如何为每个客户在 BigQuery 中以宽格式更改 label 获取正确的最小、最大日期？

Question

I have a table that records customer purchases, for example:

customer_id	label	date	purchase_id	price
2	A	2022-01-01	asd	10
3	A	2022-01-01	asdf	5
4	B	2022-02-04	asdfg	200
2	A	2022-01-03	asdjg	4
3	B	2022-02-01	dfs	20
2	G	2022-04-05	fdg	40
2	G	2022-04-10	fdg	40
2	A	2022-06-06	fgd	20

I want to see how many days/money each customer has spent in each label, so far what I'm doing is:

SELECT
customer_id,
label,
COUNT(DISTINCT(purchase_id) as orders_count,
SUM(price) as total_spent,
min(date) as first_date,
max(date) as last_date,
DATE_DIFF(max(date), min(date), DAY) as days
FROM
TABLE
WHERE
date > '2022-01-01'
GROUP BY
customer_id,
label

which gives me a long table, like this:

customer_id	label	orders_count	total_spent	first_date	last_date	days
2	A	3	34	2022-01-01	2022-06-06	180
2	G	1	40	2022-04-05	2022-04-10	5

etc

Just for simplicity I show a few columns, but customers have orders all the time. The issue with the above is that, for example for customer 2 , that he starts with label A, then changes to G, then he is back to A so this is not visible in the results table (min(date) is correct, but max(date) takes their 2nd A max(date)) and that I'd prefer to have it in wide format. For instance, ideally, columns called next_label_{i} that you get values for each changing label would be the best for me.

Could you advise me of a way of a) dealing with accomodating with this label change(future label change is the same as an earlier label) and b) a way to produce it into a wide format?

Thanks

edit: example output (correct date, wide format) [columns would go as wide as the max number of unique labels for any customer]

customer_id	first_label	first_first_date	first_last_date	first_total_spent	first_days	next_label	next_first_date	next_last_date	next_days	next_label_2	next_first_date_2	next_last_date_2	next_days_2
2	A	2022-01-01	2022-01-03	2	14	G	2022-04-05	2022-04-05	0	A	2022-06-06	2022-06-06	0

etc

Sorry this is not exactly accurate (missing the orders_count, total_spent) but it's a pain in the ass for format it here, but hopefully you get the idea. In principle, it's something as if you used python's pivot_table on the previous dataset.

Alternatively, I'd be glad for just a solution in the long format that distinguishes between a customer's label and the same customer's repeated label ( as in customer 2 who starts with A and after changing to G, returns to A)

Answer 1

Could you advise me of... b) a way to produce it into a wide format?

First, I want to say that I hope you have really good reason to get that output as usually it is not what is considered a best practices and rather is being left for presentation layer to handle.

With that in mind - consider below approach

select * from (
  select customer_id, offset, purchase.*
  from (
    select customer_id, 
      array_agg((struct(label, date, purchase_id, price)) order by date) purchases
    from your_table 
    group by customer_id
  ), unnest(purchases) purchase with offset
  order by customer_id, offset
)
pivot (
  any_value(label) label, 
  any_value(date) date, 
  any_value(purchase_id) purchase_id, 
  any_value(price) price
  for offset in (0,1,2,3,4,5)
)    

if applied to sample data in your question - output is

Note: Above has silly assumption that you know the max number of steps (in this case I used 6 - from 0 till 5). There are plenty of posts here on SO that shows how to use same technique to make it dynamic. I do not want to duplicate them as it is against SO policies. So, just do your extra homework on this:o)