Update value based on value from another record of same table - sql

Question

Here I have a sample table of a website visitors. As we can see, sometimes visitor don't provide their email. Also they may switch to different email addresses over period.
**
Original table:
**
I want to update this table with following requirements:
First time when a visitor provides an email, all his past visits will be tagged to that email
Also all his future visits will be tag to that email until he switches to another email.
**
Expected table after update:
**
I was wondering if there is a way of doing it in Redshift or T-Sql?
Thanks everyone!

Answer 1

In SQL Server or Redshift, you can use a subquery to calculate the email:
select t.*,
coalesce(email,
max(email) over (partition by visitor_id, grp),
max(case when activity_date = first_email_date then email end) over (partition by visitor_id)
)
from (select t.*,
min(case when email is not null then activity_date end) over
(partition by visitor_id order by activity_date rows between unbounded preceding and current row) as first_email_date,
count(email) over (partition by visitor_id order by activity_date between unbounded preceding and current row) as grp
from t
) t;
You can then use this in an update:
update t
set emai = tt.imputed_email
from (select t.,
coalesce(email,
max(email) over (partition by visitor_id, grp),
max(case when activity_date = first_email_date then email end) over (partition by visitor_id)
) as imputed_email
from (select t.,
min(case when email is not null then activity_date end) over
(partition by visitor_id order by activity_date) as first_email_date,
count(email) over (partition by visitor_id order by activity_date) as grp
from t
) t
) tt
where tt.visitor_id = t.visitor_id and tt.activity_date = t.activity_date and
t.email is null;

Answer 2

If we suppose that the name of the table is Visits and the primary key of that table is made of the columns Visitor_id and Activity_Date then you can do in T-SQL following:
using correlated subquery:
update a
set a.Email = coalesce(
-- select the email used previously
(
select top 1 Email from Visits
where Email is not null and Activity_Date < a.Activity_Date and Visitor_id = a.Visitor_id
order by Activity_Date desc
),
-- if there was no email used previously then select the email used next
(
select top 1 Email from Visits
where Email is not null and Activity_Date > a.Activity_Date and Visitor_id = a.Visitor_id
order by Activity_Date
)
)
from Visits a
where a.Email is null;
using window function to provide the ordering:
update v
set Email = vv.Email
from Visits v
join (
select
v.Visitor_id,
coalesce(a.Email, b.Email) as Email,
v.Activity_Date,
row_number() over (partition by v.Visitor_id, v.Activity_Date
order by a.Activity_Date desc, b.Activity_Date) as Row_num
from Visits v
-- previous visits with email
left join Visits a
on a.Visitor_id = v.Visitor_id
and a.Email is not null
and a.Activity_Date < v.Activity_Date
-- next visits with email if there are no previous visits
left join Visits b
on b.Visitor_id = v.Visitor_id
and b.Email is not null
and b.Activity_Date > v.Activity_Date
and a.Visitor_id is null
where v.Email is null
) vv
on vv.Visitor_id = v.Visitor_id
and vv.Activity_Date = v.Activity_Date
where
vv.Row_num = 1;

Answer 3

For each visitor_id you can update the null email value with the previus non-null value. In case there is none, you will use the next non-null value.You can get those values as follows:
select
v.*, v_prev.email prev_email, v_next.email next_email
from
visits v
left join visits v_prev on v.visitor_id = v_prev.visitor_id
and v_prev.activity_date = (select max(v2.activity_date) from visits v2 where v2.visitor_id = v.visitor_id and v2.activity_date < v.activity_date and v2.email is not null)
left join visits v_next on v.visitor_id = v_next.visitor_id
and v_next.activity_date = (select min(v2.activity_date) from visits v2 where v2.visitor_id = v.visitor_id and v2.activity_date > v.activity_date and v2.email is not null)
where
v.email is null