Add columns to SQL query and filter by min(date) and sum(price) - sql

Question

I am trying to generate a list of users who's first purchase was in December 2018 and have spent over 100 dollars since then in SQL. I'm able to generate the list of users, but I'm unable to determine what their first purchase was or other variables and it appears to be an issue since the columns I'm trying to include are neither grouped nor aggregated so I'm hoping someone can point me in the right direction as I'm new to SQL.
Here's my code to generate the list I want to add more columns to:
select billing_address.name, contact_email, min(processed_at) as First_Purchase_Date, sum(total_price) as Total_Revenue
FROM (
SELECT *, ROW_NUMBER() OVER(PARTITION BY id) AS instance
FROM `table.orders`
) orders -- identify duplicate rows
WHERE instance = 1
group by contact_email, billing_address.name
having min(processed_at) between '2019-01-01 00:00:00 UTC' and '2019-02-01 00:00:00 UTC' and sum(total_price) > 100
order by sum(total_price) desc
Is there some way I can modify this to pull each user's purchase from this list into a separate row and include more columns? So I'd pull in each user (and ALL of their purchases) who has a min(processed_at) in December 2018 AND their sum(total_price) > 100? something like this:
SELECT contact_email, billing_address, line_items, min(processed_at), sum(total_price) OVER (PARTITION BY contact_email)
FROM (
SELECT *, ROW_NUMBER() OVER(PARTITION BY id) AS instance
FROM `table.orders`
) orders -- identify duplicate rows
WHERE instance = 1
However, the sum(total_price) doesn't work in this case and I can't filter by min(processed_at). Can someone guide me in the right direction?

Answer 1

I think that should use window functions instead of aggregation. You can compute the date of the first purchase and the total amount spent on the fly in a subquery, without aggregating (your original group by columns become the partition columns of the window functions). Then you can use these information to filter in the outer query.
This should get you close to what you want:
select o.*
from (
select
o.*,
min(processed_at) over(partition by contact_email, billing_address) min_processed_at,
sum(total_price) over(partition by contact_email, billing_address) sum_total_price
from (
select
o.*,
row_number() over(partition by id) instance
from orders o
) o
where instance = 1
) o
where
processed_at between '2019-01-01 00:00:00 UTC' and '2019-02-01 00:00:00 UTC'
and sum_total_price > 100

Answer 2

Your question was a bit unclear as you did not provide much detail about your input tables or your expected output, so this is a guess.
The following query gets all transactions from users who meet the criteria:
-- BigQuery StandardSQL
with ordered_orders as (
--rank each ID by processed_at date first to last
select *, row_number() over(partition by id order by processed_at asc) as rn
from `table.orders`
),
first_criteria as (
-- select IDs where first processed_at date is in 2018-12
select id, processed_at as first_order_date
from ordered_orders
where rn = 1
and extract(year from processed_at) = 2018
and extract(month from processed_at) = 12
),
second_criteria as (
-- further select IDs who meet first criteria and have a total of > 100
select id, sum(total_prices) as total_revenue
from ordered_orders
inner join first_criteria using(id)
group by id
having total_revenue > 100
),
orders_with_criteria as (
-- get all orders for users who meet both criteria
select ordered_orders.* except(rn), first_order_date, total_revenue
from ordered_orders
inner join first_criteria using(id)
inner join second_criteria using(id)
),
-- select any fields you want
select * from orders_with_criteria
I prefer liberal use of CTEs in cases like this to keep the logic clear.
I also wouldn't be surprised if this query doesn't work as you intend. I think it is highly doubtful that the ID column in your orders table refers to the customer id, which is what you/we are partitioning on. Depending on who set up your tables, id probably refers to the order id. If you have a customer_id (or account #, etc), then I would use that instead of id in the query.

Answer 3

No need to use row_number() in BigQuery for this:
SELECT billing_address.name, contact_email,
MIN(processed_at) as First_Purchase_Date,
SUM(total_price) as Total_Revenue,
ARRAY_AGG(o ORDER BY processed_at LIMIT 1) as first_order
FROM `table.orders` o
WHERE instance = 1
GROUP BY contact_email, billing_address.name
HAVING MIN(processed_at) >= '2019-01-01 00:00:00 UTC' AND
MIN(processed_at) < '2019-02-01 00:00:00 UTC' AND
SUM(total_price) > 100
ORDER BY SUM(total_price) desc;
This returns the entire first order as a struct. You can select specific columns, if you prefer.