Query for negative account balance period in bigquery - google-bigquery

Question

I am playing around with bigquery and hit an interesting use case. I have a collection of customers and account balances. The account balances collection records any account balance change.
Customers:
+---------+--------+
| ID | Name |
+---------+--------+
| 1 | Alice |
| 2 | Bob |
+---------+--------+
Accounts balances:
+---------+---------------+---------+------------+
| ID | customer_id | value | timestamp |
+---------+---------------+---------+------------+
| 1 | 1 | -500 | 2019-02-12 |
| 2 | 1 | -200 | 2019-02-10 |
| 3 | 2 | 200 | 2019-02-10 |
| 4 | 1 | 0 | 2019-02-09 |
+---------+---------------+---------+------------+
The goal is to find out, for how long a customer has a negative account balance. The resulting collection would look like this:
+---------+--------+---------------------------------+
| ID | Name | Negative account balance since |
+---------+--------+---------------------------------+
| 1 | Alice | 2 days |
+---------+--------+---------------------------------+
Bob is not in the collection, because his last account record shows a positive value.
I think following steps are involved:
get last account balance per customer, see if it is negative
go through the account balance values until you hit a positive (or no more) value
compute datediff
Is something like this even possible in sql? Do you have any ideas on who to create such query? To get customers that currently have a negative account balance, I use this query:
SELECT customer_id FROM (
SELECT t.account_balance, ROW_NUMBER() OVER (PARTITION BY customer_id ORDER BY timestamp DESC) as seqnum FROM `account_balances` t
) t
WHERE seqnum = 1 AND account_balance<0

Answer 1

Below is for BigQuery Standard SQL
#standardSQL
SELECT customer_id, name,
SUM(IF(negative_positive < 0, days, 0)) negative_days,
SUM(IF(negative_positive = 0, days, 0)) zero_days,
SUM(IF(negative_positive > 0, days, 0)) positive_days
FROM (
SELECT customer_id, negative_positive, grp,
1 + DATE_DIFF(MAX(ts), MIN(ts), DAY) days
FROM (
SELECT customer_id, ts, SIGN(value) negative_positive,
COUNTIF(flag) OVER(PARTITION BY customer_id ORDER BY ts) grp
FROM (
SELECT *, SIGN(value) = IFNULL(LEAD(SIGN(value)) OVER(PARTITION BY customer_id ORDER BY ts), 0) flag
FROM `project.dataset.balances`
)
)
GROUP BY customer_id, negative_positive, grp
)
LEFT JOIN `project.dataset.customers`
ON id = customer_id
GROUP BY customer_id, name
You can test, play with above using sample data from your question as in below example
#standardSQL
WITH `project.dataset.balances` AS (
SELECT 1 customer_id, -500 value, DATE '2019-02-12' ts UNION ALL
SELECT 1, -200, '2019-02-10' UNION ALL
SELECT 2, 200, '2019-02-10' UNION ALL
SELECT 1, 0, '2019-02-09'
), `project.dataset.customers` AS (
SELECT 1 id, 'Alice' name UNION ALL
SELECT 2, 'Bob'
)
SELECT customer_id, name,
SUM(IF(negative_positive < 0, days, 0)) negative_days,
SUM(IF(negative_positive = 0, days, 0)) zero_days,
SUM(IF(negative_positive > 0, days, 0)) positive_days
FROM (
SELECT customer_id, negative_positive, grp,
1 + DATE_DIFF(MAX(ts), MIN(ts), DAY) days
FROM (
SELECT customer_id, ts, SIGN(value) negative_positive,
COUNTIF(flag) OVER(PARTITION BY customer_id ORDER BY ts) grp
FROM (
SELECT *, SIGN(value) = IFNULL(LEAD(SIGN(value)) OVER(PARTITION BY customer_id ORDER BY ts), 0) flag
FROM `project.dataset.balances`
)
)
GROUP BY customer_id, negative_positive, grp
)
LEFT JOIN `project.dataset.customers`
ON id = customer_id
GROUP BY customer_id, name
-- ORDER BY customer_id
with result
Row customer_id name negative_days zero_days positive_days
1 1 Alice 3 1 0
2 2 Bob 0 0 1