I have a SQL Server table of Customer's Events:
CREATE TABLE CustomerEvent
(
CustomerID int,
EventType int,
EventDate datetime
)
There can be many EventTypes for the same customer in one day.
EventTypes are like
1 - CheckIn
2 - CheckOut
3 - ExamStart
4 - ExamEnd
Now I want to select Customers that are currently (today) on premises. That's Clients who had checked in, but hadn't checked out, no matter if they taking Exam currently or not. Can this be done as a SQL view, or do I have to write a stored procedure for that?
You want today. So I would suggest not exists:
select ce.customerid
from customerevent ce
where eventtype = 1 and
event_date >= current_date and
event_date < current_date + interval '1 day' and
not exists (select 1
from customerevent ce2
where ce2.customerid = ce.customerid and
ce2.eventtype = 2 and
ce2.eventdate > ce.eventdate
);
You can easily incorporate this into a view.
Note: date/time functions are notoriously database specific, so the exact syntax for "today" may vary.
EDIT:
In SQL Server, this can be written as:
select ce.customerid
from customerevent ce
where eventtype = 1 and
convert(date, event_date) >= concat(date, current_date) and
not exists (select 1
from customerevent ce2
where ce2.customerid = ce.customerid and
ce2.eventtype = 2 and
ce2.eventdate > ce.eventdate
);
You can use aggregation, and filter with a having clause that compares the last check in of each customer to their last check out:
create view customerview as
select customerid
from customerevent
group by customerid
having
max(case when eventtype = 1 then eventdate end)
> max(case when eventtype = 2 then eventdate end)
or (
max(case when eventtype = 1 then eventdate end) is not null
and max(case when eventtype = 2 then eventdate end) is null
)
The second condition in the having condition handles customers that checked in at least once but never checked out.
We can simplify the query a little with coalesce() and a fixed date that you are sure to be prior to any row in your table:
having max(case when eventtype = 1 then eventdate end)
> max(case when eventtype = 2 then eventdate else '19700101' end)
Related
I have table that stores the employe info in multiple rows and it having the common name for it along with its user login time and log out time for website, and would like to achieve the result and it may contains multiple names such as (N1,N2,N3..etc)
Name,Key,Time,
N1,TotalExp,No
N1,TotalYears,5
N1,LoggedIn,10:00:00
N1,LoggedOut,20:00:00
Expected Output will like below,
N1,TotalExp,TotalYrs,LoggedDifference
N1,No,5,10
Any one help me to achieve this
Even it's a fact that the design of your database doesn't look well, you can query your data this way:
with your_data as (
select 'N1' as Name,'TotalExp' as [Key],'No' as Time union all
select 'N1','TotalYears','5' union all
select 'N1','LoggedIn','10:00:00' union all
select 'N1','LoggedOut','20:00:00'
)
select
Name,
max(case when [Key] = 'TotalExp' then Time else null end) as TotalExp,
max(case when [Key] = 'TotalYears' then Time else null end) as TotalYrs,
datediff(
hour,
max(case when [Key] = 'LoggedIn' then convert(time, Time) else null end),
max(case when [Key] = 'LoggedOut' then convert(time, Time) else null end)
) as LoggedDifference
from your_data
group by Name
You can test on here
I am trying to create a query that will group data by CT ID and Date that have all 3 MachineID's (1, 10, and 20) and at least one different Sawing Pattern Name.
This Image shows a highlighted example of the data I'm trying to get back and the code i'm currently using
I'm trying to only show data similar to the highlighted rows in the image (CT ID 501573833) and exclude the data in the rows around it where the Sawing Pattern Name is the same at all 3 MachineID's.
Your description suggests group by and having. The conditions you describe can all go in the having clause:
select ct_id, date
from t
group by ct_id, date
having sum(case when machineid = 1 then 1 else 0 end) > 0 and
sum(case when machineid = 10 then 1 else 0 end) > 0 and
sum(case when machineid = 20 then 1 else 0 end) > 0 and
min(sawing_pattern_name) <> max(sawing_pattern_name)
Seems to me that an EXISTS could be useful here.
