamping each record I upload to it with an 'effective date' similar to how peoplesoft works, if anybody is familiar with that pattern.
Then, I could issue a select statement and join on the max effective date
select UTC_USEC_TO_MONTH(timestamp) as month, sum(amt)/100 as sales
from foo.orders as all
join (select id, max(effdt) as max_effdt from foo.orders group by id) as latest
on all.effdt = latest.max_effdt and all.id = latest.id
group by month
order by month;
Unfortunately, I believe this won't scale because of the big query 'small joins' restriction, so I wanted to see if anyone else had thought around this use case.
Yes, adding a timestamp for each record (or in some cases, a flag that captures the state of a particular record) is the right approach. The small side of a BigQuery "Small Join" can actually return at least 8MB (this value is compressed on our end, so is usually 2 to 10 times larger), so for "lookup" table type subqueries, this can actually provide a lot of records.
In your case, it's not clear to me what the exact query you are trying to run is.. it looks like you are trying to return the most recent sales times of every individual item - and then JOIN this information with the SUM of sales amt per month of each item? Can you provide more info about the query?
It might be possible to do this all in one query. For example, in our wikipedia dataset, an example might look something like...
SELECT contributor_username, UTC_USEC_TO_MONTH(timestamp * 1000000) as month,
SUM(num_characters) as total_characters_used FROM
[publicdata:samples.wikipedia] WHERE (contributor_username != '' or
contributor_username IS NOT NULL) AND timestamp > 1133395200
AND timestamp < 1157068800 GROUP BY contributor_username, month
ORDER BY contributor_username DESC, month DESC;
...to provide wikipedia contributions per user per month (like sales per month per item). This result is actually really large, so you would have to limit by date range.
UPDATE (based on comments below) a similar query that finds "num_characters" for the latest wikipedia revisions by contributors after a particular time...
SELECT current.contributor_username, current.num_characters
FROM
(SELECT contributor_username, num_characters, timestamp as time FROM [publicdata:samples.wikipedia] WHERE contributor_username != '' AND contributor_username IS NOT NULL)
AS current
JOIN
(SELECT contributor_username, MAX(timestamp) as time FROM [publicdata:samples.wikipedia] WHERE contributor_username != '' AND contributor_username IS NOT NULL AND timestamp > 1265073722 GROUP BY contributor_username) AS latest
ON
current.contributor_username = latest.contributor_username
AND
current.time = latest.time;
If your query requires you to use first build a large aggregate (for example, you need to run essentially an accurate COUNT DISTINCT) another option is to break this query up into two queries. The first query could provide the max effective date by month along with a count and save this result as a new table. Then, could run a sum query on the resulting table.
You could also store monthly sales records in separate tables, and only query the particular table for the months you are interested in, simplifying your monthly sales summaries (this could also be a more economical use of BigQuery). When you need to find aggregates across all tables, you could run your queries with multiple tables listed after the FROM clause.

Selecting records from the past three months

I have 2 tables from which i need to run a query to display number of views a user had in the last 3 months from now.
So far I have come up with: all the field types are correct.
SELECT dbo_LU_USER.USERNAME
, Count(*) AS No_of_Sessions
FROM dbo_SDB_SESSION
INNER JOIN dbo_LU_USER
ON dbo_SDB_SESSION.FK_USERID = dbo_LU_USER.PK_USERID
WHERE (((DateDiff("m",[dbo_SDB_SESSION].[SESSIONSTART],Now()))=0
Or (DateDiff("m",[dbo_SDB_SESSION].[SESSIONSTART],Now()))=1
Or (DateDiff("m",[dbo_SDB_SESSION].[SESSIONSTART],Now()))=2))
GROUP BY dbo_LU_USER.USERNAME;
Basically, the code above display a list of all records within the past 3 months; however, it starts from the 1st day of the month and ends on the current date, but I need it to start 3 months prior to today's date.
Also to let you know this is SQL View in MS Access 2007 code.
Thanks in advance
Depending on how "strictly" you define your 3 months rule, you could make things a lot easier and probably efficient, by trying this:
SELECT dbo_LU_USER.USERNAME, Count(*) AS No_of_Sessions
FROM dbo_SDB_SESSION
INNER JOIN dbo_LU_USER
ON dbo_SDB_SESSION.FK_USERID = dbo_LU_USER.PK_USERID
WHERE [dbo_SDB_SESSION].[SESSIONSTART] between now() and DateAdd("d",-90,now())
GROUP BY dbo_LU_USER.USERNAME;
(Please understand that my MS SQL is a bit rusty, and can't test this at the moment: the idea is to make the query scan all record whose date is between "TODAY" and "TODAY-90 days").