SELECT
[CT ID],
[MachineID],
[Sawing Pattern name],
[Time],
CAST([Time] AS DATE) AS [Date]
FROM [DataCollector].[dbo].[Maxicut] t
WHERE EXISTS
(
SELECT 1
FROM [DataCollector].[dbo].[Maxicut] d
WHERE d.[CT ID] = t.[CT ID]
AND CAST(d.[Time] AS DATE) = CAST(t.[Time] AS DATE)
AND d.[MachineID] != t.[MachineID]
AND REPLACE(d.[Sawing Pattern name],',','') != REPLACE(t.[Sawing Pattern name],',','')
);
I have the table in PostgreSQL DB
Need to calculate SUM of counts for each event_type (example for 4 and 1)
When I use query like this
SELECT account_id, date,
CASE
WHEN event_type = 1 THEN SUM(count)
ELSE null
END AS shows,
CASE
WHEN event_type = 4 THEN SUM(count)
ELSE null
END AS clicks
FROM widgetstatdaily WHERE account_id = 272 AND event_type = 1 OR event_type = 4 GROUP BY account_id, date, event_type ORDER BY date
I receive this table
With <null> fields. It's because I have event_type in select and I need to GROUP BY on it.
How I can make query to receive grouped by account_id and date result without null's in cells? Like (first row)
272 2018-03-28 00:00:00.000000 57 2
May be I can group it after receiving result
You need conditional aggregation and some other fixes. Try this:
SELECT account_id, date,
SUM(CASE WHEN event_type = 1 THEN count END) as shows,
SUM(CASE WHEN event_type = 4 THEN count END) as clicks
FROM widgetstatdaily
WHERE account_id = 272 AND
event_type IN (1, 4)
GROUP BY account_id, date
ORDER BY date;
Notes:
The CASE expression should be an argument to the SUM().
The ELSE NULL is redundant. The default without an ELSE is NULL.
The logic in the WHERE clause is probably not what you intend. That is fixed using IN.
try its
SELECT account_id, date,
SUM(CASE WHEN event_type = 1 THEN count else 0 END) as shows,
SUM(CASE WHEN event_type = 4 THEN count else 0 END) as clicks
FROM widgetstatdaily
WHERE account_id = 272 AND
event_type IN (1, 4)
GROUP BY account_id, date
ORDER BY date;
I'm trying to analyze a funnel using event data in Redshift and have difficulties finding an efficient query to extract that data.
For example, in Redshift I have:
timestamp action user id
--------- ------ -------
2015-05-05 12:00 homepage 1
2015-05-05 12:01 product page 1
2015-05-05 12:02 homepage 2
2015-05-05 12:03 checkout 1
I would like to extract the funnel statistics. For example:
homepage_count product_page_count checkout_count
-------------- ------------------ --------------
100 50 25
Where homepage_count represent the distinct number of users who visited the homepage, product_page_count represents the distinct numbers of users who visited the homepage after visiting the homepage, and checkout_count represents the number of users who checked out after visiting the homepage and the product page.
What would be the best query to achieve that with Amazon Redshift? Is it possible to do with a single query?
I think the best method might be to add flags to the data for the first visit of each type for each user and then use these for aggregation logic:
select sum(case when ts_homepage is not null then 1 else 0 end) as homepage_count,
sum(case when ts_productpage > ts_homepage then 1 else 0 end) as productpage_count,
sum(case when ts_checkout > ts.productpage and ts.productpage > ts.homepage then 1 else 0 end) as checkout_count
from (select userid,
min(case when action = 'homepage' then timestamp end) as ts_homepage,
min(case when action = 'product page' then timestamp end) as ts_productpage,
min(case when action = 'checkout' then timestamp end) as ts_checkout
from table t
group by userid
) t
The above answer is very much correct . I have modified it for people using it for AWS Mobile Analytics and Redshift.
select sum(case when ts_homepage is not null then 1 else 0 end) as homepage_count,
sum(case when ts_productpage > ts_homepage then 1 else 0 end) as productpage_count,
sum(case when ts_checkout > ts_productpage and ts_productpage > ts_homepage then 1 else 0 end) as checkout_count
from (select client_id,
min(case when event_type = 'App Launch' then event_timestamp end) as ts_homepage,
min(case when event_type = 'SignUp Success' then event_timestamp end) as ts_productpage,
min(case when event_type = 'Start Quiz' then event_timestamp end) as ts_checkout
from awsma.v_event
group by client_id
) ts;
Just in case more precise model required: when product page can be opened twice. First time before home page and second one after. This case usually should be considered as conversion as well.
Redshift SQL query:
SELECT
COUNT(
DISTINCT CASE WHEN cur_homepage_time IS NOT NULL
THEN user_id END
) Step1,
COUNT(
DISTINCT CASE WHEN cur_homepage_time IS NOT NULL AND cur_productpage_time IS NOT NULL
THEN user_id END
) Step2,
COUNT(
DISTINCT CASE WHEN
cur_homepage_time IS NOT NULL AND cur_productpage_time IS NOT NULL AND cur_checkout_time IS NOT NULL
THEN user_id END
) Step3
FROM (
SELECT
user_id,
timestamp,
COALESCE(homepage_time,
LAG(homepage_time) IGNORE NULLS OVER(PARTITION BY user_id
ORDER BY time)
) cur_homepage_time,
COALESCE(productpage_time,
LAG(productpage_time) IGNORE NULLS OVER(PARTITION BY distinct_id
ORDER BY time)
) cur_productpage_time,
COALESCE(checkout_time,
LAG(checkout_time) IGNORE NULLS OVER(PARTITION BY distinct_id
ORDER BY time)
) cur_checkout_time
FROM
(
SELECT
timestamp,
user_id,
(CASE WHEN event = 'homepage'
THEN timestamp END) homepage_time,
(CASE WHEN event = 'product page'
THEN timestamp END) productpage_time,
(CASE WHEN event = 'checkout'
THEN timestamp END) checkout_time
FROM events
WHERE timestamp > '2016-05-01' AND timestamp < '2017-01-01'
ORDER BY user_id, timestamp
) event_times
ORDER BY user_id, timestamp
) event_windows
This query fills each row's cur_homepage_time, cur_productpage_time and cur_checkout_time with recent timestamp of event occurrences. So in case for some specific time (read row) event occured then particular column is not NULL.
More info here.
I am trying to count events (which are rows in the event_table) in the year before and the year after a particular target date for each person. For example, say I have a person 100 and target date is 10/01/2012. I would like to count events in 9/30/2011-9/30/2012 and in 10/02/2012-9/30/2013.
My query looks like:
select *
from (
select id, target_date
from subsample_table
) as i
left join (
select id, event_date, count(*) as N
, case when event_date between target_date-365 and target_date-1 then 0
when event_date between target_date+1 and target_date+365 then 1
else 2 end as after
from event_table
group by id, target_date, period
) as h
on i.id = h.id
and i.target_date = h.event_date
The output should look something like:
id target_date after N
100 10/01/2012 0 1000
100 10/01/2012 1 0
It's possible that some people do not have any events in the before or after periods (or both), and it would be nice to have zeros in that case. I don't care about the events outside the 730 days.
Any suggestions would be greatly appreciated.
I think the following may approach what you are trying to accomplish.
select id
, target_date
, event_date
, count(*) as N
, SUM(case when event_date between target_date-365 and target_date-1
then 1
else 0
end) AS Prior_
, SUM(case when event_date between target_date+1 and target_date+365
then 1
else 0
end) as After_
from subsample_table i
left join
event_table h
on i.id = h.id
and i.target_date = h.event_date
group by id, target_date, period
This is a generic answer. I don't know what date functions teradata has, so I will use sql server syntax.
select id, target_date, sum(before) before, sum(after) after, sum(righton) righton
from yourtable t
join (
select id, target_date td
, case when yourdate >= dateadd(year, -1, target_date)
and yourdate < target_date then 1 else 0 end before
, case when yourdate <= dateadd(year, 1, target_date)
and yourdate > target_date then 1 else 0 end after
, case when yourdate = target_date then 1 else 0 end righton
from yourtable
where whatever
group by id, target_date) sq on t.id = sq.id and target_date = dt
where whatever
group by id, target_date
This answer assumes that an id can have more than one target date.